Computer Science ›› 2022, Vol. 49 ›› Issue (6): 276-286.doi: 10.11896/jsjkx.210900127

• Artificial Intelligence • Previous Articles     Next Articles

Application of Machine Learning in Financial Asset Pricing:A Review

XU Jie1, ZHU Yu-kun1, XING Chun-xiao2   

  1. 1 PBC School of Finance,Tsinghua University,Beijing 100084,China
    2 Beijing National Research Center for Information Science and Technology(BNRist),Tsinghua University,Beijing 100084,China
  • Received:2021-09-15 Revised:2021-12-05 Online:2022-06-15 Published:2022-06-08
  • About author:XU Jie,born in 1986,Ph.D.His main research interests include machine learning,asset pricing and quantitative trading.
    XING Chun-xiao,born in 1967,Ph.D supervisor.His main research interests include deep learning,big data and knowledge engineering,and fintech.
  • Supported by:
    Key research and Development Plan of Ministry of Science and Technology:Research on the Theory and Techno-logy of Modern Service Trusted Transaction(2018YFB1402701).

Abstract: The key problem of financial asset allocation is asset price.Asset pricing is the core content of modern finance,which indicates that asset pricing law has always been one of the hot topics of financial research.This paper reviews the methods used by machine learning in the field of asset pricing and research progresses,classifies machine learning asset pricing method into machine learning method based on the characteristics processing and deep learning method based on end-to-end processing,compares the differences between different algorithms in principle and application scenarios,points out the applicability and limitations of the two kinds of machine learning methods,prospects the research direction on machine learning asset pricing in the future.

Key words: Asset pricing, Deep learning, Machine learning, Portfolio, Price forecasting

CLC Number: 

