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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.

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  • 标题: 基于AC均线预测的股票交易策略及实证Stock Trading Strategies Based on the AC Algorithm Moving Average Line Forecast and Empirically Study

    作者: 田益祥, 田伟

    关键字: 均线预测, AC, 交易策略Moving Average Forecast; AC; Trading Strategies

    期刊名称: 《Finance》, Vol.2 No.1, 2012-01-17

    摘要: 预测股价的趋势和拐点,特别是预测个股股价的拐点,一直是投资者和学术界十分关注的焦点问题,也是投资者短期投资成功的关键。本文利用均线的特点,结合相似体合成(AC)算法的优势,尝试对股价短期走势和拐点进行预测。在此基础上,提出一套短期股票投资的智能交易策略。任选30只股票进行实证说明交易策略的有效性,结果表明:基于AC算法均线预测的股票交易策略取得了显著的超额收益,小盘股投资效果优于大盘股。 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’.

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