Patent Publication Number: US-2019172142-A1

Title: Model investment portfolios and funds containing combined holdings only found in managed accounts with the greatest performances by time period, risk level, and asset class; including artificial intelligence-enhanced versions

Description:
RELATED APPLICATION 
     This application claims priority to U.S. Provisional Patent Application Ser. No. 62/480,903, filed on Apr. 3, 2017 and entitled “‘TRANSGENIC’MODEL PORTFOLIOS AND FUNDS”, which is incorporated herein by reference in its entirety. 
    
    
     COPYRIGHT 
     A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. 
     BACKGROUND OF THE INVENTION 
     The invention generally relates to financial systems, trading, and managing financial instruments. More specifically, the invention relates to methods and systems for creating model investment portfolios and funds comprised of tradable financial instruments (securities) found in a plurality of professionally managed accounts that have proven to deliver the greatest and most consistent returns, including by time period, risk level, and asset class; and to artificial-enhanced versions of them. 
     RELATED ART 
     Advisors and automated tools are available to help investors construct investment portfolio strategies to meet changing financial objectives and market conditions. These portfolio strategies allocate assets across different classes of financial instruments, each representing varying degrees of expected risk and return. Often the most difficult aspect of portfolio construction and management is selecting specific holdings for each class. 
     Some investors seeking to outperform market averages can afford to engage registered investment advisors (RIAs) to help manage portfolios. An RIA is registered with the U.S. Securities and Exchange Commission (SEC) or one or more U.S. States to do business as a financial advisor. RIAs manage client portfolios as fiduciaries, and often are given discretionary authority to buy and sell financial instruments without a client&#39;s further consent. Based on Form ADV filed with the Securities and Exchange Commission, required by companies managing assets in excess of $25 million, there are more than 25 million “managed accounts” with discretionary trading authority (MAs). 
     By contrast, investors with more limited resources may only be able to access professional money management talent by investing in mutual funds. There are only a few thousand such mutual funds. In essence, each of these professionally managed mutual fund portfolios competes with more than 2500 times the number of professionally managed MAs. As such, compared to publicly available mutual funds, private MAs have more than 2500 times the opportunity to “beat the market.” 
     In an Apr. 12, 2017 article titled “Wall Street Rout: Indexes Beat Stockpickers 92% of the Time,” The Wall Street Journal stated: “Over the 15 years ending December 2016, 95.4% of U.S. mid-cap funds, 93.2% of U.S. small-cap funds and 92.2% of U.S. large-cap funds trailed their respective benchmarks.” Despite these miserable performance records, mutual funds extract approximately $100 billion annually in fees from investors. The invention allows anyone to access and leverage the much larger, and most proven, private MA talent through unique models and funds. 
     BRIEF SUMMARY OF THE INVENTION 
     MA portfolios reflect the human intelligence of the professionals responsible for their constitution and evolution. The invention combines the most proven MA intelligence, and makes it publicly available to investors through model portfolios and funds; as well as artificial intelligence (AI) enhanced versions. 
     Discovering the Best Professional Intelligence 
     The invention analyzes large numbers of existing MA histories over varying time periods, by risk level, and by asset class. MAs with the greatest and/or most consistent returns (Performance) by time period and risk level and/or asset class contain holdings that will be mirrored in the invention&#39;s respective model portfolios and funds (MPFs). 
     For example, the S&amp;P 500 Health Care Index comprises those companies included in the S&amp;P 500 that are classified as members of the Global Industry Classification Standard (GICS) healthcare sector. The present invention will examine MA histories for the presence of these companies. Where the holdings meet threshold conditions, such as absolute value and/or percent of portfolio value, Performance of the healthcare holdings in each such portfolio will be compared to others and the S&amp;P 500 Health Care Index. An exemplary embodiment might only utilize MAs with +3 sigma (3σ) healthcare Performances of MAs examined, which are three standard deviations above the norm (top 0.0013; or 13% of 1%). 