  • TP181
[1] LO A W,MACKINLAY A C.Stock market prices do not follow random walks:Evidence from a simple specification test[J].The Review of Financial Studies,1988,1(1):41-66.
[2] SHAH D,ISAH H,ZULKERNINE F.Stock market analysis:A review and taxonomy of prediction techniques[J/OL].International Journal of Financial Studies,2019,7(2):26.
[3] HARVEY C R,LIU Y.A census of the factor zoo[J/OL].SSRN,2019,3341728.
[4] RUNDO F,TRENTA F,DI STALLO A L,et al.Machine lear-ning for quantitative finance applications:A survey[J/OL].Applied Sciences,2019,9(24):5574.
[5] JURCZENKO,EMMANUEL E D.Machine Learning for Asset Management:New Developments and Financial Applications[M].John Wiley & Sons,2020.
[6] CHONG E,HAN C,PARK F C.Deep learning networks for stock market analysis and prediction:Methodology,data representations,and case studies[J].Expert Systems with Applications,2017,83:187-205.
[7] NTI I K,ADEKOYA A F,WEYORI B A.A systematic review of fundamental and technical analysis of stock market predictions[J/OL].Artificial Intelligence Review,2019:1-51.
[8] JIANG W.Applications of deep learning in stock market prediction:recent progress[J].arXiv:2003.01859,2020.
[9] DIXON M F,HALPERIN I.The four horsemen of machinelearning in finance[J/OL].SSRN,2019,3453564.
[10] ZHENG A,CASARI A.Feature engineering for machine lear-ning:principles and techniques for data scientists[M/OL].O’Reilly Media,Inc.,2018.
[11] LETTAU M,PELGER M.Factors that fit the time series and cross-section of stock returns[J].The Review of Financial Stu-dies,2020,33(5):2274-2325.
[12] KELLY B T,PRUITT S,SU Y.Characteristics are covariances:A unified model of risk and return[J].Journal of Financial Economics,2019,134(3):501-524.
[13] LETTAU M,PELGER M.Estimating latent asset-pricing factors[J].Journal of Econometrics,2020,218(1):1-31.
[14] ONATSKI A.Asymptotics of the principal components estimator of large factor models with weakly influential factors[J].Journal of Econometrics,2012,168(2):244-258.
[15] AIT-SAHALIA Y,XIU D.Using principal component analysis to estimate a high dimensional factor model with high-frequency data[J].Journal of Econometrics,2017,201(2):384-399.
[16] AVELLANEDA M,HEALY B,PAPANICOLAOU A,et al.PCA for Implied Volatility Surfaces[J].The Journal of Financial Data Science,2020,2(2):85-109.
[17] CARAIANI P.The predictive power of singular value decomposition entropy for stock market dynamics[J].Physica A:Statistical Mechanics and its Applications,2014,393:571-578.
[18] GU R,SHAO Y.How long the singular value decomposed entropy predicts the stock market?-Evidence from the Dow Jones Industrial Average Index[J].Physica A:Statistical Mechanics and Its Applications,2016,453:150-161.
[19] WANG D.Adjustable robust singular value decomposition:Design,analysis and application to finance[J].Data,2017,2(3):29.
[20] BACK A D,WEIGEND A S.A first application of independent component analysis to extracting structure from stock returns[J].International Journal of Neural Systems,1997,8(4):473-484.
[21] BARILLAS F,SHANKEN J.Comparing asset pricing models[J].The Journal of Finance,2018,73(2):715-754.
[22] FULOP A,YU J.Bayesian analysis of bubbles in asset prices[J/OL].Econometrics,2017,5(4):47.
[23] TURNER J A.Momentum Portfolios and the Capital Asset Pricing Model:A Bayesian Approach[J/OL].Quarterly Journal of Finance and Accounting,2010:43-59.
[24] SCHORFHEIDE F,SONG D,YARON A.Identifying long-run risks:A Bayesian mixed frequency approach[J].Econometrica,2018,86(2):617-654.
[25] BUSSE J A,IRVINE P J.Bayesian alphas and mutual fund persistence[J].The Journal of Finance,2006,61(5):2251-2288.
[26] GEWEKE J,AMISANO G.Hierarchical Markov normal mix-ture models with applications to financial asset returns[J].Journal of Applied Econometrics,2011,26(1):1-29.
[27] PSARADAKIS Z,SOLA M,SPAGNOLO F.On Markov error correction models,with an application to stock prices and dividends[J].Journal of Applied Econometrics,2004,19(1):69-88.
[28] GU S,KELLY B,XIU D.Autoencoder asset pricing models[J/OL].Journal of Econometrics,2020.
[29] SUIMON Y,SAKAJI H,IZUMI K,et al.Autoencoder-BasedThree-Factor Model for the Yield Curve of Japanese Government Bonds and a Trading Strategy[J].Journal of Risk and Financial Management,2020,13(4):82.
[30] LV S,HOU Y,ZHOU H.Financial Market Directional Forecasting With Stacked Denoising Autoencoder[J].arXiv:1912.00712,2019.
[31] HUANG C F.A hybrid stock selection model using genetic algorithms and support vector regression[J].Applied Soft Computing,2012,12(2):807-818.
[32] LEE M C.Using support vector machine with a hybrid feature selection method to the stock trend prediction[J].Expert Systems with Applications,2009,36(8):10896-10904.
[33] KARATHANASOPOULOS A,THEOFILATOS K A,SERMPINIS G,et al.Stock market predictionusing evolutionary support vector machines:an application to the ASE20 index[J].The European Journal of Finance,2016,22(12):1145-1163.
[34] ABEDIN M Z,GUOTAI C,MOULA F E,et al.Topological applications of multilayer perceptrons and support vector machines in financial decision support systems[J].International Journal of Finance & Economics,2019,24(1):474-507.
[35] HUANG W,NAKAMORI Y,WANG S Y.Forecasting stockmarket movement direction with support vector machine[J].Computers & Operations Research,2005,32(10):2513-2522.
[36] CHEN W H,SHIH J Y,WU S.Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets[J].International Journal of Electronic Finance,2006,1(1):49-67.
