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Subsampled factor models for asset pricing: The rise of Vasa

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  • Gianluca De Nard
  • Simon Hediger
  • Markus Leippold
Abstract
We propose a new method, variable subsample aggregation (VASA), for equity return prediction using a large‐dimensional set of factors. To demonstrate the effectiveness, robustness, and dimension reduction power of VASA, we perform a comparative analysis between state‐of‐the‐art machine learning algorithms. As a performance measure, we explore not only the global predictive but also the stock‐specific R2's and their distribution. While the global R2 reflects the average forecasting accuracy, we find that high variability in stock‐specific R2's can be detrimental for the portfolio performance. Since VASA shows minimal variability, portfolios formed on this method outperform the portfolios based on random forests and neural nets.

Suggested Citation

  • Gianluca De Nard & Simon Hediger & Markus Leippold, 2022. "Subsampled factor models for asset pricing: The rise of Vasa," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1217-1247, September.
    • Handle: RePEc:wly:jforec:v:41:y:2022:i:6:p:1217-1247
    DOI: 10.1002/for.2859
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    References listed on IDEAS

    as
    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Holthausen, Robert W. & Larcker, David F., 1992. "The prediction of stock returns using financial statement information," Journal of Accounting and Economics, Elsevier, vol. 15(2-3), pages 373-411, August.
    3. Bali, Turan G. & Cakici, Nusret & Whitelaw, Robert F., 2011. "Maxing out: Stocks as lotteries and the cross-section of expected returns," Journal of Financial Economics, Elsevier, vol. 99(2), pages 427-446, February.
    4. Heitor Almeida & Murillo Campello, 2007. "Financial Constraints, Asset Tangibility, and Corporate Investment," The Review of Financial Studies, Society for Financial Studies, vol. 20(5), pages 1429-1460, 2007 12.
    5. Richardson, Scott A. & Sloan, Richard G. & Soliman, Mark T. & Tuna, Irem, 2005. "Accrual reliability, earnings persistence and stock prices," Journal of Accounting and Economics, Elsevier, vol. 39(3), pages 437-485, September.
    6. Joachim Freyberger & Andreas Neuhierl & Michael Weber & Andrew KarolyiEditor, 2020. "Dissecting Characteristics Nonparametrically," Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
    7. Barth, ME & Elliott, JA & Finn, MW, 1999. "Market rewards associated with patterns of increasing earnings," Journal of Accounting Research, Wiley Blackwell, vol. 37(2), pages 387-413.
    8. Frederico Belo & Xiaoji Lin & Santiago Bazdresch, 2014. "Labor Hiring, Investment, and Stock Return Predictability in the Cross Section," Journal of Political Economy, University of Chicago Press, vol. 122(1), pages 129-177.
    9. Tobias J. Moskowitz & Mark Grinblatt, 1999. "Do Industries Explain Momentum?," Journal of Finance, American Finance Association, vol. 54(4), pages 1249-1290, August.
    10. repec:bla:jfinan:v:43:y:1988:i:2:p:507-28 is not listed on IDEAS
    11. Palazzo, Berardino, 2012. "Cash holdings, risk, and expected returns," Journal of Financial Economics, Elsevier, vol. 104(1), pages 162-185.
    12. Banz, Rolf W., 1981. "The relationship between return and market value of common stocks," Journal of Financial Economics, Elsevier, vol. 9(1), pages 3-18, March.
    13. repec:bla:jfinan:v:44:y:1989:i:2:p:479-86 is not listed on IDEAS
    14. Kewei Hou & Chen Xue & Lu Zhang, 2015. "Editor's Choice Digesting Anomalies: An Investment Approach," The Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 650-705.
    15. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    16. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    17. Itay Kama, 2009. "On the Market Reaction to Revenue and Earnings Surprises," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 36(1‐2), pages 31-50, January.
    18. Richard H. R. Hahnloser & Rahul Sarpeshkar & Misha A. Mahowald & Rodney J. Douglas & H. Sebastian Seung, 2000. "Correction: Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit," Nature, Nature, vol. 408(6815), pages 1012-1012, December.
    19. Basu, S, 1977. "Investment Performance of Common Stocks in Relation to Their Price-Earnings Ratios: A Test of the Efficient Market Hypothesis," Journal of Finance, American Finance Association, vol. 32(3), pages 663-682, June.
    20. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    21. Robert F. Engle & Olivier Ledoit & Michael Wolf, 2019. "Large Dynamic Covariance Matrices," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 363-375, April.
