- Volume 10 Issue 1
The application of neural networks to stock forecasting has received a great deal of attention because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from data, which is required to describe nonlinear input-output relations of stock forecasting. The paper builds neural network models to forecast daily KOrea composite Stock Price Index (KOSPI), and their performance is demonstrated. MAPEs of NN1 model show 0.427 and 0.627 in its learning and test, respectively. Based on the predicted KOSPI price, the paper proposes an alpha trading for trades in Exchange Traded Funds (ETFs) that fluctuate with the KOSPI200. The alpha trading is tested with data from 125 trade days, and its trade return of 7.16 ~ 15.29 % suggests that the proposed alpha trading is effective.
Convergence;Stock Price Forecasting;Stock Price Modeling;Neural Network;ETF;Trading;Alpha Trading;Time Series Forecasting
- H. S. Hwang & J. S Oh. (2009). Time Series Stock Prices Prediction Based on Fuzzy Model, Journal of The Korean Institute of Intelligent Systems, 19(5), 689-694. https://doi.org/10.5391/JKIIS.2009.19.5.689
- W. Huang, Y. Nakamori & S. Y. Wang. (2005). Forecasting Stock Market Movement Direction with Support Vector Machine, Computers & Operations Research, 32(10), 2513-2522. https://doi.org/10.1016/j.cor.2004.03.016
- R. C. Cavalcante, R. C. Brasileiro, V. L. F. Souza, J. P. Nobrega & A. L. I. Oliveira. (2016). Computational Intelligence and Financial Markets: A Survey and Future Directions, Expert Systems with Applications, 55, 194-211. https://doi.org/10.1016/j.eswa.2016.02.006
- H. Amin, H. M. Moein & E. Morteza. (2016). Stock market index prediction using artificial neural network, Journal of Economics, Finance and Administrative Science. 10.1016/j.jefas.2016.07.002.
- H. S. Hwang. (2018). Daily Stock Price Forecasting Using Deep Neural Network Model, Journal of the Korea Convergence Society, 9(6), 39-44.
- L. D. Persio & O. Honcha. (2017). Recurrent Neural Networks Approach to The Financial Forecast of Google Assets, Int. J. of Mathetics and Computers in Simulation, 11, 7-13.
- X. Ding X, Y. Zhang, T. Liu & J. Duan. (2015). Deep Learning for Event-driven Stock Prediction, Proc. of the 24th Int. Joint Conf. on Artificial Intelligence, 2327-2333.
- Qiu M, Song Y (2016) Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model. https://doi.org/10.1371/journal.pone.0155133 https://doi.org/10.1371/journal.pone.0155133
- S. H. Koh. (2016). A Converging Approach on Investment Strategies, Past Financial Information, and Investors' Behavioral Bias in the Korean Stock Market, Journal of the Korea Convergence Society, 7(6), 205-212.
- T. Chandima & M. Sidney, M.. Musa & H. Cameron. (2007). Predicting stock market index trading signals using neural networks. Pro. of the 14th Annual Global Finance Conference (GFC'07), 171-179.
- L. J. Kyung-Soo & M. Boris. (2017). Forecasting ETFs with Machine Learning Algorithms, http://dx.doi.org/10.2139/ssrn.2899520. https://doi.org/10.2139/ssrn.2899520
- K. H. Lee & G. S. Jo. (1999). Expert System for Predicting Stock Market Timing Using A Candlestick Chart, Expert System With Applications, 16, 357-364. https://doi.org/10.1016/S0957-4174(99)00011-1
- L. C. H. Leon, A. Liu & W. S. Chen. (2006). Pattern Discovery of Fuzzy Time Series for Financial Prediction, IEEE Trans. Knowledge and Data Engineering, 18(5), 613-625. https://doi.org/10.1109/TKDE.2006.80
- O. Ican & T. B. Celik. (2017). Stock Market Prediction Performance of Neural Networks: A Literature Review, International Journal of Economics and Finance, 9(11), 100-108.
- M. F. Moller. (1993). A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning, Neural Networks, 6, 525-533. https://doi.org/10.1016/S0893-6080(05)80056-5