This application claims the benefit of U.S. provisional patent application No.61/930,807, filed on 23/1/2014, the entire contents of which are incorporated herein by reference. This application is related to application No.14/216,390 filed on date 3 and 17 of 2014 and application No.14/216,936 filed on date 3 and 17 of 2014, both of which are incorporated herein by reference in their entirety.
Background
The management of portfolios has been the subject of significant theory and research. Theoretical consideration of investment portfolio: how wealth should be invested, and how expected return for a portfolio can be maximized by carefully selecting the proportions of the various assets for a given amount of portfolio risk, or equivalently how risk can be minimized by carefully selecting the proportions of the various assets for a given level of expected return. While a certain rate of return may be expected, the estimates of individual holdings in a portfolio may deviate upward or downward from the expected rate of return. This upward or downward variation from the expected value is known as variance (variance) or volatility. Over time, a security should theoretically have a valid boundary for expected fluctuations and revenues. According to theory, securities with higher expected risks will have higher expected revenues.
Standard Pul 500 is the largest stock benchmark in the world. Billions of dollars are invested in either this benchmark or in funds based on this benchmark. Since the end of 1999, a wide market index such as standard pul 500 has experienced a long-term gain in a wide stock index that underperforms. For example, an investor according to standard pul 500 at the end of 1999 has a reduction of approximately 20% by the end of 2010 after 10 years. Until the end of 2012, standard pul 500 had positive benefits for investors at the end of these 1999 years, including many large endowment funds and donation funds. During this same period, a wide fund holding a government or corporate debt has positive revenue, and the corporate debt earns much more than the government debt during this period. This premium is due to the additional risk that corporate bonds have compared to government bonds. These markets have their annual fluctuations, but over a reasonable period of time, these securities all have positive revenues and have differences that are expected based on risk. These claims cannot be made for stock indices such as the standard pooler 500, the standard pooler 500 devaluates and actually underperforms on an absolute basis over a long period of time relative to lower risk indices holding corporate liabilities or government liabilities.
The standard pul 500 is market-weighted like most of the extensive market indices. This means that the weight of an individual company in the index is proportional to the market value for the other companies in the index. Standard Pull 500 is not controlled to ensure that: a single security or a group of securities having a common risk does not become a too large part of the portfolio. That is, the type of control used in the scientific field and engineering process in which the overall control is used to limit the effect that a portion of the population may have on the entire population being measured is not used in the broad market index. These controls limit the impact of both positive and negative aspects. In population studies, control is used to generate a standard model of the potential population. Because there is no control over the benchmarks currently used to invest in stock securities, there is no guarantee that: these historical returns from the end of 1999 to the present are a representation of stock securities in general. It is all known that a weighting strategy (market-value weighting without control) has resulted in lower than average gains over longer periods of time.
The results of the major index since 1999 seem to be inconsistent with the major theory of pricing and effective market theory for investment securities. Most work on the effective market follows the pioneering work of markovitz (Markowitz) and sharp (sharp), which others, such as morton (Merton), have made notable additions to later. The theory shows that: individual securities are priced at a level that is expected to produce risk adjusted return relative to other investment securities, and the risk adjusted return rate is obtained by the portfolio of securities having a higher likelihood of having a given time period or during several time periods by following certain rules. Rules proposed by markwitz and others are designed to assist investors and managers in selecting the most efficient portfolio design by analyzing various possible portfolios for a given security.
By selecting securities that do not "move" exactly together, the model shows the investor how to reduce his risk. The basic model in this field is known as the mean-variance model because the model is based on the expected yield (mean) and standard deviation (variance) of various portfolios. When developing the original mean variance model, markovitz assumes: a portfolio that gives the greatest return for a given risk or a portfolio that gives the least risk for a given return is an active portfolio. Thus, the following rules are used to select portfolios: (a) from portfolios having the same return, the investor will tend to select a portfolio having a lower risk, and (b) from portfolios having the same risk level, the investor will tend to select a portfolio having a higher return rate. While individual securities may underperform over a longer period of time, the rules developed for effective portfolio construction are designed to reduce the likelihood that a portfolio on a security will underperform.
One explanation for the inconsistency between modern portfolios and theoretical portfolios that develop valid market assumptions is: modern investment portfolios are of greater size and complexity than the theoretical examples. Theoretical models tend to describe diversification using individual securities and within a portfolio consisting of a few securities of single and low double digits. A number of basic articles were published after the personal retirement account (IRA) created by the employee retirement income guarantee act (ersa) in 1974, and after the first exponential fund introduced in 1976, before the development of a common fund that extended in the 90 s of the 20 th century from the 80 s of the 20 th century. An article published by markoviz in the journal of finance on portfolio selection was written in 1952. According to the first share of the east survey conducted in 1952 by the New York Stock Exchange (NYSE), only 6.5 million americans have common stocks (approximately 4.2% of the U.S. population). The article "imported Model for Portfolio Analysis" was written by the Shapu in 1963 and his book "Portfolio theory and Capital Markets" was written in 1970, both before the prosperity of creating a mutual fund by ERISA, globalization and modern technology, and before investors began to recognize the unique problems of managing such a large fund.
Modern portfolios manage billions of dollars, and to reduce exposure to non-systematic risks, portfolios require many securities with diverse risk groups. At this scale, it is challenging to establish an effective portfolio. The absolute size of the investments made by common institutions has grown exponentially today. Furthermore, the potential universe of securities has grown in ways that are heterogeneous and complex. In the united states, the total investment in mutual funds was $ 13 trillion in 2012. The U.S. public securities account for less than 20% of the total global securities by company. In addition, most U.S. companies rely primarily on global economy. This diversity and interconnectivity has increased every year. The need to control non-systematic risks involved in such portfolios of companies is also increasing every year.
This leads to several problems: 1) the time period for testing the capitalization model is too short and more time should be given; 2) theoretical errors where risk and benefit are not necessarily correlated; 3) the inefficient design of market weighted portfolio securities, and other examples that require controls that contrast this model. In other words, there is a need for new standard cases for constructing portfolios of investment securities and for addressing the increasing complexity of present companies and the size and diversity of present funds by applying the methodology and rationale of markovitz and sharp to the complexity of present large-scale funds.
Current system classification poses difficulties in building new models of the potentially effective portfolio of these large-scale modern investment instruments. These systems are similar to the basic articles in finance, were created before the advent of large digital databases, and were modeled in accordance with the era's databases such as the Dewey Decimal System (Dewey decemal System) and the standard industry classification System. These systems classify graded stocks. Each category is a fixed hierarchy with a single parent class per stock; a parent has a single parent, etc. Each parent class has a description, but does not have the notion of specific intrinsic properties that relate stocks under one parent class to stocks under another. Without this description, it is difficult to understand which varied risks a company will be exposed to, and thus how many companies in a large portfolio share similar or related risks. The difficulty of categorizing these types is becoming more and more evident with the increasing size and diversity of today's corporate complexities and today's funds. Despite the fact that one of the greatest risks of the market-value weighting strategy is the lack of control for single risk exposure, foam prices, or a large number of non-systematic price corrections, there are currently limited tools to systematically address these issues. Therefore, there is a need for a multivariate classification system that is supported by current data processing that can provide these tools and that can build multiple different portfolios to test the efficiency of each portfolio and test criteria.
Wave nature
Volatility of pricing continues to occur each time the price fluctuates. Volatility is an important factor in the performance of an investment portfolio and these price fluctuations create obstacles to the growth of an investment portfolio. For example, daily volatility has shown that revenue from leveraging Exchange Traded Funds (ETF) is being hurt. (see TonyCopper, Alpha Generation and Risksmoothening Using Managed volume (2010)).
In an effort to reduce the impact of volatility on portfolios, various weighting schemes have been proposed. For example, one method described in U.S. patent No.8,306,892 operates by calculating weights based on market value, total domestic production value, and geographic area. In another example described in U.S. patent No.8,131,620, a portfolio of securities is weighted based on market value and positive dividend return. Many other portfolio weighting schemes exist. None of these weighting schemes fully implement the markov model, and in particular, normalized risk/benefit is a statistical process that requires matching the number of securities with the degree of association between particular securities.
Some examples, such as the example described in U.S. patent No.8,005,740, use the North American Industrial Classification System (NAICS) part for weighting. A weighting scheme based on NAICS or global industry classification criteria (GICS) associates companies by their location at a fixed level. There are two main limitations of the fixed NAICS and GICS hierarchy: 1) any entries are irrelevant and cannot be compared without a common parent; 2) only any items in the same parent can be compared according to the metrics used by the GICS or NAICS to define the group (the name of the group indicates the metrics separating it, e.g. "consumer" vs "commercial" can be relevant to the customer group).
