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https://doi.org/10.1073/pnas.1802987115

has been cited by the following article:

• TITLE: Possibility for Short-Term Forecasting of Japanese Stocks Return by Randomly Distributed Embedding Theory

  AUTHORS: Seisuke Sugitomo, Keiichi Maeta

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

  JOURNAL NAME: Journal of Mathematical Finance, Vol.9 No.3, July 8, 2019

  ABSTRACT: 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.