WO2001037166A2

WO2001037166A2 - Systems for generating custom trade strategies and methods therefor

Info

Publication number: WO2001037166A2
Application number: PCT/US2000/030254
Authority: WO
Inventors: Chan Chiu
Original assignee: Abovetrade.Com
Priority date: 1999-11-18
Filing date: 2000-11-01
Publication date: 2001-05-25
Also published as: WO2001037166A8; AU1456601A

Abstract

A computer-implemented method for creating a custom trade strategy, which furnishes custom trade recommendation for a first marketable security. The method includes electronically facilitating selection by a user of a user-selected pool of analysts from available analysts furnishing analyst recommendations via the Internet. The user-selected pool analysts represent a subset of the available analysts. The method also includes providing a cross-incorporation algorithm to apply to analyst recommendations received from the user-selected pool of analysts via the Internet. The method further includes forming the custom trade strategy from both the user-selected pool of analysts and the cross-incorporation algorithm, wherein the custom trade strategy is configured to provide the custom trade recommendation for the first marketable security when the custom trade strategy is applied against a set of analyst recommendations from the user-selected pool of analysts. Optionally, there is included performing a historical validation on the custom trade strategy against historical data pertaining to the first marketable security to obtain a performance report. The performance report is indicative of a performance of the custom trade strategy for the first marketable security if the custom trade strategy had been followed in the past for the first marketable security. The historical data includes historical pricing data of the marketable security and historical analyst recommendations from the user-selected pool of analysts pertaining the marketable security.

Description

SYSTEMS FOR GENERATING CUSTOM TRADE STRATEGIES AND

METHODS THEREFOR

BACKGROUND OF THE INVENTION

The present invention relates to innovative techniques for creating custom trade strategies for marketable securities. More particularly, the present invention relates to improved computer-implemented systems and methods for creating custom trade strategies and for utilizing the custom trade strategies to assist users in trading marketable securities.

Marketable securities refer generally to marketable financial instruments such as stocks, bonds, mutual funds, puts, calls, options, warrants, and the like. Every day in the United States alone, billions of dollars worth of marketable securities change hands, generating a significant amount of profit or loss for the trading participants. Because of the enormous stakes involved, participants involved in the trading of marketable securities are always looking for improved trading strategies that may give them an edge in spotting and executing the trades in the most profitable manner.

To cater to the needs of those participants, an industry specialized in the gathering, reporting, and analyzing of the data underlying the marketable securities and in issuing trading recommendations therefor has evolved. By way of example, thousands of people earn their living daily by gathering various types of information thought to have the potential of affecting the performance of marketable securities. The information gathered may include, for example, the fundamentals of companies that underlie the marketable securities, the long-term interest rate, relevant current events, market trends, insider trading data, overall health of a particular industry, current inventory level, and the like. Securities analysts may then apply their own analytical processes to the information gathered in order to arrive at a trading recommendation for their clients. The resultant recommendation may then be utilized by the participants in order to execute their own trades. To facilitate discussion, Fig. 1 illustrates a plurality of data sources 102, 104, 106, 108, 110, and 112, representing the data sources that may be employed by analysts A-E to derive the trading recommendations for selected marketable securities. Within the constraints of practicality in terms of time and resources, most analysts cannot pay equal attention to every piece of information thought to have an impact, however small, on the performance of a particular marketable security. Accordingly, different analysts may place different emphases on different sources of data and may employ different analytical techniques (or combinations of analytical processes and human intuition) in the derivation of their trading recommendations. By way of example, analyst A may employ an analytical tool 120, which may be based on the interest rate 102, the current events 104, and the fundamentals 106, in order to come up with his trading recommendations. Analyst B may rely on a different combination of data sources, such as the current events 104, the fundamentals 106, and the long-term debt 108, in deriving her trading recommendations. Depending on their beliefs in the significance of specific data sources on the future performance of selected marketable securities, other analysts may rely on fewer or a greater number of data sources. By way of example, analyst D may believe that there is a very strong correlation between certain insider trading actions and the future performance of a marketable security and may base his analysis primarily on the insider trading data source 110.

Despite the use of increasingly sophisticated analytical techniques, each analyst's recommendation remains at best a highly educated guess pertaining to the future performance of a particular marketable security. Accordingly, at a different periods in time, different recommendations (and concomitantly different analysts) have proven to be more effective than others at recommending timely, profitable trades. Thus, trading participants remain highly interested in finding trade recommendations that more consistently increase investment returns while minimizing risks.