Without control, a random set of securities may have periods of significant estimation of the up-and-down oscillations from one time period to another. These large swings in value may not be caused by variables such as metering attributes or designations such as "growth" or "value" stocks. Conversely, these large swings in value may be caused by specific intrinsic properties of the individual companies comprising the group. The estimation swing may be caused by: for example, companies have long relied on a particular commodity when it suddenly loses its value; there is excessive prosperity and unmet demand for products of companies or industries in terms of demand prospects; companies have long fixed cost contracts and the actual cost changes that their product competitors can achieve; or certain assets and product categories that a company has too high a weight in terms of product mix lose their value; or for other reasons.
There are many reasons for random foam. In some cases, the random foam is a wide market (also called system) foam; in other cases, the random bubbles are limited to constituent groups (e.g., assets or industries). There are certain events that seemingly are systematic (because they make much, e.g., internet foam) but non-systematic. In either case, the impact on the investor's return can be very negative.
Random walk theory represents the inability to address volatility and apparent randomness of the returns of stock-based investment securities. The random walk theory considers that: most random choices of stock-based investment securities will do as well as proactive management choices of stock-based securities. Random walk theory is the root cause of the index fund and is a broad support for the academic world's passive index fund. The random walk theory "taking the logical extreme …, that is, the monkey whose eyes are covered, may choose an investment portfolio that will perform as well as the investment portfolio chosen by the expert when throwing a dart. (B.Malkiel, A random walk Down walk Street, 10 th edition, 2012)
Many different weighting strategies have been proposed to address the problem of random volatility of such stock-based investment instruments. These indices perform poorly compared to debt stock indices, thereby highlighting that these passive indices are constantly plagued by the same random assumption.
Historically, healthcare has also been plagued by the same random problems. In healthcare, this assumption is designed such that random patients go to random physicians who have the same probability of receiving random responses. The probability of random responses was considered by many to be very high before modern medical and modern statistical control groups. The healthcare industry slowly addresses this problem by creating detailed patient profiles and developing statistical methodologies using information from these profiles to control the underlying characteristics of a given population. This work has occurred incrementally as each disease area and government agency studied and understood a series of natural deviations, until advanced domain-specific architectures have evolved.
The systems and methods described herein may be used in investment management by controlling certain types of random events that affect the overall randomness of the risk and yield of an investment security. Random movement of investment securities creates a barrier to revenues, especially large downward movements caused by events such as bankruptcy or the breaking of non-systematic foam. In these cases, it is not expected that the investment securities will rebound to the levels that existed before. In these cases, the affected security is re-priced because the market suddenly identifies that the security is being priced too high.
Non-systematic foam and bankruptcy are associated with non-systematic factors, such as potentially inherent attributes of an asset, company, or industry associated with a particular investment security. The main problem with risk management of large portfolios of securities is the inability to control the occurrence of these types of events. If a portfolio inadvertently weighs too much on a security or group of securities having a common foam or bankruptcy risk, the proceeds are greatly affected by the relatively small number of securities in the portfolio. In some cases, too high a weight in terms of certain non-systematic variables has had a similar systematic impact on the portfolio. This is obviously the case for internet foam. In the year 2000 of the calendar year, there was a 9.09% reduction in market-weighted standard pil 500. This is the worst year historically for standard pul 500. In that year, 16 stocks declined by 49.8% while the rest of the market rose by 4.28%. Unfortunately for investors, these 16 companies are all industries that move information, store information, or process information, 24.8% of the total portfolio.
Previous efforts to increase portfolio revenues have presented at least two problems: 1) many sub-optimal groups, and 2) no control of changes or associations between or within groups to ensure that each group operates in a predictable group-specific manner. The existing large-scale diverse portfolio of securities lacks control over its constituent groups, and market weighting or even weighting does not solve the problem of overall control.
Problem of scale
The above problems are particularly acute in large-scale portfolio of securities for a number of reasons. It is particularly difficult to control the different attributes associated with securities without reliable and efficient system attributes and hierarchical systems using hierarchical comprehensive hierarchies. Various example reasons why scale management is difficult are set forth below.
(a) Constitution of ownership restrictions: for many funds and fund managers, there is a limit to the proportion of companies they may own. For example, at 5% stock keeping, there are 13-D files and oversight. Many funds will not or cannot cross this threshold.
(b) Mobility restrictions of ownership: the more individual securities a fund possesses, the more difficult it is to sell the fund in terms of the liquidity of the stocks. Furthermore, because of scale, many funds have absolute dollar limits or dollar-equivalent limits on ownership. If a fund has 50 billion dollars to invest, a 1 million dollar investment may be considered too little.
(c) Larger funds require a large number of securities to fill the portfolio: due to the above identified factors and other practical problems, larger funds require investing in a large number of companies due to liquidity problems and ownership problems. Throughout an economic system, there are many bonds, and the greater the number of companies, the more difficult it is to track and supervise the potential bonds and risks from these bonds. The main part of these associations is due to non-system attributes associated with the company's supplier, product, industry, business, geographic location, etc. For investment portfolios with large numbers of securities, it is very likely to become too focused on non-systematic risk classification. Understanding and controlling different risk potential groups is difficult without a reliable and efficient attribute system and hierarchical comprehensive hierarchy for controlling these different attributes.
(d) The fact that investors do not pick all qualified companies to invest is: larger funds require investing in larger companies due to the above identified factors and many more practical problems. The companies available in the group change over time. Furthermore, over time, these securities have variable weights and are aggregated differently depending on what companies are present in which taxonomy at any given point in time. In addition to changing over time, the industry, department, or company choice also changes with geographic location. Indeed, departmental differences may be a greater cause of price variation between geographic locations than the underlying currencies driving the products. For example, in technical stocks, the U.S. portfolio is more important than the European and Latin America portfolios. Europe and latin america are relatively important in terms of commodity and raw materials. If the goal of the fund manager is monetary differences, it is important to control these departmental changes. First, it should be understood that there are different sets of potential risks at any given point in time and in any particular geographic location or category, and then these different potential risks cannot be controlled with currently known techniques.
(e) The attribute risk is multidimensional because the attribute risk is a centralized risk: the single or multiple attributes help to differentiate between risks of individual companies. For example, identifying companies in the semiconductor industry is a distinguishable risk. Further, the type of semiconductor (e.g., storage, handling, connection) is important, as it is a characteristic of the required raw material and customer. These variables, however, are often grouped in a category of large-scale funds. The existing classification of current systems tends to be standardized on a global basis and cannot differentiate between these factors. The inability to represent articulated multi-attribute risks is a significant limitation for existing large-scale investment portfolios.
Non-systematic events may occur with systematic impact if the portfolio and especially large-scale portfolios are not better controlled. Examples of non-systematic events are provided below. The known and existing systems do not address the potential statistical causes of systematic effects of random fluctuations in the composition of large-scale portfolios of securities. However, in the case of improved control, the effects of non-systematic events may be limited.
Detailed Description
Risk introduction
A security is a financial instrument representing the right of ownership (stock) in a publicly traded company, a creditor relationship (bond) with a government department or company, or ownership represented by an option. Securities are interchangeable, negotiable financial instruments of a type that represent financial value. A company or entity that issues securities is known as an issuer. The price of a security is based on its expected return during the time of ownership. In turn, the expected revenue is based on the expected quality and performance of the potential entities associated with the securities.
Investments in the investment industry are made with value-added or benefit or potential risk expectations or variances in these benefits. The investment security has two main performance indicators: the rate of return over a particular period of time and the probability or risk of achieving the expected rate of return. These two metrics are related: the higher the expected risk, the higher the expected revenue. In other words, a higher level of risk should be compensated by a higher level of revenue. The probability of return is associated with the variance of the expected outcome for a given investment security. The actual revenue required for the investing industry may be constrained by a number of factors including: market conditions, the provision of a given investment capital, or anticipated inflation or deflation of currency. However, for a given market at a given time, the associated profitability in relation to the investment security will include risks associated with: risk related to the type of investment security and potential industries or attributes associated with the investing industry.
Securities vary according to their profitability characteristics and expectations. Each security represents a particular ownership location at a particular company. Each type, such as bond, stock instrument or derivative, has its own specific ownership and investment characteristics. The expected revenue from a security is based on the type of security and its characteristics, as well as the potential performance of the associated entity in relation to the ownership represented by the security. The expected revenue and the actual revenue may be substantially different for any security. The difference between the expected revenue and the actual revenue is the risk of the security.
There are two main types of risks in investing in securities. The first type, systematic risk or market risk, refers to events that affect the overall outcome, such as broad market revenue, overall economic system resource holding or total revenue, etc. In many environments, events such as earthquakes and major weather disasters pose an overall risk, as these events affect not only the distribution but also the total amount of resources. The second type, non-systematic risk (also known as special risk or business risk), refers to the risk that an entity associated with a security experiences a change in wealth or even fails due to any number of factors. The systematic risk is related to the total risk of the total investment domain. Non-systematic risks relate to entity-specific risks for entities whose securities have ownership locations.
