Zusammenfassung
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.
Beschreibung
Avoiding Backtesting Overfitting by Covariance-Penalties: an empirical investigation of the ordinary and total least squares cases
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