While the analysts may have their reasons for believing that a particular set of data sources may have more of an influence over the future performance of a marketable securities than other data sources, participants similarly may also have their beliefs. Currently, the trade recommendations are based on the analysts' choice of data sources, not the trading participants'. If a trader wishes to take advantage of an analyst's experience and insights, that trader must accept the analyst's choice of the data sources examined as well as the analyst's own emphasis on particular data sources. In other words, there is very little that an average trader can do to influence the choice of data sources and/or the degree of emphasis placed on individual specific data sources underlying the generated recommendations.

Some traders employ a plurality of recommendations from different analysts and attempt to intuit a trade action (e.g., buy, hold, or sell) from the different conflicting recommendations. However, this approach leaves much to be desired as it is difficult to maintain consistency or to avoid being unduly influenced by emotion while mentally weighing the conflicting recommendations and externalities.

In view of the foregoing, there are desired computer-implemented systems and methods for creating and utilizing custom trade strategies for marketable securities that leverage on the analysts' experience and insights while allowing participants to have more influence over the choice of data sources employed in the generation of the trade recommendations.

SUMMARY OF THE INVENTION

The invention relates, in one embodiment, to a computer-implemented method for creating a custom trade strategy, which furnishes custom trade recommendation for a first marketable security. The method includes electronically facilitating selection by a user of a user-selected pool of analysts from available analysts furnishing analyst recommendations via the Internet. The user-selected pool analysts represent a subset of the available analysts. The method also includes providing a cross-incorporation algorithm to apply to analyst recommendations received from the user-selected pool of analysts via the Internet. The method further includes forming the custom trade strategy from both the user-selected pool of analysts and the cross- incorporation algorithm, wherein the custom trade strategy is configured to provide the custom trade recommendation for the first marketable security when the custom trade strategy is applied against a set of analyst recommendations from the user- selected pool of analysts. Optionally, there is included performing a historical validation on the custom trade strategy against historical data pertaining to the first marketable security to obtain a performance report. The performance report is indicative of a performance of the custom trade strategy for the first marketable security if the custom trade strategy had been followed in the past for the first marketable security. The historical data includes historical pricing data of the marketable security and historical analyst recommendations from the user-selected pool of analysts pertaining the marketable security.

In another embodiment, the invention relates to a computer-implemented method for providing a custom trade recommendation to a user based on an output of a custom trade strategy, which furnishes custom trade recommendation for a first marketable security. The method includes providing a custom trade strategy, which includes a user-selected pool of analysts from available analysts furnishing analyst recommendations via the Internet and a cross-incorporation algorithm applied against analyst recommendations from the user-selected pool of analysts. The user-selected pool of analysts represents a subset of the available analysts that was previously selected by the user. There is included aplying the custom trade strategy against the first marketable security to derive the custom trade recommendation. The application preferably employs the latest analyst recommendations obtained from the user- selected pool of analysts.

In yet another embodiment, the invention relates to a computer-implemented system for providing a custom trade recommendation to a user based on an output of a custom trade strategy, which furnishes a custom trade recommendation for a first marketable security. The system includes a first module for receiving from a user a first set of choices among available analysts furnishing analyst recommendations via the Internet, the choices resulting in a user-selected pool of analysts. The user- selected pool of analysts represents a subset of the available analysts. There is included a second module configured to receive, responsive to the first set of choices, analyst recommendations from the user-selected pool of analysts from the Internet responsive to the choices. There is further included a third module, operatively coupled with the second module, to cross-incorporate the analyst recommendations from the user-selected pool of analyst and to output the custom trade recommendation.

These and other features of the present invention will be described in more detail below in the detailed description of the invention and in conjunction with the following figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar elements and in which:

Fig. 1 illustrates the manner in which analysts may generate their recommendations in the prior art.

To facilitate discussion, Fig. 2A illustrates in a highly schematic format how a user may create various aggregate recommendation strategies from the analysts' recommendations and/or filters.

Various additional exemplary aggregate recommendation strategies are shown in Figs. 2B-2E.

Fig. 3 illustrates, in accordance with one embodiment of the present invention, the steps that may be employed in creating an aggregate recommendation strategy and in generating an aggregate recommendation therefrom.

Fig. 4 illustrates a custom trade strategy which involves the optional use of the user-specified rules to further refine the aggregate recommendation strategy to derive at the a more customized custom trade recommendation.

Fig. 5 illustrates, in accordance with one embodiment of the present invention, the steps involved in historical validation of a custom trade strategy.

Fig. 6 illustrates, in accordance with one aspect of the present invention, the steps involved when a user wishes to employ archived custom trade strategies to assist in the future execution of his or her own trades. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention will now be described in detail with reference to a few preferred embodiments thereof as illustrated in the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without some or all of these specific details. In other instances, well known process steps and/or structures have not been described in detail in order to not unnecessarily obscure the present invention.

