Defending the Reliability of Investoristics’ 4-Factor Model Backtest

Introduction

At Investoristics, we recognize that backtesting is often met with skepticism, particularly when results indicate significant outperformance over historical periods. Critics argue that backtests are prone to overfitting, survivorship bias, and unrealistic assumptions. However, not all backtests are created equal. The rigor and methodology behind a strategy significantly influence how well it translates into real-world performance.

Our proprietary 4-factor model, rooted in quarterly rebalancing, fundamentally driven selection, S&P 500-only stock universe, and strict risk management rules, presents a uniquely strong case for real-world applicability. This paper will outline why the backtested results of our 4-factor model are not only credible but also likely to produce similar real-world performance.

1. The Stability of a Quarterly Rebalancing Approach

One of the strongest defenses of our backtest’s reliability is the use of a quarterly rebalancing schedule. Unlike high-frequency or daily trading strategies, which often suffer from market noise, excessive trading costs, and execution challenges, a quarterly rebalance minimizes distortions caused by short-term volatility while allowing fundamental factors to play out.

A quarterly rebalance strikes a balance between responsiveness and stability, ensuring that stocks are not traded too frequently (which can lead to excessive slippage and costs) while still being reactive enough to changing economic conditions. This approach aligns well with institutional investment practices, reinforcing its practical viability.

2. Limiting Stock Selection to S&P 500 Companies

Another cornerstone of our strategy is its exclusive focus on S&P 500 stocks. This choice eliminates many of the common pitfalls of backtests that involve small-cap or illiquid securities, which often exhibit extreme returns in simulations but are difficult to execute in real-world portfolios.

S&P 500 stocks are:

• Highly liquid, reducing execution risk and minimizing slippage.
• Well-covered and fundamentally transparent, ensuring reliable financial data.
• Resilient and proven businesses, limiting the likelihood of survivorship bias contaminating backtest results.

By limiting stock selection to one of the most established indices globally, our strategy avoids many of the structural weaknesses that cause backtests to diverge from real-time performance.

3. The Power of Investoristics’ Fundamental-Only Selection Criteria

Unlike many quantitative strategies that rely on technical indicators, complex algorithms, or machine learning models that can lead to overfitting, our 4-factor model is based exclusively on fundamental analysis.

Our selection process prioritizes:

• Quality (strong return on equity, stable financials)
• Growth (earnings growth, revenue expansion)
• Momentum (avoiding value traps by ensuring fundamental strength is recognized by the market)
• Value (ensuring strong fundamental companies are purchased at reasonable prices)

By relying on clear, logical, and historically validated financial metrics, rather than opaque statistical patterns, our model ensures that stock selection is economically sound, not merely a function of past data patterns.

4. Proven Performance Across Economic Cycles

A major flaw in many backtests is their reliance on data from only favorable market conditions. This can produce misleading results, as strategies that perform well in bull markets may collapse during downturns. However, our 4-factor model has been rigorously tested across multiple economic cycles, including:

• The Dot-Com Crash (2000-2002)
• The Global Financial Crisis (2008-2009)
• The COVID-19 Market Shock (2020)

Crucially, our strategy has demonstrated not only resilience but also superior recovery performance after downturns, indicating that it effectively identifies undervalued opportunities during market stress. The ability to perform well in both up and down markets provides a strong argument that our strategy is not simply benefitting from specific historical trends but is truly robust across conditions.

5. Avoiding Overfitting: No Machine Learning or Optimization Tuning

Many skeptical investors dismiss backtests due to the overuse of machine learning models or hyper-optimized weighting schemes, which can introduce biases that do not hold up in real markets. However, our strategy avoids such pitfalls by using:

• Fixed fundamental criteria rather than dynamically adjusted weightings.
• Pre-determined ranking metrics based on decades of financial research.
• No reliance on proprietary black-box models, ensuring transparency and real-world applicability.

By keeping our methodology straightforward and aligned with long-standing investment principles, Investoristics mitigates the risk of backtest overfitting, making it far more likely to translate into future success.

6. Value Identification and Stronger Results After Market Turmoil

One of the most compelling arguments for our model’s validity is that its strongest results tend to follow periods of market stress. This pattern suggests that our model successfully identifies mispriced opportunities—particularly in value-oriented stocks—during times of pessimism.

Rather than simply riding momentum or benefiting from bull market conditions, our strategy exhibits a mean-reversion dynamic, where undervalued but fundamentally strong stocks generate significant outperformance once normal market conditions resume. This is consistent with well-documented market anomalies where value-oriented investors, equipped with strong fundamental criteria, outperform over the long term.

7. Quality, Momentum, and Growth Factors Strengthen Reliability

Beyond simple value metrics, our 4-factor model integrates quality and momentum signals, ensuring that selected stocks are not just “cheap” but also financially sound and demonstrating positive investor recognition. The inclusion of:

• Quality metrics (e.g., strong return on equity, stable earnings growth) filters out weak companies that might appear statistically cheap but are actually deteriorating businesses.
• Momentum indicators help avoid prolonged underperformance and increase the likelihood of capturing stocks that are beginning to be revalued by the market.
• Growth factors ensure that the companies selected have expanding earnings and revenue, reducing the risk of stagnation.

By combining these factors, our strategy mitigates the common weaknesses of traditional value investing (e.g., value traps) while still capitalizing on the long-term outperformance of value-oriented stocks.

Conclusion: Expecting Real-World Results to Track Backtests Closely

Considering the strengths outlined above, it is reasonable to expect that our 4-factor model’s real-world results should closely track its backtested performance. The key reasons for confidence include:

• A quarterly rebalance that avoids excessive trading costs and market noise.
• A focus on high-quality, liquid S&P 500 stocks, reducing execution risk.
• A purely fundamental-based approach, eliminating overfitting concerns.
• Proven resilience across economic cycles, showing robust real-world applicability.
• A value-driven selection process that historically performs best after downturns, reinforcing its ability to identify mispriced opportunities.
• The use of quality, growth, and momentum filters, ensuring that stock selection is fundamentally sound and supported by market trends.

While no backtest can fully guarantee future performance, the structural integrity, economic logic, and historical robustness of Investoristics’ 4-factor model strongly support the expectation that real-world performance will closely mirror backtested results. This is not a hypothetical exercise in data mining but rather a disciplined, well-reasoned investment framework designed to persist across changing market environments.