A Stock Trading Strategy Based on Time-Varying Quantile Theory

Tiantian Liu; Ning Qiu; Wentao Gu

doi:10.20965/jaciii.2015.p0417

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JACIII Vol.19 No.3 pp. 417-422

doi: 10.20965/jaciii.2015.p0417

(2015)

Paper:

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A Stock Trading Strategy Based on Time-Varying Quantile Theory

Tiantian Liu, Ning Qiu, and Wentao Gu

Department of Statistics, Zhejiang Gongshang University
Hang Zhou 310018, China

Received:

December 29, 2013

Accepted:

February 27, 2015

Published:

May 20, 2015

Keywords:

time-varying quantile, kernel density, trading strategy, NARX model

Abstract

Many of the trading strategies viewed as highly important by to financial market investors, we developed based on fundamental and technical analysis. We propose a stock trading strategy based on time-varying quantile analysis and apply it to the stock market in the People’s Republic of China. Comparing results for both the buy-and-hold strategy and a popular NARX-based neural network trading strategy showed that our strategy performed well.

Cite this article as:

T. Liu, N. Qiu, and W. Gu, “A Stock Trading Strategy Based on Time-Varying Quantile Theory,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.3, pp. 417-422, 2015.

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References

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[15] A. Esfahanipour and S. Mousavi, “A genetic programming model to generate risk-adjusted technical trading rules in stock markets,” Expert Systems with Applications, Vol.38, pp. 8438-8445, 2011.
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[17] A. Péerez, “Comments on ‘Kernel density estimation for time series data,’” Int. J. of Forecasting, Vol.28, pp. 15-19, 2012.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] E. E. Peters, “Fractal market analysis: Applying chaos theory to investment and economics,” Willey & Sons, 1994.

[2] [2] T. Kimoto, K. Asakawa, M. Yoda, and M. Takeoka, “Stock market prediction system with modular neural network,” Proc. of IJCNN, 1990.

[3] [3] K. Kamijo and T. Tanikawa, “Stock price pattern recognition: a recurrent neural network approach,” Int. Joint Conf. on Neural Networks, 1990.

[4] [4] H. Ahmadi, “Testability of the arbitrage pricing theory by neural networks,” Int. Conf. on Neural Networks, pp. 1385-1393, 1990.

[5] [5] S. S. Kim, “Time-delay recurrent neural network for temporal correlations and prediction,” Neurocomputing, pp. 253-263, 1998.

[6] [6] E. Saad, D. Prokhorov, and D. Wunsch, “Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks,” IEEE Trans. Neural Netw, Vol.6, pp. 1456-1470, 1998.

[7] [7] R. D. F. Harris and F. Yilmaz., “A momentum trading strategy based on the low frequency component of the exchange rate,” J. of Banking & Finance, Vol.33, pp. 1575-1585, 2009.

[8] [8] X. Li, Z. Deng, and J. Luo, “Trading strategy design in financial investment through a turning points prediction scheme,” Expert Systems with Applications, Vol.36, pp. 7818-7826, 2009.

[9] [9] N. Baba and T. Kawachi, “A New Trial for Improving the Traditional Technical Analysis in the Stock Markets,” Knowledge-Based Intelligent Information and Engineering Systems Lecture Notes in Computer Science, Vol.3213, pp. 434-440, 2004.

[10] [10] M. N. Vora, “Genetic Algorithm for trading signal generation,” Int. Conf. on Business and Economics Research, 2010.

[11] [11] H. Kim and K. Shin, “A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets,” Applied Soft Computing, Vol.7, pp. 569-576, 2007.

[12] [12] D. Bao and Z. Yang, “Intelligent stock trading system by turning point confirming and probabilistic reasoning,” Expert Systems with Applications, Vol.34, pp. 620-627, 2008.

[13] [13] X. Lin, Z. Yang, and Y. Song, “Intelligent stock trading system based on improved technical analysis and Echo State Network,” Expert Systems with Applications, Vol.38, pp. 11347-11354, 2011.

[14] [14] Pei-Chann Chang, T. Warren Liao, Jyun-Jie Lin, Chin-Yuan Fan., “A dynamic threshold decision system for stock trading signal detection,” Applied Soft Computing, 11, 3998-4010,(2011).

[15] [15] A. Esfahanipour and S. Mousavi, “A genetic programming model to generate risk-adjusted technical trading rules in stock markets,” Expert Systems with Applications, Vol.38, pp. 8438-8445, 2011.

[16] [16] A. Harvey and V. Oryshchenko, “Kernel density estimation for time series data,” Int. J. of Forecasting, Vol.28, pp. 3-14, 2012.

[17] [17] A. Péerez, “Comments on ‘Kernel density estimation for time series data,’” Int. J. of Forecasting, Vol.28, pp. 15-19, 2012.