In accordance with one aspect of the present invention, there are provided systems and methods, implemented on a distributed computer network such as the Internet, for permitting a user to create custom trade strategies that leverage on the research, analyses, experience, and insights of the analysts while allowing the user a greater degree of customization over the sources of data and filtering mechanisms employed in arriving at custom trade recommendations. In one embodiment, the user is allowed to select which analyst should be included in a user-selected pool of analysts from which recommendations are received. The received recommendations from the user-selected pool of analysts are then employed to generate an aggregate recommendation.

In an exemplary case, the user may select a particular analyst for inclusion in the pool of analysts based on the sources of data employed by that particular analyst and/or the manner with which that particular analyst employs a particular data source. By way of example, a particular user may believe that the inflationary rate may carry a high predictive value pertaining to the future performance of a particular marketable security or groups of marketable securities. In this situation, that user may choose to include more analysts whose recommendations are based on the inflationary rate in the user-selected pool of analysts in order to increase the emphasis on this particular data source in the custom trade strategy. By permitting the user to vary the size of the pool of analysts as well as the number of analysts associated with a particular source of data, the invention allows the user to exercise a greater degree of control over the influence of a specific data source on the aggregate recommendation. More importantly, this degree of customization is achieved while allowing the user to continue to leverage on the analysts' research, analyses, experience, and insights.

Once the user-selected pool of analysts is established, the individual recommendations of the analysts may be gathered through the distributed computer network (such as the Internet), and a cross-incorporation algorithm may be applied to generate an aggregate recommendation from the pool of recommendations gathered. The aggregate recommendation represents a single recommendation and can be generated from the pool of recommendations using any suitable cross-incorporation algorithm, i.e., any mathematical combination of the recommendations in the pool. By way of example, each recommendation in the pool may be given an equal weight, and an average or median may be taken to generate the aggregate recommendation. Preferably, all recommendations in a particular pool of recommendations should adhere to one recommendation convention (e.g., from strong sell to strong buy or from Aaa to Ccc) to simplify the calculation of the aggregate recommendation. In one embodiment, conversion functions may be provided with the cross-incorporation algorithm to conform the individual analysts' recommendations to one uniform convention in order to simplify the generation of the aggregate recommendation.

In another embodiment, different weights can be accorded to different recommendations or groups of recommendations in the user-selected pool of recommendations in order to allow the user to vary the emphasis accorded to different analysts and/or data sources. Once the user-selected pool of analysts is established and the appropriate cross-incorporation algorithm is designated, a custom trade strategy may be created therefrom to guide the user in future trades.

Furthermore, the user may also choose to incorporate one or more analyst recommendations pertaining to a second marketable security, which is different from the first marketable security of primary interest for trading purpose, into the user- selected pool of analyst recommendations. Users may choose to do so because of a belief that the combination of these two (or three or even more) marketable securities may yield a custom trade recommendation that has a greater predictive power on the future direction of the first (target) marketable security. In other words, the performance of a particular custom trade strategy may be improved in many instances when recommendations pertaining other marketable securities are involved.

In one embodiment of the present invention, the user may further apply one or more market/industry-specific filters (which may be preconfigured or may be custom created by the user) against the generated aggregate recommendation in order to further refine the aggregate recommendation. In a sense, these filters are like additional analysts, whose recommendations are to be taken into account when generating the aggregate recommendation. Incorporating such filtering may alter the timing and/or nature of the original aggregate recommendation. In general, these market/industry-specific filters rely on indicators that affect broad sectors of the market or a specific industry therein and tend to be non-specific to any particular securities.

By way of example, a particular market filter may employ key economic indicators such as various interest rates, business industry cycles, and news from the Federal government over a period of several months in order to obtain an overall economic environment of the U.S. market. That filter may then be applied to a specific market (e.g., S&P 500 or NASDAQ) as a way of checking a security's volatility against the economic climate. Another market filter may involve cross- industry reviews of the daily supply-and-demand trends of an individual market such as the S&P 500 index or the NASDAQ to determine whether it supports historically favorable conditions. This analysis may comprise the previous several weeks and considers pervasive inflationary indicators such as market volatility, interest rate changes, and inflation figures in order to put a recommendation into the context of a specific market and lowers risk by checking a stock volatility against the economic climate. Other filters may be more industry specific (herein "industry-specific filters"), which pertains only to conditions or data from a particular industry such as semiconductor, transportation, etc.

By filtering the security-specific aggregate recommendation through a market/industry-specific filter, the performance of a particular custom trade strategy may be improved in many instances. The resultant aggregate recommendation may then be employed as the custom trade recommendation to guide the participant in future trades.

