DNN models based on dimensionality reduction for stock trading
暂无分享,去创建一个
Dong Wang | Yang Xiang | Meizi Li | Dongdong Lv | Dong Wang | Yang Xiang | D. Lv | Meizi Li
[1] Henry C. W. Lau,et al. A business process activity model and performance measurement using a time series ARIMA intervention analysis , 2009, Expert Syst. Appl..
[2] Yingying Xu,et al. Technological progress, globalization and low-inflation: Evidence from the United States , 2019, PloS one.
[3] Yulei Rao,et al. A deep learning framework for financial time series using stacked autoencoders and long-short term memory , 2017, PloS one.
[4] Norton Trevisan Roman,et al. Forecasting stock market index daily direction: A Bayesian Network approach , 2018, Expert Syst. Appl..
[5] Jianjun Xu,et al. Deep Learning with Gated Recurrent Unit Networks for Financial Sequence Predictions , 2018 .
[6] Jianxue Chen. SVM application of financial time series forecasting using empirical technical indicators , 2010, 2010 International Conference on Information, Networking and Automation (ICINA).
[7] Ming-Chi Lee,et al. Using support vector machine with a hybrid feature selection method to the stock trend prediction , 2009, Expert Syst. Appl..
[8] An-Sing Chen,et al. Time-varying Variance Scaling: Application of the Fractionally Integrated ARMA Model , 2019, The North American Journal of Economics and Finance.
[9] Lee-Ing Tong,et al. Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming , 2011, Knowl. Based Syst..
[10] Zhenhong Du,et al. Red tide time series forecasting by combining ARIMA and deep belief network , 2017, Knowl. Based Syst..
[11] Li Tang,et al. Predicting the direction of stock markets using optimized neural networks with Google Trends , 2018, Neurocomputing.
[12] Shouyang Wang,et al. Forecasting stock market movement direction with support vector machine , 2005, Comput. Oper. Res..
[13] Xiao Zhong,et al. Forecasting daily stock market return using dimensionality reduction , 2017, Expert Syst. Appl..
[14] Ha Young Kim,et al. Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models , 2018, Expert Syst. Appl..
[15] Chung-Ho Su,et al. A hybrid fuzzy time series model based on ANFIS and integrated nonlinear feature selection method for forecasting stock , 2016, Neurocomputing.
[16] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[17] Shouyang Wang,et al. A causal feature selection algorithm for stock prediction modeling , 2014, Neurocomputing.
[18] Amy Loutfi,et al. A review of unsupervised feature learning and deep learning for time-series modeling , 2014, Pattern Recognit. Lett..
[19] Matthew Dixon,et al. Sequence Classification of the Limit Order Book Using Recurrent Neural Networks , 2017, J. Comput. Sci..
[20] Jui-Chung Hung,et al. A fuzzy GARCH model applied to stock market scenario using a genetic algorithm , 2009, Expert Syst. Appl..
[21] Daniela M. Witten,et al. An Introduction to Statistical Learning: with Applications in R , 2013 .
[22] Wei-Chang Yeh,et al. Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm , 2011, Appl. Soft Comput..
[23] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[24] Herbert Kimura,et al. Literature review: Machine learning techniques applied to financial market prediction , 2019, Expert Syst. Appl..
[25] Sahil Shah,et al. Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques , 2015, Expert Syst. Appl..
[26] Stephen C. H. Leung,et al. A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression , 2015, Knowl. Based Syst..
[27] Wing Lon Ng,et al. Enhancing risk-adjusted performance of stock market intraday trading with Neuro-Fuzzy systems , 2014, Neurocomputing.
[28] Guo-qiang Xie. The Optimization of Share Price Prediction Model Based on Support Vector Machine , 2011, 2011 International Conference on Control, Automation and Systems Engineering (CASE).
[29] Wen Long,et al. Deep learning-based feature engineering for stock price movement prediction , 2019, Knowl. Based Syst..
[30] Mohammad Mahdi Rounaghi,et al. Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model , 2016 .
[31] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[32] Jason Laws,et al. Trading futures spread portfolios: applications of higher order and recurrent networks , 2008 .
[33] Ligang Zhou,et al. Predicting the listing statuses of Chinese-listed companies using decision trees combined with an improved filter feature selection method , 2017, Knowl. Based Syst..
[34] Yurong Liu,et al. A survey of deep neural network architectures and their applications , 2017, Neurocomputing.
[35] Bruno Biais,et al. High Frequency Trading , 2012 .
[36] Vinícius M. A. de Souza,et al. Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model , 2019, Inf. Sci..
[37] P. Dash,et al. A hybrid stock trading framework integrating technical analysis with machine learning techniques , 2016 .
[38] Chulwoo Han,et al. Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies , 2017, Expert Syst. Appl..
[39] Seda Tolun,et al. Dimension reduction in mean-variance portfolio optimization , 2018, Expert Syst. Appl..
[40] Georgios P. Kouretas,et al. Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH model , 2010 .
[41] Cheng-Lung Huang,et al. A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting , 2009, Expert Syst. Appl..
[42] H. Bourlard,et al. Auto-association by multilayer perceptrons and singular value decomposition , 1988, Biological Cybernetics.
[43] Patrick P. K. Chan,et al. LG-Trader: Stock trading decision support based on feature selection by weighted localized generalization error model , 2014, Neurocomputing.
[44] Nicolas Huck,et al. Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 , 2017, Eur. J. Oper. Res..
[45] Rui Ferreira Neves,et al. Combining Principal Component Analysis, Discrete Wavelet Transform and XGBoost to trade in the financial markets , 2019, Expert Syst. Appl..
[46] Dymitr Ruta. Automated Trading with Machine Learning on Big Data , 2014, 2014 IEEE International Congress on Big Data.
[47] Yingjun Chen,et al. Integrating principle component analysis and weighted support vector machine for stock trading signals prediction , 2018, Neurocomputing.
[48] Meizi Li,et al. Selection of the optimal trading model for stock investment in different industries , 2019, PloS one.
[49] Anders Lindén,et al. Recombinant human IL-26 facilitates the innate immune response to endotoxin in the bronchoalveolar space of mice in vivo , 2017, PloS one.
[50] Erdogan Dogdu,et al. A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters , 2017 .
[51] Przemyslaw Grzegorzewski,et al. Stock Trading with Random Forests, Trend Detection Tests and Force Index Volume Indicators , 2013, ICAISC.
[52] Chih-Fong Tsai,et al. Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches , 2010, Decis. Support Syst..
[53] K. P. Soman,et al. NSE Stock Market Prediction Using Deep-Learning Models , 2018 .
[54] Kamil Zbikowski,et al. Using Volume Weighted Support Vector Machines with walk forward testing and feature selection for the purpose of creating stock trading strategy , 2015, Expert Syst. Appl..
[55] Douglas A. Wolfe,et al. Nonparametric Statistical Methods , 1973 .