[37] MORITZ B,ZIMMERMANN T.Tree-based conditional portfolio sorts:The relation between past and future stock returns[J/OL].SSRN,2016,2740751.
[38] NTI K O,ADEKOYA A,WEYORI B.Random forest based feature selection of macroeconomic variables for stock market prediction[J/OL].American Journal of Applied Sciences,2019,16(7):200-212.
[39] KRAUSS C,DO X A,HUCK N.Deep neural networks,gra-dient-boosted trees,random forests:Statistical arbitrage on the S&P 500[J].European Journal of Operational Research,2017,259(2):689-702.
[40] THAKKAR A,CHAUDHARI K.A comprehensive survey onportfolio optimization,stock price and trend prediction using particle swarm optimization[J/OL].Archives of Computational Methods in Engineering,2020:1-32.
[41] RATHER A M,AGARWAL A,SASTRY V N.Recurrent neural network and a hybrid model for prediction of stock returns[J].Expert Systems with Applications,2015,42(6):3234-3241.
[42] PARRACHO P,NEVES R,HORTA N.Trading in financialmarkets using pattern recognition optimized by genetic algorithms[C]//Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation.2010:2105-2106.
[43] MERELLO S,RATTO A P,ONETO L,et al.Ensemble Application of Transfer Learning and Sample Weighting for Stock Market Prediction[C]//2019 International Joint Conference on Neural Networks(IJCNN).2019:1-8.
[44] NAM K H,SEONG N Y.Financial news-based stock movement prediction using causality analysis of influence in the Korean stock market[J].Decision Support Systems,2019,117:100-112.
[45] NGUYEN T T,YOON S.A novel approach to short-term stock price movement prediction using transfer learning[J].Applied Sciences,2019,9(22):4745.
[46] LI X,XIE H,LAU R Y K,et al.Stock prediction via sentimental transfer learning[J].IEEE Access,2018,6:73110-73118.
[47] LEE T H,YANG Y.Bagging binary and quantile predictors for time series[J].Journal ofEconometrics,2006,135(1/2):465-497.
[48] SUN J,LI H,FUJITA H,et al.Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting[J].Information Fusion,2020,54:128-144.
[49] RUNDO F,TRENTA F,DI STALLO A L,et al.Machine lear-ning for quantitative finance applications:A survey[J].Applied Sciences,2019,9(24):5574.
[50] GUDELEK M U,BOLUK S A,OZBAYOGLU A M.A deeplearning based stock trading model with 2-D CNN trend detection[C]//2017 IEEE Symposium Series on Computational Intelligence.2017:1-8.
[51] HOSEINZADE E,HARATIZADEH S.CNNPred:CNN-based stock market prediction using several data sources[J].arXiv:1810.08923,2018.
[52] KIM T,KIM H Y.Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data[J/OL].PloS one,2019,14(2):e0212320.
[53] BOROVKOVA S,TSIAMAS I.An ensemble of LSTM neural networks for high-frequency stock market classification[J].Journal of Forecasting,2019,38(6):600-619.
[54] YILDIZ Z C,YILDIZ S B.A portfolio construction frameworkusing LSTM-based stock markets forecasting[J/OL].International Journal of Finance & Economics,2020.
[55] CHEN S H,HSIEH Y L.Reinforcement learning in experimental asset markets[J].Eastern Economic Journal,2011,37(1):109-133.
[56] CHOI J J,LAIBSON D,MADRIAN B C,et al.Reinforcementlearning and savings behavior[J].The Journal ofFinance,2009,64(6):2515-2534.
[57] CAO J,CHEN J,HULL J,et al.Deep hedging of derivativesusing reinforcement learning[J].arXiv:2013.16409,2021.
[58] CAO Y,ZHAI J.Estimating price impact via deep reinforcement learning[J/OL].International Journal of Finance &Economics,2020.
[59] LEE J,KIM R,KOH Y,et al.Global stock market predictionbased on stock chart images using deep Q-network[J].IEEE Access,2019,7:167260-167277.
[60] DING X,ZHANG Y,LIU T,et al.Deep learning for event-dri-ven stock prediction[C]//Twenty-fourth International Joint Conference on Artificial Intelligence.2015.
[61] XU Y,ZHAO J.Can sentiments on macroeconomic news explain stock returns? Evidence form social network data[J/OL].International Journal of Finance & Economics,2020.
[62] FANG L,PERESS J.Media coverage and the cross-section ofstock returns[J].The Journal of Finance,2009,64(5):2023-2052.
[63] LIU J,LU Z,DU W.Combining enterprise knowledge graph and news sentiment analysis for stock price prediction[C]//Procee-dings of the 52nd Hawaii International Conference on System Sciences.2019.
[64] CHEN Y,WEI Z,HUANG X.Incorporating corporation rela-tionship via graph convolutional neural networks for stock price prediction[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management.2018:1655-1658.
[65] DENG S,ZHANG N,ZHANG W,et al.Knowledge-driven stock trend prediction and explanation via temporal convolutional network[C]//Companion Proceedings of The 2019 World Wide Web Conference.2019:678-685.
[66] TIWARI S,PANDIT R,RICHHARIYA V.Predicting future trends in stock market by decision tree rough-set based hybrid system with HHMM[J/OL].Iternational Journal of Electronics and Computer Science Engineering,2010,1(3).
[67] CREIGHTON J,ZULKERNINE F H.Towards building a hybrid model for predicting stock indexes[C]//2017 IEEE International Conference on Big Data(Big Data).2017:4128-4133.
[68] ASSIS C A S,PEREIRA A C M,CARRANO E G,et al.Restricted Boltzmann machines for the prediction of trends in financial time series[C]//2018 International Joint Conference on Neural Networks.2018:1-8.
[69] CHEN W,YEO C K,LAU C T,et al.Leveraging social media news to predict stock index movement using RNN-boost[J].Data & Knowledge Engineering,2018,118:14-24.
[70] CHEN Y,LIN W,WANG J Z.A dual-attention-based stockprice trend prediction model with dual features[J].IEEE Access,2019,7:148047-148055.
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