Abstract
In general, it is not always easy to estimate stock prices exactly and get profits. Until today, many researchers have attacked to this subject, but could not report the successful estimation methods even if various approaches or many heuristics were applied in our knowledge-oriented society. This is because the fluctuation of stock prices is inherently characterized as random walk. In this paper, we address a short-term-specific up/down fluctuation estimation method of stock prices. Our approach is first to select 16 brand companies in Japan Stock Market as the fundamental stock features, and then to define analytically 8 stock attributes as input parameters for our 3-level neural network. We used 32,000 samples of 2,000 days from 16 brands: the first 1,000 days samples were used as learning data for our neural network; and the last 1,000 days samples were as test data. Our experiments showed that the up/down fluctuation estimation method in the short-term from the end value of today to the start value of tomorrow functions effectively.
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Watanabe, T., Iwata, K. (2009). Estimation for Up/Down Fluctuation of Stock Prices by Using Neural Network. In: Lytras, M.D., Ordonez de Pablos, P., Damiani, E., Avison, D., Naeve, A., Horner, D.G. (eds) Best Practices for the Knowledge Society. Knowledge, Learning, Development and Technology for All. WSKS 2009. Communications in Computer and Information Science, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04757-2_19
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DOI: https://doi.org/10.1007/978-3-642-04757-2_19
Publisher Name: Springer, Berlin, Heidelberg
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