In one embodiment, one or more user-specified rules may be applied against the aggregate recommendation (which may or may not include filtering) in order to reflect the specific trading preference of a particular user with regard to one or more user-specified criteria. By way of example, a particular user may indicate that a particular custom trade strategy (which may or may not include market/industry- specific filters) be followed until a particular benchmark is achieved. The user may further specify the specific trading action to be taken when the benchmark is achieved. In one example, the participant may specify that a particular custom trade strategy be followed but the securities should be sold if a certain percentage gain is achieved in a certain amount of time or if the securities reach a certain price point. Thus, the user- specific rule may override or modify the aggregate recommendation (which may or may not include the market/industry-specific filters and/or other marketable securities) to further refine it and to arrive at the custom trade recommendation.

The use of market/industry-specific filters and/or additional marketable securities and/or the user-specified rule, and the various selectable combinations that they offer provide the user with yet additional levels of customization for the custom trade strategy.

To facilitate discussion, Fig. 2A illustrates in a highly schematic format how a user may create various aggregate recommendation strategies from the analysts' recommendations and/or filters. In the example of Fig. 2 A, a particular user may believe that broad market trends in combination with the insider trading information may provide the most reliable projection of the future performance of a particular marketable security. Accordingly, this user may create an aggregate recommendation strategy based on the recommendations of analysts D and E and using cross- incorporation algorithm 202. As mentioned earlier, the recommendations of the analysts in the user-selected pool of analysts may be cross-incorporated using any mathematical combination, and may result in each recommendation having the same or different degree of contribution to the determination of the aggregate recommendation. In the case of the cross-incorporation algorithm 206, the user also simultaneously incorporates broad market filter(s) 130 and industry-specific filter(s) 132 to create the aggregate recommendations.

Another user may believe that broad market trends and long-term debt may provide a more reliable prediction of a marketable security's future performance, with insider trading information playing a negligible role. Accordingly, an aggregate recommendation strategy may be created by combining the recommendations of analysts C and E (along with industry-specific filter(s) 132) using aggregate recommendation algorithm 204. As a further example, a particular user may believe that a stock's future performance depends mostly on the interest rate and current events, as well as the fundamentals and long term debt of the company that underlies the marketable securities, with the current events and fundamentals having a greater influence in the future performance of that stock. Accordingly, this user may create an aggregate recommendation strategy from the recommendations of analysts A and B (both of whom employ the current events and the fundamentals in their analyses) using aggregate recommendation algorithm 206.

Of course, if there are multiple analysts employing the same set of data sources, a user may choose to include all of the analysts covering the same set of data sources in the pool, only selected analysts covering that same set of data sources in the pool, or simply the one analyst whose recommendation the user deems most valuable in the pool.

It should also be noted at this point that there is no requirement that an analyst be an individual formally trained in the securities trade. Any recommendation from any individual or group of individuals may be employed as long as the user feels that the recommendation employed is useful in generating a profitable aggregate recommendation. By way of example, recommendations may come from stockbrokers, day traders, academics, or even lay people. If a user desires, he or she may, for example, select a recommendation from an institution that specializes in securities trading. At the other end of the scale, the user may, for example, select a recommendation from an individual issuing securities trading recommendations based on sports scores. Indeed, any data source or recommendation, whether deemed to be reliable or unreliable according to commonly accepted financial principles, may be included in the pool by the user.

Various additional aggregate recommendation strategies are shown in Figs. 2B-2E hereinbelow. The aggregate recommendations therefrom may be employed as custom trade recommendations, or they may be refined further with user-specified trading rules prior to becoming custom trade recommendations. Note that these aggregate recommendation strategies are only exemplary and not exhaustive. In Fig. 2B, a cross-incorporation strategy is created from analyst recommendations on stock X only (stock X representing the stock of interest for trading purpose). The user- selected pool of analysts includes analysts A, B, and C in this example. In Fig. 2C, additional filters are included (such as market filter 130 and industry-specific filter 132). As mentioned, filters may be employed because a user may believe that the inclusion of a filter increases trading performance or reduces risk.

The cross-incorporation strategy of Fig. 2D differs^" from that of Fig. 2B in that it employs a recommendation pertaining to a different marketable security (stock Y) in deriving the aggregate recommendation. Note that this recommendation pertaining to a different marketable security (stock Y) may be obtained from an analyst who is also selected for his/her recommendation on the target marketable security (stock X), or may be obtained from a different analyst altogether. The use of one or more recommendations involving one or more additional marketable securities has been discussed earlier.

The cross-incorporation strategy of Fig. 2E differs from that of Fig. 2D in that it further employs filters. As in the case of Fig. 2C, filters may be employed because a user may believe that the inclusion of a filter increases trading performance or reduces risk. In Fig. 2E, the cross-incorporation strategy therein employs both the filters and the additional marketable securities in the generation of the aggregate recommendation.

