August 26, 2019
The Machines Are Coming
Maya Ortiz de Montellano, CFA Senior Investment Analyst
- The potential of Artificial Intelligence (AI) in asset management is considerable, with applications that may touch all aspects of the investment management process.
- Global Manager Research is seeing a broad range of AI implementation in its coverage of traditional and hedged strategies. Asset managers are incorporating AI-based techniques in their investment practices from idea generation to execution.
- While advances in AI implementation are accelerating, there are inherent limitations associated with AI that likely will mean that its greatest impacts will be in areas of finance that are the least efficient or where it increases human capacity (at least in the near term).
AI is moving out of the laboratory and into our daily financial lives, from digital assistants in banking apps to fraud detection algorithms used by credit card companies. Worldwide spending on AI systems is expected to double to $79.2 billion in 2022, with the banking sector expected to be second only to the retail industry in total dollar amount.1 Macro trends are driving the adoption of AI systems across the financial services industry—trends such as mounting regulatory reporting, cheaper processing power, the exponential increase in data production, and consumer preferences. We are seeing the world’s large asset managers and niche hedge funds spend heavily on developing or acquiring AI-enabled systems to automate some front and back office functions. While these firms have had some success in using AI to inform investment decisions, we believe there is a long way to go before asset managers and hedge funds will be able to deploy AI at scale to develop a competitive advantage through unique investment strategies. When it comes to investment portfolios, it is a subdiscipline of AI called machine learning (ML) that is already making an impact.
The machines in question are computers, or technically, graphic processing units (GPU) that process data in parallel at unimaginable speed, mimicking the processes of the human mind. ML as a discipline combines knowledge based in statistics, computer science, engineering, and mathematics. At the most basic level, an algorithm that performs linear regression while automatically incorporating new data could be considered ML. For investment managers, ML’s potential is twofold:
- ML techniques can free up the modeling process behind quantitative investment strategies by allowing researchers to leave behind the assumption that there is a linear relationship between data and variables.
- Second, ML has the potential to find actionable information from large unstructured data sets—including data that is not categorized into rows and columns (as in a spreadsheet).
ML—putting vast amounts of data to work
The explosive growth of available data and processing power available in the public cloud is behind the race to adopt ML techniques. Predictions about the growth of data keep escalating, with a current prediction that there will be 40 times more data bytes than there are stars in the observable universe by 2020.2 ML models are adept in situations in which there may be little intuition about why data might be related. That is its Achilles heel as well, but we will come back to that point. First, we will review a few points on how ML models work and where they are being used today.
Advanced ML techniques depend heavily on having access to vast amounts of data to identify patterns or signals that lead to a desired outcome. The observed outcomes from that process inform or train the system in how to adapt its parameters. ML is already being successfully deployed within investment management in areas where data is plentiful. This includes quantitative data like the millions of tick data points generated on stock exchanges, or qualitative data like news feeds on social media that may indicate something about the likelihood of a stock’s price rising or falling.
In our coverage of traditional and alternative investments, Global Manager Research is already seeing a broad range of ML deployment. That coverage includes niche hedge funds that are deploying autonomous ML-based investment strategies. Yet, the overwhelming majority of asset managers are using ML techniques to enhance an investment management process that depends on human intervention and decision making.
Irrespective of whether a manager employs a fundamental or systematic process, asset managers already are using ML across the four traditional steps in the investment process: idea generation (data intake), security selection (modeling), portfolio construction, and execution.
- Starting with the first step, ML enables asset managers to harness the vast potential of alternative data sets to generate ideas and to inform the investment decision making. For example, asset managers may incorporate satellite image processing of car parking lots to better estimate consumer visits for a retailer, or consumer credit card data for insights into consumer purchases or debt levels. However, the informative power of alternative data sets often poses its own challenges, when it becomes widely available (thus losing its predictive power), or due to a lack of precision (cars in parking lots may not translate to sales).3 Still, ML techniques are adept at parsing large data sets; for example, one systematic fund manager uses ML techniques to process a data set that has the equivalent of one trillion rows of data.4
- In security selection and modeling, asset managers employ ML techniques to model alpha signals. Alpha is excess return over the market-based return. These are signals with predictive power potential about the likelihood that the price of a security may rise or fall. Systematic, ML-based investment strategies generally act on the basis of hundreds or thousands of signals with varying degrees of capacity over shorter time frames. The problem is that these signals often become crowded, with too many other market participants chasing too few alpha signals and the signal loses its predictive power or staying power. As such, the signals are said to “decay,” and ML techniques assist in adapting the investment strategy to account for that eventuality. A fundamental security selection process may benefit from ML-based natural language processing that screens transcripts of management guidance for key words that indicate a change in tone about upcoming prospects. The lesser frequency of these quarterly data points, however, constrains their applicability to some extent. In the third instance, ML plays a role in helping asset managers optimize portfolios to account for changing conditions or market regimes in which certain models or signals may prove less effective.
