Modern Approaches of Financial Data Analysis for ASEAN Entrepreneurs
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 tha
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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. 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, 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.
I. Song (&) School of Business/IT, James Cook University, Singapore, Singapore e-mail: [email protected] B. Anselme School of Business/IT, James Cook University, Singapore, Singapore e-mail: [email protected] B. Anselme Ecole Nationale Superieur de l’Informatique pour l’ Industrie et l’Entreprise (ENSIIE), Évry, France P. Mandal College of Business, Lamar University, Beaumont, TX, USA e-mail: [email protected] J. Vong Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore e-mail: [email protected] © Springer Science+Business Media Singapore 2017 P. Mandal and J. Vong (eds.), Entrepreneurship in Technology for ASEAN, Managing the Asian Century, DOI 10.1007/978-981-10-2281-4_1
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Keywords Clustering Classification
I. Song et al.
Financial analyses Stock price prediction Prediction
1 Introduction Today most of the global monetary mass is invested in financial places, on coupons, debt financing, raw materials, features, or stocks. Optimizing investment strategies in these markets has become one of today’s most important research topics. The most common methodologies are: portfolio management that aims to reduce the risk taken in investment by diversifying the range of investment (Paranjape-Voditel and Deshpande 2013), arbitrage by detecting anomaly in prices and take a free lunch, pricing by calculating the real value of stocks, and finally standard trading with two majors types, which are the fundamental (Lev and Thiagarajan 1993) and the technical/quantitative (Lo et al. 2000) analysis approaches. The technical/quantitative analysis approaches include using mathematical tools in order to predict trends, discovering patterns for machine-based trading, and predicting medium-/short-term trends. The fundamental analysis approaches include using micro- and macroeconomics indicators
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