Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market

Seung Hwan Jeong, Hee Soo Lee, Hyun Nam and Kyong Joo Oh
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Seung Hwan Jeong: Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea
Hee Soo Lee: Department of Business Administration, Sejong University, Seoul 05006, Korea
Hyun Nam: Department of Investment Information Engineering, Yonsei University, Seoul 03722, Korea
Kyong Joo Oh: Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea

Sustainability, 2021, vol. 13, issue 3, 1-16

Abstract: Research on stock market prediction has been actively conducted over time. Pertaining to investment, stock prices and trading volume are important indicators. While extensive research on stocks has focused on predicting stock prices, not much focus has been applied to predicting trading volume. The extensive trading volume by large institutions, such as pension funds, has a great impact on the market liquidity. To reduce the impact on the stock market, it is essential for large institutions to correctly predict the intraday trading volume using the volume weighted average price (VWAP) method. In this study, we predict the intraday trading volume using various methods to properly conduct VWAP trading. With the trading volume data of the Korean stock price index 200 (KOSPI 200) futures index from December 2006 to September 2020, we predicted the trading volume using dynamic time warping (DTW) and a genetic algorithm (GA). The empirical results show that the model using the simple average of the trading volume during the optimal period constructed by GA achieved the best performance. As a result of this study, we expect that large institutions will perform more appropriate VWAP trading in a sustainable manner, leading the stock market to be revitalized by enhanced liquidity. In this sense, the model proposed in this paper would contribute to creating efficient stock markets and help to achieve sustainable economic growth.

Keywords: dynamic time warping; genetic algorithm; sliding window; volume forecasting; volume weighted average price (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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