Setting Realistic Expectations: How Monte Carlo Enhances Backtesting

This blog discusses a common misconception in backtesting: the belief that past results will repeat themselves exactly in the future. However, that’s rarely the case. Backtests can provide a helpful foundation, but actual future outcomes often vary. Sometimes they may be better, sometimes worse, and other times close to expectations. Let’s explore how we can use a probabilistic approach, particularly with Monte Carlo simulations, to understand potential outcomes more realistically.

The Problem with Relying Solely on Backtest Results

Imagine backtesting a strategy and seeing solid results. It’s tempting to assume that these outcomes will be consistent in real-world applications. Yet, the truth is that there are numerous paths the strategy could take in the future. Only one of these materialized in the backtest. The other possible paths — some performing better, some worse — are equally plausible. So, rather than expecting one specific outcome, it’s more practical to prepare for a range of possibilities.

How Monte Carlo Simulation Helps Define Expected Ranges

Monte Carlo simulation is a tool for estimating the range of possible outcomes for a strategy by simulating thousands of scenarios based on historical monthly return data. Let’s say we have a backtested strategy that allocates evenly across momentum, value, low volatility, quality, and alpha factors. By running Monte Carlo simulations, we create different possible paths the strategy could follow in the future, providing a range of potential returns, volatility, and drawdowns.

Building the Simulation: The Process Explained

1. Historical Data & Monthly Changes: We start with actual monthly returns, such as a 9.7% return in the first month, 5.35% in the next, and so on. By reordering these monthly returns, we simulate thousands of alternative outcomes.

2. Simulated Scenarios: In this analysis, we use 217 months of data, creating approximately 10,000 scenarios. Each of these scenarios reflects a unique path the strategy could have taken, allowing us to explore the range of potential outcomes.

3. Outcome Interpretation: We analyze the median, 95th percentile (best-case), and 5th percentile (worst-case) outcomes, along with the probabilities of achieving specific returns.

Visualizing the Results: What Each Line Represents

• Red Lines: These represent the extremes. The top red line shows the 95th percentile outcome, where only 5% of results are higher. The bottom red line shows the 5th percentile, where only 5% of outcomes are lower.
• Blue Lines: The blue lines represent individual simulated paths, with denser areas indicating higher probabilities.
• Black Line: This is the median or base-case scenario.
• Green Line: Representing a fixed deposit (FD) return, it provides a benchmark to compare the strategy’s performance.

Interpreting the Simulation: Key Insights

Monte Carlo simulations provide insights that go beyond the single-point estimate from backtesting. Here are a few significant takeaways:

1. Probability of Beating Fixed Deposits: In this simulation, there’s a 91% chance that the strategy will outperform FD returns, assuming an 8% CAGR benchmark. This is particularly meaningful for investors seeking returns above traditional fixed income options.

2. Range of Expected Returns:
— 0% or Higher: The probability of positive returns over the investment horizon is almost certain.
— Above 8% CAGR: There’s a 91% probability, indicating high confidence in outperforming FDs.
— 12% CAGR (Comparable to Nifty): The probability is around 63.6%, showing a reasonable chance of outperforming the broader market.
— 14% or Higher: Drops to about 45.8%, highlighting that ambitious returns are possible but not guaranteed.

3. Backtesting vs. Realistic Expectations: Past performance, such as 16.5% rolling returns, shows potential, but simulations highlight the variability in future outcomes. This prepares investors for a range of outcomes rather than expecting past performance to repeat exactly.

Strategies for Improving Outcome Predictability

For those looking to reduce uncertainty, here are some tips:

1. Extend the Investment Horizon: A longer time horizon, such as 10 years instead of 5, reduces volatility and narrows the range of outcomes. Over a longer period, short-term fluctuations are smoothed out, making returns more predictable.

2. Consider SIP (Systematic Investment Plan): Regular investments, such as SIPs, dollar-cost-average the investment, which further stabilizes returns over time.

Findings on Extended Investment Horizons and SIPs

1. Extended Investment Horizon Narrows the Range of Outcomes:
Extending the time horizon for investments reduces the impact of short-term fluctuations and results in a narrower range of potential returns. This approach leads to more predictable outcomes over time.

2. Reduced Risk of Losing Capital with Longer Time Frames:
Investing over a longer period, such as 10 years, significantly reduces the risk of ending with less than the initial investment. This is especially reassuring for investors who prefer stability and less volatility in their portfolio.

3. SIPs Reduce Volatility and Improve Predictability:
Regular investments through SIPs create a more predictable return trajectory by averaging out market highs and lows. This systematic approach reduces the impact of market fluctuations, resulting in a steadier long-term growth path.

4. SIPs vs Lump Sum Investment:
While lump sum investments offer immediate full market exposure, SIPs are more effective in reducing risk and creating a more predictable range of outcomes over time, especially in volatile markets.

5. Narrower Return Range with SIP Over Extended Periods:
By investing through SIPs over a longer period, the distribution of potential returns becomes more predictable. This helps investors set more realistic expectations, reducing uncertainty compared to shorter investment horizons.

6. Increased Likelihood of Outperforming Fixed Deposits (FDs):
SIPs over longer periods have a higher probability of outperforming fixed deposit returns. Even in worst-case scenarios, the strategy tends to outperform FDs, making it a more attractive option for long-term growth.

7. SIP Provides Consistency Over Time:
The longer the SIP period, the greater the consistency in returns, allowing investors to better forecast their potential portfolio value.

Monte Carlo Simulation: The Value of Probabilistic Thinking

Using Monte Carlo simulation shifts the focus from fixed outcomes to a range of possibilities, aligning with probabilistic thinking. Instead of viewing backtesting as a promise, Monte Carlo simulations allow investors to see likely outcomes, setting realistic expectations for returns, volatility, and drawdowns.

Conclusion: Backtesting as a Foundation, Monte Carlo for Realistic Projections

By combining backtesting with Monte Carlo simulations, investors achieve a more balanced outlook on future returns. Rather than fixating on the “best-case” scenario, investors can target the median or average expectation, which aligns better with realistic financial planning.

This shift to probabilistic thinking enhances resilience, ensuring that investors are better prepared for both positive and challenging times, and more likely to stay the course with their investment strategy.

Watch the full video on youtube here:https://youtu.be/ScbUoqn9Tr0?si=sdX7C48bEJ7YZJli

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Disclaimer: We are not SEBI registered advisors. Any content shared on or through our digital media channels is for information and education purposes only and should not be treated as investment or trading advice. Please do your own analysis or take independent professional financial advice before making any investments based on your own personal circumstances. Investment in securities is subject to market risks; please carry out your due diligence before investing. And last but not least, past performance is not indicative of future returns.