The expected profitability (and volatility) of an investment security depends on factors including market forces and forces that depend on the particular investment security and its underlying characteristics. The former forces are systematic and affect a broad class of securities. The latter being specific and specific to each particular investment security depending on the particular attributes of each particular investment security. The variation in return for investment securities that depends on the latter depends on the particular attributes of the particular securities.
Risk of non-systematics
Non-systematic or business risks are specific to the quality and attributes of a particular entity associated with a security. The wealth change or even bankruptcy of a particular business is related to the inherent attributes of the business itself. These attributes include any number of factors including business, business administration/personnel, business services, business products, business customers, customers of business customers, availability of supply, strength of business provider or particular qualifications and liabilities of business. Events related to any of these things, or any combination of these things, may cause the wealth of the business to change, and in doing so, the expected revenue of the business associated with the entity.
In addition to individual companies, a portfolio of securities may be affected by certain non-systematic risks if the portfolio is too exposed to or too concentrated on those non-systematic risks. One of the main reasons for having a portfolio is to reduce this exposure to non-systematic risks by: the portfolio is spread over many investments with unique non-systematic risks so that the non-systematic risks do not materially alter the wealth or expected return of the total portfolio. This strategy is relatively easy for individual investors who can decentralize a portfolio in relatively small amounts into relatively small numbers of individual securities. However, this strategy has proven difficult for larger-scale investors, such as pension funds or donation funds that have billions of dollars or billions of dollar equivalents to invest. These large-scale investors must invest in hundreds or thousands of securities at any given point in time, representing billions of dollars in value. Minimizing the impact of non-systematic risk factors of the portfolio has proven to be very difficult for investors with such investment scales, and investors tend to be too heavily weighted in larger industry bubbles, and negatively impacted by repetitive technical bubbles and persistent too high a weight on large bankruptcy or large degradation classes of financial instruments (e.g., mortgage securities). The invention disclosed herein provides a method for a portfolio manager to systematically control these non-systematic portfolio risks, which are disproportionate and negatively impact large-scale portfolios.
Properties
The system described herein may operate by assigning one or more attributes to a company associated with an investment security. The methods described herein may be implemented on a computing device to segment investment securities using attributes associated with a company or attributes related to the investment securities associated with potential investment securities. These attributes may be used as markers of particular risks associated with an event such as bankruptcy or foam. These attributes allow a portfolio manager to stratify or subdivide a portfolio into groups according to particular attributes, where each group represents a risk associated with a particular attribute. The hierarchical children of these parent groups have unique risks between groups and shared common risks shared with their parents.
After the portfolio is stratified, weights may be assigned to the stratified elements, and a reconstruction plan for systematic based weighting may be performed. In this manner, a portfolio manager may understand and manage the specific risks in a portfolio. Furthermore, the specific risk can be designed by arbitrarily setting the weight for the layered units. In some embodiments, the administrator may use the weights to form a hierarchy of multiple levels of different groups and subgroups to determine the expected risk at the beginning of the process, and then weight the groups according to the expected risk results.
The methods described herein calculate, based on asset industry-specific attributes, a weight for a portfolio of investment securities having specific properties that differ from an uncontrolled portfolio of the same investment security. As described in more detail below, the present invention reduces the randomness of the return of individual sponsorship industry using sponsorship industry-specific attributes by establishing a portfolio of investment securities that reduces the impact of the risk of a particular sponsorship industry attribute by layering the specific attribute and its risk in a controlled manner over a controlled overall set of investment portfolios representing both the set defined by the common attribute and the set comprising the particular investment securities sharing the attributes associated with the set.
Layering
To control non-systematic risks, a portfolio manager must control the business specific risks present in any portfolio. These risks may be company related risks, industry related risks, product related risks, customer related risks, or vendor related risks, among others. The larger the portfolio becomes, the more difficult it is for the portfolio manager to understand what specific non-systematic risks the portfolio is exposed to. The risk component stratification approach described herein reduces the negative impact on the attribute-specific volatility of the portfolio as a whole.
The system described herein can be used to create standardized barcodes that identify a large number of commercial attributes. The system may assign standardized barcodes having standardized attributes to the securities in the portfolio. Based on the attributes of such barcodes, the specific non-systematic risk exposure present in the portfolio can be identified and controlled. When certain non-systematic risk exposures are identified, the method can be used to control these non-systematic risks by limiting the exposure of the portfolio to these risks.
The system may be used to create a hierarchical level of specific risk groups across which securities on a portfolio are assigned and to select expected exposures to the hierarchical risk groups by applying calculated or user-provided weights for identifying non-systematic risks. Thus, layering can be used to systematically control exposure to non-systematic risks. These exposures can then be managed over time by creating rebalancing rules that re-set the exposure of the portfolio to these identified non-systematic risks on an appropriate periodic schedule. In this manner, the exposure of large-scale portfolio of securities to a large number of non-systematic risks can be systematically determined and managed.
Businesses that have a common intrinsic property or set of properties are related to events associated with that property or set of properties. The indicators of relevance will vary with the importance level of the attributes of a particular business. For example, if all network equipment companies share the same customer, the loss of a primary customer (e.g., a northbound electric network, a gigante network company) will affect all companies. However, the impact of the scenario where the north electrical network is the only customer of the company is greater than the scenario where the north electrical network is less than 5% of the company's business. In this manner, the group companies in the risk group defined by the attributes provide the portfolio manager with a method of grouping the securities in the group that are related to the particular attribute associated with the event. Furthermore, most properties are then part of a larger property group. All companies sharing a northbound power network are part of a network device group, which in turn is part of a communication device group, which in turn is part of a larger digital technology group. In this manner, the use of specific attributes enables portfolio managers to group securities by broad and narrow classifications and the importance of those classifications to individual securities.
The layered processing may include: the population is divided into independent subsets (called layers) within which independent samples of a particular population can be placed. Stratification is an important tool in statistics where, in order to create a sample set for a particular population, where the proportion of selected samples from each layer is assigned, the population is divided into parts or subsets (known as layers). By creating defined subsets that are assigned defined proportions, statistics can create meaningful control over the overall outcome.
The result of the hierarchical universe is referred to as a control group because the components and weights of the subsets are defined and can be tested. In any population, there tends to be random variations in which a subset of the population has a different characteristic than the population as a whole. The effect of these abnormal sub-populations may be mitigated by grouping the populations into sub-populations that are expected to behave differently and then ensuring that some of each sub-population is used when studying the population as a whole. As an example, if the yield of workers is being studied, it may be found that workers are less efficient on monday morning than other times of the week. However, if the 20 days of operation in the year are sampled randomly, a sample set that is abnormally biased toward Monday may be received randomly. This is not a representation of work because the data set is biased toward a period of work inefficiency. In an effort to eliminate this bias, the overall set may be layered across five subsets consisting of one subset for each day of the week. When random sampling is performed, each subset will be assigned an equal number of working days, such that the entire sample is made up of five subsets each having an equal number of example days. In this way, the layering can limit the bias of the sample set and increase the probability of representing the result.
The layering methodology is common in the examples cited above. Layering provides the following controls that may: 1) ensuring an unbiased sample set as a representation of the entire population; or 2) ensure that certain deviations create results that are representative of a potential population that is desirable, but not essential. Examples of the former are clinical trials or trials in social science. In these cases, the experimenter is attempting to form a representative sample set that can change the assumptions to investigate how the assumptions affect the controlled population. An example of the latter is risk management, where different overall subsets are uncorrelated and have highly disparate presence or fluctuations. In this case, the hierarchy may want to bias the sample set towards a particular sub-class, e.g., a subset with relatively high or low fluctuation. In both cases, the layering enables a statist to build a sample set with predictable results based on the type of layering model implemented. In layered sampling, layers are formed based on shared properties or characteristics of members. These attributes may be based on a measure of the overall relative quantity, such as the size, speed, or lifetime of the overall. Furthermore, the attribute may be based on a physically identifiable attribute such as the color of hair, the color of skin or the color of eyes, right-handed or left-handed.
In the context of an investment security, the value of the investment security may be directly or indirectly related to: 1) a type of asset or a type of operation directly or indirectly associated with a security, and/or 2) a particular attribute associated with an asset or an operation directly or indirectly associated with a security.
The total expected return for the portfolio can be determined based on the expected return for each individual investment security and its weight in the portfolio. The total volatility of the portfolio can be determined based on the volatility and weight of the individual investment securities and the pairwise correlations of these individual investment securities with each other. Thus, overall volatility can be controlled and reduced by stratifying portfolios into groups having relatively high intra-group correlations and relatively low inter-group correlations. Volatility can be controlled by dividing the investment securities into related clusters, i.e., groups formed based on shared and unshared attributes corresponding to risk.
