Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500

European Journal of Operational Research

Volumn 259, Issue 2, 2017, Pages 689-702

  Krauss, Christopher ^a   Do, Xuan Anh ^a   Huck, Nicolas ^b  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b UNIVERSITÉ DE LORRAINE   (France)

Author keywords

Deep learning; Ensemble learning; Finance; Gradient boosting; Random forests

Indexed keywords

COMMERCE; DECISION TREES; FINANCE; FINANCIAL MARKETS; LEARNING SYSTEMS; PROBABILITY;

DEEP LEARNING; DEEP NEURAL NETWORKS; ENSEMBLE LEARNING; GRADIENT BOOSTING; MACHINE LEARNING RESEARCH; PROBABILITY FORECASTS; RANDOM FORESTS; STATISTICAL ARBITRAGE;

ELECTRONIC TRADING;

EID: 85006747541     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2016.10.031     Document Type: Article

References (69)

1. Aiolfi, M., Timmermann, A., Persistence in forecasting performance and conditional combination strategies. Journal of Econometrics 135:1 (2006), 31–53.
2. Allis, L., Searching for solutions in games and artificial intelligence, 1994, University Limburg, Maastricht, The Netherlands Ph.D. thesis.
3. Atsalakis, G.S., Valavanis, K.P., Surveying stock market forecasting techniques – Part II: Soft computing methods. Expert Systems with Applications 36:3 (2009), 5932–5941.
4. Avellaneda, M., Lee, J.-H., Statistical arbitrage in the US equities market. Quantitative Finance 10:7 (2010), 761–782.
5. Bacon, C.R., Practical portfolio performance: Measurement and attribution, 2nd Wiley finance, 2008, Wiley, Chichester, England and Hoboken, NJ.
6. Bates, J.M., Granger, C.W.J., The combination of forecasts. Journal of the Operational Research Society 20:4 (1969), 451–468.
7. Bella, A., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J., On the effect of calibration in classifier combination. Applied Intelligence 38:4 (2013), 566–585.
8. Bianchini, M., Scarselli, F., On the complexity of neural network classifiers: A comparison between shallow and deep architectures. IEEE Transactions on Neural Networks and Learning Systems 25:8 (2014), 1553–1565.
9. Bogomolov, T., Pairs trading based on statistical variability of the spread process. Quantitative Finance 13:9 (2013), 1411–1430, 10.1080/14697688.2012.748934.
10. Breiman, L., Random forests. Machine Learning 45:1 (2001), 5–32.
11. Candel, A., Parmar, V., LeDell, E., & Arora, A. (2016). Deep learning with H2O (5th edition). http://h2o.ai/resources.
12. Carhart, M.M., On persistence in mutual fund performance. Journal of Finance, 52(1), 1997, 57, 10.2307/2329556.
13. Clegg, M., Krauss, C., Pairs trading with partial cointegration, 2016, University of Erlangen-Nürnberg FAU Discussion Papers in Economics. https://www.econstor.eu/bitstream/10419/140632/1/858609614.pdf.
14. Click, C., Malohlava, M., Parmar, V., Roark, H., & Candel, A. (2016). Gradient boosted models with H2O (6th edition). http://h2o.ai/resources/.
15. Dietterich, T.G., Ensemble methods in machine learning. Multiple classifier systems, 2000, Springer, 1–15.
16. Dixon, M., Klabjan, D., Bang, J.H., Implementing deep neural networks for financial market prediction on the Intel Xeon Phi. Proceedings of the eighth workshop on high performance computational finance, 2015, 1–6.
17. Do, B., Faff, R., Does simple pairs trading still work?. Financial Analysts Journal 66:4 (2010), 83–95.
18. Enke, D., Thawornwong, S., The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications 29:4 (2005), 927–940, 10.1016/j.eswa.2005.06.024.
19. Fama, E.F., French, K.R., Multifactor explanations of asset pricing anomalies. Journal of Finance 51:1 (1996), 55–84, 10.2307/2329302.
