Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning

Published Online:https://doi.org/10.1287/mnsc.2021.4189

References

  • Adebiyi AA, Adewumi AO, Ayo CK (2014) Comparison of ARIMA and artificial neural networks models for stock price prediction. J. Appl. Math. 1:1–7.CrossrefGoogle Scholar
  • Atsalakis GS, Valavanis KP (2009) Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems Appl. 36(3):5932–5941.CrossrefGoogle Scholar
  • Barroso P, Santa-Clara P (2015) Momentum has its moments. J. Financial Econom. 116(1):111–120.CrossrefGoogle Scholar
  • Beaver W, McNichols M, Price R (2007) Delisting returns and their effect on accounting-based market anomalies. J. Accounting Econom. 43(2–3):341–368.CrossrefGoogle Scholar
  • Bianchi D, Büchner M, Tamoni A (2021) Bond risk premia with machine learning. Rev. Financial Stud. 34(2):1046–1089.CrossrefGoogle Scholar
  • Butt H, Virk N (2020) Momentum crashes and variations to market liquidity Internat. J. Finance & Econom., ePub ahead of print August 21, https://doi.org/10.1002/ijfe.2249.Google Scholar
  • Chong E, Han C, Park FC (2017) Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems Appl. 83:187–205.CrossrefGoogle Scholar
  • Dai W, Wu JY, Lu CJ (2012) Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes. Expert Systems Appl. 39(4):4444–4452.CrossrefGoogle Scholar
  • Daniel K, Moskowitz TJ (2016) Momentum crashes. J. Financial Econom. 122(2):221–247.CrossrefGoogle Scholar
  • Daniel K, Jagannathan R, Kim S (2019) A hidden Markov model of momentum. Working paper, Columbia University.Google Scholar
  • de Oliveira FA, Nobre CN, Zárate LE (2013) Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index—Case study of PETR4, Petrobras, Brazil. Expert Systems Appl. 40(18):7596–7606.CrossrefGoogle Scholar
  • DeMiguel V, Martin-Utrera A, Nogales FJ, Uppal R (2020) A transaction-cost perspective on the multitude of firm characteristics. Rev. Financial Stud. 33(5):2180–2222.CrossrefGoogle Scholar
  • Fama EF, French KR (2015) A five-factor asset pricing model. J. Financial Econom. 116(1):1–22.CrossrefGoogle Scholar
  • Feng G, Polson N, Xu J (2018) Deep learning factor alpha. Preprint, submitted September 23, https://dx.doi.org/10.2139/ssrn.3243683.Google Scholar
  • Freyberger J, Neuhierl A, Weber M (2020) Dissecting characteristics nonparametrically. Rev. Financial Stud. 33(5):2326–2377.CrossrefGoogle Scholar
  • Geczy CC, Samonov M (2016) Two centuries of price-return momentum. Financial Anal. J. 72(5):32–56.CrossrefGoogle Scholar
  • Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press. http://www.deeplearningbook.org.Google Scholar
  • Green J, Hand JR, Zhang XF (2017) The characteristics that provide independent information about average us monthly stock returns. Rev. Financial Stud. 30(12):4389–4436.CrossrefGoogle Scholar
  • Gu S, Kelly B, Xiu D (2020) Empirical asset pricing via machine learning. Rev. Financial Stud. 33(5):2223–2273.CrossrefGoogle Scholar
  • Gu S, Kelly BT, Xiu D (2021) Autoencoder asset pricing models. J. Econometrics 222(1):429–450.CrossrefGoogle Scholar
  • Jegadeesh N, Titman S (1993) Returns to buying winners and selling losers: Implications for stock market efficiency. J. Finance 48(1):65–91.CrossrefGoogle Scholar
  • Kazem A, Sharifi E, Hussain FK, Saberi M, Hussain OK (2013) Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Appl. Soft Comput. 13(2):947–958.CrossrefGoogle Scholar
  • Khashei M, Bijari M (2010) An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems Appl. 37(1):479–489.CrossrefGoogle Scholar
  • Khashei M, Bijari M (2011) A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl. Soft Comput. 11(2):2664–2675.CrossrefGoogle Scholar
  • Kim KJ, Ahn H (2012) Simultaneous optimization of artificial neural networks for financial forecasting. Appl. Intelligence 36:887–898.CrossrefGoogle Scholar
  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444.CrossrefGoogle Scholar
  • Ledoit O, Wolf M (2012) Nonlinear shrinkage estimation of large-dimensional covariance matrices. Ann. Statist. 40(2):1024–1060.CrossrefGoogle Scholar
  • Liao Z, Wang J (2010) Forecasting model of global stock index by stochastic time effective neural network. Expert Systems Appl. 37(1):834–841.CrossrefGoogle Scholar
  • Messmer M (2017) Deep learning and the cross-section of expected returns. Working paper, University of St. Gallen.Google Scholar
  • Moritz B, Zimmermann T (2016) Tree-based conditional portfolio sorts: The relation between past and future stock returns. Working paper, Ludwig Maximilian University of Munich (LMU).Google Scholar
  • Patel J, Shah S, Thakkar P, Kotecha K (2015) Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems Appl. 42(1):259–268.CrossrefGoogle Scholar
  • Rather AM, Sastry V, Agarwal A (2017) Stock market prediction and portfolio selection models: A survey. OPSEARCH 54:558–579.CrossrefGoogle Scholar
  • Wang J, Wang J (2015) Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks. Neurocomputing 156:68–78.CrossrefGoogle Scholar
  • Wang JZ, Wang JJ, Zhang ZG, Guo SP (2011) Forecasting stock indices with back propagation neural network. Expert Systems Appl. 38(11):14346–14355.CrossrefGoogle Scholar
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