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首页> 外文期刊>International journal of finance & economics >High-frequency trading from an evolutionary perspective: Financial markets as adaptive systems
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High-frequency trading from an evolutionary perspective: Financial markets as adaptive systems

机译：从进化的角度看高频交易：金融市场作为自适应系统

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The recent rapid growth of algorithmic high-frequency trading strategies makes it a very interesting time to revisit the long-standing debates about the efficiency of stock prices and the best way to model the actions of market participants. To evaluate the evolution of stock price predictability at the millisecond timeframe and to examine whether it is consistent with the newly formed adaptive market hypothesis, we develop three artificial stock markets using a strongly typed genetic programming (STGP) trading algorithm. We simulate real-life trading by applying STGP to millisecond data of the three highest capitalized stocks: Apple, Exxon Mobil, and Google and observe that profit opportunities at the millisecond time frame are better modelled through an evolutionary process involving natural selection, adaptation, learning, and dynamic evolution than by using conventional analytical techniques. We use combinations of forecasting techniques as benchmarks to demonstrate that different heuristics enable artificial traders to be ecologically rational, making adaptive decisions that combine forecasting accuracy with speed.
机译：最近，算法高频交易策略迅速发展，这使得它成为一个非常有趣的时机，可以重新审视关于股票价格效率以及建模市场参与者行为的最佳方法的长期争论。为了评估毫秒级时间段内股票价格可预测性的演变并检查其是否与新形成的自适应市场假设相一致，我们使用强类型遗传规划（STGP）交易算法开发了三个人工股票市场。我们通过将STGP应用于苹果，埃克森美孚和谷歌这三只市值最高的股票的毫秒数据来模拟现实交易，并观察到毫秒时间范围内的获利机会可以通过涉及自然选择，适应，学习的进化过程更好地建模，并且动态演化比使用常规分析技术要好。我们使用预测技术的组合作为基准来证明不同的启发式方法使人工交易者具有生态理性，并做出将预测准确性与速度相结合的自适应决策。

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