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What is Backtesting overfitting, and why should you avoid it?

Backtesting Overfitting

Backtest overfitting, selection bias, and false results are some of the issues in finance studies. These problems affect not only the researchers but also practitioners and investors in the field. Before we formally move into the topic of backtesting and overfitting in investment strategies, let’s get to the bottom of the basics of backtesting overfitting. This article will give you a systematic approach to answer the question – ‘why should you avoid the backtesting overfitting?’

What is Backtesting Overfitting?

Backtesting is a historical simulation of how a study or research would have performed in the past. These performances can be the result of some noise or signal. Noise is a false interpretation of facts and gaining results from it. At the same time, a signal is an ideal strategy to publish a study showing the actual outcome. Theoretically, the practice is assumed to be fit when the strategy infers profit signals by adopting the actual result following an empirical research strategy for obtaining the outcome.

On the other hand, it is overfitting when the practice is profiting from noise. This noise is called a false positive, which is an undesirable input in the process. If we simply put things together, the backtest overfitting means inserting false-positive results in a research or study to show a profit. That is what we should avoid as these strategies show false positive feedback and profiting from noise. This issue is considered one of the vital problems in finance, as the signal-to-noise ratio is low. As a result, the strategies may likely come as overfit or unfit.

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Another concern in this process for increasing the probability of backtest overfitting (PBO) is selection bias.

Selection bias is when research only focuses on the positive results of a study. However, this leads to winning research by optimizing the study towards the winning pool. Unfortunately, the whole procedure leaves loopholes in the process while creating the probability of backtest overfitting. Because the result or hypothesis that worked best in the past may not work in the future, this system’s ecosystem only shows the effect of working best, but the future outcome is negative. Therefore, the selection bias cannot work without instilling backtest overfitting, which is malpractice or negligence. Thus, the selection bias combined with the practice leads to the most inflated performance.

Impacts of backtesting overfitting

Overfitting and its related issues are creating many practical problems in a financial investigation and real-time losses. These problems are the impact of these false-positive hypotheses or trials.

  • Transparency: The problem of overfitting and selection bias affects a study’s clarity. This may create misleading results, ignorance, and abusing backtested effects. These eventually weaken the legitimacy of actual outcomes, followed by different testing biases.
  • Deflated Performance: Having a less established watchdog to prevent backtesting overfitting increases the possibility of misleading financial research results. This causes investors to pay the price for a study that has a false-positive result. For example, an economic researcher may simulate millions of investment strategies and pick the one that shows the best performance. Scientists recognized this methodological error as selection bias. That one single success is showcased among the number of trials. Without knowing the result of the researchers’ backtesting, the investors buy the strategy from firms. This eventually leads to product loss and financial loss caused by deflated performance.
  • Probability of Loss: The impact of backtest overfitting creates false discoveries and may result in a loss in field implementation. These investment strategies make unnecessary losses for investors. On top of that, the Sharpe ratio in these false positives tends to fail in the practical realm. The over-fit models are challenging to identify too. As a result, the investments fail to reach their return, followed by risks and losses.
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Why Should You Avoid Back Testing Overfitting?

For avoiding selection bias and ultimately producing backtest overfitting, researchers should follow a more realistic approach. As a researcher, one should be dedicated to result-oriented research for maintaining the qualitative trend of economic analysis. To gain the right results out of the study, one needs to follow the correct computational procedures. To that end, there are many reasons for avoiding backtesting overfitting to get a legit outcome. Let’s break why you should prevent backtesting overfitting.

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To Maintain Transparency

To avoid selection bias, a researcher should be transparent and not conceal information. It will define the legitimacy of the research and the hard-won procedure of obtaining possible success. As a result, the study will not lose its value. On the contrary, it will eventually increase its exposure, and the researcher will gain accolades.

Maintaining the Sharpe Ratio

Sharpe ratio indicates the positive outcome of an investment as a return. The Sharpe ratio helps us evaluate the risk and return to help investors generate higher returns for the optimal bet. When investors receive a strategy, they will receive a better Sharpe ratio to determine how to bring the best outcome out of it.

Legitimate Empirical Findings

Gaining legitimacy while doing empirical research is mandatory for achieving a low signal-to-noise ratio. However, when a researcher conducts multiple tests on the same data, the possibility of finding false data increases. Furthermore, when the research uses a finite number of independent datasets without controlling the misleading positives, it may lead to overfitting. Therefore, we should be avoiding backtesting overfitting to maintain the legitimacy of the research as an empirical researcher.

