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Yazann Romahi at AI Frontiers : The Pitfalls of Using AI in Financial Investing

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Because of the success of momentum based strategies, most AI practitioners come into finance thinking they can achieve easy wins by applying AI to time series analysis. We outline how this can be a trap, and other common misconceptions about AI in finance. We discuss the value of new sources of data and how we have used them successfully. By way of example we walk through an application of natural language processing to enhance our equity long/short and event driven hedge fund strategies.

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Yazann Romahi at AI Frontiers : The Pitfalls of Using AI in Financial Investing

  1. 1. FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE The Pitfalls of Using AI in Financial Investing November 2018 Yazann Romahi, PhD
  2. 2. 1 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE Areas of AI Applications in Finance Trading Fraud Detection Credit LendingImage Recognition Robo-Advisors For illustrative purposes only $ Asset Management
  3. 3. 2 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE The March of Quantitative Methods in Financial Investing Markowitz Modern Portfolio Theory Louis Bachelier’s Thesis published Rapid Advances in Portfolio Theory 1980s First CTA funds 1990s Growth of Quant Equity Long Short Hedge Funds 2000s Increasing Use of AI in Trading Strategies 2009- Growth of Alternative Beta Growth of Quantitative Investing Strategies New sources Of Data Rapidly Being Created 1900 1952 1960s 1980 2010 2014 𝒌=𝟎 𝒏 𝒏 𝒌 𝒙 𝒌 For illustrative purposes only
  4. 4. 3 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE Understanding Approaches to Building Investment Strategies $370 Bn $973 Bn Hedge Fund and CTA AUM Trend Following  Trend-following hedge funds (also known as CTAs) are exclusively quantitatively based  Typically employ momentum and reversion signals at different time frequencies  AI is used, but traditional methods are more prevalent Fundamental Equity  Quantitative, equity long/short approaches began to gain prominence in the early 90s.  Fundamental signals (e.g. valuation, quality) are an important component of these processes  New sources of data are creating new opportunities for alpha generation Source: BarclayHedge as of 2Q 2018. For illustrative purposes only
  5. 5. 4 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE An Expansion of the Quant’s Toolkit NEW TECHNIQUES TRADITIONAL DATA NEWDATA TRADITIONAL TECHNIQUES Building a neural network (machine learning) on textual data Building random forests for fraud detection Time series extracted from satellite data of industrial sites in China Regression on Earnings Yield; Time series analysis using econometrics For illustrative purposes only
  6. 6. 5 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE New Data Sources Abound of the data available today has been collected in the last two years90% Alternative Data Individuals Business Processes Sensors Social Media Transaction Data Satellites News and Reviews Corporate Data Geo-location Web Searches, Personal Data Government Agencies Data Other Sensors For illustrative purposes only
  7. 7. 6 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE Pitfalls of Time Series Analysis in Trading Strategies New sources of data have cross-sectional depth, but lack time-series depth1 Probability of random noise ~30% Not statistically significant (p=0.12) For illustrative purposes only Monthly Trading Model (55% Success Rate) Weekly Trading Model (55% Success Rate)
  8. 8. 7 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE Pitfalls of Time Series Analysis in Trading Strategies Financial time-series are non-stationary2 Stationary Data -15% -10% -5% 0% 5% 10% 15% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec S&P 500 (Daily Returns, 2008) -1,000 -800 -600 -400 -200 0 200 400 600 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 Total Nonfarm Payrolls (Change, Thousands of Persons) Source: Bloomberg, as of November 2018. For illustrative purposes only
  9. 9. 8 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 S&P500Level Example Financial Time Series Pitfalls of Time Series Analysis in Trading Strategies Everything should be made as simple as possible, but not simpler3 Traditional Methods • Require modeler to determine functional form • Econometrics methods are often perfectly adequate Artificial Intelligence • Significantly higher degrees of freedom allows for flexibility, but is often less statistically robust due to inability to properly test out of sample Source: Bloomberg, as of November 2018. For illustrative purposes only 0 20 40 60 80 100 120 ModelError Model Complexity Model Complexity vs. Error Total Model Error In-sample Error Best Model
  10. 10. 9 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE 100 120 140 160 180 200 220 240 Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Sep-17 Oct-17 Subject Matter Expertise is Key: An Example Corporate activity is a source of binary idiosyncratic risk Stock price movements following confirmation or denial of a merger agreement News articles are a source of useful information, if appropriately handled For illustrative purposes only
