大規模言語生成モデルによる分類精度の向上

LSTM によるTOPIX Core30 企業の分類分析

抄録

News has great impacts on asset prices in the financial markets. Many attempts have been reported to ascertain how news influences stock prices. However, the limitations in the number of available data sets usually become the hurdle for the model accuracy. In this study, Reuter's news texts were classified based on each return through LSTM models. As a result of the analysis, we found the prediction accuracy could be improved by using texts generated by GPT-2. Further examination of the classification model will be planned.