{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/stock-price-correlation-coefficient","title":"Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model","arxiv_id":"1808.01560","date":"2018-08-05","proceeding":null,"authors":["Hyeong Kyu Choi"],"abstract":"Predicting the price correlation of two assets for future time periods is\nimportant in portfolio optimization. We apply LSTM recurrent neural networks\n(RNN) in predicting the stock price correlation coefficient of two individual\nstocks. RNNs are competent in understanding temporal dependencies. The use of\nLSTM cells further enhances its long term predictive properties. To encompass\nboth linearity and nonlinearity in the model, we adopt the ARIMA model as well.\nThe ARIMA model filters linear tendencies in the data and passes on the\nresidual value to the LSTM model. The ARIMA LSTM hybrid model is tested against\nother traditional predictive financial models such as the full historical\nmodel, constant correlation model, single index model and the multi group\nmodel. In our empirical study, the predictive ability of the ARIMA-LSTM model\nturned out superior to all other financial models by a significant scale. Our\nwork implies that it is worth considering the ARIMA LSTM model to forecast\ncorrelation coefficient for portfolio optimization.","url_abs":"http://arxiv.org/abs/1808.01560v5","url_pdf":"http://arxiv.org/pdf/1808.01560v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"stock-price-correlation-coefficient","repo_url":"https://github.com/imhgchoi/Corr_Prediction_ARIMA_LSTM_Hybrid","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"stock-price-correlation-coefficient","repo_url":"https://github.com/morpheu513/DA_project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"stock-price-correlation-coefficient","repo_url":"https://github.com/morpheu513/NASDAQ_stock_analysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"portfolio-optimization","task_name":"Portfolio Optimization"},{"task_slug":"stock-market-prediction","task_name":"Stock Market Prediction"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}