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Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016

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Return predictability has been a controversial topic in finance for a long time. We show there is substantial predictive power in combining forecasting variables. We apply correlation screening to combine twenty variables that have been proposed in the return predictability literature, and demonstrate forecasting power at a six-month horizon. We illustrate the economic significance of return predictability through a simulation which takes positions in SPY proportional to the model forecast.

The simulated strategy yields annual returns more than twice that of the buy-and-hold strategy, with a Sharpe ratio four times as large. This application of big data ideas to return predictability serves to shift the sentiment associated with market timing.

Publicado en: Economía y finanzas

Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016

  1. 1. Market Timing, Big Data, and Machine Learning Xiao Qiao University of Chicago and Hull Investments LLC
  2. 2. Overview “Big data” has invaded all aspects of our lives Applications are only getting broader Finance could significantly benefit from utilizing more sophisticated predictive tools Market-timing is a natural application of predictive analytics I will present a simple but effective market-timing model X. Qiao, Hull Investments Market Timing and Big Data 1
  3. 3. Big Data and Machine Learning
  4. 4. Origin Story Where does the word “big data” come from? John Mashey, Silicon Graphics (SGI) in the mid 1990s Slide deck titled “Big Data and the Next Wave of InfraStress” in 1998 Mashey: “I wanted the simplest, shortest phrase to convey that the boundaries of computing keep advancing” Frank Diebold, University of Pennsylvania “ ‘Big Data’ Dynamic Factor Models for Macroeconomic Measurement and Forecasting” at the Eighth World Congress of the Econometric Society in 2000 Used the word in an economics/statistics sense rather than a computing context X. Qiao, Hull Investments Market Timing and Big Data 2
  5. 5. Age of Machines X. Qiao, Hull Investments Market Timing and Big Data 3
  6. 6. Predictive Analytics Will Dominate “Every part of your business will change based on what I consider predictive analytics of the future” Ginni Rometty Chairwoman, President, and CEO of IBM X. Qiao, Hull Investments Market Timing and Big Data 4
  7. 7. Supervised Learning X. Qiao, Hull Investments Market Timing and Big Data 5
  8. 8. Deep Learning Deep learning/neural net Reinforcement learning “Intelligent” system, not just raw computation power X. Qiao, Hull Investments Market Timing and Big Data 6
  9. 9. Big Data is Everywhere Finance could benefit from this data revolution Both financial and non-financial data have exploded along with the big data trend Predictive tools applied to finance Forecasting the stock market X. Qiao, Hull Investments Market Timing and Big Data 7
  10. 10. Forecasting the Stock Market
  11. 11. 30-Second Primer on the Equity Premium Equity Premium Excess return that investing in the stock market provides over a risk-free rate [Investopedia] Why is it important? Offers insight to understanding a key risk-reward trade-off in the marketplace and investor preferences Used in asset allocation Individual and institutional investors Input for cost of capital calculations, affect corporate decisions X. Qiao, Hull Investments Market Timing and Big Data 8
  12. 12. Can We Forecast the Equity Premium? NO... Merton, Samuelson, Malkiel Goyal and Welch (2008) YES... Fama, Shiller, Campbell Cochrane (2008) X. Qiao, Hull Investments Market Timing and Big Data 9
  13. 13. We Can’t Produce a Good Ex Post Estimate Robert Merton, 1997 Nobel laureate: Merton (1980) calls attempts to precisely estimate the equity premium “a fool’s errand” X. Qiao, Hull Investments Market Timing and Big Data 10
  14. 14. We Can’t Predict Where the Market is Going Paul Samuelson, 1970 Nobel laureate: “Participation in market timing implies a degree of self-confidence bordering on hubris and self-deception” X. Qiao, Hull Investments Market Timing and Big Data 11
  15. 15. Don’t Even Try Burton Malkiel, author of A Random Walk Down Wall Street: Warned the audience “Don’t try to time the market. No one can do it. It’s dangerous.” at a conference in 2013 X. Qiao, Hull Investments Market Timing and Big Data 12
