The R&D Tax Credit Aspects of Artificially Intelligent Hedge Funds



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Hedge-Funds
        Today, thousands of hedge funds are in existence, collectively managing over $1 trillion in assets. These funds, which are unregulated and only available to qualified investors, pursue myriad different trading strategies including but not limited to long/short equity, market neutral, event driven, macro, and distressed securities investing. Each fund employs and adheres to its own unique investment philosophy and process; however, every fund can generally be classified as being either fundamental or quantitative in style.

        In the “quant” fund realm, many funds are scaling up investments in “machine learning”, a subset of artificial intelligence (AI) that is attracting considerable attention in the money management industry.

        Hedge funds developing and refining their own proprietary machine learning technology should be aware of federal and state Research and Development Tax Credits that are available as a result of their innovation.


The R&D Tax Credit

        Enacted in 1981, the Federal Research and Development (R&D) Tax Credit allows a credit of up to 13 percent of eligible spending for new and improved products and processes. Qualified research must meet the following four criteria:

  • New or improved products, processes, or software
  • Technological in nature
  • Elimination of uncertainty
  • Process of experimentation

        Eligible costs include employee wages, cost of supplies, cost of testing, contract research expenses, and costs associated with developing a patent. On December 18, 2015 President Obama signed the bill making the R&D Tax Credit permanent.  Beginning in 2016, the R&D credit can be used to offset Alternative Minimum tax and startup businesses can utilize the credit against payroll taxes.


Rise of the Machines

        While it’s true that computerized investment approaches have been around for decades, machine learning, a form of artificial intelligence where dynamic algorithms comb through immense sets of data seeking patterns and predictive outcomes, expands upon traditional algorithmic trading models which are often rules-based and static in nature.
 
        For example, a standard algorithmic investment model might short the 30 best performing stocks in a generic index over a specified time frame while simultaneously snatching-up the 30 worst performers over the same period, hoping to benefit from a market reversal. Such a model, while systematic and complex, is also rigid and fails to account for changes in things beyond company fundamentals and market technicals.

        By contrast, many quant funds in recent years have migrated toward artificial intelligence where machines can scrutinize large amounts of financial and non-financial data in the blink of an eye. Specifically, several quant shops today employ a type of machine learning called deep learning, which trains machines to recognize patterns in big data by incorporating human-like observations of pictures, words and audio . This capability, in turn, enables machines to be able to recognize potential actionable investment ideas often well before any human might be able to do the same.  
    
        Consider, for instance, the hypothetical AI powered hedge fund that looks at weather patterns and satellite images of crops in order to make a bet on commodities . In this regard, an artificially intelligent machine is able to identify, analyze and adapt to real-time changes in the natural environment in ways that traditional statistical-based analysis cannot account for.


The Pioneers

        The technology involved in creating artificially intelligent machines is cutting-edge.  These machines do not just collect data and process information; they draw inferences, answer questions, recommend actions and display data in visual layouts with state-of-the-art features. Described below is a summary of some of the key players in the hedge fund industry that have embraced machine learning as a natural extension of their quantitative investment DNA.   

Bridgewater Associates
        Bridgewater Associates, the world’s largest hedge fund manager with approximately $150 billion under management, is turning towards artificial intelligence.  In March 2015 the firm started a new artificial-intelligence unit with about half a dozen people led by David Ferrucci, the all-star engineer from International Business Machines Corp that developed the Watson computer to beat human players on “Jeopardy” during his time there.

        The unit is tasked with creating algorithms to make predictions based on historical data and probabilities.  But the technology (which is still rather confidential) is far more innovative than its ancestors from the late 1990’s who used quantitative analysis to predict similar trends.  The difference is that the latest technology has self-learning capabilities, can communicate with humans and is capable of analyzing far more complex and voluminous data.
     
