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This is a joint effort on collecting latest papers related to quantitative finance. Please fork to add your wisdom!
Atsalakis G S, Valavanis K P. Surveying stock market forecasting techniques Part II: Soft computing methods. Expert Systems with Applications, 2009, 36(3):5932–5941. (link)
Cai X, Lin X. Feature Extraction Using Restricted Boltzmann Machine for Stock Price Predic- tion. 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE), 2012. 80–83.(link)
Nair B B, Dharini N M, Mohandas V P. A stock market trend prediction system using a hybrid decision tree-neuro-fuzzy system. Proceedings - 2nd International Conference on Advances in Recent Technologies in Communication and Computing, ARTCom 2010, 2010. 381–385. (link)
Lu C J, Lee T S, Chiu C C. Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 2009, 47(2):115–125. (link)
Creamer G, Freund Y. Automated trading with boosting and expert weighting. Quantitative Finance, 2010, 10(4):401–420. (link)
Batres-Estrada, Bilberto. "Deep learning for multivariate financial time series." (2015). (link)
Xiong, Ruoxuan, Eric P. Nicholas, and Yuan Shen. "Deep Learning Stock Volatilities with Google Domestic Trends." arXiv preprint arXiv:1512.04916 (2015).(link)
Sharang, Abhijit, and Chetan Rao. "Using machine learning for medium frequency derivative portfolio trading." arXiv preprint arXiv:1512.06228 (2015).(link)
Dempster, Michael AH, and Vasco Leemans. "An automated FX trading system using adaptive reinforcement learning." Expert Systems with Applications 30.3 (2006): 543-552. (link)
Tan, Zhiyong, Chai Quek, and Philip YK Cheng. "Stock trading with cycles: A financial application of ANFIS and reinforcement learning." Expert Systems with Applications 38.5 (2011): 4741-4755. (link)
Rutkauskas, Aleksandras Vytautas, and Tomas Ramanauskas. "Building an artificial stock market populated by reinforcement‐learning agents." Journal of Business Economics and Management 10.4 (2009): 329-341.(link)
Deng, Yue, et al. "Deep Direct Reinforcement Learning for Financial Signal Representation and Trading." (2016).(link)
Bollen J, Mao H, Zeng X. Twitter mood predicts the stock market. Journal of Computational Science, 2011, 2(1):1–8. (link)
Preis T, Moat H S, Stanley H E, et al. Quantifying trading behavior in financial markets using Google Trends. Scientific reports, 2013, 3:1684. (link)
Moat H S, Curme C, Avakian A, et al. Quantifying Wikipedia Usage Patterns Before Stock Market Moves. Scientific Reports, 2013, 3:1–5. (link)
Ding, Xiao, et al. "Deep learning for event-driven stock prediction." Proceedings of the 24th International Joint Conference on Artificial Intelligence (ICJAI’15). 2015. (link)
Fehrer, R., & Feuerriegel, S. (2015). Improving Decision Analytics with Deep Learning: The Case of Financial Disclosures. arXiv preprint arXiv:1508.01993. (link)
Nevmyvaka Y, Feng Y, Kearns M. Reinforcement learning for optimized trade execution. Proceedings of the 23rd international conference on Machine learning ICML 06, 2006, 17(1):673–680. (link)
Ganchev K, Nevmyvaka Y, Kearns M, et al. Censored exploration and the dark pool problem. Communications of the ACM, 2010, 53(5):99. (link)
Kearns M, Nevmyvaka Y. Machine learning for market microstructure and high frequency trading. High frequency trading - New realities for traders, markets and regulators, 2013. 1–21. (link)
Sirignano, Justin A. "Deep Learning for Limit Order Books." arXiv preprint arXiv:1601.01987 (2016). (link)
Deng, Yue, et al. "Sparse coding-inspired optimal trading system for HFT industry." IEEE Transactions on Industrial Informatics 11.2 (2015): 467-475.(link)
Ahuja, Saran, et al. "Limit order trading with a mean reverting reference price." arXiv preprint arXiv:1607.00454 (2016). (link)
Aït-Sahalia, Yacine, and Jean Jacod. "Analyzing the spectrum of asset returns: Jump and volatility components in high frequency data." Journal of Economic Literature 50.4 (2012): 1007-1050. (link)
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