Vol.2 No.1 (January 2012)
Stock Trading Strategies Based on the AC Algorithm Moving Average Line Forecast and Empirically Study
Forecasting the trends and inflection point of the price, especially stock price, is the focus of the investors and the academic, and the key issues whether the short-term investment will success or not. This paper attempts to predict the trends and inflection point of the short-term stock price by the Analogy Com- plexion (AC) algorithm, taking advantage of the moving average’s features and superiority. Based on it, we propose a set of intelligent trading strategy used to short-term stock investment. To illustrate the effect- tiveness of the strategy, we randomly selected 30 stocks. The empirical result shows that the trading strategy based on the AC moving average forecasting receives a significant excess return and the performance of small- cap stocks is better than the large-cap stocks’.
田益祥 , 田伟 (2012) 基于AC均线预测的股票交易策略及实证。 金融， 2， 30-35. doi: 10.12677/fin.2012.21003
 P. Xidonas, D. Askounis and J. Psarras. Common stock portfolio selection: A multiple criteria decision making methodology and an application to the Athens Stock Exchange. Operational Re- search, 2009, 9(1): 55-79.
 S. G. M. Fifield, D. M. Power and D. G. S. Knipe. The perfor- mance of moving average rules in emerging stock markets. Applied Financial Economics, 2008, 19(18): 1513-1532.
 J. Pinto, R. Neves and N. Horta. Fitness function evaluation for MA trading strategies based on genetic algorithms. New York: GECCO’11 Proceedings of the 13th Annual Conference Compa- nion on Genetic and Evolutionary Computation, 2009: 819-820.
 K. Y. Huang, C. J. Jane. A hybrid model for stock market fore- casting and portfolio selection based on ARX, grey system and RS theories. Expert Systems with Applications, 2009, 36(3): 5387-5392.
 E. N. Lorence. Athmospheric predictability is revealed by naturaly occurring analogues. Journal of the Atmospheric Sciences, 1969, 26: 636-646.
 贺昌政. 自组织数据挖掘与经济预测[M]. 北京: 科学出版社, 2005.