Song, Insu, Anselme, Bryan, Mandal, Purnendu, and Vong, John (2017) Modern approaches of financial-data analysis. In: Mandal, Purnendu, and Vong, John, (eds.) Entrepreneurship in Technology for ASEAN. Managing the Asian Century . Springer, pp. 1-14.

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Abstract

Short and medium term predictions of stock prices have been important problems in financial analysis. In the past, various different approaches have been used including statistical analysis, fundamental analysis, and more recently advanced approaches that use machine learning and data mining techniques. However, most of existing algorithms do not incorporate all available information of the market. By using more informative and relevant data, prediction results will better reflect market reality. This would benefit in reducing the inaccuracy of predicting due to randomness in stock prices, by using trend rather than a single stock price variation. For instance some stock prices are correlated and/or dependent with/on each other and market mood. In this paper, we review the existing techniques of stock prices and time series predictions, and the classification and clustering methods. Based on the literature analysis, we propose a method for incorporating related stock trend information: clustering related companies using machine learning approaches. We report on a preliminary analysis results using monthly adjusted closing prices of 100 companies collected over a 15-month period.

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