Predicting Stock Price Trend Using MACD Optimized by Historical Volatility
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-25
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Jian& Kim, Junseok. 2018. Predicting Stock Price Trend Using MACD Optimized by Historical Volatility. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1209600
Modern Language Association (MLA)
Wang, Jian& Kim, Junseok. Predicting Stock Price Trend Using MACD Optimized by Historical Volatility. Mathematical Problems in Engineering No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1209600
American Medical Association (AMA)
Wang, Jian& Kim, Junseok. Predicting Stock Price Trend Using MACD Optimized by Historical Volatility. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1209600
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1209600