20. Fama, E.F., French, K.R., A five-factor asset pricing model. Journal of Financial Economics 116:1 (2015), 1–22, 10.1016/j.jfineco.2014.10.010.
21. Fernandes, M., Medeiros, M.C., Scharth, M., Modeling and predicting the CBOE market volatility index. Journal of Banking & Finance 40 (2014), 1–10.
22. Freund, Y., Schapire, R.E., A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55:1 (1997), 119–139.
23. Friedman, J.H., Stochastic gradient boosting, 1999, Stanford University Working paper.
24. Friedman, J.H., Stochastic gradient boosting. Computational Statistics & Data Analysis 38:4 (2002), 367–378.
25. Gatev, E., Goetzmann, W.N., Rouwenhorst, K.G., Pairs trading: Performance of a relative-value arbitrage rule. Review of Financial Studies 19:3 (2006), 797–827.
26. Gedeon, T.D., Data mining of inputs: Analysing magnitude and functional measures. International Journal of Neural Systems 8:02 (1997), 209–218.
27. Genre, V., Kenny, G., Meyler, A., Timmermann, A., Combining expert forecasts: Can anything beat the simple average?. International Journal of Forecasting 29:1 (2013), 108–121.
28. Goodfellow, I., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y., Maxout networks. Proceedings of the 30th International Conference on Machine Learning, 2013, 1319–1327.
29. H2O (2016). H2O documentation. http://h2o-release.s3.amazonaws.com/h2o/rel-tibshirani/3/docs-website/h2o-docs/index.html and http://docs.h2o.ai.
30. Hansen, L.K., Salamon, P., Neural network ensembles. IEEE Transactions on Pattern Analysis & Machine Intelligence 12:10 (1990), 993–1001.
31. Hastie, T., Tibshirani, R., Friedman, J.H., The elements of statistical learning: Data mining, inference, and prediction, 2nd Springer series in statistics, 2009, Springer, New York.
32. Huck, N., Pairs selection and outranking: An application to the S&P 100 index. European Journal of Operational Research 196:2 (2009), 819–825.
33. Huck, N., Pairs trading and outranking: The multi-step-ahead forecasting case. European Journal of Operational Research 207:3 (2010), 1702–1716.
34. Huck, N., Pairs trading: Does volatility timing matter?. Applied Economics 47:57 (2015), 6239–6256, 10.1080/00036846.2015.1068923.
35. Jacobs, H., What explains the dynamics of 100 anomalies?. Journal of Banking & Finance 57 (2015), 65–85, 10.1016/j.jbankfin.2015.03.006.
36. Jacobs, H., Weber, M., On the determinants of pairs trading profitability. Journal of Financial Markets 23 (2015), 75–97, 10.1016/j.finmar.2014.12.001.
37. James, G.M., Witten, D., Hastie, T., Tibshirani, R., An introduction to statistical learning: With applications in R. Springer texts in statistics, 2013, Springer, New York 4th printing, 2014.
38. Khandani, A.E., Lo, A.W., What happened to the quants in August 2007? Evidence from factors and transactions data. Journal of Financial Markets 14:1 (2011), 1–46, 10.1016/j.finmar.2010.07.005.
39. Krauss, C., Statistical arbitrage pairs trading strategies: Review and outlook. Journal of Economic Surveys, forthcoming, 2016, 10.1111/joes.12153.
40. Krauss, C., Stübinger, J., Nonlinear dependence modeling with bivariate copulas: Statistical arbitrage pairs trading on the S&P 100, 2015, University of Erlangen-Nürnberg IWQW Discussion Paper Series. https://www.econstor.eu/bitstream/10419/125514/1/844416606.pdf.
41. LeCun, Y., Bengio, Y., Hinton, G., Deep learning. Nature 521:7553 (2015), 436–444, 10.1038/nature14539.
42. Leung, M.T., Daouk, H., Chen, A.-S., Forecasting stock indices: A comparison of classification and level estimation models. International Journal of Forecasting 16:2 (2000), 173–190.
