英文标题：
《Forecasting stock market returns over multiple time horizons》
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作者：
Dimitri Kroujiline, Maxim Gusev, Dmitry Ushanov, Sergey V. Sharov and
Boris Govorkov
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最新提交年份：
2016
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英文摘要：
In this paper we seek to demonstrate the predictability of stock market returns and explain the nature of this return predictability. To this end, we introduce investors with different investment horizons into the news-driven, analytic, agent-based market model developed in Gusev et al. (2015). This heterogeneous framework enables us to capture dynamics at multiple timescales, expanding the model\'s applications and improving precision. We study the heterogeneous model theoretically and empirically to highlight essential mechanisms underlying certain market behaviors, such as transitions between bull- and bear markets and the self-similar behavior of price changes. Most importantly, we apply this model to show that the stock market is nearly efficient on intraday timescales, adjusting quickly to incoming news, but becomes inefficient on longer timescales, where news may have a long-lasting nonlinear impact on dynamics, attributable to a feedback mechanism acting over these horizons. Then, using the model, we design algorithmic strategies that utilize news flow, quantified and measured, as the only input to trade on market return forecasts over multiple horizons, from days to months. The backtested results suggest that the return is predictable to the extent that successful trading strategies can be constructed to harness this predictability.
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中文摘要：
在本文中，我们试图证明股票市场收益的可预测性，并解释这种收益可预测性的性质。为此，我们将具有不同投资视野的投资者引入Gusev等人（2015）开发的新闻驱动、分析、基于代理的市场模型。这种异构框架使我们能够在多个时间尺度上捕获动态，扩展模型的应用程序并提高精度。我们从理论和实证上研究异质模型，以突出某些市场行为背后的基本机制，例如牛市和熊市之间的转换以及价格变化的自相似行为。最重要的是，我们应用该模型表明，股票市场在日内时间尺度上几乎是有效的，能够快速适应即将到来的新闻，但在较长的时间尺度上变得效率低下，在较长的时间尺度上，新闻可能会对动态产生长期的非线性影响，这归因于在这些时间尺度上作用的反馈机制。然后，利用该模型，我们设计了算法策略，利用量化和测量的新闻流作为多个时段（从几天到几个月）市场回报预测的唯一输入。回溯测试结果表明，如果能够构建成功的交易策略来利用这种可预测性，那么回报是可预测的。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：General Finance 一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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一级分类：Physics 物理学
二级分类：Physics and Society 物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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