Abstract
The market analysis is one of the important tasks for text mining. In this situation, Web news has an important role to predict stock prices. In this paper, we propose a method to predict the Nikkei Stock Average, which is one of the most important stock market indexes. We extract viewpoints from experts’ articles for analyzing Web news. The extracted words are index words in the vector space of a machine learning technique. We also incorporate word embedding and bootstrap approaches into our method. It predicts “UP” or “DOWN” of the next day by using the articles of a day. We also evaluate our method with not only one-day prediction but also simulated trading. The experimental result shows that index words based on expert articles were effective for both one-day prediction and simulated trading.
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Notes
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- 3.
Since the number of BoWs is approximately 10,000, BoK-E is approximately 10% of the BoW.
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Note that we never use experts’ articles for the vectorization. We just use expert’s articles for extracting index words for the vector space.
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More precisely, the model predicts the next day’s “UP” or “DOWN” from the target day.
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Loss-cutting is a financial word. It is an order to buy or sell stocks automatically in the case that unrealized capital losses become larger.
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Short selling is a financial word and the practice of selling stocks that are not currently owned by borrowing them from a securities company.
- 9.
More properly, the borrowed stocks are paid back to the securities company. The balance is the benefit of the day.
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Although we evaluated other methods, such as the method with BoT-E, the BoK-E also produced the best performance in terms of this simulated trading.
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Ichinose, K., Shimada, K. (2018). Stock Market Prediction Using Keywords from Expert Articles. In: Ghazali, R., Deris, M., Nawi, N., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2018. Advances in Intelligent Systems and Computing, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-319-72550-5_39
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DOI: https://doi.org/10.1007/978-3-319-72550-5_39
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