     Combining the Best Intelligence 
     Continuing the example above, for time period X across a large number of MAs with healthcare holdings, the best Performances are determined relative to the others and the S&amp;P 500 Health Care Index. Since MPFs include holdings from multiple MAs, the invention determines optimal holdings in healthcare MPFs based on backtesting possible combinations of the best MA healthcare performances, using historical closing price information to determine what return, risk, and consistency would have been achieved. 
     For every 10,000 MA histories examined, only 13 would be 3σ Performers. Yet there would be six billion possible combinations of these 13 (13 factorial; or 13!) for each time period and weighting assumption (such as using the same relative weight for each 3σ Performance). There are more than a million any-date-to-any-date pairs over 6 years of trading, with nearly an infinite number of weighting possibilities for each pair. Through backtesting, the invention seeks optimally Performing combinations (OPCs) for each risk level and asset class within any desired time period. 
     This very “big data” intensive analytical effort can be approached using conventional brute force computational systems, and/or with self/deep-learning artificial intelligence (AI) algorithms to selectively narrow data sets and evaluate non-obvious combinations. 
     Combining multiple MA holdings create new Performance and risk profiles. For example, there was a period in time when an investor could purchase shares of Berkshire Hathaway, Inc., while also investing in Fidelity&#39;s Magellan Fund, essentially betting on legendary investors Warren Buffet and Peter Lynch. But there was no single investment offering available based on various combinations of both Buffet&#39;s and Lynch&#39;s holdings. Had possible combinations been backtested, new and different risk, return, and consistency profiles could have been calculated. The invention can determine any possible combination of any plurality of MAs, in whole or by asset class; each combination having a unique Performance and risk profile not previously available. 
     MPFs Synchronize with Sub-Advising MAs 
     MAs, referred to as “sub-advising” when used in combination to form MPFs, continue to operate as before. The RIAs involved simply continue to manage the client accounts. When a sub-advising MA makes a trade, the sub-advised MPF will, as soon as possible after, update models and trade funds proportionally to maintain the relative MA weightings that define the MPF. 
     MPFs—Best Active Management Possible at Passive Prices 
     A Mar. 30, 2017 Bloomberg article stated: “The evidence has piled up in recent years that the vast majority of active managers fail to beat the market net of their fees. A common reaction is that beating the market is too difficult and that it&#39;s therefore a waste of time and money to try. But just the opposite is true . . . the problem is not that active managers fail to outperform the market; it&#39;s that they keep that outperformance for themselves through high fees.” 
     Because any MPF trading activity which mirrors sub-advising MA trades is always helpful for the involved MAs and their advisors and investors, since an increase in the same activity (whether buying or selling) nudges the market in the desired direction, MPFs justify a lower cost passive-like pricing model for a higher value active trading performance. Sub-advisors don&#39;t need additional incentive to continue investing wisely for their clients (in exemplary implementations no one will even know who they are), and the cost of computer-trading MPFs can be lower than the cost of tracking an index (fewer trades). As such, a MPF investor might pay a fraction of the expected management fee for an active investment program that consistently beats an index having similar or greater risk. 
     Making MPFs Available 
     MPFs as model portfolios can advise investors or financial professionals (“notify me when any instrument in this MPF trades”), interactively engage with advisors (“how would the model portfolio change if constrained to market decline scenarios only”) (Augmented Advisory™), or automatically trade positions in an investment account (“make this healthcare MPF X % of a portfolio”). Deploying MPFs as tradable funds, including mutual and/or exchange-traded funds (ETFs), or portions of funds, will make them available to the full investing public. 
