چکیده مقاله
The success of any investment portfolio always depends on the future behavior and price events ofassets Therefore, the better one can predict the future of an asset, the more profitable decisions can bemade Today, with the expansion of machine learning models and their advanced sub branch i e deeplearning, it is possible to better predict the future of assets and make decisions based on thosepredictions In this article, a deep learning method called CNN LSTM with multiple parallel inputs isintroduced and is shown that it is able to provide a more accurate prediction of asset returns for the nextperiod than other machine learning and deep learning models Then, these forecasts will be used in twostages to build the portfolio First, the assets that have the highest predicted return are selected, and thenin the second step, Markowitz's mean variance model will be used to obtain the optimal ratio of theselected assets for trading in the next period The model test is performed on the assets randomlyselected from different New York Stock Exchange industries based on the 11 Global IndustryClassification Standard GICS Stock Market Sectors
کلیدواژهها
نویسندگان
Mahdi Ashrafzadeh
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran,Iran;
Hatef Kiabakht
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran,Iran;
شیوه ارجاع
Ashrafzadeh, Mahdi and Kiabakht, Hatef,1402,Portfolio optimization based on return prediction using multiple parallelinput CNN-LSTM,The 9th International Conference on Industrial and Systems Engineering,Mashhad
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