Define a limit
Investment of securities: as used herein, an investment security is defined as a financial instrument that may represent any or all of the following: ownership locations in a company (stock) or collection of assets; creditor relationship with company; an individual or governmental agency (bond) established directly or indirectly through the issuer's assets; or rights or ownership represented by options or other derivative tools. An investment security may be an interchangeable, negotiable financial instrument representing a financial value associated with an entity. The value of an investment security may be based on the type of security, the type of relationship with the issuer, and the type of assets and debts directly or indirectly associated with the security.
The attributes are as follows: the entities represented by the investment securities may be associated with attributes. The system may identify a variety of attributes associated with the entity. As a non-exclusive example, the system may operate on the following categories of attributes: (1) global dependent, or (b) intrinsic. The population-related attribute may be, for example, a scoring system that designates a high/low volume security or a growth/value security. The system may be configured to identify multiple types of intrinsic properties. By way of non-limiting example, the types of intrinsic properties may be: intrinsic properties of the grammar structure, context properties, metering properties, and market-based properties. Some intrinsic properties may also be considered absolute. An example of a metering attribute may be total debt and an example of a market-based attribute may be market value. Examples of context attributes may include: (a) geographic location attributes, (b) attributes pertaining to the company's assets (e.g., "big containers" and "small containers" for shipping companies), (c) attributes related to products (e.g., "luxury" and "not luxury" items of clothing), (d) attributes related to customers (e.g., lists of particular customers), and (e) attributes related to vendors (e.g., lists of particular vendors). The system may identify any combination of different types of attributes.
Any combination of multiple attributes may be formed into a composite attribute. Any combination of intrinsic properties may be considered a composite intrinsic property, while any combination of related properties or any combination of intrinsic and related properties together may form a composite related property. The composite attribute may be defined as a new single attribute.
In some cases, the attributes may be defined to include attributes related to the entity associated with the investment security, and accordingly exclude attributes of the investment security itself. For those embodiments, the system may be configured to define attributes to specifically exclude attributes related to: the type of investment security (e.g., stock, debt, or derivative) and characteristics of the investment security, such as preference, due date, duration, or bargain price. In those configurations, those attributes that are excluded are not considered attributes because the attributes that the lock includes are related to the company or asset with which the investment security is associated, not to the investment security itself.
In some implementations, the intrinsic attributes may be defined to exclude metering attributes and performance attributes. The intrinsic properties included may be important properties, characteristics, or intrinsic characteristics of the underlying entities or assets associated with the investment security. For example, the intrinsic properties may define: what a company does, such as manufacturing or shipping; attributes related to the company's products, such as the vehicle, computer, sofa, and type of vehicle, computer, or sofa; attributes related to a company's customers, such as customers or businesses; attributes relating to a customer of the customer; attributes related to the geographic location of the business or attributes related to individual businesses of the business; attributes related to the product and the materials that the company uses to provide its product; attributes relating to any of a number of industries or portions of industries that a company may operate; attributes related to the business structure of a company, such as integrated, non-integrated, forward-integrated, backward-integrated, or networked; company-based management, decision-making, and policy-based management or policies, such as unique risks; a financial lever; attributes relating to any of a number of government or macro economic risks associated with a particular business or country of business trade; attributes associated with a metering risk or trading risk identified by a business that is core of its trade; or risks associated with a classification dependent upon a particular business or department by the investment community. At any given point in time, any of these attribute factors or industrial events related to these attribute factors may affect the risk associated with the investment securities associated with entities having these attributes. While the inherent attributes may provide relevant orders or offers, the inherent attributes are not necessarily literally ranked.
In some embodiments, the population-related attribute may be defined to include a characteristic based on any one of: a rating system; a scoring system that compares rate-based metering or performance characteristics of entities or assets associated with the investment securities at some point in time and then groups the investment securities that are scored by their associated scores; or any system that identifies through any type of scoring system that assigns different identification values to the same entity, product, or asset at different times based on their scores; and a ranking system. In these systems, because these systems are point-in-time measurement systems that group investment securities based on measurements at a given point in time, the same entity or the same group of assets may be assigned different values at different points in time. Securities that satisfy a classification depend on the score of the company or group of assets at that point in time, not necessarily on the particular underlying business or group of assets being scored.
A layered synthesis unit: as used herein, a hierarchical synthesis unit is defined as a hierarchical organization for investment securities, comprising: 1) a parent group defined by one or more attributes, wherein all members of the parent group have in common the attributes used to define the parent group; and 2) at least two sub-groups of the parent group, the sub-groups being considered as children of the parent group and/or siblings of each other. All members of the subgroup have in common the attributes used to define the subgroup. Further, all members of the child group have in common the attribute for defining the parent group of the child group. Any hierarchical synthesis unit and sub-units within a hierarchical synthesis unit may include any number of other sub-units that comply with the rules of their parent units or sub-units. In some cases, a hierarchical synthesis unit may include only a parent group and two child units. In other cases, the hierarchical synthesis unit may include as many portions as the original synthesis unit parent will support in size and diversity.
Layered comprehensive investment portfolio: as used herein, a hierarchical composite portfolio is defined as comprising at least two hierarchical composite units, wherein attributes of parent classes in the composite units represent risk groups such that: 1) the parent risk group has a different risk distribution relative to other parent risk groups; and 2) all subunits comprising the investment securities in the risk group are defined as hierarchically integrated units.
The composition unit parent may satisfy the condition of sharing a particular common attribute or set of common attributes with the member, although other constraints may exist in the parent group of the hierarchical composition unit. The parent group of the plurality of hierarchical synthesis units may include: a hierarchical composite portfolio defined as a portfolio that creates a composite unit to address defined differential risks through the composite unit comprising the hierarchical composite portfolio.
Syntactic structure properties
The above-described attributes may be expressed as domain-specific grammars that define the structure of the hierarchical synthesis unit and the hierarchical synthesis portfolio. The structure may be defined by using a domain-specific grammar and a domain-specific grammar location, including identifying attributes related to the domain-specific data entities associated with the grammar location. Grammar tags may have relational attributes that relate grammar positions to one another.
As used herein, a grammar can be thought of as a set of rules. The grammar position is based on the valid position of the rule set. Symbols in the database may be used to tag data entities. Grammar tags may be used to tag associations between symbols and rules. The grammar tag associates the data entity tagged by the symbol with other data entities in the domain based on the rule set established by the grammar. The process of grammar tagging provides a means for associating domain-specific information. Information in the domain is obtained and tagged with rules related to the information in the domain. Grammar tags may be dynamic.
The syntax tags for the hierarchical synthesis unit may be expressions that serve as labels for the tags. Such expressions may adhere to syntax that can be represented in a syntactic expression or equivalent notation in a BNF notation. Any expression or sub-expression of the grammar containing elements having a range of possible values may be hierarchically organized, in which case the expression or sub-expression describes a dimension comprising a region or contiguous sub-region within a multidimensional space. By default, syntax elements specified by hierarchy are interpreted from left to right according to the position of the syntax elements within the expression, in successive levels from top to bottom within the hierarchy.
A grammar can represent a hierarchy of coordinates that provide continuous specialization; the degree of specialization increases with the depth of the hierarchy. The grammar may also provide for stepwise serialization in multiple levels; the degree of serialization increases as the number of elements at successive levels increases.
Furthermore, syntax elements share approximate syntax positions in terms of successive levels of specialization and/or degrees of serialization for both:
a) a parent class of syntax elements in the hierarchy; and
b) sibling classes of syntax elements in similar locations across different levels in the same domain according to the same syntax.
A syntax element may be considered to have an approximate syntax position if it is relatively close to other elements based on its hierarchical specialization or sequence position. These relationships allow comparison of values across grammar positions. By default, syntax elements specified by hierarchy are organized alphabetically and/or numerically within a given hierarchy level.
The syntactical tags of the attributes associate the data entities with the shared attributes by assigning the data entities to elements in a common set of syntactical tags. The grammar tag associates the data entity with other data entities in the domain according to the grammar association of the data entity. Thus, the grammar tag inherently groups and/or aggregates data entities sharing the grammar tag. In some cases, the grammar tags may be used to create a specification model for the portfolio, discussed in more detail below.
An example representation of a grammar is shown in fig. 8A and 8B. A graphical representation of the syntax is shown in fig. 9.
Portfolio architecture creation
Constructing large-scale portfolios of securities is challenging for a number of reasons. It is difficult without a reliable and efficient property system and a hierarchical system that uses hierarchical integration levels to control different properties. The systems and methods described herein are capable of designing and managing large-scale based risk exposure independently and together.
The portfolio of investment securities designed is the following set of securities: the risk/benefit distributions are designed (or selected) from the overall or potential risk groups used to build the consolidated potential security to be different from the uncontrolled groups.
A hierarchical composite portfolio comprising investment securities may generate new units based on a dynamic combination of entities of similar kinds, the new units comprising a portion of each of the components combined to create a new entity having different characteristics than the respectively obtained potential components. Dynamic characteristics mean that the characteristics of the investment security change and change over time. The investment portfolio can be configured to possess the dynamic characteristics to create a reliable portfolio that substantially maintains the characteristics of the investment portfolio over time.
