Open AccessPossibility for Short-Term Forecasting of Japanese Stocks Return by Randomly Distributed Embedding Theory
Author(s) -
Seisuke Sugitomo,
Keiichi Maeta
Publication year - 2019
Publication title -
journal of mathematical finance
Language(s) - English
Resource type - Journals
eISSN - 2162-2434
pISSN - 2162-2442
DOI - 10.4236/jmf.2019.93015
Subject(s) - embedding , lasso (programming language) , econometrics , term (time) , regression , attractor , computer science , operator (biology) , state variable , linear regression , regression analysis , mathematics , statistics , artificial intelligence , machine learning , mathematical analysis , biochemistry , physics , chemistry , repressor , quantum mechanics , world wide web , transcription factor , gene , thermodynamics
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