Performance evaluation holds paramount importance in the trading framework, as it provides a basis for assessing the effectiveness of models and strategies. Evaluating a model’s performance involves considering key metrics during the out-of-sample period. This section explores three crucial characteristics: consistent low-volatility results, stable accuracies through time, and the incorporation of economically intuitive robust features.
- Volatility as a Measure – A desirable strategy should not yield highly volatile results. Ideally, it should navigate various market fluctuations without drastic returns. Ensuring consistency in the future performance of a strategy is challenging. The preferred approach involves employing time series cross-validation and calculating the standard deviation of results. The subsequent section illustrates this technique using an example to derive expected accuracy measures with confidence intervals.
- Maintaining Model Stability – Stability in accuracy is paramount for a model’s reliability. Achieving a consistently high accuracy is crucial, considering the inherent variability in financial time series data. The goal is to maintain an average accuracy between 52% and 54%, providing a margin to account for transaction costs and fees. Cross-validation proves useful in assessing accuracy over multiple runs, even with a limited number of iterations. Another approach involves utilizing autocorrelation and measuring accuracy, especially in autoregressive (AR) models. For instance, employing a grid search method with various lagged variables allows for the determination of model stability.
- Grid Search Example: A grid search method, exemplified using the Dry Baltic index, involves calculating accuracies for different lags in a support vector machine autoregressive algorithm. The results showcase a stable model, with an average accuracy of 76.60% and low volatility (1.07%).
- Challenges with Financial Time Series: Applying the same methodology to daily returns of NZDUSD reveals challenges. The model performs poorly, with an average accuracy of 45.40% and significant volatility (3.03%). This highlights the complexity of achieving stability in financial time series predictions.
- Importance of Relevant Variables – When creating models, especially non-autoregressive ones, selecting variables that explain variations in the dependent variable is crucial. Intuitive and economically relevant features enhance a model’s robustness. A cautionary note is provided against using variables that lack causality, emphasizing that correlation does not imply causation. In the realm of data science, adherence to this principle is essential.
- Model Interpretability – In addition to the discussed metrics, the interpretability of the model should be considered. A model that provides insights into the underlying factors driving its predictions adds value to the decision-making process.
- Risk Management Integration – Integrating risk management practices into the evaluation process ensures that the model not only performs well but also considers the associated risks. This involves evaluating the downside risk and potential drawdowns.
The ultimate goal is often alpha generation – the pursuit of profits that exceed market benchmarks. To gauge the success of a trading strategy, investors and traders focus on performance metrics. In this article, we explore key measures, from gross versus net return to advanced ratios like Sharpe, Sortino, Calmar, and the profit factor.
1. Gross VS Net Return: – The fundamental measure of trading success lies in the returns – both gross and net. Gross return reflects the asset’s appreciation or depreciation, while net return factors in transaction costs, taxes, and other fees. The distinction between these returns becomes vital in assessing the actual profit earned from an investment.
Net Return = Gross Return - (Transaction Costs + Taxes + Fees)
2. Sharpe, Sortino, and Calmar Ratios: Navigating Risk-Adjusted Profitability
Sharpe Ratio: The Sharpe ratio stands out as a widely used metric for risk-adjusted profitability. Calculated as the excess return relative to the risk taken, a Sharpe ratio higher than 1 is desirable. However, limitations include time dependency, bias from illiquid holdings, unsuitability for non-normal returns, and disregard for asset correlations.
Sortino Ratio: Similar to the Sharpe ratio, the Sortino ratio divides excess returns by the standard deviation of negative returns (downside deviation). A Sortino ratio of 2 is considered adequate, making it particularly useful for assessing high-volatility investments.
Calmar Ratio: Specifically tailored for hedge funds and CTA performance, the Calmar ratio divides the portfolio’s return by its maximum drawdown. This ratio offers insights into a strategy’s resilience during challenging market conditions.
3. Information Ratio: The Information Ratio (IR) hinges on two critical concepts: active return and active risk. Active return, also known as excess return, is the portfolio return minus a benchmark, while active risk, or tracking error, represents the standard deviation of active returns. The IR, therefore, is the ratio of active return to active risk.
4. Profit Factor:
For a quick assessment of a strategy’s profitability, the profit factor comes into play. It is calculated as the total gross profit divided by the total gross loss in absolute values. A profit factor exceeding 1 indicates that gross profit surpasses gross loss, resulting in a positive balance and return.
Profit Factor = \frac{Total Gross Profit}{Total Gross Loss}
