{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T16:58:58Z","timestamp":1791478738921,"version":"4.3.4"},"reference-count":34,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,2,12]],"date-time":"2019-02-12T00:00:00Z","timestamp":1549929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61104166"],"award-info":[{"award-number":["61104166"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This paper takes 50 ETF options in the options market with high transaction complexity as the research goal. The Random Forest (RF) model, the Long Short-Term Memory network (LSTM) model, and the Support Vector Regression (SVR) model are used to predict 50 ETF price. Firstly, the original quantitative investment strategy is taken as the research object, and the 15 min trading frequency, which is more in line with the actual trading situation, is used, and then the Delta hedging concept of the options is introduced to control the risk of the quantitative investment strategy, to achieve the 15 min hedging strategy. Secondly, the final transaction price, buy price, highest price, lowest price, volume, historical volatility, and the implied volatility of the time segment marked with 50 ETF are the seven key factors affecting the price of 50 ETF. Then, two different types of LSTM-SVR models, LSTM-SVR I and LSTM-SVR II, are used to predict the final transaction price of the 50 ETF in the next time segment. In LSTM-SVR I model, the output of LSTM and seven key factors are combined as the input of SVR model. In LSTM-SVR II model, the hidden state vectors of LSTM and seven key factors are combined as the inputs of the SVR model. The results of the two LSTM-SVR models are compared with each other, and the better one is applied to the trading strategy. Finally, the benefit of the deep learning-based quantitative investment strategy, the resilience, and the maximum drawdown are used as indicators to judge the pros and cons of the research results. The accuracy and deviations of the LSTM-SVR prediction models are compared with those of the LSTM model and those of the RF model. The experimental results show that the quantitative investment strategy based on deep learning has higher returns than the traditional quantitative investment strategy, the yield curve is more stable, and the anti-fall performance is better.<\/jats:p>","DOI":"10.3390\/a12020035","type":"journal-article","created":{"date-parts":[[2019,2,13]],"date-time":"2019-02-13T02:49:44Z","timestamp":1550026184000},"page":"35","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Research on Quantitative Investment Strategies Based on Deep Learning"],"prefix":"10.3390","volume":"12","author":[{"given":"Yujie","family":"Fang","sequence":"first","affiliation":[{"name":"SHU-UTS SILC Business School, Shanghai University, Shanghai 201899, China"},{"name":"Carey Business School, The Johns Hopkins University, Baltimore, MD 20036, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0631-8370","authenticated-orcid":false,"given":"Juan","family":"Chen","sequence":"additional","affiliation":[{"name":"SHU-UTS SILC Business School, Shanghai University, Shanghai 201899, China"},{"name":"Smart City Research Institute, Shanghai University, Shanghai 201899, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengxuan","family":"Xue","sequence":"additional","affiliation":[{"name":"SHU-UTS SILC Business School, Shanghai University, Shanghai 201899, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.knosys.2018.10.034","article-title":"Deep learning-based feature engineering for stock price movement prediction","volume":"164","author":"Long","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"18569","DOI":"10.1007\/s11042-016-4159-7","article-title":"Stock prediction using deep learning","volume":"76","author":"Ritika","year":"2017","journal-title":"Mutimedia Tools and Appl."},{"key":"ref_3","first-page":"1","article-title":"Deep Learning for Forecasting Stock Returns in the Cross-Section","volume":"3","author":"Masaya","year":"2018","journal-title":"Stat. Financ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0925-2312(03)00372-2","article-title":"Financial time series forecasting using support vector machines","volume":"55","author":"Kim","year":"2003","journal-title":"Neurocomputing"},{"key":"ref_5","unstructured":"Heaton, J.B., Polson, N.G., and Witte, J.H. (arXiv, 2016). Deep Learning in Finance, arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sun, S., Wei, Y., and Wang, S. (2018, January 11\u201313). AdaBoost-LSTM Ensemble Learning for Financial Time Series Forecasting. Proceedings of the International Conference on Computational Science, Wuxi, China.","DOI":"10.1007\/978-3-319-93713-7_55"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_8","unstructured":"Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., and Iosifidis, A. (2018). Temporal Bag-of-Features Learning for Predicting Mid Price Movements Using High Frequency Limit Order Book Data. IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1016\/j.ijforecast.2010.07.003","article-title":"Forecasting exchange rate volatility using high-frequency data: Is the euro different?","volume":"27","author":"Chortareas","year":"2011","journal-title":"Int. J. Forecast."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.ijforecast.2009.01.007","article-title":"Forecasting exchange rates with a large Bayesian VAR","volume":"25","author":"Carriero","year":"2009","journal-title":"Int. J. Forecast."