Application of Machine Learning in Stock Market Return Forecasting
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With the development of our country's financial market, people pay more attention to the stock market return forecast, which can bring the investors a lot of income and also become a tool of risk management. Machine learning (ML) and artificial intelligence (AI) are becoming increasingly sophisticated and have achieved amazing results in many fields. Machine learning and deep learning algorithm models can process huge amounts of data, from which they can quickly process and analyze laws, many scholars try to apply various machine learning models to the financial field. This paper first expounds the theoretical basis of stock market return prediction, and summarizes the main literature in this field in recent years, from the traditional stock prediction model to the machine learning prediction model. Finally, the traditional stock prediction model ARIMA model and the deep learning LSTM neural network model are selected for comparative empirical research. The sample data are from the Tushare big data community, taking the new energy vehicle stock BYD (002594) as an example. The empirical results show that the prediction accuracy and stability of LSTM neural network are far higher than ARIMA model, and it will have broad application prospects in financial prediction and other fields in the future.
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Markowitz H. Portfolio selection[J]. Journal of Finance ,1952,7 (1):77-91.
Fama E F,French K R. 1993. Common Risk Factors in the Returns on Stocks and Bonds [J].Journal of Financial Economics,33(1).
Carhart M M. 1997. On Persistence in Mutual Fund Performance [J].Journal of Finance,52(1).
Li Fujun, Jiang Fuwei, Yang Hua. The Influence of investors' rational Characteristics on Momentum Effect: Evidence based on China's A-share Market [J]. Macroeconomic Research, 2019, (11).
Fama E F,French K R. 2015. Incremental Variables and the Investment Opportunity Set [J].Journal of Financial Economics,117(3).
Braun P A, Nelson D B, Sunier A M. Good news, bad news, volatility, and betas[J]. The Journal of Finance, 1995, 50(5): 1575-1603.
Awartani B M A, Corradi V. Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries[J]. International Journal of forecasting, 2005, 21(1): 167-183.
Hung J C, Lee M C, Liu H C. Estimation of value-at-risk for energy commodities via fat-tailed GARCH models[J]. Energy Economics, 2008, 30(3): 1173-1191.
Wei Yanhua, Zhang Shiying. Correlation Analysis of Financial markets: Copula-GARCH Model and its application [J]. Systems Engineering, 2004, 22(4): 7-12.
Xiong Zhengde, Wen Hui, Xiong Yipeng. An empirical study on volatility spillover effect between foreign exchange Market and stock market in China: Analysis of multivariate BEKK-GARCH (1, 1) model based on wavelet multi-resolution [J]. Chinese Management Science,2015, 23(4): 30-38.
Akita R, Yoshihara A, Matsubara T, et al. Deep learning for stock prediction using numerical and textual information[C]//2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS). IEEE, 2016: 1-6.
Gu J, Wang Z, Kuen J, et al. Recent advances in convolutional neural networks[J]. Pattern recognition, 2018, 77: 354-377.
Leippold M, Wang Q, Zhou W. Machine learning in the Chinese stock market[J]. Journal of Financial Economics, 2022, 145(2): 64-82.
Chen Weihua, XU Guoxiang. Research on Prediction Accuracy of Stock Market Volatility Based on Deep Learning and Stock Forum Data [J]. Management World, 2018, 34(1): 180-181.
Li Bin, SHAO Xinyue, Li Yueyang. Research on fundamental quantitative Investment driven by Machine learning [J]. Chinese Industrial Economy, 2019 (8): 61-79.
Leung M T, Daouk H, Chen A S. Forecasting stock indices: a comparison of classification and level estimation models[J]. International Journal of forecasting, 2000, 16(2): 173-190.
Chevapatrakul T. Return sign forecasts based on conditional risk: Evidence from the UK stock market index[J]. Journal of Banking & Finance, 2013, 37(7): 2342-2353.
Pönkä H. Predicting the direction of US stock markets using industry returns[J]. Empirical Economics, 2017, 52(4): 1451-1480.
Nair B B, Mohandas V P, Sakthivel N R. A decision tree-rough set hybrid system for stock market trend prediction[J]. International Journal of Computer Applications, 2010, 6(9): 1-6.
Panigrahi S S, Mantri J K. Epsilon-SVR and decision tree for stock market forecasting[C]//2015 International Conference on Green Computing and Internet of Things (ICGCIoT). IEEE, 2015: 761-766.
Wang Ling, Hu Yang. Stock Data Mining Based on C4.5 Decision Tree [J]. Computer and modernization, 2015 (10): 21-24.
Deng Nansha, Su Wen. Case study of Stock market prediction Analysis based on Data mining technology [J]. Science and Technology and Enterprises, 2012 (18): 272-274.
Wang Yu. Stock Prediction model based on Cart Tree and Boosting Algorithm [D]. Harbin: Harbin University of Science and Technology, 2018.
Breiman L. Random forests[J]. Machine learning, 2001, 45(1): 5-32.
Khandani A E, Kim A J, Lo A W. Consumer credit-risk models via machine-learning algorithms[J]. Journal of Banking & Finance, 2010, 34(11): 2767-2787.
Butaru F, Chen Q, Clark B, et al. Risk and risk management in the credit card industry[J]. Journal of Banking & Finance, 2016, 72: 218-239.
Moritz B, Zimmermann T. Tree-based conditional portfolio sorts: The relation between past and future stock returns[J]. Available at SSRN 2740751, 2016.
Patel J, Shah S, Thakkar P, et al. Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques[J]. Expert systems with applications, 2015, 42(1): 259-268.
Wang Shuyan, Cao Zhengfeng, Chen Mingzhi. Application of random forest in quantitative stock selection [J]. Operation Research and Management ,2016, 25(3): 163-168.
Zhang Xiao, Wei Zengxin. Application of Random Forest in stock trend prediction [J]. China Management Informatization, 2018, 21(3): 120-123.
Friedman J H. Greedy function approximation: a gradient boosting machine[J]. Annals of statistics, 2001: 1189-1232.
Krauss C, Do X A, Huck N. Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500[J]. European Journal of Operational Research, 2017, 259(2): 689-702.
Zhang Xiao, WEI Zengxin, Yang Tianshan. Application of GBDT combination Model in Stock prediction [J]. Journal of Hainan Normal University: Natural Science Edition, 2018, 31(1): 73-80.
Deng Jing. GBDT Multi-factor Stock Selection Model Based on Factor Deviation [J]. Software Guide, 2021.
Hutchinson J M, Lo A W, Poggio T. A nonparametric approach to pricing and hedging derivative securities via learning networks[J]. The journal of Finance, 1994, 49(3): 851-889.
Yao J, Tan C L. A case study on using neural networks to perform technical forecasting of forex[J]. Neurocomputing, 2000, 34(1-4): 79-98.
Karathiya M B, Sakshi D S, Sakshi R S. Stock Prediction by Fuzzy Neural Networks[C]//2012 Second International Conference on Advanced Computing & Communication Technologies. IEEE, 2012: 79-82.
Hu H, Tang L, Zhang S, et al. Predicting the direction of stock markets using optimized neural networks with Google Trends[J]. Neurocomputing, 2018, 285: 188-195.
Fischer T, Krauss C. Deep learning with long short-term memory networks for financial market predictions[J]. European journal of operational research, 2018, 270(2): 654-669.
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