Unveiling the Hidden Complexities of Low-Risk Investing: Insights from Our Latest Research

The Core Question

How do portfolio construction choices impact the performance of low-risk strategies? While the academic literature has extensively documented the low-risk anomaly, many practical questions remain unanswered. Specifically, we investigate how choices such as the risk estimation methodology, weighting schemes, transaction costs, and portfolio constraints shape portfolio outcomes.

What We Do

Our research analyzes ~10,000 portfolios across 130,000+ performance metrics to quantify the variability introduced by different portfolio construction and evaluation choices. By leveraging decades of data from January 1978 to December 2023, we develop a comprehensive framework to assess the influence of:

• Risk Estimation Methodology: Comparing beta-based method with volatility-based estimators (e.g., historical volatility, exponentially weighted moving averages (ewma), idiosyncratic volatility with respect to the Fama and French factor models).

• Portfolio Types and Constraints: Analyzing the effects of size and price filters as wells as leverage constraints on performance.

• Portfolio Size and Weighting: The impact of the number of portfolio holdings and their weighting.

• Rebalancing Frequency: Evaluating how often portfolios should be updated to strike a balance between responsiveness and transaction costs.

• Transaction Costs: Incorporating realistic cost assumptions to simulate real-world implementation.

Our decision tree framework, a visual representation of the portfolio construction and evaluation process, serves as the backbone of our analysis:

The figure illustrates all possible decision paths for our portfolio construction and evaluation. We consider the paths of eight portfolio construction decision forks and two performance evaluation forks for our sorting portfolios.

Key Findings

1. Methodological Choices Are Critical

The choice of the risk estimation methodology and weighting scheme emerge as primary drivers of performance variability. For instance: Volatility-based estimators, such as historical volatility and exponentially weighted moving averages, outperform beta-based approaches in risk-adjusted terms. Equally-weighted portfolios often deliver better returns than value-weighted ones, albeit with higher transaction costs.

2. Transaction Costs Matter (A Lot)

Ignoring transaction costs can lead to misleading conclusions. Portfolios with high turnover, such as those employing ewma and beta estimators or frequent rebalancing, see significant performance erosion once costs are accounted for. Our findings underscore the importance of incorporating realistic cost assumptions when designing low-risk strategies.

3. Simpler Can Be Better

Portfolios with size and price filters are more robust, as they exclude illiquid or costly-to-trade stocks. Additionally, less frequent rebalancing—quarterly or annually—outperform higher-frequency strategies, striking a better balance between adaptability and cost efficiency.

4. Nonstandard Errors Are a Hidden Risk

We quantify the variability introduced by arbitrary methodological decisions, referred to as "nonstandard errors." These errors often rival or exceed traditional standard errors in magnitude, particularly in unconstrained portfolio designs. This finding highlights the need for cautious interpretation of (single) backtest results and the need to robustify across parametrizations.

The plot displays the average annualized Sharpe ratios of long-only and limited-short portfolios after accounting for transaction costs. Each bar represents portfolios sharing a specific decision variable within a given decision fork, differentiated by colors. The black lines show the inter quartile range of Sharpe ratios. Sharpe ratios are calculated based on 11,598 daily out-of-sample returns from January 1978 to December 2023.

Practical Recommendations for Investors

Our study bridges the gap between academic insights and real-world implementation, offering actionable takeaways for investors:

1.     Choose Your Risk Estimator Wisely: Opt for volatility-based estimators, which tend to be more stable and cost-efficient than beta-based methods.

2.     Don’t Overlook Costs: Incorporate transaction costs into your strategy evaluation to ensure realistic performance expectations.

3.     Simplify Where Possible: Use filters for size and price to avoid costly and illiquid stocks and consider less frequent rebalancing to reduce turnover.

4.     Focus on Robust Metrics: Evaluate portfolios across multiple performance measures, such as Sharpe ratios, maximum drawdowns, and net returns, to ensure alignment with your investment objectives.

The Bigger Picture

The low-risk anomaly continues to challenge conventional thinking, offering a compelling case for rethinking risk-return trade-offs. However, as our research demonstrates, the path to optimal portfolio construction is fraught with methodological complexities. By carefully considering factors such as transaction costs, estimator choices, and portfolio constraints, investors can design strategies that are not only effective but also implementable in real-world settings.

We invite you to explore the full details of our study in the working paper:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5105457