A method for creating a hierarchical composite investment portfolio using a domain specific grammar for investment securities may comprise the steps of: 1) grouping investment securities having a common risk attribute; 2) the grouped investment securities are layered into the following sub-groups: a) are associated with different risks while b) remain associated with the risk characteristics of the group to which it belongs.
In one embodiment, the hierarchical composite portfolio can include an identification of a plurality of investment securities and associated weights. As a non-limiting example, identification and weighting may be performed using computerized processing according to the example method illustrated in fig. 1. As shown in FIG. 1, the method may first generate a hierarchical portfolio architecture (1125), and then generate a resulting list of investment securities and weights (1150). In an initial step, the layering module (1105) may receive as input investment security attributes (1120) and a hierarchy of attribute rules (1122), both the investment security attributes (1120) and the hierarchy of attribute rules (1122) may be stored in one or more computerized data storage devices. As a non-limiting example, the investment security attributes may be selected from those examples provided in the above definitions. Other attributes and other attribute types may be used.
As described above, the attribute rules may be provided by a grammar for portfolio architecture. The grammar rules may define relationships between the attributes and the investment securities associated with the attributes.
The hierarchical model (1105) may also include a selection sub-module (1110) to receive as input a selection of attributes and/or rules (1121) from a user. In some embodiments, rules and/or the structure of rules may be predetermined. For example, a syntax including a rule for describing a company is shown in fig. 8A to 8B. In other embodiments, a pre-existing rule set may be edited by the user, or the rule set may be user-defined. Rules, such as those illustrated in fig. 8A-8B, define relationships between syntax elements. The attributes selected by the user are then applied to the grammar. In other implementations, the user may be provided with an interface for creating new rules (1121), and then the new rules (1121) are input to the layering module (1105).
In some implementations, the rule statement may be a boolean statement in the form of "attribute," "operator," "value," that returns true or false based on its attributes for an entity or an entity-associated investment security. In some implementations, a rule may be a boolean expression that combines (via boolean operators) one or more rule statements. Example rules are shown by the lines in fig. 9.
In some embodiments, a hierarchy of rules may be defined as a relationship between a set of two or more rules that defines an order in which the rules are applied, subject to the following constraints: the constraint is that any entity or investment security of an entity that does not satisfy a rule at a node of the hierarchy will not pass the rules of any of the subclasses of the parent class. The hierarchical submodule (1115) may be configured to create a hierarchical portfolio architecture (1125) based on a hierarchy of rules (1122), investment security attributes (1120), which are optional at this stage, inputs (1121) on the creation and selection of rules for the rules, or a list (1131) of other identifications of investment securities. The hierarchical portfolio architecture (1125) can then be electronically represented and stored on a computerized data storage device.
The rules may be used as statements that filter entities and investment securities based on attributes. A hierarchy may be used to define relationships between rules that specify the order in which the rules are applied. Any companies excluded from the high ranking will also be excluded from the lower ranking group. The multi-property system described herein can be configured by changing the population of any parent or child by changing one (or more) of the properties defining the parent or child.
Example graphical and textual representations of the resulting hierarchical portfolio architecture are shown in fig. 3 and 4. FIG. 3 shows example attributes and their grammatical positions. The attribute-based rules shown in FIG. 3 are presented graphically in FIG. 4. The rules shown in fig. 3 describe a high hierarchy level, including: two groups of business locations (1; 1205) with real estate and equipment material manufacturers (2; 1210). The rules in fig. 3 also describe the enterprise location of the real estate developer (1. a; 1215), the real estate operator (1. B; 1220), the REIT/real estate lessor (1. C; 1225), the material manufacturer for information handling devices (2. a; 1230), the manufacturer of materials for non-information handling devices (2. B; 1235). These enterprise locations are shown at level two of the hierarchical architecture. The rules in fig. 4 include several third level relationships. The third level defines: relationships for customer real estate developers (1, A.i; 1240), industrial real estate developers (1. A.ii; 1245) under the real estate developers (1. A; 1215); the relationships of North American real estate operator (1. B.i; 1250), European real estate operator (1. B.i; 1255) and Asian real estate operator (1. B.i; 1260) under real estate operator (1. B; 1220); and low lever REIT (1. C.i; 1265) and lever REIT (1. C.ii; 1270) under REIT/real estate lender (1. C; 1225). Further relationships are shown under group (2. A; 1230) and group (2. B; 1235), but are not described further herein.
Many attributes may be used to create a portfolio architecture. The portfolio architecture can include nested sets of tiers. As a non-limiting example, in some instances, the groups may be formed by referencing attributes that are common to all entities in the universe, such that at each level, each element of the universe is in exactly one group. In some embodiments, the groups may be subdivided into any number of child sub-groups, and the number need not be the same for each group in the original parent group, and this subdivision process may be performed any number of times, each time adding a level to the hierarchy in a "top-down" manner. In some embodiments, hierarchical synthesis units are used to build larger hierarchical synthesis units, as well as create hierarchies in a "bottom-up" manner. In some embodiments, a combination of "top-down" and "bottom-up" approaches may be used. Regardless of the method of construction, the resulting hierarchical portfolio architecture (1125) can include an electronic representation of a collection of attributes arranged hierarchically according to defined attribute rules.
Weighting of investment securities
The tiered synthesis portfolio can include one or more tiered syntheses that maintain a defined risk exposure by weighting the components of the tiered synthesis accordingly.
The hierarchies described herein may be adjusted in a variety of ways to enable a user to control the entirety of the investment security and thereby control the results caused by events associated with the population of investment securities. The bias in the portfolio return may be created based on changes made to any or all of the following: 1) gross changes in investment securities; 2) a way to layer the population of investment securities (portfolio architecture); and, 3) the manner in which hierarchical elements are weighted at arbitrary locations within the hierarchical hierarchy.
When the portfolio architecture has been determined, a weighting may be determined for the investment securities. As a non-limiting example, the weighting function may be any of the following functions: for a particular group in the hierarchical portfolio architecture, a value between 0 and 1 is returned indicating a weight associated with the group related to the siblings of the group in the hierarchical portfolio architecture. In some embodiments, the sum of the weighting functions of all siblings at each level may be equal to 1.
In some embodiments, the weight of a security is a function of its position in the hierarchy only. As a non-limiting example, the weights may be divided evenly among all of the sub-classes in a given parent group of sub-classes. That is, if the first level contains 10 groups, each group will be given a weight of 10%. If one of these groups contains 4 subgroups, each subgroup will be given a weight of 25% of its parent group, resulting in a weight of 25% by 10% to 2.5%; meanwhile, if the different top level groups have 5 subclass groups, each subclass group has a weight of 20% by 10% to 2%. This process may be repeated for each level, ultimately yielding a weight for each bottom level group. A similar process may be applied to the securities within each underlying rank group, resulting in a weight for each security of the universe.
In some embodiments, the weighting algorithm may be executed by a computer as follows:
in other embodiments, the weight of any group may be a function of the attributes of the companies of that group. As a non-limiting example, groups (formed using any attribute) may be weighted by a function of one or more of the attributes common to securities in the universe. As a non-limiting example, a group may be weighted within a parent group of the group in proportion to the total debt of all securities in the group. In some embodiments, the function depends on a single attribute, and in other embodiments, the function depends on multiple attributes. In some embodiments, the same function is used to weight each group in the architecture. In other embodiments, different functions may be used to weight different groups in the hierarchy. In some embodiments, the weighting may be performed by a computer as follows:
referring to the example of fig. 1, the computerized weighting module (1130) receives a hierarchical portfolio architecture (1125). As shown in fig. 2, the weighting module may also be configured to receive an identification of the investment security (1131), and an identification of an attribute of the investment security associated with the investment security (1132). The weighting module may then generate a list of investment securities and weights (1150). The weighting module is shown in further detail in fig. 6. As shown, the system may receive a selection and/or identification of an investment security to be weighted (1305). The investment securities to be weighted may be located at any one or more points of the hierarchical levels described above. Then, a weight for a single security and a weight for a group of securities for the current level may be calculated (1310). In some implementations, the calculation may be started at the top level of the hierarchical hierarchy. At the current level, weighting schemes and rules for the level may be identified (1315). The weighting factor may be calculated by dividing the unprocessed weighting proportion by n, which is the number of investment securities or the number of groups of securities (1320). As a non-limiting example, referring to fig. 4, the top rank weight may be calculated as 50% for group 1 and 50% for group 2. At the second level, groups 1A to 1C may be weighted with.50 × 33 ═ 0.165 or 16.5%, respectively.
Any positive or negative weighted bias may be applied (1325) before or after the calculation of the weights. The bias is applied by adding, subtracting, multiplying, dividing or other operations on the weights. Any bias applied to a group or investment security requires that a corresponding opposite bias be applied elsewhere in the same group or peer group of the same rank. If the bottom level can be reached and the bottom level weighting is complete, the weighting process can be terminated. Otherwise, processing may continue at the next level.
