مقاله انگلیسی رایگان در مورد سیستم پیش بینی روند قیمت سهام داده محور جدید – الزویر 2018

Stock price trend prediction is a classic and interesting topic that has attracted many researchers and participants in multiple disciplines such as economics, financial engineering, statistics, operations research, and machine learning. Although a lot of efforts have been paid during the past several decades (Abarbanell & Bernard, 1992; Adam, Marcet, & Nicolini, 2016; Adebiyi, Adewumi, & Ayo, 2014; Blume, Easley, & O’hara, 1994; Göçken, Özçalıcı, Boru, & Dosdogru, ˘ 2016), accurate forecast of the stock price, even its movements, is still not easy to achieve hitherto, though some advanced machine learning techniques have been utilized. For instance, Kim (2003) used support vector machines to predict the direction of the daily socket price movements in Korea, obtaining a hit rate 56%. Schumaker and Chen (2009) included the text mining technique into socket price forecast, achieving a hit rate 57%. Tsai and Wang (2009) combined the decision tree and neural networks to make prediction to Taiwan stock market. The accuracy of the hybrid model achieves around 70%. However, their test data sets were relatively small, only including dozens of stocks. According to a recent empirical study (Gerlein, McGinnity, Belatreche, & Coleman, 2016), the prediction accuracies of several machine learn- ing models (such as C4.5, K∗, logistic model tree, etc.) are in the range of 48% ∼ 54%. Traditional technical analysts have developed many indices and sequential analytical methods that may reflect the trends in the movements of the stock price. However, technical analysis contradicts with the efficient-market hypothesis but they cannot make generalised inferences regarding the accuracy. For example, the efficient-market hypothesis states that as long as the market is weak-form efficient, the price of a stock follows the random walk model (Fama, 1995) and cannot be predicted by analyzing prices from the past. Meanwhile, the prices are affected by many macroeconomical factors, fundamental factors of companies and the involvement of public investors. Therefore, some criticism of technical analysis is that it only considers transactional data of stocks and completely ignores the fundamental factors of companies (Nassirtoussi, Aghabozorgi, Wah, & Ngo, 2014; Patel, Shah, Thakkar, & Kotecha, 2015) which might be helpful, if the market is in weakform efficiency. The fundamental factors of a company cover many aspects such as basic financial status, marketing and development strategies, political events, general economic conditions, commodity price indices, interest rate changes, movements of other stock markets, expectations and psychology of investors, and so on. Comprehensively figuring out the impact of these compound factors on the movement of the stock price is obviously out of the capability of human analysts.