Which approach is commonly used to mitigate issues with price returns?

Log returns are commonly used to mitigate issues with price returns due to their mathematical properties that facilitate better analysis of financial data. Unlike simple price returns, which can be influenced by the compounding effect and can produce misleading results when dealing with multi-period returns, log returns provide a more consistent framework for evaluating performance over time.

Log returns are calculated by taking the natural logarithm of the ratio of current price to previous price. This approach has several advantages: it is time additive, meaning that the log returns for multiple periods can simply be summed to find the total return for those periods, allowing for easier calculations in models that require this kind of aggregation. Moreover, log returns are also useful for statistical analysis, as they tend to follow a normal distribution more closely than simple returns, making them more suitable for various statistical methods and financial modeling.

The other approaches, while useful in different contexts, do not specifically address the issues related to the compounding of returns or the ease of mathematical manipulation that log returns offer. For example, signal testing and backtesting focus on the validation and effectiveness of trading strategies rather than the underlying returns themselves, while benchmarking involves comparing performance against a standard index or portfolio but does not directly alter how returns are measured.