43. Lo, A.W., Hedge funds: An analytic perspective, rev. and expanded Advances in financial engineering, 2010, Princeton University Press, Princeton.
44. Longin, F., Solnik, B., Extreme correlation of international equity markets. Journal of Finance 56:2 (2001), 649–676.
45. Mina, J., Xiao, J.Y., Return to RiskMetrics: The evolution of a standard. RiskMetrics Group, 2001.
46. Moore, G., Cramming more components onto integrated circuits. Electronics 38:8 (1965), 114–117.
47. Moritz, B., Zimmermann, T., Deep conditional portfolio sorts: The relation between past and future stock returns, 2014 Working paper, Ludwig Maximilian University Munich and Harvard University.
48. Newbold, P., Granger, C.W.J., Experience with forecasting univariate time series and the combination of forecasts. Journal of the Royal Statistical Society: Series A (General), 1974, 131–165.
49. Pesaran, M.H., Timmermann, A., A simple nonparametric test of predictive performance. Journal of Business & Economic Statistics 10:4 (1992), 461–465.
50. Peterson, B.G., Carl, P., Performance Analytics: Econometric tools for performance and risk analysis. R package, 2014.
51. Pole, A., Statistical arbitrage: Algorithmic trading insights and techniques, 2008, John Wiley & Sons, Hoboken, N.J.
52. R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/.
53. Rubin, R., Collins, M., How an exclusive hedge fund turbocharged its retirement plan. Bloomberg Business, 2015 http://www.bloomberg.com/news/articles/2015-06-16/how-an-exclusive-hedge-fund-turbocharged-retirement-plan.
54. Ryan, J.A., Ulrich, J.M., R package xts: Extensible time series. R package, 2014.
55. Sadka, R., Liquidity risk and the cross-section of hedge-fund returns. Journal of Financial Economics 98:1 (2010), 54–71.
56. Schapire, R.E., The strength of weak learnability. Machine learning 5:2 (1990), 197–227.
57. Sermpinis, G., Theofilatos, K., Karathanasopoulos, A., Georgopoulos, E.F., Dunis, C., Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and particle swarm optimization. European Journal of Operational Research 225:3 (2013), 528–540, 10.1016/j.ejor.2012.10.020.
58. Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., et al. Mastering the game of go with deep neural networks and tree search. Nature 529:7587 (2016), 484–489.
59. S&P Dow Jones Indices (2015). Equity S&P 500. http://us.spindices.com/indices/equity/sp-500.
60. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15:1 (2014), 1929–1958.
61. Stock, J.H., Watson, M.W., Combination forecasts of output growth in a seven–country data set. Journal of Forecasting 23:6 (2004), 405–430.
62. Takeuchi, L., Lee, Y.-Y. A., Applying deep learning to enhance momentum trading strategies in stocks, 2013, Stanford University Working paper.
63. The Economist (2016). A game-changing result. http://www.economist.com/news/science-and-technology/21694883-alphagos-masters-taught-it-game-electrifying-match-shows-what.
64. Timmermann, A., Chapter 4 forecast combinations. Elliott, G., Granger, C.W.J., Timmermann, A., (eds.) Handbook of economic forecasting, vol. 1, 2006, Elsevier, 135–196, 10.1016/S1574-0706(05)01004-9 http://www.sciencedirect.com/science/article/pii/S1574070605010049.
65. Ulrich, J., R package TTR: Technical trading rules. R package, 2013.
66. Varian, H.R., Big data: New tricks for econometrics. Journal of Economic Perspectives 28:2 (2014), 3–27.
67. Whaley, R.E., The investor fear gauge. Journal of Portfolio Management 26:3 (2000), 12–17.
68. Zeiler, M. D. (2012). ADADELTA: An adaptive learning rate method. arXiv preprint arXiv: 1212.5701.
69. Zhang, G., Patuwo, B.E., Hu, M.Y., Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting 14:1 (1998), 35–62.