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http://dx.doi.org/10.13088/jiis.2022.28.4.157

Classification Algorithm-based Prediction Performance of Order Imbalance Information on Short-Term Stock Price  

Kim, S.W. (Graduate School of Business IT, Kookmin University)
Publication Information
Journal of Intelligence and Information Systems / v.28, no.4, 2022 , pp. 157-177 More about this Journal
Abstract
Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.
Keywords
Limit Order Book; Order Imbalance Information; Classification Algorithms; KOSPI200 Index Futures; Day Trading;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 Berkman, H., Koch, P. D., Tuttle, L., & Zhang, Y. J. (2012). Paying attention: Overnight returns and the hidden cost of buying at the open. Journal of Financial and Quantitative Analysis, 47(4), 715-741. https://doi.org/10.1017/S0022109012000270   DOI
2 Chen, Y., & Hao, Y. (2017). A feature weighted support vector machine and k-nearest neighbor algorithm for stock market indices prediction. Expert Systems with Applications, 80, 340-355. https://doi.org/10.1016/j.eswa.2017.02.044   DOI
3 Han, S. B. (2017). Foreigners' short selling and price pressures in the Korean stock market. Journal of Industrial Economics and Business, 30(6), 2119-2139. https://doi.org/10.22558/jieb.2017.12.31.6.2119   DOI
4 Kim, S. W. (2022). Performance on Altcoin investment using technical trading rules. Journal of the Korean Academia-Industrial, 23(6), 198-207. https://doi.org/10.5762/KAIS.2022.23.6.198   DOI
5 Kim, T. D., & Ok, K. (2015). Private information and trading behavior: KOSPI200 Futures Markets. Journal of Derivatives and Quantitative Studies, 23(2), 207-241. https://doi.org/10.1108/JDQS-02-2015-B0003   DOI
6 Kumbure, M. M., Lohrmann, C., Luukka, P., & Porras, J. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, 197, 1-41. https://doi.org/10.1016/j.eswa.2022.116659   DOI
7 Cao, L., & Tay, F. E. H. (2001). Financial forecasting using support vector machines. Neural Computing & Applications, 10, 184-192. https://doi.org/10.1007/s005210170010   DOI
8 Cenesizoglu, Dionne, T., G., & Zhou, X. (2022). Asymmetric effects of the limit order book on price dynamics. Journal of Empirical Finance, 65, 77-98. https://doi.org/10.1016/j.jempfin.2021.11.002   DOI
9 Cont, R., Stoikov, & Talreja, R. (2010). A stochastic model for order book dynamics. Operations Research, 58(3), 549-563. https://doi.org/10.1287/opre.1090.0780   DOI
10 Harris, L., & Panchapagesan, V. (2005). The information content of the limit order book: Evidence from NYSE specialist trading decisions. Journal of Financial Markets, 18(1), 25-67. https://doi.org/10.1016/j.finmar.2004.07.001   DOI
11 Kang, J., & Ryu, D. (2010). Which trades move asset prices? An analysis of futures trading data. Emerging Markets Finance & Trade, 46, 7-22. https://doi.org/10.2753/REE1540-496X4603S101   DOI
12 Kim, Y., Choi, H. S., & Kim, S. W. (2020). A study on risk parity asset allocation model with XGBoost. Journal of Intelligence and Information Systems, 26(1), 135-149. https://doi.org/10.13088/jiis.2020.26.1.135   DOI
13 Ranaldo, A. (2004). Order aggressiveness in limit order book markets. Journal of Financial Markets, 7(1), 53-74. https://doi.org/10.1016/S1386-4181(02)00069-1   DOI
14 Kim, S. W. (2019). Performance analysis on day trading strategy with bid-ask volume. The Journal of the Korea Contents Association, 19(7), 36-46. https://doi.org/10.5392/JKCA.2019.19.07.036   DOI
15 Kim, S. W., & Ahn, H. (2010). Development of an intelligent trading system using support vector machines and genetic algorithms. Journal of Intelligence and Information Systems, 16(1), 71-92.
