评估具有不同存储器长度的自适应交易策略的性能
Evaluating the performance of adapting trading strategies with different memory lengths
摘要：
我们提出了一个基于少数者博弈的预测模型，在这个模型中，交易者利用策略的过去表现来连续地评估具有不同记忆长度的一套完整的交易策略。基于所选择的交易策略，他们确定他们对单一资产在接下来的时间段内的运动的预测。我们发现，使用标准普尔500指数的股票，我们的预测模型产生了超过51.5%的高成功率，并产生比买入并持有策略更高的回报。即使考虑到交易成本，我们发现使用预测将产生更好的投资组合。
英文摘要：
We propose a prediction model based on the minority game in which traders continuously evaluate a complete set of trading strategies with different memory lengths using the strategies' past performance. Based on the chosen trading strategy they determine their prediction of the movement for the following time period of a single asset. We find empirically using stocks from the S&P500 that our prediction model yields a high success rate of over 51.5% and produces higher returns than a buy-and-hold strategy. Even when taking into account trading costs we find that using the predictions will generate superior investment portfolios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
