portfolio optimization based on return prediction using multiple parallel input cnn-lstm

The success of any investment portfolio always depends on the future behavior and price events of assets. therefore, the better one can predict the future of an asset, the more profitable decisions can be made. today, with the expansion of machine learning models and their advanced sub-branch i.e. deep learning, it is possible to better predict the future of assets and make decisions based on those predictions. in this article, a deep learning method called cnn-lstm with multiple parallel inputs is introduced and is shown that it is able to provide a more accurate prediction of asset returns for the next period than other machine learning and deep learning models. then, these forecasts will be used in two stages to build the portfolio. first, the assets that have the highest predicted return are selected, and then in the second step, markowitz s mean-variance model will be used to obtain the optimal ratio of the selected assets for trading in the next period. the model test is performed on the assets randomly selected from different new york stock exchange industries based on the 11 global industry classification standard (gics) stock market sectors.