An electronic representation of the weighted investment securities may then be input as an indication to, by way of non-limiting example, exchange traded funds (EFT) or other financial instruments such as hedged funds, mutual funds, limited liability partners or other investment instruments.
In an alternative embodiment, the steps of the method for layering and weighting may be rearranged. For example, a list of investment securities may be introduced anywhere in the portfolio design process. Investment securities and/or reorganization processes may be selected prior to layering to create exposure to a particular universe. Any of the hierarchy, weighting scheme, and/or rebalancing scheme may be selected or chosen before or after the investment security is selected.
Alternative orderings and variations of the steps for creating a portfolio of investment securities as described above may be made. For example, referring to FIG. 1, identification of an investment security (1131) may be provided to the tier module (1105). In this arrangement, the hierarchical submodule can generate a hierarchical portfolio architecture (1125) of the investment securities, and then the hierarchical portfolio architecture (1125) is input to the weighting module (1130).
Recombination and re-weighting
Further, some embodiments may include periodically recombining the assigned weights to maintain a desired risk exposure. The tiered portfolio can include: one or more hierarchical composite units that maintain a defined risk exposure by weighting and periodically recombining the assigned weights for the components of the hierarchical portfolio accordingly to maintain the desired risk exposure. Referring to the embodiments shown in fig. 1, 2 and 5, the steps shown may be performed at any point to create a re-weighted portfolio based on modified inputs, such as modified weighting rules. Referring to fig. 5, in other embodiments, the re-weighting may be provided by a separate re-weighting module (1155). A re-weighting module (1155) receives a list of target exposures (1151) assigned to portfolio hierarchy positions. The re-weighting module then selects a new investment security for inclusion in the hierarchical composite portfolio.
Hierarchical composite investment portfolio scoring
Using the methods described herein, scores may be calculated for tiered portfolios. The score may be a characteristic of the portfolio and may be used in a variety of contexts. In some embodiments, the target score may be a quantifiable number that the portfolio is expected to achieve. In other embodiments, the target score may be a set of attributes that the investor wants the portfolio to have. The portfolio score may be a value or vector of values calculated from the portfolio that may be compared to a target score that an investor has for the portfolio. The target score may be a theoretical value or an estimated value.
The goal score may be used as a way to optimize the portfolio. The investor can pick a target score and the system can then be used to build an optimized hierarchical composite portfolio for that score. Alternatively, the target score may be used to establish an investment portfolio that reflects the potential overall performance. That is, the target score may be a measure of how the population is expected to perform, and hierarchical synthesis may be used to measure the performance of the population. Given a weighted list of investment securities for a portfolio and a target score, a score for the portfolio may be calculated based on portfolio-derived attributes.
The goal score may form an estimate of how the desired portfolio performed or how the manager intended the portfolio to perform based on the characteristics of the portfolio. The target score may be obtained by measuring performance of any or all of the following: individual companies, randomly sampled individual companies, hierarchical units, and/or synthesis.
The goal score may also be identified as a goal score that the investor requires as part of the investment goal. Here, the investor may want to use hierarchical synthesis to achieve a predetermined target score. By establishing groups based on common attributes, risk groups may be formed. These risk groups may then be appropriately weighted to obtain a target score, resulting in a portfolio with known bias.
In some embodiments, the hierarchical composite portfolio can be designed to meet a user-defined goal score. As a non-limiting example, the target score may include any or all of the following: (a) an absolute benefit target (e.g., expected roll rate), (b) a risk/benefit metric (e.g., a sharp ratio, a sotrino ratio, or alpha (alpha)), or (c) a risk target measured by volatility (e.g., a drop potential difference or beta (beta)). In some implementations, the target score may be a one-dimensional vector or a multi-dimensional vector of values or elements, such as those examples provided above. For example, the target score may be [ actual profit-no risk interest ]/[ expected profit-no risk interest ], wherein the target score is greater than or equal to one.
A method for building a hierarchical synthesis with a target score according to one embodiment is described below with reference to FIG. 7. As an initial step, the user establishes a population of investments by identifying a universe of investment securities (7005). For example, the universe may be a financial company and an energy company in the United states. Next, the universe of securities is filtered (7015). The company's universe is then layered 7020. Through this process, companies are placed into groups of hierarchical units, levels, based on common characteristics.
After the overall hierarchy, metrics to be used in evaluating the portfolio are identified. The metric used may depend on the population being layered. For example, the metrics for an investment-level bond portfolio can be expected yield or expected fluctuations, while the metrics for a stock portfolio can be expected risk and expected profit. When the metric criteria have been identified, a target score may be established (7010). The goal score is the goal that the user wants to see the portfolio achieve, and the goal is measured by the identified metrics. For example, a target score for an investment-level bond portfolio may be an expected yield and expected fluctuations that an investor wants the portfolio to achieve. Example embodiments of the goal score are described below.
When the target score is set, a synthetic portfolio of designs may be created (7020). Integration may be a combination of two or more hierarchical units. The synthesis may be designed to achieve a target score. The synthesis can be designed in the following way: strategically weighting hierarchical units and companies within hierarchical units (7025), and re-weighting companies within hierarchical units (7030). The weighting and re-weighting process may include changing the composition of the population (adding or deleting components in the population that meet the population criteria).
The composite may be tested according to the target score (7035). If the goal score is accepted, then the process may reach completion. If the goal score is not met, then some or all of the various parameters including 1) the hierarchy rules (e.g., architecture), 2) the weighting rules, 3) universe of filtering by hierarchy and weighting, and 4) rebalancing/restructuring strategies may be adjusted. The process may be repeated until a portfolio is created with a satisfactory score.
Hierarchical synthesis may be used as a way to optimize the portfolio. As described above, the synthesis of the design may be configured to meet the target score. Here, the target score may be considered an investment target. For example, the goal may be to build a composite whose yield, performance, variance, and/or other quality matches that summarized in the goal score.
Thus, instead of establishing the most representative portfolio for a potential population, the following portfolios may be created: lower ranked groups are strategically weighted so that the portfolio will most closely match its target score. Here, satisfying the portfolio and establishing the portfolio integration enables the identification of significantly different risk groups within the population. Because these risk groups are identified, weights may be strategically assigned across the risk groups to satisfy the goal scores.
In investing securities, a major concern of investors is risk and income. Thus, in some embodiments, the goal score may reflect an investment goal of the portfolio quantified relative to risk and benefit characteristics of the portfolio. The goal of building investment complexes is to design risks and benefits by integrating design and weighting potential constituents. The designed portfolio can produce a composite score (calculated by combining individual security data affected by multiple attributes) that can reliably yield a theoretical estimate.
Using the methods described herein, the synthesis can be designed to improve these inherent properties. Specific characteristics may be created for use in specific environments. In an investment security, a composite may be formed to manage composite scores. Hierarchical synthesis may be used to obtain a target score. The layering enables grouping of identified risks within a portfolio. Thus, when creating a design portfolio that meets a target score, the risk that the portfolio will be exposed to can be better understood qualitatively and quantitatively.
Investment statistics for hierarchical synthetic portfolios
Portfolios generated according to the methods described herein may be scored using modified versions of known statistical analyses, including alpha (alpha), beta (beta), and sharp and sotrino ratios. The score may be generated based on a standard hierarchical model portfolio and variations on the standard portfolio. For example, the tiered alpha may be calculated as a risk adjusted premium for a standard tiered portfolio. A hierarchical beta is also calculated for a hierarchical portfolio relative to a hierarchical standardized market in which the hierarchical standardized market is defined as having a beta of 1.
In some embodiments, since the market may be defined as a hierarchical portfolio of a contextual subset of the total market, the standard hierarchical beta may also be defined in terms of context. For example, as a non-limiting example, a subset of context may be defined as a department, an industry, a geographic location, a time, dictionary terminology, and so forth.
Standard case for hierarchical portfolios
Markwitz assumes that several portfolios are established to determine one that most effectively represents a group. A single model was built to test how the most representative of the treatments were to build a standard model. In building the standard model, there is control over the potential overall and target scores that are estimated to be derived from the standard case. In financial theory, the portfolio of individual securities is used to assume the model developed by the portfolio. There is an inconsistency due to the fact that the current standard case of weighting portfolios against investment securities, market values, does not result in a target score or profitability assumed by theory.
Using the systems and methods described herein, a standard tiered portfolio can be defined. The hierarchical elements may be used as tools for building standard models and developing standard goal scores. The population of investment securities may be subdivided using categories of reliable and efficient investment securities to validate the standard study. The user may develop a standard score to test the hypothesis and validate benchmarks used in comparative studies of other tiered portfolios. The system may be configured such that a standard hierarchical portfolio can be used to derive a target score. A goal score, such as a goal alpha score, for the tiered portfolio can be defined relative to a benchmark goal score.
