Predicting Stock Price Trend Using MACD Optimized by Historical Volatility

Jian Wang · Junseok Kim

52 citations0/0 files storedMathematical Problems in Engineering

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

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.

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References

2013Value and Momentum Everywhere 2011Time Series Momentum 2011Forecasting stock indices with back propagation neural network 2018Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets 2017Forecasting price movements using technical indicators: Investigating the impact of varying input window length 2015Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks 2009A combination of hidden Markov model and fuzzy model for stock market forecasting 2017Wavelet Neural Network Prediction Method of Stock Price Trend Based on Rough Set Attribute Reduction

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