     Artificial Intelligence Enhanced MPFs 
     Artificial intelligence (AI) can potentially augment and enhance human intelligence OPCs, and be offered as separate model and fund choices. A Jun. 14, 2017 Wall Street Journal article illustrates an example:
         “[Economists] downloaded all the 10-K and 10-Q filings with the Securities and Exchange Commission from 1994 through 2014 and used textual-analysis software to create a similarity score showing how the language in corporate filings differed one period to the next.   They then looked at stock performance following filings. The finding: Shares of companies that had significant changes did much worse than those of companies that didn&#39;t. This was particularly true when it came to changes in the risk factors section of 10-Ks.”   [A strategy of buying shares of companies with no significant risk-factor changes and betting against companies with major changes would have returned more than 22 percentage points more than the overall market annually.]
 
AI algorithms, whether developed to be task-specific or machine/deep-learned, could examine such “research data” relating to OPC companies, altering and backtesting weighted holdings in the OPCs. Alongside non-AI models and funds (most proven jockeys), AI-augmented versions combining the very best human and artificial intelligence (most proven jockeys riding the best mathematical horses) could be offered as separate models and funds.
       

    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         FIG. 1  is a block diagram of a structure embodiment according to the present invention. 
         FIG. 2  is a schematic flow chart depicting the program flow of a software application in the structure of  FIG. 1 , according to one embodiment of the present invention. 
         FIG. 3  is a schematic flow chart depicting the program flow of a software application in the structure of  FIG. 1 , according to a more specific embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     An exemplary embodiment of the present invention is discussed in detail below. It should be understood that this specific implementation is done for illustration purposes. Other components and configurations may be used without departing from the spirit and scope of the present invention. 
     The presently described embodiment relates to investment model portfolio construction, public or private fund offerings based on those models, and artificial intelligence-enhanced versions of both the models and funds. Embodiments of the present technology include methods and computer systems for generating investment portfolio models and funds based on historical performances of professionally “managed accounts” (MAs). 
     Referring to  FIG. 1 , MA database  130 , metrics database  140 , and research database  150  receive and store data using a computing system  100  (e.g., a conventional computer standing alone or connected to a server (not shown)). Computing system  100  comprises at least a processor  110  and a memory  120 . Memory  120  contains MA database  130 , metrics database  140 , and research database  150 , and software application  160  which comprises a plurality of instruction routines which are executed by processor  110  to carry out particular steps in the method of the presently described technology. External trading system  170  informs software application  160  when a security in a MPF MA is traded. 
     Processor  110  may be contained within a single computer system such as system  100 , or distributed among multiple computer systems. Likewise, MA database  130 , metrics database  140 , and research database  150  may be contained within a single computer system such as system  100 , or distributed among multiple computer systems. Software application  160  may incorporate application logic and instruction routines within a single application module or across multiple application modules, which may be contained in a single computer system such as system  100 , or distributed among multiple computer systems. 
     Data may be downloaded from an internet server (not shown) into MA database  130 , metrics database  140 , and research database  150 , or transferred from a local storage medium (not shown), for example. The data which is stored in MA database  130 , metrics database  140 , and research database  150  may include data for any time period. 
     In accordance with at least one embodiment of the present technology, the securities in MA database  130  include all initial securities holdings, plus purchases and sales, for each MA within a prescribed time period. The metrics database  140  includes information for each security in MA database  130 , including asset classification, related dividends or interest, and daily closing prices for the prescribed time period, as well as benchmark and statistical information to compare and/or compute various performance metrics. The research database  150  includes information for each security in MA database  130 , including governmental filings and company related news. Software application  160  utilizes MA database  130  and metrics database  140  to compute daily values, and changes in values, for each security held in each MA for each desired time period. Software application  160  further computes return, risk, and consistency profiles for each MA, and MA asset class, in MA database  130  within the prescribed time period. Software application  160  also backtests various combinations of MAs and MA asset classes, and updates MPFs based on MA trade information received from trading system  170 . 
     Referring again to  FIG. 1 , software application  160  is executed by processor  110  in order to carry out all or some of program flow  200 , as shown in  FIG. 2 , in accordance with one embodiment of the present technology. 