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.neucom.2015.03.100","article-title":"Neural networks performance in exchange rate prediction","volume":"172","author":"Galeshchuk","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.neucom.2015.04.071","article-title":"Forecasting exchange rate using deep belief networks and conjugate gradient method","volume":"167","author":"Shen","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1016\/S0925-2312(01)00676-2","article-title":"Modified support vector machines in financial time series forecasting","volume":"48","author":"Tay","year":"2002","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.knosys.2016.12.019","article-title":"Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble","volume":"120","author":"Sun","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/72.935093","article-title":"Financial time series prediction using least squares support vector machines within the evidence framework","volume":"12","author":"Van","year":"2001","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s13042-015-0359-0","article-title":"A novel hybrid model using teaching-learning-based optimization and a support vector machine for commodity futures index forecasting","volume":"9","author":"Das","year":"2018","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1080\/14697688.2015.1032546","article-title":"Modelling high-frequency limit order book dynamics with support vector machines","volume":"15","author":"Kercheval","year":"2015","journal-title":"Quant. Financ."},{"key":"ref_18","unstructured":"Fan, A., and Palaniswami, M. (2001, January 15\u201319). Stock selection using support vector machines. Proceedings of the International Joint Conference on Neural Networks, Washington, DC, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1109\/TNN.2003.820556","article-title":"Support vector machine with adaptive parameters in financial time series forecasting","volume":"14","author":"Cao","year":"2003","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1016\/j.eswa.2007.08.088","article-title":"Particle swarm optimization for parameter determination and feature selection of support vector machines","volume":"35","author":"Lin","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1007\/s00521-011-0721-4","article-title":"A hybrid procedure with feature selection for resolving stock\/futures price forecasting problems","volume":"22","author":"Hsu","year":"2013","journal-title":"Neural Comput. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3055","DOI":"10.1016\/j.neucom.2009.03.015","article-title":"Improving options price forecasts with neural networks and support vector regressions","volume":"72","author":"Liang","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., and Gabbouj, M. (September, January 28). Using deep learning to detect price change indications in financial markets. Proceedings of the 2017 25th European Signal Processing Conference (EUSIPCO), Kos, Greece.","DOI":"10.23919\/EUSIPCO.2017.8081663"},{"key":"ref_24","unstructured":"Kai, C., Zhou, Y., and Dai, F. (November, January 29). A LSTM-based method for stock returns prediction: A case study of China stock market. Proceedings of the 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"58","DOI":"10.4236\/jmf.2018.81005","article-title":"Predicting Equity Price with Corporate Action Events Using LSTM-RNN","volume":"8","author":"Minami","year":"2018","journal-title":"J. Math. Financ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., and Iosifidis, A. (2017, January 24\u201327). Forecasting stock prices from the limit order book using convolutional neural networks. Proceedings of the 2017 IEEE 19th Conference on Business Informatics (CBI), Thessaloniki, Greece.","DOI":"10.1109\/CBI.2017.23"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_28","first-page":"1","article-title":"Supervised Sequence Labelling with Recurrent Neural Networks","volume":"385","author":"Graves","year":"2008","journal-title":"Stud. Comput. Intell."},{"key":"ref_29","unstructured":"Sun, R.Q. (2015). Research on Price Trend Prediction Model of US Stock Index Based on LSTM Neural Network. [Master\u2019s Thesis, Capital University of Economics and Business]."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2279","DOI":"10.1080\/02664763.2017.1414166","article-title":"Predicting Risk for Adverse Health Events Using Random Forest","volume":"45","author":"Guy","year":"2018","journal-title":"J. Appl. Stat."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support vector machines","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_32","first-page":"203","article-title":"Support Vector Regression","volume":"11","author":"Awad","year":"2007","journal-title":"Neural Inf. Process. Lett. Rev."},{"key":"ref_33","first-page":"164","article-title":"A Step-by-step Guide of Options Investment","volume":"22","author":"Hu","year":"2018","journal-title":"China Econ. Publ. House"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1086\/260062","article-title":"The Pricing of Options and Corporate Liabilities","volume":"81","author":"Black","year":"1973","journal-title":"J. Polit. Econ."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/12\/2\/35\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:31:33Z","timestamp":1760185893000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/12\/2\/35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,12]]},"references-count":34,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["a12020035"],"URL":"https:\/\/doi.org\/10.3390\/a12020035","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,12]]}}}