In an initial step, a theoretical score or an estimated score may be defined. Using adjustments based on changing any or all of the following: 1) gross changes in investment securities; 2) how to stratify the population of investment securities; and 3) how to weight hierarchical elements within a hierarchical hierarchy, a portfolio can be designed to: 1) creating a representative result for a given population (referred to herein as the normal case); 2) a result of the deflection in the first direction; or 3) a bias toward the second direction.
Depending on how the adjustment is made, the bias may be toward a subset of populations within a particular population set of investment securities, such as a group of geographic locations or a group of times or a particular inherent attribute class (or subset of attribute classes). Within a hierarchical level for a given population, a particular deviation (or lack thereof) may be managed by the hierarchy itself (through structure or attribute selection) or a weighting assigned to a particular hierarchical element.
A non-standard integration is an integration that is designed to vary according to standard conditions. The variance from the standard case is considered the alpha of the design or algorithm. Using the present invention, the negative variance can be designed as an alpha for short investment positions. The designed positive variance can be designed as an alpha for long investment positions. For example, the distribution may be standard (based on standard cases) or non-standard. The non-standard distribution may be either positive (to the right of the standard) or negative (to the left of the standard). As described above, adjustments to the weights may be used to generate portfolios having these types of distributions.
Data set normalization and probability formation
Financial benchmark indices are often used to assess the performance of financial instruments. The standard pul 500 index is an example of one such benchmark index for stock-oriented funds. The reman brother composite bond index is a benchmark index for bond funds. The standard Pul 500 index is market weighted such that the market value of an individual stock is used to weight the value of the stocks in the index. Thus, a change in market value for a relatively large company has a disproportionate impact on the index. As tools representing relatively large companies fluctuate in value, funds tracking these indices also experience a corresponding fluctuation in value.
Mathematical processing according to the methods described herein can be applied to larger sets of economic data to reduce fluctuations and randomness in the results (e.g., return on investment). In some embodiments, multivariate algorithms can be used to organize larger data sets. The method can be used to generate causal relationships and perform real-time analysis.
The system may also be configured to normalize a data set representing the investment security. The normalization process includes statistical classification based on attributes of entities associated with the investment security. The attributes used for normalization may be those types of attributes described above, or other attributes related to the businesses and assets of the entity associated with the security.
A plurality of investment securities may be organized into a statistical classification. A user interface for selecting among the attributes may be provided by the system. The system may include a statistical classification editor (referred to as a thesaurus editor in some embodiments). Statistical classification may be defined within a system using an editor. The statistical classification may be defined as any one or more of the above attributes, alone or in combination with each other. Statistical classification may also be defined based on the syntax and coding system described above. In some cases, the statistical classification may also be a hierarchical unit.
Example of bankruptcy
The following example illustrates a comprehensive use case for investment securities. In this example, a hierarchical composite portfolio of investment-level corporate debt securities is created.
Investment-level debts are securities of a particular kind with a definite expected rate of return and a definite risk. Each bond is rated by a third party rating facility. The rating captures the likelihood of a debt default by a bond issuer. In the case of default risk, which is one of the most appropriate risks in the investment of such securities, corporate bonds with the same rating will have similar return on expiration, holding other variables (e.g., expiration), constants. Assuming that all payments (coupon payments and face value) are made as expected, the return-to-maturity is the annual rate that would be obtained by a bond holder holding a bond due at a given current price of the bond. In other words, the return-to-maturity is a discount rate at which the current value of the cash flow of the bond is equal to the current value of the bond. The revenue for a given due date will be the same or within a very narrow range for all bonds with similar ratings from these institutions. That is, the performance of investment-level company liability securities is predictable.
Although different investment level liability securities may have the same probability of breach, the one or more events that trigger a breach may vary depending on the issuer. That is, different companies may face different risk factors relating to the specific intrinsic value of a company and its business. While some of these factors may be unique to the company, others may be common to groups of companies. Such risks may generally include: industrial risk, product risk, customer risk, susceptibility to interest rate, geographic location, political, or economic factors outside of the control of the company, or risks associated with the CEO or management of the company. There are many company-specific attributes that may depend on the risk of a company's breach. These attributes may include, but are not limited to:
1) financial lever: some companies are more or less leveraging than others;
2) property-based assets or native operations: such attributes are not metering attributes or performance attributes, but rather attributes that define what a company does, such as manufacturing, shipping; attributes related to the company's products such as vehicle, computer or sofa and the type of vehicle, computer or sofa; attributes relating to a company's clients, such as customers or businesses; attributes relating to a customer of the customer; attributes relating to geographic location of a business or its individual businesses; attributes related to the product and the materials used by the company to provide its product; attributes relating to any of a number of industries or portions of industries that a company may operate; attributes related to the business structure of a company, such as integrated, non-integrated, forward integrated, backward integrated, or networked; attributes relating to any of a number of government or macro economic risks associated with a particular trade or country of commercial trade; attributes associated with a metering risk or trading risk identified by a business that is core of its trade; or risks associated with a classification dependent upon a particular business or department by the investment community. At any given point in time, any of these factors or any of the industrial events associated with these factors may cause or increase the risk of bankruptcy for any particular company.
3) Management or strategy: companies have unique risks based on their management, their decisions, and strategies.
4) Company asset value: bankruptcy (a type of breach) fundamentally changes the price of investment securities issued by a single company. At the time of filing for a bankruptcy, the assumption of revenue based on the operations performed is changed to include the analysis of the clearing plan and the right of each individual of the company to invest in securities. In this case, the investor evaluates their ability to receive payment in respect of a given security based on the position of the investment security in the issuing company's capital structure. The investment security may have been designated as clearing priority. These clearing priorities specify which securities receive which proceeds and when if the potential properties of the company are sold or processed.
Each of these attributes is a potential source of default risk or bankruptcy risk for fixed income investors. Some of these attributes may be related to a group of companies (e.g., the company that produces the vehicle, or a company that is business in new orleans). Thus, a portfolio that does not control a particular attribute may be inadvertently exposed to a particular risk of focus. Other companies in the group may also be affected when a member of the group breaches a property or applies for a property.
The present invention comprises a method for establishing a hierarchical composite portfolio of investment level company liability securities in the following manner: exposure to bankruptcy risks, corporate events, and other such non-systematic risk factors is limited by managing exposure to the portfolio of any particular company or industry. In a market weighted liability portfolio, the certificates are weighted in proportion to the size of the issuance relative to the total size of all issuances in the portfolio. In such an unmanaged weighting scheme, the weighting may be too high in the portfolio for the company or industry that issued the large number of debts. If one of these companies or industries has a negative event, such as bankruptcy, the portfolio itself will be severely impacted. A hierarchical composite portfolio is a tool that limits the specific exposure to the amount of computation, as well as limits on the impact of individual companies and a wide range of industries or macroscopic economies.
An example of one embodiment is provided for applying the present invention to manage the risk of default for a portfolio of investment-level companies. Each liability security has a risk level that is directly dependent on the value of the clearing of the potential assets of the company. This risk is clearly separated from the market risk associated with the supply and the demand of the debt security itself, as well as from market factors that may affect the profitability required for a given investment security at a given point in time, e.g., the risk-free interest rate at that point in time).
The system described herein avoids such non-systematic risks throughout the portfolio; that is, the system can reduce or eliminate the significant impact of individual securities or groups of securities. This may be achieved by grouping companies in risk groups (tiers) based on non-systematic attributes, such as grouping companies together with similar products, or similar customer base. Suitably performed stratification ensures that a single non-systematic risk does not represent a significant risk to the portfolio as a whole. In such a tiered integration, bankruptcy risks are spread across enough unique groups to minimize the impact of bankruptcy of any one group or company.
The invention can be used to create layers as follows. For investment-grade bonds, there are three types of causes of degradation or bankruptcy: 1) company-specific risks; 2) industry-specific risks; and 3) product specific risks. Investment-level bonds of a given rating should have the same probability of risk of degradation or risk of bankruptcy, but the rating does not provide information about the likely cause of bankruptcy. Indeed, the factors that may cause a publisher to default may be fundamentally different for bonds of the same rating. However, these bankruptcy factors are inherently related to the inherent attributes of the issuing company. Using these attributes, the bonds can be grouped into risk groups based on the characteristics of the bond issuer in relation to the issuer's bankruptcy factor. This process may be repeated to form a nested hierarchy of risk groups, where each child group has its own risk and also has a risk associated with a parent group. These risk groups are then layers that can be used to build a tiered investment complex. By layering the investment across these layers, the chances that a negative event of a single company or industry may severely impact the portfolio can be substantially mitigated.
