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Abstract

News articles have great impacts on asset prices in the financial markets. Many attempts havebeen reported to ascertain how news influences stock prices. However, the limitations in the number ofavailable data sets usually become the hurdle for the model accuracy. In this study, we propose a newsevaluation model utilizing GPT-2. A news evaluation model is a model that evaluates news articlesdistributed to financial markets based on price fluctuation rates and predicts fluctuations in stock prices.Reuter's news texts are classified based on each return through LSTM models. Using co-occurrencenetwork analysis, we reviewed the overview of the news articles retrieved. News articles generated by GPT-2 was used with original news articles, and the model accuracy was examined. The results showed thatcreated news articles are influential over the prediction of stock price fluctuation.