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Maximizing the Performance

The performance of a research outcome is maximized when its discoveries identify a high signal-to-noise ratio. This is because there would be less noise or unnecessary data in the research. The most crucial would be its signal data. However, when a computational process is weak in calibrating the parameters of an investment strategy, it will weaken its performance. Such a gap in the algorithm creates an opportunity for noise-integrated future signals. This may result in a weak signal-to-noise ratio. Therefore, it is essential to avoid backtesting overfitting to maximize the research performance in finance.

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How to identify the Probability of Backtest Overfitting and Avoid It

Backtesting is a process of validating via simulation an algorithmic strategy of research. As a researcher, if anyone can identify the risks and the positive return of investment through this approach in their work, it will validate their findings. Therefore, identifying the trends of research through backtesting and the probability of backtesting will help the researcher to avoid it. Thus, it can bring more funds and investment to the project.

For this, you will need a general framework with combinatorically symmetric cross-validation (CSCV) to measure the probability rate of backtest overfitting. This process helps to detect biased results in the context of investment strategies.

Here’s what you need to know to identify the overfitting probabilities.

  1. Estimate the precise characterization of the event of backtest overfitting where the maximum performance in-sample (IS) must underperform with the remaining out-of-sample (OOS) configurations.
  2. A general framework will assess the probability based on in-sample (IS) and out-of-sample (OOS) test results.
  3. Establishing a null hypothesis confirming the production of backtest overfitting will develop an algorithm for testing the procedure.

In addition, the comparisons of other approaches with the CSCV would help prevent overfitting in research when the study is adopted for implementation. Thus, practitioners would require the researcher to withhold the data for sample testing. Independent testing and procedure to validate the OOS performance, also known as the hold-out or method, should be applied. Judging the study’s hypothesis assessed with IS and OOS performance levels, the investor can decide whether to reject or accept the investment if the research is manipulated with overfitting. This approach seems better than many other approaches to find the probable backtesting overfitting with maximum performance output.

The probability of backtest overfitting (PBO) can be evaluated by assessing the probable loss, performance degradation, and stochastic dominance of the strategy. The problem of backtesting overfitting is a recognized factor in producing inaccurate solutions. The loopholes formed in the process combined with valid literature have made it more difficult for practitioners and investors. However, this approach has an advantage in assessing many probable successes for backtesting performance with time series.

Moreover, this deterministic approach of identifying whether the study is overfitting or not is a model-free and nonparametric testing algorithm. This easily applicable algorithm follows Bayesian inference, machine learning, experimental mathematics, information theory, and decision theory to address the particular problem of the backtest by collecting elements. Finally, this CSCV implementation is a performance statistic that takes out-of-sample (OOS) without any fixed hold-out and swaps all in-sample (IS) datasets.

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Possible Solution and Future Prospects

Numerous studies have tried to find possible solutions to the problem of backtest overfitting. This study attempts to understand the detailed implementations of combinatorically symmetric cross-validation (CSCV) to avoid these overfitting problems. This study also describes the CSCV method. It explains how it produces reasonable estimates to identify the Probability of Back Testing Overfitting (PBO). The easy application and devising method could help us find the solution to overfitting and get 95 percent assurance on detecting the PBO.

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We believe this study will raise more significant concern for the backtesting overfitting issue and identify the possibilities of computing an algorithm to acquire more accurate backtest results. Such algorithms and their powerful tools to achieve a reasonable estimation of the probability of getting a better Sharpe ratio will be a great asset in the process of evaluating your study. That will also assist in determining the Minimum Track Record Length (MinTRL) in the study. This Cross-Validation Evaluation will create a sustainable approach to deal with financial overfitting by assessing the risk metric with the number of parameters and data used to define the trading strategy.

In addition, the results coming from a similar study also shows the potential of the CSCV approach. These allow investors to check the rate of probability of backtesting overfitting and avoid the issues. This algorithmic system is a solution for a researcher to give their findings a better output to make them worthy of high investment return.

Final Thoughts

To get qualitative research with a good Sharpe ratio, a researcher should conduct a performance-oriented strategy. Biases in-sample (IS) should be prevented to avoid overfitting. Most importantly, it should follow a systematic approach to measure probable backtesting overfitting. Our findings and the detailed discussion on Combinatorially Symmetric Cross-Validation (CSCV) implementation method will help you estimate the probability with its in-sample statistics with great ease.  Since it is a model-free approach, its forecasting will be more accurate than the study and its probable performance result. Therefore, this method will help you reach the best height in your research. We believe, as a researcher, our insight on this approach to assess backtesting overfitting and how to avoid the problem would assist you in getting a better result for thresholding your finding and optimizing the performance of your research.

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