  11. 11. 10 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE 2% RELEVANT NEWSMILLIONS OF ARTICLES PER YEAR1 Investment Process Application of AI: NewsFilter 1 For illustrative purposes only 2 Awarded based on use of machine-learning based News-Filter Most Cutting-Edge IT Initiative Best use of Emerging or Innovative Technology AWARDS2 AI can provide a solution to managing large data sets more effectively allowing it to be systematically incorporated in factor portfolios
  12. 12. 11 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE Investment Process Application of AI: NewsFilter Machine learning algorithm can improve performance by reducing idiosyncratic risk Source: J.P. Morgan Asset Management, Bloomberg 15 20 25 30 35 1-Sep 15-Sep 29-Sep 13-Oct 27-Oct 10-Nov 24-Nov 8-Dec 22-Dec General Cable Corporation ~75% increase in price post rumor Identification of M&A rumor led to short constraint on General Cable in equity factor models, preventing loss of 25bps at portfolio level
  13. 13. 12 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE Areas of AI Applications at J.P. Morgan Asset Management Trading Using reinforcement learning in enhanced trading Earnings Revisions Analysis of company earnings reports and Q&A yielding better estimates of earnings Image Recognition Analysis of industrial site satellite imagery in China as a leading indicator of industrial production Thematic Portfolios Using NLP of news, social media and financial reports, to create on demand thematic portfolio ideas $
  14. 14. 13 | FOR EDUCATIONAL PURPOSES ONLY | NOT INTENDED TO BE USED AS ADVERTISING OR SALES LITERATURE Disclosures The views contained herein are not to be taken as advice or a recommendation to buy or sell any investment in any jurisdiction, nor is it a commitment from J.P. Morgan Asset Management or any of its subsidiaries to participate in any of the transactions mentioned herein. Any forecasts, figures, opinions or investment techniques and strategies set out are for information purposes only, based on certain assumptions and current market conditions and are subject to change without prior notice. All information presented herein is considered to be accurate at the time of production. This material does not contain sufficient information to support an investment decision and it should not be relied upon by you in evaluating the merits of investing in any securities or products. In addition, users should make an independent assessment of the legal, regulatory, tax, credit and accounting implications and determine, together with their own professional advisers, if any investment mentioned herein is believed to be suitable to their personal goals. Investors should ensure that they obtain all available relevant information before making any investment. It should be noted that investment involves risks, the value of investments and the income from them may fluctuate in accordance with market conditions and taxation agreements and investors may not get back the full amount invested. Both past performance and yields are not reliable indicators of current and future results. J.P. Morgan Asset Management is the brand for the asset management business of JPMorgan Chase & Co. and its affiliates worldwide. To the extent permitted by applicable law, we may record telephone calls and monitor electronic communications to comply with our legal and regulatory obligations and internal policies. Personal data will be collected, stored and processed by J.P. Morgan Asset Management in accordance with our Company’s Privacy Policy. For further information regarding our regional privacy policies please refer to the EMEA Privacy Policy; for locational Asia Pacific privacy policies, please click on the respective links: Hong Kong Privacy Policy, Australia Privacy Policy, Taiwan Privacy Policy, Japan Privacy Policy and Singapore Privacy Policy. This communication is issued by the following entities: in the United Kingdom by JPMorgan Asset Management (UK) Limited, which is authorized and regulated by the Financial Conduct Authority; in other European jurisdictions by JPMorgan Asset Management (Europe) S.à r.l.; in Hong Kong by JF Asset Management Limited, or JPMorgan Funds (Asia) Limited, or JPMorgan Asset Management Real Assets (Asia) Limited; in Singapore by JPMorgan Asset Management (Singapore) Limited (Co. Reg. No. 197601586K), or JPMorgan Asset Management Real Assets (Singapore) Pte Ltd (Co. Reg. No. 201120355E); in Taiwan by JPMorgan Asset Management (Taiwan) Limited; in Japan by JPMorgan Asset Management (Japan) Limited which is a member of the Investment Trusts Association, Japan, the Japan Investment Advisers Association, Type II Financial Instruments Firms Association and the Japan Securities Dealers Association and is regulated by the Financial Services Agency (registration number “Kanto Local Finance Bureau (Financial Instruments Firm) No. 330”); in Australia to wholesale clients only as defined in section 761A and 761G of the Corporations Act 2001 (Cth) by JPMorgan Asset Management (Australia) Limited (ABN 55143832080) (AFSL 376919); in Brazil by Banco J.P. Morgan S.A.; in Canada for institutional clients’ use only by JPMorgan Asset Management (Canada) Inc., and in the United States by JPMorgan Distribution Services Inc. and J.P. Morgan Institutional Investments, Inc., both members of FINRA; and J.P. Morgan Investment Management Inc. In APAC, distribution is for Hong Kong, Taiwan, Japan and Singapore. For all other countries in APAC, to intended recipients only. Copyright 2018 JPMorgan Chase & Co. All rights reserved.

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