@techreport{oai:shiga-u.repo.nii.ac.jp:00013852,
 author = {Zhong,  Xin and Wang, Yiwen},
 issue = {No.E-8},
 month = {Mar},
 note = {Technical Report, The primary purpose of this study is to forecast the one-month forward Nikkei 225 stock return by employing neural networks. We first explore the predictive function of artificial neural networks by comparing the predictive power of models of different neurons and hidden layers. We find that the model with 100 neurons and two hidden layers has the best predictive ability. We also investigate the effects of different types of input variables on predictions. The results show that both technical and liquidity proxies contribute to the analysis. Finally, we combine neural networks with portfolio construction strategies and confirm that neural networks predictions can effectively distinguish good stocks and bad stocks.  In summary, this study applies the neural network to the stock market and provides a new idea for using deep learning to investment decisions., Discussion Paper, Series E, No. E-8, pp. 1-7},
 title = {Neural Network Application in Predicting Stock Returns: Evidence from Japan},
 year = {2021}
}