英文标题：
《Avoiding Backtesting Overfitting by Covariance-Penalties: an empirical
investigation of the ordinary and total least squares cases》
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作者：
Adriano Koshiyama and Nick Firoozye
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最新提交年份：
2019
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英文摘要：
Systematic trading strategies are rule-based procedures which choose portfolios and allocate assets. In order to attain certain desired return profiles, quantitative strategists must determine a large array of trading parameters. Backtesting, the attempt to identify the appropriate parameters using historical data available, has been highly criticized due to the abundance of misleading results. Hence, there is an increasing interest in devising procedures for the assessment and comparison of strategies, that is, devising schemes for preventing what is known as backtesting overfitting. So far, many financial researchers have proposed different ways to tackle this problem that can be broadly categorised in three types: Data Snooping, Overestimated Performance, and Cross-Validation Evaluation. In this paper, we propose a new approach to dealing with financial overfitting, a Covariance-Penalty Correction, in which a risk metric is lowered given the number of parameters and data used to underpins a trading strategy. We outlined the foundation and main results behind the Covariance-Penalty correction for trading strategies. After that, we pursue an empirical investigation, comparing its performance with some other approaches in the realm of Covariance-Penalties across more than 1300 assets, using Ordinary and Total Least Squares. Our results suggest that Covariance-Penalties are a suitable procedure to avoid Backtesting Overfitting, and Total Least Squares provides superior performance when compared to Ordinary Least Squares.
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中文摘要：
系统交易策略是基于规则的程序，用于选择投资组合和分配资产。为了获得某些期望的回报曲线，定量策略师必须确定大量的交易参数。回溯测试是一种利用可用历史数据确定适当参数的尝试，由于大量误导性结果，受到了高度批评。因此，人们越来越有兴趣设计评估和比较战略的程序，即设计方案来防止所谓的后验过度拟合。迄今为止，许多金融研究人员提出了解决这一问题的不同方法，大致可分为三种类型：数据窥探、高估绩效和交叉验证评估。在这篇文章中，我们提出了一种处理金融过度拟合的新方法，即协方差惩罚校正，在这种方法中，给定用于支持交易策略的参数和数据的数量，降低风险度量。我们概述了交易策略协方差惩罚修正背后的基础和主要结果。之后，我们进行了一项实证调查，使用普通最小二乘法和总体最小二乘法，将其与1300多个资产的协方差惩罚领域中的其他一些方法进行了比较。我们的结果表明，协方差惩罚是避免回测过度拟合的合适程序，与普通最小二乘法相比，总最小二乘法提供了更好的性能。
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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：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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