Fig. 3 illustrates, in accordance with one embodiment of the present invention, the steps that may be employed in creating an aggregate recommendation strategy and in generating an aggregate recommendation therefrom. In step 302, the user selects analysts from those available in the database or, preferably, through the Internet. The selection establishes both the pool of analysts as well as the makeup of the individual analysts in the pool, thereby indirectly controlling which data source(s) would be given emphasis in the generation of the aggregate recommendation. In the case of the Internet, this selection process typically occurs though an appropriate user interface, e.g., a browser-based utility running on the client computer.

In step 304, a cross-incorporation algorithm is selected. This selection may be done automatically by the computer system or may be user-selected from the various cross-incorporation algorithms available to give the user greater control over the degree of emphasis placed on each data source and/or each analyst in the pool.

In step 306, the cross-incorporation algorithm is applied to the recommendations in the pool in order to generate an aggregate recommendation for a particular marketable security or groups of marketable securities. In the case of the Internet, this calculation preferentially, but not necessarily, occurs at the server computer, which is in communication with the client computer through the Internet. Note that the aggregate recommendation strategy comprises both the analyst selection process and the application of the cross-incorporation algorithm to the chosen analysts' recommendations.

Fig. 4 illustrates a custom trade strategy which involves the optional use of the user-specified rules to further refine the aggregate recommendation strategy to derive at the a more customized custom trade recommendation. As shown in Fig. 4, the user- specified trading rules 402 are applied against the aggregate recommendation 404, which is derived from the aggregate recommendation strategy 406 (which may or may not include filters and/or recommendations pertaining to a marketable security different from the target marketable security). In one embodiment, the user may also include, either as part of the user- specified rule or as a stand-alone rule, the transaction costs involved in carrying out the trades and/or the tax implication in order to render the simulation more realistic. The output of the custom trade strategy is a customized trade recommendation 408 may then be employed to guide the participant in future trades. After the custom trade strategy is created, the user may perform historical validation to gauge the effectiveness of a particular custom trade strategy based on historical pricing data of the marketable securities involved and the historical records of the recommendations of the analysts in the pool of user-selected analysts. To ensure the integrity of the historical validation process, it is preferable that such validation be based on historical, unaltered records pertaining to the analysts' recommendations. This is an important point because, if the historical records of the analysts' recommendations can be changed, it would be difficult, if not impossible, to accurately gauge how a particular custom trade strategy would have done if employed in the past. To ensure the fidelity of the historical records of the analysts' recommendations, the system may, in one embodiment, gather and archive the analysts' recommendations in a read-only database so that, over time, those recommendations can be used to generate trustworthy historical validations.

Generally speaking, the historical records of an analyst's recommendations, including the recommended trades, the timing of each recommendation, the price of the marketable securities at the time of each recommendation, and the like, may be readily obtained from a variety of sources. By way of example, analysts routinely publish their recommendations on various marketable securities in print and on the Internet. Once a custom trade strategy is created or chosen, the user may execute the chosen custom trade strategy on the historical pricing data of the marketable securities involved to obtain a performance report. The performance report reveals, among other information, how well a particular custom trade strategy would have done if applied to the chosen marketable securities for the specified time period in the past.

Fig. 5 illustrates, in accordance with one embodiment of the present invention, the steps involved in historical validation of a custom trade strategy. In step 502, the user may choose a particular marketable securities and a particular custom trade strategy to apply against. In step 504, the user may choose a historical time period within which the custom trade strategy may be simulated. By way of example, the historical time period may range from a predefined point in time to the present, or between two predefined points in time in the past. Preferably, the chosen historical time period is sufficiently long to allow a statistically significant track record to be generated for the custom trade strategy. In step 506, the chosen custom trade strategy is applied against the historical pricing data of the chosen marketable securities at a plurality of historical time periods (e.g., one month, 3 months, one year, etc.) within the historical time period. The historical time periods are typically the times that the chosen custom trade strategy would have made a recommendation for a trade (e.g., a buy or a sell).

In step 508, a performance report is generated for the simulation of the chosen custom trade strategy based on historical data. The performance report may be filtered in accordance with some predefined criteria, which may be predefined by the system or may be user selectable. By way of example, the performance report may show the number of trades involved, the amount of gain or loss, the amount of commissions involved, and the like. The report may be generated trade-by-trade or may be generated cumulatively for the entire chosen historical time period, or for smaller increments of time therein. If desired, provisions may be made for changes in tax laws (also another historical data) and/or the performance report may be broken down year-by-year to give a better resolution of the performance of a custom trade strategy and/or more realistically reflect the effect of taxation. By performing historical validation, the user may quickly obtain an indication of how well a particular custom trade strategy would have done if applied to a particular marketable security in the past. In this manner, the user may have a better sense of how that particular custom trade strategy may fare in the future when applied to the same marketable security or to a different marketable security.

