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The financial market is a mechanism for determining the price of financial funds and trading financial assets. It is a market that enables the financing of securities and the trading of securities. The capital market is also called the “long-term financial market”, which mainly includes the stock market, fund market, and bond market. Its volatility can reflect the degree of risk of assets. The fluctuation of stock prices plays a considerable role in the appropriate timing of buying and selling stocks (Kim, Kim, 2019). For investors, the true meaning of investing in the stock market is to obtain extraordinary returns by buying low and selling high, so the prediction of stock price fluctuations has become a special focus of private investors and investment companies (Samuelson, 1975). Due to a large amount of data in stocks, the low data correlation, and the many factors that affect stock prices, financial markets are full of uncertainty, which also makes predicting stock price fluctuations a significant problem for stock researchers. Initially, people had predicted the fluctuations of stock prices several times (Brooks, 1998; Keim & Stambaugh, 1986), but the results were unsatisfactory.
Investors are not completely rational. For example, people may have positive or negative emotions at certain moments (Hiew, Huang, Mou, Li, Wu, & Xu, 2019; Baker & Wurgler, 2006; Lee, Shleifer, & Thaler, 1991; Han, 2008; Chong, Cao, & Wong, 2014). Therefore, after this hypothesis was raised, there were both support and opposition, thus becoming one of the most controversial investment theories (Cowles, 1933; Borovkova & Tsiamas, 2019). Some researchers believe that if the trading signals (Bao, Yue, & Rao, 2017) of the stocks can be found, the stocks can be bought and sold at the appropriate time to obtain relatively high profits. Finding accurate stock trading signals is the key to obtaining huge returns. That is, in stock trading, when a buy signal appears, the stock is bought, and when a sell signal appears, the stock is sold. In other words, you should buy stocks at a lower price and sell stocks at a higher price if you want to obtain higher profits or income. Stock trading signals have always been the main research objects of investors and investment companies. Researchers have also tried many times to predict the best buying and selling signals, but because many factors affect stock prices, the prediction results are not satisfactory.
Support vector machines (SVM) is a binary classification model. Although it has not appeared for a long time, it has good classification performance in the field of machine learning. Huang et al. (Huang, Nakamori, & Wang, 2005) found in 2005 that SVM has better prediction results than traditional statistical methods. Nayak et al. (Nayak, Mishra, & Rath, 2015) proposed a hybrid model for the prediction of Indian stock indexes. This model mainly uses two machine learning algorithms, namely SVM and k-Nearest Neighbor (KNN). Studies have shown that the hybrid model they proposed has good scalability and predictability for high-dimensional data. Although the prediction results of SVM are significantly improved compared to other models (Cortes & Vapnik, 1995) and (Sheta, Ahmed, & Faris, 2015), it is still not satisfactory for stock prediction. The neural network model has received particular attention. Because neural network models can identify nonlinear relationships in data sets, many researchers have analyzed, and predicted stock price fluctuations based on various neural networks, and have also obtained relatively good results. For the stocks of the Tokyo Stock Exchange, Kimoto et al. (Kimoto, Asakawa, Yoda, & Takeoka, 1990) conducted a systematic study. They proposed a stock prediction system based on neural networks, and the system achieved relatively good profits in training. It stimulates the mechanism of the human brain to process data and can detect nonlinear relationships in the data. Oliveira et al. (De Oliveira, Nobre, & Zarate, 2013) used time series analysis, technical analysis, and financial theory to establish a neural network to predict the future price trend of stocks in the short term. This research shows that the proposed model has good performance.