Abstract
With the rapid development of the financial market, many professional traders use technical indicators to analyze the stock market. As one of these technical indicators, moving average convergence divergence (MACD) is widely applied by many investors. MACD is a momentum indicator derived from the exponential moving average (EMA) or exponentially weighted moving average (EWMA), which reacts more significantly to recent price changes than the simple moving average (SMA). Traders find the analysis of 12- and 26-day EMA very useful and insightful for determining buy-and-sell points. The purpose of this study is to develop an effective method for predicting the stock price trend. Typically, the traditional EMA is calculated using a fixed weight; however, in this study, we use a changing weight based on the historical volatility. We denote the historical volatility index as HVIX and the new MACD as MACD-HVIX. We test the stability of MACD-HVIX and compare it with that of MACD. Furthermore, the validity of the MACD-HVIX index is tested by using the trend recognition accuracy. We compare the accuracy between a MACD histogram and a MACD-HVIX histogram and find that the accuracy of using MACD-HVIX histogram is 55.55% higher than that of the MACD histogram when we use the buy-and-sell strategy. When we use the buy-and-hold strategy for 5 and 10 days, the prediction accuracy of MACD-HVIX is 33.33% and 12% higher than that of the traditional MACD strategy, respectively. We found that the new indicator is more stable. Therefore, the improved stock price forecasting model can predict the trend of stock prices and help investors augment their return in the stock market.
Highlights
Securities investment is a financial activity influenced by many factors such as politics, economy, and psychology of investors
From the “zgrs-” stock data chosen in Section 4, we calculate the HVIX of the past m days and the past n days
A higher stock index means that investors feel anxiety regarding the stock market, and a lower stock index means that the rate of change of the stock price will decrease
Summary
Securities investment is a financial activity influenced by many factors such as politics, economy, and psychology of investors. Many trend analysis indicators and prediction methods for financial markets have been proposed. Pai [5] used Internet search trends and historical trading data to predict stock markets using the least squares support vector regression model. Lahmiri [6] accurately predicted the minute-ahead stock price by using singular spectrum analysis and support vector regression. Lahmiri et al [8] proposed an intelligent ensemble forecasting system for stock market fluctuations based on symmetric and asymmetric wavelet functions. Laboissiere et al [10] developed a model involving correlation analysis and artificial neural networks (NNs) to predict the stock prices of Brazilian electric companies. Lei [11] proposed a wavelet NN prediction method for the stock price trend based on rough set attribute reduction. Lahmiri [12] used variational mode decomposition to forecast the intraday stock price
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