In accordance with one aspect of the present invention, it is recognized that much work goes into the creation and historical validation of each custom trade strategy, and the database of custom trade strategies created by various users over time represent a valuable resource (e.g., for users who may not be comfortable or inclined to create their own custom trade strategies or for those who simply want to explore). Furthermore, it is recognized that if a particular marketable security receives recommendations from a large number of analysts and/or involves many user- specified trading rules and/or market filters, the possible custom trade strategy combinations that may be generated for those marketable securities and/or group of marketable securities may be unduly large. It is further recognized that if a user has access to the archive of custom trade strategies already created by other users, some of the need to generate new custom trade strategies for a particular marketable security may be reduced.

In accordance with one aspect of the present invention, custom trade strategies created by users over time are gathered, preferably via a distributed computer network such as the Internet, and archived for access by other users. More specifically, users can access (through an appropriate web page, for example) these ready-made custom trade strategies and can rank them in accordance with some performance criteria. To assure that the user receives updated performance data, the performance of the archived custom trade strategies may be periodically updated as new pricing data for the marketable securities are acquired (which may be weekly, daily, hourly or even in real time.

The ranking may, for example, allow the user to ascertain the top strategies for a particular marketable securities or group of marketable securities in view of the performance criteria employed for ranking. By way of example, a user may access the system to rank all available custom trade strategies for the stock "IBM" to ascertain the strategies that yield the best one-year investment return. Once the top strategies are found, the user may then employ those strategies in generating recommendations to assist in the future execution of his or her own trades.

The user may also employ the archived custom trade strategies to obtain a ranking for a particular analyst or specific group of analysts. By way of example, a facility may be provided for the user to search for all archived custom trade strategies which a specific analyst X is involved. The archived custom trade strategies found may then be ranked in accordance with some chosen ranking criteria (which may be entered by the user if desired). The result is a ranking of all custom trade strategies involving analyst X in accordance with the chosen ranking criteria (e.g., one-year return). This ranking may also be filtered for specific industries or groups of marketable securities, if desired. By performing such rankings, the user can ascertain the top strategies, market-wide or specific to industries, involving a specific analyst. This information may be employed to help the user in his decision pertaining to which analyst should be included in his custom trade strategy. Of course the user may perform such filtering for as many analysts as desired. To facilitate discussion, Fig. 6 illustrates, in accordance with one aspect of the present invention, the steps involved when a user wishes to employ archived custom trade strategies to assist in the future execution of his or her own trades. In step 602, the custom trade strategies as well as the associated marketable securities are archived in a database. This step 602 is typically performed over time and may be done each time a new custom trade strategy is created for a particular marketable security. In step 604, the user may enter the specific marketable securities or groups of securities for which strategy ranking is desired. By way of example, the user may specify that strategy ranking be performed for a single marketable security (e.g., the aforementioned IBM stock), any group of marketable securities (e.g., the high technology sector or the hard disk drive manufacturer group), or the entire market.

In step 606, which is also optional, the user may enter the performance criteria employed for ranking. If the user does not enter a criterion for ranking, some default criteria may apply. Exemplary criteria include one-year return, 3 -month return, one- year return adjusted for a specified degree of risk, and the like. In step 608, the system performs the strategy ranking based on the marketable securities involved (and performance criteria involved). The ranking may be performed for the top custom trade strategies (e.g., the top 10, 20, 50, or 100) or for all custom trade strategies involved. In step 610, the strategy ranking is furnished to the user to allow the user to ascertain the top strategies for a particular chosen marketable security in view of the performance criteria employed.

In one embodiment, the ranking is furnished on a web page and each marketable security in the ranking may be linked to other pages that illustrate, for example, its performance record, cumulatively for a given period of time and/or trade- by-trade, the formulation of the custom trade strategy itself, and the like. In this manner, the user not only receives the ranking but can also drill down to view the custom trade strategy and its performance in greater detail. This is particularly useful as users generally want to know further details about a particular performance result to ascertain whether the particular trade strategy involved is likely to produce a similar result in the future. Further, it is contemplated that a user may be able to combine trade strategies for different marketable securities to arrive at a trade strategy ranking for the group. Thus, the user may be able to designate any group of marketable securities and obtain custom trade strategy ranking therefor.

