Author(s)

Seisuke Sugitomo¹, Keiichi Maeta²

¹Fund Manager at Epic Partners Investment Co., Ltd., Tokyo, Japan.
²Graduate School of Mathematical Sciences, University of Tokyo, Tokyo, Japan.

In this work, we use the model-free framework, named randomly distributed embedding, which is the method that randomly selects variables from the values of many observed variables at a certain time and estimates the state of the attractor at that time, to predict the future return of Japanese stocks and show that the prediction accuracy is improved compared to the conventional methods such as simple linear regression or least absolute shrinkage and selection operator (LASSO) regression. In addition, important points to be considered when applying the randomly distributed embedding method to financial markets, and specific future practical applications will be presented.

Randomly Distributed Embedding (RDE), Least Absolute Shrinkage and Selection Operator (LASSO), Artificial Intelligence Finance, Japanese Stock Return Prediction

Sugitomo, S. and Maeta, K. (2019) Possibility for Short-Term Forecasting of Japanese Stocks Return by Randomly Distributed Embedding Theory. Journal of Mathematical Finance, 9, 266-271. doi: 10.4236/jmf.2019.93015.