TY - GEN
T1 - New Technique for Stock Trend Analysis – Volume-weighted Squared Moving Average Convergence & Divergence
AU - Au, Sze Chit
AU - Keung, Jacky Wai
PY - 2023
Y1 - 2023
N2 - In computational intelligence, Gerald Appel designed MACD, short for Moving Average Convergence /Divergence in the 1970s, a popular trading indicator used in the business data analysis of stock prices to predict future trends. While it is easy to read, MACD has two distinct disadvantages, the time lagging problem and the fake signals problem, resulting in delays in buying or selling signals and decisions. Besides, three parameters input are required for the calculation model, which is not user-friendly for new learners. This study proposes a new methodology - Volume Square-Weighted Moving Average Convergence & Divergence (VSWMACD). It aims to improve MACD performance and apply various evaluation tools to verify the enhancements. Five datasets with 200 stocks from Hong Kong Stock Market in each have been applied to the testing. The outcome shows that compared to MACD, the average Return On Investment of VSWMACD increased by around 15%, and the average Maximum Drawdown decreased by about 5%. VSWMACD is proven to reduce fake signals while earning a higher return with a lower risk than MACD. A better portfolio management can be formed. © 2023 IEEE.
AB - In computational intelligence, Gerald Appel designed MACD, short for Moving Average Convergence /Divergence in the 1970s, a popular trading indicator used in the business data analysis of stock prices to predict future trends. While it is easy to read, MACD has two distinct disadvantages, the time lagging problem and the fake signals problem, resulting in delays in buying or selling signals and decisions. Besides, three parameters input are required for the calculation model, which is not user-friendly for new learners. This study proposes a new methodology - Volume Square-Weighted Moving Average Convergence & Divergence (VSWMACD). It aims to improve MACD performance and apply various evaluation tools to verify the enhancements. Five datasets with 200 stocks from Hong Kong Stock Market in each have been applied to the testing. The outcome shows that compared to MACD, the average Return On Investment of VSWMACD increased by around 15%, and the average Maximum Drawdown decreased by about 5%. VSWMACD is proven to reduce fake signals while earning a higher return with a lower risk than MACD. A better portfolio management can be formed. © 2023 IEEE.
KW - Business analytics
KW - Business informatics
KW - Computational Intelligence
KW - Data information and knowledge
UR - http://www.scopus.com/inward/record.url?scp=85168875308&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85168875308&origin=recordpage
U2 - 10.1109/COMPSAC57700.2023.00140
DO - 10.1109/COMPSAC57700.2023.00140
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 979-8-3503-2698-7
T3 - Proceedings - International Computer Software and Applications Conference
SP - 987
EP - 988
BT - Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023
PB - IEEE
T2 - 47th IEEE Annual Computers, Software, and Applications Conference (COMPSAC 2023)
Y2 - 27 June 2023 through 29 June 2023
ER -