In accordance with yet another aspect of the present invention, the archived custom trade strategies may be employed to quickly furnish the user with a recommendation of the best buy or best buys of the day. By way of example, a user may wish to know the recommendation for a best buy for a group of marketable securities (pre-selected either by the user or by some other entities) or for the market as a whole. In one embodiment, the best buy recommendation(s) may be obtained by ranking, using the latest information available for the day, the custom trade strategies (which may pertain to the whole market or selected groups of securities per user preference) according to some ranking criteria (which may be furnished to the user or may be supplied by the user and may relate to any performance criteria, for example) and selecting from the top-ranked trade strategies those with the best buy, strong buy, or buy recommendations.

The user may also specify to be alerted (via, e.g., a supplied email address using an appropriate email server, a supplied pager number via an appropriate pager server, a supplied telephone number via an appropriate phone server, or an instant message via an appropriate web server) when a chosen custom trade strategy outputs a recommendation that satisfies the user's alert filter. By way of example, the user may ask to be alerted via any of the aforementioned communication methods when a chosen custom trade strategy generates a buy or sell recommendation for one of the marketable securities in which the user is interested. As another example, a user may ask to have the trade ranking emailed to her at specified time intervals or upon the occurrence of an event driven either by the custom trade strategy output or by some specified external event.

To promote the exchange of information, tips, and custom trade strategies, a message board may be furnished for each stock, each stock group, or each analyst to allow users to post messages pertaining to their trades, particular marketable securities, custom trade strategies they like, etc. Each message on the message board web page may be linked to the custom trade strategy under discussion and/or the custom trade strategy associated with the individual or entity posting the message. Thus, a user may not only read the message posted but may also access, via clicking on the message title for example, the underlying custom trade strategy, its trade-by- trade or cumulative performance report, and other details pertaining to the custom trade strategy involved and/or the marketable securities involved.

While this invention has been described in terms of several preferred embodiments, there are alterations, permutations, and equivalents which fall within the scope of this invention. It should also be noted that there are many alternative ways of implementing the methods and apparatuses of the present invention. It is therefore intended that the following appended claims be interpreted as including all such alterations, permutations, and equivalents as fall within the true spirit and scope of the present invention.

Claims

CLAIMSWhat is claimed is:

1. A computer-implemented method for creating a custom trade strategy, said custom trade strategy furnishing custom trade recommendation for a first marketable security, comprising: electronically facilitating selection by a user of a user-selected pool of analysts from available analysts furnishing analyst recommendations via the Internet, said user- selected pool analysts representing a subset of said available analysts; providing a cross-incorporation algorithm to apply to analyst recommendations received from said user-selected pool of analysts via said Internet; and forming said custom trade strategy from both said user-selected pool of analysts and said cross-incorporation algorithm, wherein said custom trade strategy is configured to provide said custom trade recommendation for said first marketable security when said custom trade strategy is applied against a set of analyst recommendations from said user-selected pool of analysts.

2. The method of claim 1 wherein at least one recommendation in said recommendations received from said user-selected pool of analysts pertains to a second marketable security that is different from said first marketable security.

3. The method of claim 1 wherein said first marketable security is common stocks of a publicly traded company.

4. The method of claim 1 wherein said first marketable security is mutual fund shares of a publicly traded mutual fund.

5. The method of claim 1 wherein said cross-incorporation algorithm accords equal weight to each of said analyst recommendations received from said user- selected pool of analysts.

6. The method of claim 1 wherein said cross-incorporation algorithm accords unequal weights to at least some of said analyst recommendations received from said user-selected pool of analysts.

7. The method of claim 1 wherein at least one of said analyst recommendations received from said user-selected pool of analysts represents a filter.

8. The method of claim 7 wherein said filter represents one based on key economic indicators.

9. The method of claim 7 wherein said filter represents a cross-industry filter.

10. The method of claim 7 wherein said filter represents one based on parameters specific to an industry.

11. The method of claim 1 further including performing a historical validation on said custom trade strategy against historical data pertaining to said first marketable security to obtain a performance report, said performance report being indicative of a performance of said custom trade strategy for said first marketable security if said custom trade strategy had been followed in the past for said first marketable security, said historical data including historical pricing data of said marketable security and historical analyst recommendations from said user-selected pool of analysts pertaining said marketable security.

12. The method of claim 1 wherein said historical pricing data is chosen at historical time periods where said custom trade strategy would have made a recommendation for a trade based on said historical analyst recommendations.

13. The method of claim 1 wherein said forming said custom trade strategy further includes incorporating a set of user-specified rules with an output of said cross- incorporation algorithm to generate said custom trade recommendation.

14. The method of claim 1 wherein said custom trade recommendation represents one of a strong buy, buy, hold, sell, strong sell.

15. The method of claim 1 further comprising providing a conversion function to conform said analyst recommendations from said user-selected pool of analysts to a single recommendation convention prior to application of said cross-incorporation algorithm.