        As opposed to following static instructions, programs learn as markets change and adjust to new and forthcoming information.  The programs are effective, advocates say, because they can crunch huge amounts of data in short periods, "learn" what works, and adjust their strategies on the fly.  In contrast, the typical quantitative approach may employ a single strategy or even a combination of strategies at once, but may not move between them or modify them based on what the program determines works best.

Renaissance Technologies
        Renaissance Technologies, the East Setauket, NY fund with headquarters near Stony Brook University and operations in Manhattan, has been developing innovative self-learning financial analysis technology as well.  The company was founded in 1982 by President and CEO James H. Simmons and has approximately $65 billion in assets under management, as of October 2015.  It has consistently been a leader in cutting-edge financial management technology around the world, employing artificial intelligence to make sense of patterns that are not easily decipherable to the human mind.

Two Sigma Investments
        Two Sigma is a systematic hedge fund based in New York that uses machine learning and distributed computing as part of its investment strategy.  The company is run by John Overdeck and David Siegel and has $35 billion in assets under management. Two Sigma believes that artificial intelligence and machine learning offer measurable benefits over human fundamental due diligence and traditional quant methods by supplementing statistics with real-world information.

MAN AHL
        Man Group’s AHL fund out of the United Kingdom, which has over $19 billion under management, has tested and used machine learning strategies with demonstrable success. In fact, a particular machine learning strategy helped AHL move from negative to positive returns when it was first introduced in August of 2015. Although it’s still early in the machine learning life cycle, Man Group has resolved to commit more funding to AI than to any other venture – all but affirming its massive appeal and potential.  


The Impact of Cloud Computing

        While large and powerful hedge funds have always had the means to invest in and devote resources to AI, cloud computing has made it feasible for start-ups and entrepreneurs to help hedge funds create intricate computer models. Indeed, the pooling of many computers into one powerful entity, accessible via the Internet, has allowed things to be done at unprecedented cost and scale .

        For example, Braxton McKee, founder of Ufora – a data science engineering company based in New York, has been able to leverage the powers of cloud technology to create his own models at a nearly negligible cost. Additionally, Domino Data Lab, founded by a few Bridgewater alums and based in San Francisco, enables quantitative hedge fund analysts to test new ideas, collaborate with other data enthusiasts, and seamlessly produce new trading models, all through access to the cloud .

        There is no doubt that cloud computing is a major transformational force in the IT world. In addition to enhancing IT productivity, it serves as an increasingly important platform for innovation amongst hedge funds seeking to capitalize on the use of artificial intelligence.


Challenges Ahead

        Several notable funds, as summarized previously, now employ some form of artificial intelligence to predict trends and to better understand markets and human behavior. Nonetheless, as any good quantitative analyst will agree, models are only as good as the assumptions underpinning them. Improvements in computing power and big data  analysis have opened the door for major innovations in the field of artificial intelligence; however, such advancements have also given rise to the concern of extraneous information, or “noise”, distorting the accuracy of predictive models. For this reason, some doubters believe that machine learning will quickly become an out-moded practice in the quantitative investing domain.

        In addition to being susceptible to noise, skeptics also contend that successful AI trading strategies will eventually be copied by other funds . As a consequence of widespread replication, the asymmetric information edge originally captured by the innovating fund will evaporate and subsequently be fully priced into the market.

        Artificially intelligent hedge funds are thus well-served to experiment with a plethora of different machine learning technologies and trading strategies, and should engage an R&D tax credit service provider to benefit from each new iteration and development.


Conclusion

        Automated, artificial intelligence-based trading systems are beginning to capture the attention of global hedge funds. Artificial intelligence and, more specifically, machine learning, can analyze large swaths of data with great speed and adapt to new information the way old, static models cannot. Federal and state R&D Tax credits are available to stimulate the efforts in AI hedge fund innovation and should be taken advantage of, accordingly.

Article Citation List

   


Authors

Charles R Goulding Attorney/CPA, is the President of R&D Tax Savers.

Robert Goulding is a CFA and Investment Professional with R&D Tax Savers


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