投资者羊群行为如何影响连续双向拍卖中的价格走势
文章 arXiv papers · 作者: Shingo Ichiki et al.
总结
本研究构建随机订单簿模型,描述投资者在连续双向拍卖机制中的行为;主要交易所采用这种机制。模型描述了两种群体行为:投资者可能提交顺应近期价格趋势的订单,也可能提交与趋势相反的订单。作者使用模拟价格序列,考察持续朝同一方向变动的价格区间,并比较不同投资者行为模型产生的累积分布。
趋势跟随行为会使单向价格走势区间的分布呈现幂律尾部,而逆势行为模型则产生不同的分布。分析将部分趋势形成归因于订单簿中的订单暂时聚集,并与近期价格变动方向一致。这些发现提供了一种可能机制,用于解释参与者行为与大幅单向价格走势之间的联系。不过,结果来自模型及其模拟;本文没有使用实盘订单簿数据进行实证验证,也未评估这些模式能否盈利交易。
核心观点
- 该模型描述连续双向拍卖中顺势和逆势提交订单的行为。
- 研究根据持续朝同一方向变动的区间分析模拟价格数据。
- 趋势跟随行为使价格走势区间累积分布呈现幂律尾部。
- 作者将趋势形成与顺应近期价格走势的订单暂时聚集联系起来。
- 报告的证据来自随机模型和模拟,并非有记录的实盘测试。
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全文
# Simple Stochastic Order-Book Model of Swarm Behavior in Continuous Double Auction # Simple Stochastic Order-Book Model of Swarm Behavior in Continuous Double Auction In this study, we present a simple stochastic order-book model for investors' swarm behaviors seen in the continuous double auction mechanism, which is employed by major global exchanges. Our study shows a characteristic called "fat tail" is seen in the data obtained from our model that incorporates the investors' swarm behaviors. Our model captures two swarm behaviors: one is investors' behavior to follow a trend in the historical price movement, and another is investors' behavior to send orders that contradict a trend in the historical price movement. In order to capture the features of influence by the swarm behaviors, from price data derived from our simulations using these models, we analyzed the price movement range, that is, how much the price is moved when it is continuously moved in a single direction. Depending on the type of swarm behavior, we saw a difference in the cumulative frequency distribution of this price movement range. In particular, for the model of investors who followed a trend in the historical price movement, we saw the power law in the tail of the cumulative frequency distribution of this price movement range. In addition, we analyzed the shape of the tail of the cumulative frequency distribution. The result demonstrated that one of the reasons the trend following of price occurs is that orders temporarily swarm on the order book in accordance with past price trends.
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