16. A computer-implemented method for providing a custom trade recommendation to a user based on an output of a custom trade strategy, said custom trade strategy furnishing custom trade recommendation for a first marketable security, comprising: providing a custom trade strategy, which includes a user-selected pool of analysts from available analysts furnishing analyst recommendations via the Internet and a cross-incorporation algorithm applied against analyst recommendations from said user-selected pool of analysts, said user-selected pool of analysts representing a subset of said available analysts that was previously selected by said user; applying said custom trade strategy against said first marketable security to derive said custom trade recommendation, said applying employ latest analyst recommendations obtained from said user-selected pool of analysts.

17. The method of claim 16 wherein at least one recommendation in said recommendations received from said user-selected pool of analysts pertains to a second marketable security that is different from said first marketable security.

18. The method of claim 16 wherein said first marketable security is common stocks of a publicly traded company.

19. The method of claim 16 wherein said first marketable security is mutual fund shares of a publicly traded mutual fund.

20. The method of claim 16 wherein said cross-incorporation algorithm accords equal weight to each of said analyst recommendations received from said user- selected pool of analysts.

21. The method of claim 16 wherein said cross-incorporation algorithm accords unequal weights to at least some of said analyst recommendations received from said user-selected pool of analysts.

22. The method of claim 16 wherein at least one of said analyst recommendations received from said user-selected pool of analysts represents a filter.

23. The method of claim 22 wherein said filter represents one based on key economic indicators.

24. The method of claim 22 wherein said filter represents a cross-industry filter.

25. The method of claim 22 wherein said filter represents one based on parameters specific to an industry.

26. The method of claim 16 further including performing a historical validation on said custom trade strategy against historical data pertaining to said first marketable security to obtain a performance report, said performance report being indicative of a performance of said custom trade strategy for said first marketable security if said custom trade strategy had been followed in the past for said first marketable security, said historical data including historical pricing data of said marketable security and historical analyst recommendations from said user-selected pool of analysts pertaining said marketable security.

27. The method of claim 16 wherein said historical pricing data is chosen at historical time periods where said custom trade strategy would have made a recommendation for a trade based on said historical analyst recommendations.

28. The method of claim 16 wherein said forming said custom trade strategy further includes incorporating a set of user-specified rules with an output of said cross-incorporation algorithm to generate said custom trade recommendation.

29. The method of claim 16 wherein said custom trade recommendation represents one of a strong buy, buy, hold, sell, strong sell.

30. The method of claim 16 further comprising providing a conversion function to conform said analyst recommendations from said user-selected pool of analysts to a single recommendation convention prior to application of said cross-incorporation algorithm.

31. A computer-implemented system for providing a custom trade recommendation to a user based on an output of a custom trade strategy, said custom trade strategy furnishing a custom trade recommendation for a first marketable security, comprising: a first module for receiving from a user a first set of choices among available analysts furnishing analyst recommendations via the Internet, said choices resulting in a user-selected pool of analysts, said user-selected pool of analysts representing a subset of said available analysts; a second module configured to receive, responsive to said first set of choices, analyst recommendations from said user-selected pool of analysts from said Internet responsive to said choices; a third module, operatively coupled with said second module, to cross- incorporate said analyst recommendations from said user-selected pool of analyst and to output said custom trade recommendation.

32. The system of claim 31 wherein said first module is implemented on a client computer, said second module and third module being implemented on a server computer, said server computer and said client computer being in communication via said Internet.

33. The system of claim 31 wherein at least one recommendation in said recommendations received from said user-selected pool of analysts pertains to a second marketable security that is different from said first marketable security.

34. The system of claim 31 wherein said first marketable security is common stocks of a publicly traded company.

35. The system of claim 31 wherein at least one of said analyst recommendations received from said user-selected pool of analysts represents a filter, said filter being one of a filter based on key economic indicators, a cross-industry filter, and filter based on parameters specific to an industry.

36. The system of claim 31 wherein said forming said custom trade strategy further includes incorporating a set of user-specified rules with an output of said cross-incorporation algorithm to generate said custom trade recommendation.

37. The system of claim 36 further including a fourth module configured to perform a historical validation on said custom trade strategy against historical data pertaining to said first marketable security to obtain a performance report, said performance report being indicative of a performance of said custom trade strategy for said first marketable security if said custom trade strategy had been followed in the past for said first marketable security, said historical data including historical pricing data of said marketable security and historical analyst recommendations from said user-selected pool of analysts pertaining said marketable security, said historical pricing data being chosen at historical time periods where said custom trade strategy would have made a recommendation for a trade based on said historical analyst recommendations.