    22. Michaely, Roni & Thaler, Richard H & Womack, Kent L, 1995. "Price Reactions to Dividend Initiations and Omissions: Overreaction or Drift?," Journal of Finance, American Finance Association, vol. 50(2), pages 573-608, June.
    23. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    24. Hong, Harrison & Kacperczyk, Marcin, 2009. "The price of sin: The effects of social norms on markets," Journal of Financial Economics, Elsevier, vol. 93(1), pages 15-36, July.
    25. Lakonishok, Josef & Shleifer, Andrei & Vishny, Robert W, 1994. "Contrarian Investment, Extrapolation, and Risk," Journal of Finance, American Finance Association, vol. 49(5), pages 1541-1578, December.
    26. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    27. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    28. Chordia, Tarun & Subrahmanyam, Avanidhar & Anshuman, V. Ravi, 2001. "Trading activity and expected stock returns," Journal of Financial Economics, Elsevier, vol. 59(1), pages 3-32, January.
    29. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    30. Kewei Hou & Tobias J. Moskowitz, 2005. "Market Frictions, Price Delay, and the Cross-Section of Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 981-1020.
    31. Balakrishnan, Karthik & Bartov, Eli & Faurel, Lucile, 2010. "Post loss/profit announcement drift," Journal of Accounting and Economics, Elsevier, vol. 50(1), pages 20-41, May.
    32. Jacob Thomas & Frank X. Zhang, 2011. "Tax Expense Momentum," Journal of Accounting Research, Wiley Blackwell, vol. 49(3), pages 791-821, June.
    33. John H. Cochrane, 2011. "Presidential Address: Discount Rates," Journal of Finance, American Finance Association, vol. 66(4), pages 1047-1108, August.
    34. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    35. repec:bla:jfinan:v:59:y:2004:i:2:p:623-650 is not listed on IDEAS
    36. Litzenberger, Robert H & Ramaswamy, Krishna, 1982. "The Effects of Dividends on Common Stock Prices: Tax Effects or Information Effects?," Journal of Finance, American Finance Association, vol. 37(2), pages 429-443, May.
    37. Kewei Hou & David T. Robinson, 2006. "Industry Concentration and Average Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1927-1956, August.
    38. Jeremiah Green & John R. M. Hand & X. Frank Zhang, 2017. "The Characteristics that Provide Independent Information about Average U.S. Monthly Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 30(12), pages 4389-4436.
    39. Valta, Philip, 2016. "Strategic Default, Debt Structure, and Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 51(1), pages 197-229, February.
    40. Huang, Alan Guoming, 2009. "The cross section of cashflow volatility and expected stock returns," Journal of Empirical Finance, Elsevier, vol. 16(3), pages 409-429, June.
    41. Piotroski, JD, 2000. "Value investing: The use of historical financial statement information to separate winners from losers," Journal of Accounting Research, Wiley Blackwell, vol. 38, pages 1-41.
    42. Christopher W. Anderson & Luis Garcia‐Feijóo, 2006. "Empirical Evidence on Capital Investment, Growth Options, and Security Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 171-194, February.
    43. Olivier Ledoit & Michael Wolf & Zhao Zhao, 2019. "Efficient Sorting: A More Powerful Test for Cross-Sectional Anomalies," Journal of Financial Econometrics, Oxford University Press, vol. 17(4), pages 645-686.
    44. Ou, Jane A. & Penman, Stephen H., 1989. "Financial statement analysis and the prediction of stock returns," Journal of Accounting and Economics, Elsevier, vol. 11(4), pages 295-329, November.
    45. Titman, Sheridan & Wei, K. C. John & Xie, Feixue, 2004. "Capital Investments and Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(4), pages 677-700, December.
    46. Itay Kama, 2009. "On the Market Reaction to Revenue and Earnings Surprises," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 36(1-2), pages 31-50.
    47. Liu, Weimin, 2006. "A liquidity-augmented capital asset pricing model," Journal of Financial Economics, Elsevier, vol. 82(3), pages 631-671, December.
    48. Datar, Vinay T. & Y. Naik, Narayan & Radcliffe, Robert, 1998. "Liquidity and stock returns: An alternative test," Journal of Financial Markets, Elsevier, vol. 1(2), pages 203-219, August.
    49. Re-Jin Guo & Baruch Lev & Charles Shi, 2006. "Explaining the Short- and Long-Term IPO Anomalies in the US by R&D," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(3-4), pages 550-579.
    50. Richard H. R. Hahnloser & Rahul Sarpeshkar & Misha A. Mahowald & Rodney J. Douglas & H. Sebastian Seung, 2000. "Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit," Nature, Nature, vol. 405(6789), pages 947-951, June.
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