16 Kozhan, R., & Salmon, M. (2012). The information content of a limit order book: The case of an FX market. Journal of Financial Markets, 15, 1-28. https://doi.org/10.1016/j.finmar.2011.07.002   DOI
17 Park, Y. J., Kutan, A. M., & Ryu, D. (2019). The impacts of overseas market shocks on the CDS-option basis. The North American Journal of Economics and Finance, 47, 622-636. https://doi.org/10.1016/j.najef.2018.07.003   DOI
18 Lee, W. B., & Choe, H. (2007). Short-term return predictability of information in the open limit order book. Asia-Pacific Journal of Financial Studies, 36(6), 963-1008.
19 Lohrmann, C., & Luukka, P. (2019). Classification of intraday S&P500 returns with a random forest. International Journal of Forecasting, 35, 390-407. https://doi.org/10.1016/j.ijforecast.2018.08.004   DOI
20 Malagrino, L., Roman, N. T., & Monteiro, A. M. (2018). Forecasting stock market index daily direction: A Bayesian network approach. Expert Systems with Applications, 105, 11-22. https://doi.org/10.1016/j.eswa.2018.03.039   DOI
21 Ryu, D. (2013). Price impact asymmetry of futures trades: Trade direction and trade size. Emerging Markets Review, 14, 110-130. https://doi.org/10.1016/j.ememar.2012.11.005   DOI
22 Sharpe, W. F. (1966). Mutual Fund Performance. The Journal of Business, 39(1), 119-138.   DOI
23 Song, J. H., Choi, H. S., & Kim, S. W. (2017). A study on commodity asset investment model based on machine learning technique. Journal of Intelligence and Information Systems, 23(4), 127-146. https://doi.org/10.13088/jiis.2017.23.4.127   DOI
24 Thakkar, A., & Chaudhari, K. (2022). Information fusion-based genetic algorithm with long short-term memory for stock price and trend prediction. Applied Soft Computing, 128, 1-20. https://doi.org/10.1016/j.asoc.2022.109428   DOI
25 Wang, M. C., Zu, L. P., & Kuo, C. J. (2008). The state of the electronic limit order book, order aggressiveness and price formation. Asia-Pacific Journal of Financial Studies, 37(2), 245-296.
26 Zhang, D., & Lou, S. (2021). The application research of neural network and BP algorithm in stock price pattern classification and prediction. Future Generation Computer Systems, 115, 872-879. https://doi.org/10.1016/j.future.2020.10.009   DOI
27 Lee, Y., & Kim, W. C. (2013). A stochastic model for order book dynamics: An application to Korean stock index futures. Management Science and Financial Engineering, 19(1), 37-41. https://doi.org/10.7737/MSFE.2013.19.1.037   DOI
28 Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153-163. https://doi.org/10.1016/j.eswa.2017.02.041   DOI
29 Yang, H. (2021). Investor sentiment and market dynamics: Evidence from index futures markets. Investment Analysts Journal, 50, 258-272. https://doi.org/10.1080/10293523.2021.2010376   DOI
30 Zhang, N., Lin, A., & Shang, P. (2017). Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting. Physica A, 477, 161-173. https://doi.org/10.1016/j.physa.2017.02.072   DOI
31 Cao, C., Hansch, O., & Wang, X. (2009). The information content of an open limit-order book. The Journal of Futures Markets, 29(1), 16-41. https://doi.org/10.1002/fut.20334   DOI
32 Griffiths, M. D., Smith, B. F., Turnbull, D. A. S., & White, R. W. (2000). The costs and determinants of order aggressiveness. Journal of Financial Economics, 56(1), 65-88. https://doi.org/10.1016/S0304-405X(99)00059-8   DOI
33 Yun, K. K., Yoon, S. W., & Won, D. (2021). Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process. Expert Systems with Applications, 186, 1-21. https://doi.org/10.1016/j.eswa.2021.115716   DOI
34 Cao, H., Lin, T., & Zhang, H. (2019). Stock price pattern prediction based on complex network and machine learning. Complexity, 2019(10), 1-12. https://doi.org/10.1155/2019/4132485   DOI