Example of industry Risk
The following example illustrates additional use cases for tiered integration of investment securities. In this example, a portfolio of investment securities according to the standard Pul 900 index of stocks is created. The composite is a broad-based index that includes large and medium stock securities issued by U.S. companies from various industries. The universe is a combination of a standard pul 500 index and a standard pul 400 index that track large and medium disk companies, respectively, with the standard pul 500 index and the standard pul 400 index. During a period of time, such a stock universe should display a consistent revenue premium associated with a non-venture investment such as the U.S. short term treasury bond.
In this example, the revenue of the market-weighted standard Pul 900 is compared to the revenue of the same universe of securities designed to be hierarchically integrated using the method of the present invention. Attributes related to the functional characteristics of these 900 companies are used to create hierarchically nested layers that functionally group similar companies together. These layers are used to determine a weight for each security according to the methods described herein. The portfolio is rebalanced quarterly, returning each security to its original weight.
Layering provides important benefits in environments where a particular industry experiences negative price shock (colloquially referred to as industrial foam "cracking"). As the industry foams grow, the market value of the companies in the industry grows, and thus, the weight of the industry in the market weighted portfolio grows. In market-weighted funds, which lack attribute-based control over the weight of individual companies and the weight of similar company groups, such foam can cause inadvertent overexposure to specific risk groups that include risks that affect only specific industries. When too high weight industrial foam collapses, the portfolio proportions suffer disproportionately. Even if companies other than the industrial foam perform reasonably, the negative return of the over-weighted company will result in a negative return for the entire portfolio.
However, in a hierarchical portfolio, the risk of industrial foam can be substantially mitigated by layering the universe such that the tiers correspond to different industrial risks. In this way, industry-specific risks are isolated and cannot be scaled out to cause negative performance in the portfolio.
The growth and diskless of information technology stocks from 1997 to 2000 exemplifies the benefits of a tiered portfolio. Using grammatically structured attributes, groups of companies whose business functions involve moving, storing, or processing information are defined. The companies in this group include: microsoft, Cisco, Intel, AOL, high-pass, and other such information technology companies.
The twenty largest such information technology stocks in the standard piler 900 gained weight in the late 90 s of the 19 th century, so that by 2000, these twenty stock tickets dominate the portfolio. In 1997, 1998, and 1999, the twenty tickets were collectively weighted at 11.8%, 13.7%, and 20.4% of standard piler 900, respectively. In 2000, when the foam collapsed, these stocks were devalued by 42.3%, while the standard piler 900 as a whole benefited by-6.9%. Excluding these information companies, the rest of the companies of standard pul 900 earn a 6.8% profit. That is, the "overall market" decline in 2000 was not a systematic failure; but rather as a result of uncontrolled overexposure to individual industries.
Such industry-specific risks can be controlled in a tiered portfolio. In an example hierarchical portfolio, the same twenty information companies are set at a weight of 2.0% and adjusted to that weight quarterly. In 2000, the isolated group performed poorly (devalued by 59.7%), but outside the group, the example hierarchical composite portfolio had considerable revenue. Excluding these twenty companies, the example tiered portfolio capitalizes 21.3%. Overall, in 2000, the example tiered portfolio composite earns a 17.6% profit, and a 24.5% profit over the exact same universe of market weighted portfolios.
The performance of the market weighted standard pul 900 compared to the performance of the example hierarchical composite portfolio for the same universe shows how the hierarchy can prevent non-systematic industry risks from affecting the entire portfolio.
System architecture
The systems and methods described herein may be implemented in software or hardware or any combination thereof. The systems and methods described herein may be implemented using one or more computing devices that may or may not be physically or logically separate from each other. Moreover, various aspects of the methods described herein may be combined or consolidated into other functionality. An exemplary computerized system for implementing the present invention is shown in FIG. 10.
In some embodiments, the illustrated system elements may be combined into a single hardware device or separated into multiple hardware devices. If multiple hardware devices are used, the hardware devices may be physically located close to or remote from each other.
The method can be implemented in a computer program product accessible from a computer-usable or computer-readable storage medium providing program code for use by or in connection with a computer or any instruction execution system. A computer-usable or computer-readable storage medium may be any apparatus that can contain or store the program for use by or in connection with the computer or instruction execution system, apparatus, device.
A data processing system suitable for storing and/or executing corresponding program code may include at least one processor coupled directly or indirectly to computerized data storage devices, such as memory elements. Input/output (I/O) devices (including but not limited to keyboards, display devices, pointing devices, etc.) can be coupled to the system. Network adapters may also be coupled to the system to enable the data processor to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. To provide for interaction with a user, features may be implemented on a computer having: a display device such as an LCD (liquid crystal display) or another type of monitor for displaying information to a user, and a keyboard and an input device such as a mouse or a trackball by which a user can provide input to a computer.
A computer program may be a set of instructions that can be used, directly or indirectly, in a computer. The systems and methods described herein may be implemented using the following programming languages: such as FlashTM、JAVATM、C++、C、C#、Visual BasicTM、JavaScriptTMPHP, XML, HTML, etc., or a combination of programming languages, including compiled or interpreted languages, and may be deployed in any form, including as a stand-alone program or as a module, part, subroutine, or other unit suitable for use in a computing environment. Software may include, but is not limited to, firmware, resident software, microcode, etc. Protocols such as SOAP/HTTP may be used in implementing the interface between programming modules. The components and functions described herein may be implemented using any desktop operating system executing in a visual environment or an invisible environment using any programming language suitable for software development, including but not limited to: is not limited toSame version of Microsoft WindowsTM、AppleTMMacTM、iOSTM、UnixTM/X-WindowsTM、LinuxTMAnd the like. The system may be implemented using a network application framework such as Ruby on Rails.
The processing system may be in communication with a computerized data storage system. The data storage system may include a non-relational data store or a relational data store such as MySQLTMOr other relational database. Other physical and logical database types may be used. The data store may be a database Server, such as Microsoft SQL ServerTM、OracleTM、IBM DB2TM、SQLITETMOr any other database software, relational or otherwise. The data storage portion may store information identifying the grammar tag and any information required to operate on the grammar tag. In some embodiments, the processing system may use object-oriented programming and may store data in the form of objects. In these embodiments, the processing system may use an object-relational mapping (ORM) to store data objects in a relational database. The systems and methods described herein may be implemented using any number of physical data models. In one example embodiment, an RDBMS may be used. In these embodiments, the table in the RDBMS may include a column representing the coordinates. In the case of an economic system, data representing companies, products, etc. may be stored in a table of the RDBMS. The table may have a predetermined relationship between the data. The table may also have a modifier component associated with the coordinates.
Suitable processors for executing a program of instructions include, but are not limited to: general and special purpose microprocessors, and the sole processor or one of multiple processors or cores of any type of computer. The processor may receive and store instructions and data from a computerized data storage device, such as read-only memory, random access memory, both read-only memory and random access memory, or any combination of the data storage devices described herein. The processor may include any processing circuitry or control circuitry operative to control the operation and performance of the electronic device.
The processor may also include, or may be operatively coupled to communicate with, one or more data storage devices for storing data. As non-limiting examples, such data storage devices may include: magnetic disks (including internal hard disks and removable magnetic disks), magneto-optical disks, read-only memories, random access memories, and/or flash memory devices. Storage devices suitable for tangibly embodying computer program instructions and data may further include: all forms of non-volatile memory including, for example, semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory may be implemented by or incorporated into an ASIC (application specific integrated circuit).
The systems, modules, and methods described herein may be implemented using any combination of software elements or hardware elements. The systems, modules, and methods described herein may be implemented using one or more virtual machines operating alone or in combination with each other. Any applicable virtualization method may be used to encapsulate a physical computing machine platform into a virtual machine executing under the control of virtualization software running on a hardware computing platform or host machine. The virtual machine may have virtual system hardware and guest operating system software.
The systems and methods described herein may be implemented in the following computer systems: a computer system including a back-end component, such as a data processor, or including middleware such as an application server or an Internet server; or a computer system including a front-end component, such as a client computer having a graphical user interface or an Internet browser; or any combination thereof. The components of the system can be connected by any form of digital data communication or medium of digital data communication such as a communication network. Examples of communication networks include, for example, a LAN, a WAN, and the computers and networks forming the Internet.
One or more embodiments of the invention may be practiced with other computer system configurations, including: handheld devices, microprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a network.
While one or more embodiments of the present invention have been described, various modifications, additions, substitutions and equivalents thereof are included within the scope of the present invention.
In the description of the embodiments, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments of the claimed subject matter. It is to be understood that other embodiments may be utilized and that changes or variations, such as structural changes, may be made. Such embodiments, adaptations, or variations do not necessarily depart from the scope relative to the intended claimed subject matter. Although the steps herein may be presented in a certain order, in some cases the order may be changed so that certain inputs are provided at different times or in a different order without changing the functionality of the systems and methods described. The disclosed steps may also be performed in a different order. Moreover, the various computations herein need not be performed in the order disclosed, and other embodiments using alternative orders of computation may be readily implemented. In addition to being reordered, a computation can also be decomposed into sub-computations with the same result.