英文标题：
《Optimal Dynamic Strategies on Gaussian Returns》
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作者：
Nick Firoozye and Adriano Koshiyama
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最新提交年份：
2019
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英文摘要：
Dynamic trading strategies, in the spirit of trend-following or mean-reversion, represent an only partly understood but lucrative and pervasive area of modern finance. Assuming Gaussian returns and Gaussian dynamic weights or signals, (e.g., linear filters of past returns, such as simple moving averages, exponential weighted moving averages, forecasts from ARIMA models), we are able to derive closed-form expressions for the first four moments of the strategy\'s returns, in terms of correlations between the random signals and unknown future returns. By allowing for randomness in the asset-allocation and modelling the interaction of strategy weights with returns, we demonstrate that positive skewness and excess kurtosis are essential components of all positive Sharpe dynamic strategies, which is generally observed empirically; demonstrate that total least squares (TLS) or orthogonal least squares is more appropriate than OLS for maximizing the Sharpe ratio, while canonical correlation analysis (CCA) is similarly appropriate for the multi-asset case; derive standard errors on Sharpe ratios which are tighter than the commonly used standard errors from Lo; and derive standard errors on the skewness and kurtosis of strategies, apparently new results. We demonstrate these results are applicable asymptotically for a wide range of stationary time-series.
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中文摘要：
本着趋势跟踪或均值回归的精神，动态交易策略代表了现代金融的一个仅被部分理解但利润丰厚且普遍存在的领域。假设高斯回报和高斯动态权重或信号（例如，过去回报的线性过滤器，如简单移动平均、指数加权移动平均、ARIMA模型的预测），我们能够根据随机信号和未知未来回报之间的相关性，推导出策略回报前四个矩的闭合表达式。考虑到资产配置的随机性，并对策略权重与收益的相互作用进行建模，我们证明了正偏度和过度峰度是所有正夏普动态策略的基本组成部分，这通常是经验观察到的；证明总最小二乘法（TLS）或正交最小二乘法比OLS更适合最大化夏普比率，而典型相关分析（CCA）同样适用于多资产情况；推导夏普比率的标准误差，其比Lo中常用的标准误差更为严格；并推导出策略的偏度和峰度的标准误差，这显然是新的结果。我们证明了这些结果对于大范围的平稳时间序列是渐近适用的。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Quantitative Finance 数量金融学
二级分类：Trading and Market Microstructure 交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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