     Referring to  FIG. 2 , in step  220  of flow  200 , for a prescribed time period software application  160  computes and groups MAs in  210  by risk level. In step  230  of flow  200 , software application  160  computes returns and consistency performance for each MA. In step  240  of flow  200 , software application  160  identifies and ranks MA performances within each risk level. In step  250  of flow  200 , software application  160  backtests various combinations and weightings of MA performances ranked in step  240  to determine the sub-advising MAs that will be mirrored in model portfolios for each risk level. In step  260  of flow  200 , model portfolios for each risk level are created from the highest performing combinations identified in step  250  backtesting. 
     Separately, in step  225  of flow  200 , for a prescribed time period software application  160  subdivides MA holdings in  210  by asset classes. In step  235  of flow  200 , software application  160  computes returns and consistency performance for each MA asset class. In step  245  of flow  200 , software application  160  identifies and ranks MA performances within each asset class. In step  255  of flow  200 , software application  160  backtests various combinations and weightings of the MA performances ranked in step  245  to determine the sub-advising sub-divided MAs that will be mirrored in model portfolios for each asset class. In step  265  of flow  200 , model portfolios for each asset class are created from the highest performing combinations identified in step  255  backtesting. 
     Traded funds are created in step  270  of flow  200  that contain the same securities holdings in the same proportions as model portfolios from steps  260  and  265 . Using research database  150 , artificial intelligence algorithms in step  280  of flow  200  are applied to MPFs from steps  260  and  265  and backtested to determine any additional performance gains that would have been achieved had alterations in holdings or weightings been made during the specified period. In step  290  of flow  200 , superior outcomes determined in step  280  will create AI-enhanced versions of the MPFs from steps  260  and  265 . In step  295  of flow  200 , MA trading system  170  communicates any purchase or sale transactions in sub-advising MAs so model portfolios can be proportionally updated and funds proportionally traded. 
       FIG. 3  shows a more specific embodiment of the present invention. Referring again to  FIG. 1 , software application  160  is executed by processor  110  in order to carry out program flow  300 , as shown in  FIG. 3 . 
     Referring to  FIG. 3 , in step  320  of flow  300 , using the most recent five full years of managed account histories from step  310 , application  160  computes and assigns MAs in  310  to one of twenty risk levels based on portfolio beta (historical risk) and standard deviation (volatility/consistency). In step  330  of flow  300 , software application  160  computes rolling returns and consistency performance for each MA. In step  340  of flow  300 , software application  160  identifies and ranks MA performances within each risk level based on rolling time-weighted rate of returns. In step  350  of flow  300 , software application  160  backtests various combinations and weightings of MA performances ranked in step  340  to determine the MAs that will be mirrored in model portfolios for each risk level. In step  360  of flow  300 , model portfolios for each risk level are created from the highest performing combinations identified in step  350  backtesting. 
     Separately, in step  325  of flow  300 , using the most recent five full years of managed account histories from step  310  software application  160  subdivides MA holdings in  310  by asset classes. In step  335  of flow  300 , software application  160  computes returns and consistency performance for each MA asset class. In step  345  of flow  300 , software application  160  identifies and ranks MA performances within each asset class. In step  355  of flow  300 , software application  160  backtests various combinations and weightings of the MA performances ranked in step  345  to determine the MAs that will be mirrored in model portfolios for each asset class. In step  365  of flow  300 , model portfolios for each asset class are created from the highest performing combinations identified in step  355  backtesting. 
     Publicly accessible funds are created in step  370  of flow  300  that contain the same holdings in the same proportions as model portfolios from steps  360  and  365 . Using research database  150 , artificial intelligence algorithms in step  380  of flow  300  are applied to MPFs from steps  360  and  365  and backtested to determine any additional performance gains that would have been achieved had alterations in holdings or weightings been made during the specified period. In step  390  of flow  300 , superior outcomes determined in step  380  will create AI-enhanced versions of the MPFs from steps  360  and  365 . In step  395  of flow  300 , MA trading system  170  communicates any purchase or sale transactions in sub-advising MAs so model portfolios can be proportionally updated and funds proportionally traded.