  16. 16. Counter Argument Finance theory tells us the equity premium varies through time Changing investment opportunity set, Merton (1973) Linked to fluctuations in the macroeconomy Influenced by investor disposition We should be able to forecast this quantity using the appropriate information set Variables that capture the state of the economy Variables that reveal investor expectations Variables that show mispricing X. Qiao, Hull Investments Market Timing and Big Data 13
  17. 17. Evidence that Favors Predictability Many predictors proposed in literature: Price ratios, bond spreads, technicals, etc Fama and French (1988) Campbell and Shiller (1988a, 1988b) Cochrane (2008) X. Qiao, Hull Investments Market Timing and Big Data 14
  18. 18. What Has Changed Data explosion The availability of both financial and non-financial data have massive increased Predictive analytics Modern machine learning techniques, making sense of all of the data Evolution of the academic literature Increasing evidence that market movements can be anticipated X. Qiao, Hull Investments Market Timing and Big Data 15
  19. 19. What We Do “A Practitioner’s Defense of Return Predictability” by Blair Hull and Xiao Qiao Leverage these recent changes to build a market-timing strategy! It is possible to time the market, and beneficial to do so Double the return of buy-and-hold with half the risk What’s different in this paper? Consider predictability from the investor’s perspective Use daily, weekly, monthly, and quarterly data Easy to replicate, data available upon request X. Qiao, Hull Investments Market Timing and Big Data 16
  20. 20. A Practitioner’s Defense of Return Predictability Available on SSRN
  21. 21. Research Question Participate in the return predictability debate: Can we predict the equity premium well enough to form a trading strategy? Resolve the predictability debate from the investor’s perspective Traditionally the question has been “Can we predict the equity premium statistically” Focus on economic magnitudes, not big t-stats X. Qiao, Hull Investments Market Timing and Big Data 17
  22. 22. This Paper Evaluate and combine 20 predictors from literature Capture diverse information sets Correlation screening to remove the least informative variables Form rolling window forecasts of the equity premium Construct market-timing strategy based on forecasts, invest in SPY Backtest shows 12% annual returns in 2001-2015, twice as high as buy-and-hold Sharpe ratio of 0.85, four times that of buy-and-hold Smaller drawdowns Eliminate look-ahead bias by including variables as they are discovered in the literature, get same results X. Qiao, Hull Investments Market Timing and Big Data 18
  23. 23. Data Bulk of data from Bloomberg, Federal Reserve Bank of St. Louis, U.S. Census Bureau Short interest of Rapach, Ringgenberg, and Zhou (2015) from Matt Ringgenberg Construct 20 variables from the predictability literature Price ratios: dividend yield, price to earnings, CAPE, etc Rates: bond yield, default spread, term spread, etc Real economy: Baltic Dry Index, new orders/sales, cay Technical: moving average, PCA-tech Sell in May, variance risk premium, CPI, short interest X. Qiao, Hull Investments Market Timing and Big Data 19
  24. 24. Information in Return Predictors Correlation matrix of predictor variables. Positive correlations are in green and negative correlations are in red. X. Qiao, Hull Investments Market Timing and Big Data 20
  25. 25. Relationship with Market Returns Correlation between predictors and one-, three-, six-, and 12-month future market returns. Positive correlations are in green; negatives are in red. X. Qiao, Hull Investments Market Timing and Big Data 21
  26. 26. Econometric Model: Kitchen Sink Kitchen Sink (KS): Include all predictors except price ratios, which are replaced by PCA-price for a total of 16 variables Re m,t→t+130 = αKS + βKS xt + KS,t→t+130 xt =     x1,t x2,t ... x16,t     =     PCA − pricet BYt ... SIt     Simulated Strategy: Fit model every 20 days to get ˆβKS Use ˆβKS along with updated predictors to construct daily forecast Take position in SPY proportional to forecast, adjust daily Process repeats every 20 days X. Qiao, Hull Investments Market Timing and Big Data 22
  27. 27. Econometric Model: Correlation Screening Correlation Screening (CS): Include a predictor only if its correlation with 130-day future market returns exceed 10% Re m,t→t+130 = αCS + βCS ˜xt + CS,t→t+130 ˜xt =     x1,t I|ρ1,m|>0.1 x2,t I|ρ2,m|>0.1 ... x16,t I|ρ16,m|>0.1     ρi,m = Corr(xi,t , Re m,t→t+130) Real-Time Correlation Screening (RTCS): CS, only include variables that have been discovered Re m,t→t+130 = αRTCS + βRTCS ˘xt + RTCS,t→t+130 ˘xt = ˜xt|xi,t has been discovered Backtest strategies the same way as KS X. Qiao, Hull Investments Market Timing and Big Data 23