- Finally, execution is one of the areas in which ML is frequently conflated with more straightforward program trading in which rules and heuristics are specified in a program in an attempt to achieve improved trade execution. These programs help to account for impact costs and control for risk. Indeed, bank platforms are seeking to deploy ML-based trading platforms. Yet, in the heavily regulated financial services domain, the deployment of true ML-based trading is still in its early days. In this instance as well, it is the exponential increase in data availability that is driving advancement—as exchanges (such as NASDAQ) have enhanced transparency by releasing all outstanding buy and sell limit order process and volumes in real time. This, sometimes is referred to as market microstructure data.5
The reality of ML—its application for asset managers has a long way to go
Certainly, the number of fund managers deploying ML-based investment strategies is growing rapidly, but the degree to which ML is incorporated in the investment process still varies widely. In response to a BarclaysHedge poll of hedge funds last year, 56% of respondents reported using AI or ML in their investment process, with 33% reporting its usage in risk management, and 27% in trade execution.6 Still, with all the progress that has been made, ML’s potential in modeling financial assets still appears to be in its infancy.
A vexing problem for ML is that the problems it aims to solve range from random to deterministic. Financial data tends to have a high degree of noise, and it is prone to exogenous shocks. On the other side is the predictability of the physics that govern the development of a driverless car. If the training data used to inform the model changes, it could result in a very different model—this is the variance problem. For example, the autonomous vehicle learning algorithm that trains in the deserts of Arizona may come to a different model specification than a model trained in the mountains of Colorado. Similarly, ML systems have resoundingly beat human world champions at the games of chess and Go (a strategy board game), but games have states and actions (i.e., queen to rook five) that allow a ML algorithm to learn what actions to deploy in different states. In financial markets, the state is not always obvious. Are we just in a late cycle economic expansion, or headed for more serious economic headwinds?
As market makers look to deploy ML-driven trading platforms, or as asset managers seek unique ML-based investment strategies, they must contend with regulatory and transparency requirements, along with the fact that it is the tails of the portfolio return distribution that often matter most to investors —and not simply the average. The consequences of an unintended “computer glitch,” which resulted from faulty testing of new program trading software, led to a $460 million loss for one financial services firm in less than 30 minutes.7 It took the firm’s programmers that amount of time to identify the root cause and shut down eight servers. It is possible to imagine that a properly trained ML-based execution platform that is penalized for unexplained losses would have responded in a fraction of that time, thus curtailing the losses. On the other hand, we believe the need to explain why a model chose an action are reasons enough to keep human overseers in charge.
ML—challenges and opportunities
Let’s return to discussion of that Achilles heel for ML. Probably the greatest challenge for wider ML application in the investment arena is that investors want to understand why they are making money or losing it. ML algorithms that are fed large amounts of data will eventually find a pattern, even if the pattern (correlation) is of a spurious nature such as stock prices rise when rain falls in New Mexico. ML in finance is subject to interpretability. That means that it is difficult to deploy a ML model if there is no rational – easily understandable explanation behind its success.
As we have noted, ML is already playing a role in investment portfolios, and that role is expected to grow. Wells Fargo Asset Management is in the process of deploying a stock research tool that employs ML techniques to leverage the capabilities of its portfolio management teams by broadening the scope of the idea generation process, or to help corroborate the human intuition of its team. WFAM is also developing a virtual analyst tool that has the potential to make security recommendations across bonds and stocks for portfolio managers’ consideration.
In the nearer term, ML’s greatest impacts likely will be in areas of finance such as real estate, regulatory supervision, and standardized asset allocation models (i.e., robo-advisors). While asset management firms are developing (and deploying) potentially profitable ML strategies, autonomous ML hedge funds are still few and far between. In this area, the limits include the difficulties of implementing ML, along with the competition. The most profitable ML strategies often do not scale (meaning only a limited amount of capital may be deployed into the strategy), and the anomalies they exploit are transitory. Essentially, they are at risk of being “competed away” by another machine.
