Browse > Article
http://dx.doi.org/10.7465/jkdi.2017.28.4.819

Analysis of intraday price momentum effect based on patterns using dynamic time warping  

Lee, Chunju (Division of Investment Information Engineering, Yonsei University)
Ahn, Wonbin (Department of Industrial Engineering, Yonsei University)
Oh, Kyong Joo (Department of Industrial Engineering, Yonsei University)
Publication Information
Journal of the Korean Data and Information Science Society / v.28, no.4, 2017 , pp. 819-829 More about this Journal
Abstract
The aim of this study is to analyze intraday price momentum. When price trends are formed, price momentum is the phenomenon that future prices tend to follow the trend. When the market opened and closed, a U-shaped trading volume pattern in which the trading volume was concentrated was observed. In this paper, we defined price momentum as the 10 minute trend after market opening is maintained until the end of market. The strategy is to determine buying and selling in accordance with the price change in the initial 10 minutes and liquidating at closing price. In this study, the strategy was empirically analyzed by using minute data, and it showed effectiveness, indicating the presence of an intraday price momentum. A pattern in which returns are increasing at an early stage is called a J-shaped pattern. If the J-shaped pattern occurs, we have found that the price momentum phenomenon tends to be stronger than otherwise. The DTW algorithm, which is well known in the field of pattern recognition, was used for J-shaped pattern recognition and the algorithm was effective in predicting intraday price movements. This study showed that intraday price momentum exists in the KOSPI200 futures market.
Keywords
DTW (dynamic time warping); intraday price momentum; J-shaped price pattern;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 Asness, C. S., Moskowitz, T. J. and Pedersen, L. H. (2013). Value and momentum everywhere. The Journal of Finance, 68, 929-985.   DOI
2 Chung, S. H. and Oh, K. J. (2014). Using genetic algorithm to optimize rough set strategy in KOSPI200 futures market. Journal of the Korean Data & Information Science Society, 25, 281-292.   DOI
3 Conrad, J. and Kaul, G. (1998). An anatomy of trading strategies. Review of Financial Studies, 11, 489-520.   DOI
4 Gao, L., Han, Y., Li, S. Z. and Zhou, G. (2015). Market intraday momentum, Washington University, St. Louis.
5 Griffin, J. M., Ji, X. and Martin, J. S. (2003). Momentum investing and business cycle risk: Evidence from pole to pole. The Journal of Finance, 58, 2515-2547.   DOI
6 Hurst, B., Ooi, Y. H. and Pedersen, L. H. (2017). A century of evidence on trend-following investing, AQR Capital Management.
7 Jain, P. C. and Joh, G. H. (1988). The dependence between hourly prices and trading volume. Journal of Financial and Quantitative Analysis, 23, 269-283.   DOI
8 Jegadeesh, N. and Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48, 65-91.   DOI
9 Kim, D. H. and Shu, H. J. (2008). Empirical study on the performance of style momentum strategies in the korean stock market. Korean Journal of Business Administration, 21, 1945-1975.
10 Keogh, E. J. and Pazzani, M. J. (1999). Scaling up dynamic time warping to massive dataset. European Conference on Principles of Data Mining and Knowledge Discovery, Springer, Berlin, Heidelberg.
11 Kim, S. (2012). A study on the profitability of the trading strategies using past returns. Asian Review of Financial Research, 25, 203-246.
12 Kim, Y. B. (2004). Conditional contrarian strategy and trading volume effect in the Korean stock market. Journal of Industrial Economics and Business, 17, 505-524.
13 Kim, H. H. and Oh, K. J. (2012). Using rough set to develop the optimization strategy of evolving timedivision trading in the futures market. Journal of the Korean Data & Information Science Society, 23, 881-893.   DOI
14 Oh, K. J. and Kim, Y. M. (2013). An intelligent early warning system for forecasting abnormal investment trends of foreign investors. Journal of the Korean Data & Information Science Society, 24, 223-233.   DOI
15 Kwon, D. and Lee, T. (2014). Hedging effectiveness of KOSPI200 index futures through VECM-CC-GARCH model. Journal of the Korean Data & Information Science Society, 25, 1449-1466.   DOI
16 Lee, S. J. and Oh, K. J. (2011). Finding the optimal frequency for trade and development of system trading strategies in futures market using dynamic time warping. Journal of the Korean Data & Information Science Society, 22, 255-267.
17 Lo, W. and MacKinlay, A. (1990). When are contrarian profits due to stock market overreaction? Review of Financial Studies, 3, 175-205.   DOI
18 Meinard, M. (2007). Information retrieval for music and motion, Springer, Berlin, Heidelberg.
19 Moskowitz, T. J., Ooi, Y. H. and Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104, 228-250.   DOI
20 Park, K. I. and Jee, C. (2006). Contrarian strategy based on past stock return and volatility. The Korean Journal of Financial Management, 23, 1-25.