  28. 28. Realized Returns vs. Forecasts Vertical axis: actual returns; horizontal axis: forecast returns. The black solid line is drawn at 45 degrees. The green dashed line is the best linear fit. X. Qiao, Hull Investments Market Timing and Big Data 24
  29. 29. Kitchen Sink Performance X. Qiao, Hull Investments Market Timing and Big Data 25
  30. 30. Correlation Screening Performance CS Selected Variables X. Qiao, Hull Investments Market Timing and Big Data 26
  31. 31. Real-Time Correlation Screening Performance RTCS Selected Variables X. Qiao, Hull Investments Market Timing and Big Data 27
  32. 32. Performance Comparison KS returns the same as buying SPY, with half of the volatility CS and RTCS double the return on SPY, with half of the risk Market-timing strategies all have smoother return profiles than SPY, smaller drawdown X. Qiao, Hull Investments Market Timing and Big Data 28
  33. 33. Annual Returns CS and RTCS have smaller ranges compared to that of SPY X. Qiao, Hull Investments Market Timing and Big Data 29
  34. 34. Annual Returns Need to stay disciplined...Consecutive under-performance compared to SPY X. Qiao, Hull Investments Market Timing and Big Data 30
  35. 35. Annual Returns Model performed well in large downturns in 2002 and 2008 X. Qiao, Hull Investments Market Timing and Big Data 31
  36. 36. Caveats Too good to be true? Two of the three largest market downturns are in the test period, the third is the Great Depression Maybe we just got lucky with 2001-2015, what about earlier periods? Only a true out-of-sample test will remove all doubts, time will tell X. Qiao, Hull Investments Market Timing and Big Data 32
  37. 37. Paper Summary Combine 20 variables using correlation screening Market-timing strategies based on return forecasts Outperform buy-and-hold with twice the returns and half of the volatility Consider more sophisticated ways to combine variables X. Qiao, Hull Investments Market Timing and Big Data 33
  38. 38. Implications of Market Timing
  39. 39. Actual Strategy Performance X. Qiao, Hull Investments Market Timing and Big Data 34
  40. 40. Tactical Asset Allocation The typical advice from financial advisers is to put 60% of one’s portfolio into equities Optimal if expected returns were constant Necessarily suboptimal with time-varying returns Investors should dynamically adjust their equity position Depending on forward-looking expected returns Two problems arise with adjusting equity expose Must be able to reliably forecast returns Constantly monitor forecast to take appropriate positions X. Qiao, Hull Investments Market Timing and Big Data 35
  41. 41. Tactical Asset Allocation A market-timing portfolio is a superior alternative to buy-and-hold Automatically adjusts the equity exposure based on return forecasts Investors can replace their buy-and-hold allocation with the market-timing portfolio Don’t have to worry about monitoring forecasts Achieve higher returns with less risk Get the same diversification benefits across asset classes X. Qiao, Hull Investments Market Timing and Big Data 36
  42. 42. Broader Impact of Market Timing Investors already market time... BUT They tend to buy at tops and sell at bottoms This behavior makes prices more volatile than it should be Destabilizing to the marketplace X. Qiao, Hull Investments Market Timing and Big Data 37
  43. 43. Benefits of Market Timing To use this type of strategy requires a “contrarian spirit” There can be no emotion, fear, or greed Result in a more stable marketplace X. Qiao, Hull Investments Market Timing and Big Data 38
  44. 44. Summary Big data has taken over many aspects of everyday life Its influence is only getting bigger Finance can benefit by riding this trend Market timing with many variables is just the tip of the iceberg Nobel laureates say no one can time the market Big data and new technology make it possible Academic literature has shifted X. Qiao, Hull Investments Market Timing and Big Data 39
  45. 45. Future Directions Different horizons Multiple models Incorporate even more data More sophisticated predictive tools X. Qiao, Hull Investments Market Timing and Big Data 40
  46. 46. Final Thought It was considered irresponsible to time the market in the last 30 years, but it will be considered irresponsible NOT to time the market in the next 30 years. X. Qiao, Hull Investments Market Timing and Big Data 41
  47. 47. Variables Selected with Correlation Screening CS Performance
  48. 48. Variables Selected with RTCS RTCS Performance

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