1 IDC, “Worldwide Semiannual Artificial Intelligence Systems Guide,” March 11, 2019.
2 Domo, “Data Never Sleeps 7.0.”
3 Bloomberg, “Parking Lots Don’t Tell the Whole Story: The Trouble with Alternative Data,” November 29, 2018.
4 Two Sigma hedge fund.
5 “Machine Learning for Market Microstructure and High Frequency Trading,” by Michael Kearns and Yuriy Nevmyvaka (2013).
6 Institutional Investor, “More Hedge Funds Using AI Machine Learning,” July 19, 2018.
7 “The Rise and Fall of Knight Capital—Buy High, Sell Low. Rinse and Repeat,” August 5, 2018.
Global Manager Research (“GMR”) is a division of Wells Fargo Investment Institute, Inc. (“WFII”). WFII is a registered investment adviser and wholly owned subsidiary of Wells Fargo Bank, N.A., a bank affiliate of Wells Fargo & Company.
The information in this report was prepared by Global Manager Research (“GMR”). Opinions represent GMR’s opinion as of the date of this report and are for general information purposes only and are not intended to predict or guarantee the future performance of any individual security, market sector or the markets generally. GMR does not undertake to advise you of any change in its opinions or the information contained in this report. Wells Fargo & Company affiliates may issue reports or have opinions that are inconsistent with, and reach different conclusions from, this report. Past performance is no guarantee of future results. GMR may provide research analysis for Wells Fargo affiliated mutual funds, private funds and other products, which may also be advised by WFII or a Wells Fargo affiliate (“Wells Fargo”). The analysis utilizes the same processes and scrutiny as for non-affiliated products and WFII is committed to providing research that is fair and unbiased, but a conflict may arise as Wells Fargo may benefit from a favorable recommendation for an affiliated product.
The information contained herein constitutes general information and is not directed to, designed for, or individually tailored to, any particular investor or potential investor. This report is not intended to be a client-specific suitability analysis or recommendation, an offer to participate in any investment, or a recommendation to buy, hold or sell securities. Do not use this report as the sole basis for investment decisions. Do not select an asset class or investment product based on performance alone. Consider all relevant information, including your existing portfolio, investment objectives, risk tolerance, liquidity needs and investment time horizon. The material contained herein has been prepared from sources and data we believe to be reliable but we make no guarantee to its accuracy or completeness.
GMR uses qualitative and quantitative methods to assess investment products to develop due diligence opinions. In general, due diligence opinions entail a thorough assessment of an investment product and the assignment of one of five assessment recommendations: Recommended, Watch, Supported, Sell or Sunset. GMR may change an investment product’s assessment recommendation from time to time. GMR due diligence assessments are generally described as: “Recommended”, where assessment criteria indicate an investment product is in good standing and GMR has high conviction in it. “Recommended: Watch Level I", where an event has occurred and is being evaluated. Pending the outcome of the evaluation, GMR maintains its recommendation for new purchases. "Recommended: Watch Level II", where an event has occurred that may have the potential to impact longer term investment prospects and is being evaluated. Pending the outcome of the evaluation, GMR maintains its recommendation for new purchases. "Watch Level III", where an event has occurred that has elevated concern regarding this product's longer term investment prospects. GMR recommends restricting new flows into the product until our evaluation is complete. “Supported” where a product is in good standing and is considered acceptable to own. “Sell” where assessment criteria indicate an investment product is recommended for exit in the near-term; and “Sunset” where assessment criteria indicate an investment product should be exited over an appropriate period of time as determined by the client’s specific situation.
Wells Fargo Asset Management (WFAM) is a trade name used by the asset management businesses of Wells Fargo & Company. Wells Fargo Funds Management, LLC, a wholly owned subsidiary of Wells Fargo & Company, provides investment advisory and administrative services for Wells Fargo Funds. Other affiliates of Wells Fargo & Company provide subadvisory and other services for the funds. The funds are distributed by Wells Fargo Funds Distributor, LLC, Member FINRA, an affiliate of Wells Fargo & Company.
Wells Fargo Advisors is registered with the U.S. Securities and Exchange Commission and the Financial Industry Regulatory Authority, but is not licensed or registered with any financial services regulatory authority outside of the U.S. Non-U.S. residents who maintain U.S.-based financial services account(s) with Wells Fargo Advisors may not be afforded certain protections conferred by legislation and regulations in their country of residence in respect of any investments, investment transactions or communications made with Wells Fargo Advisors.
Wells Fargo Advisors is a trade name used by Wells Fargo Clearing Services, LLC and Wells Fargo Advisors Financial Network, LLC, Members SIPC, separate registered broker-dealers and non-bank affiliates of Wells Fargo & Company.