Concept:

VolatilityShield Diversified is a long-term quantitative model based on cross-sectional momentum, designed to analyze a curated selection of low-volatility assets (ETFs) spanning low-volatility equity, bonds, and commodities. The model aims to emphasize historically top performing assets (with performance measured in reference to specific momentum metric) while maintaining balanced allocations across classes and subclasses, with an attempt to prioritize diversification by incorporating a higher proportion of top-performing allocations.

The model has been back-tested over 21 years, and historical simulations suggest it may have demonstrated consistent performance, particularly during market downturns and bear markets, where its impact might have been minimal in hypothetical simulations. A complex, dynamic momentum metric drives the model’s approach by identifying possible trends in asset momentum and selecting specific assets that may exhibit certain historical momentum patterns.

The model operates with low-frequency rebalancing, focusing on long-term performance by utilizing adjusted daily closing prices for hypothetical calculations, performance tracking, and simulated allocation adjustments. Users reviewing the model may simulate the theoretical rebalancing actions based on received email notifications, typically at the end of each month during regular conditions, or more frequently during emergency scenarios.

Model’s Inception Date: 2003-10-31

Rebalance Regime

Under normal market conditions, the model simulates monthly rebalancing, generally aligning with the last trading day of each month. However, rebalancing may occur at other times if hypothetical emergency conditions are detected. The model exclusively analyzes long positions and does not involve shorting. During historical bear market scenarios, the model has simulated allocations to certain bond ETFs to manage volatility and attempt to stabilize returns.

Normal Market Positions/Allocations

In typical conditions, the model simulates rebalancing actions involving the five best measured assets from its selected diversified basket. These allocations are dynamically optimized using a mathematical approach aims at minimizing portfolio volatility and enhancing returns through adjustments to the composition of historically selected assets.

Emergency Allocations

The model’s algorithm attempts to monitor market conditions using a quantitative framework with rigorously tested historical signals. If bear market conditions are identified, the model transitions to emergency allocations in simulations, hypothetically closing all normal allocations. Once recovery signals are detected, the model exits emergency mode and resumes hypothetical rebalancing with the three best measured assets from its selected basket.

The emergency alert system uses a quantitative method designed to identify both major and minor bear market events. This framework allows the model to simulate transitions between normal and emergency allocations, with emergency allocations typically including short-term bond ETFs as part of a defensive posture endeavor during downturns.

Diversification

VolatilityShield analyzes a diversified asset basket that includes equities, bonds, and commodities, providing insights into historical exposure across various subclasses and market sectors. With U.S. large-cap blend and value equities, corporate and government bonds of varied durations, and commodities such as gold, the model attempts to reflect a comprehensive approach to asset allocation. Additionally, the model incorporates bond-specific subclasses, including government, corporate, and municipal bonds, to provide insights into debt market segments alongside other classes.

The model applies two approaches to diversification:

A) Horizontal Asset Diversification:
This involves allocations across multiple assets, spanning different classes, subclasses, and sectors. By analyzing top-performing assets from various financial market categories at the end of each month during normal market conditions, the model seeks to illustrate how diversification may help reduce volatility and minimize the risk of significant losses.

B) Vertical Time Diversification:
This approach focuses on dynamic rebalancing over time. As the model adjusts its simulated positions, it transitions between various classes, subclasses, and sectors month by month. This ongoing rebalancing process aims to highlight how diversification may evolve over time, potentially contributing to reduced volatility and mitigating negative returns in back-tested scenarios.

This multi-dimensional approach to diversification, as reflected in historical performance metrics, may have demonstrated lower maximum drawdowns and reduced overall volatility in back-testing, providing insights into how diversification could contribute to managing risk.

• Assets Classifications :

  Classes covered by the model:

  • Equities: Exposure to large-cap stocks and broad market indexes.

  • Fixed Income: Comprehensive coverage of bond markets, including government, corporate, and municipal bonds.

  • Commodities: Investments in gold and a diversified range of broad commodities.

  Subclasses covered by the model:

  • Treasury Bonds:

    • Short-term (0-3 months, 1-3 years).

    • Intermediate-term (7-10 years).

    • Long-term (20+ years).

  • Corporate Bonds:

    • Short-term and intermediate-term corporate bond ETFs.

  • Municipal Bonds: National municipal bond exposure.

  • High-Yield Bonds: Short-term high-yield bond strategies.

  • Aggregate Bonds: Broad U.S. bond market exposure covering investment-grade debt.

  • Commodities: Broad commodity strategies with long-dated focus and gold-specific investments.

  • Equities:

    • S&P 500 ETFs (Core, Low Volatility).

    • Sector-focused ETFs (Consumer Staples, Consumer Discretionary).

  Sectors covered by the model:

  • Consumer Staples: Defensive equities in essential goods and services.

  • Consumer Discretionary: Cyclical equities tied to non-essential consumer spending.

  • Gold: Focused investment in gold as a store of value.

  • Broad Commodities: Exposure to a diverse set of commodity markets.

  • Treasuries: Government-issued bonds across multiple maturities.

  • Corporate Bonds: Debt securities issued by corporations.

Model’s Benchmark

The VolatilityShield Quantitative Model utilizes a blended benchmark for comparative analysis, reflecting historical performance data from U.S.-traded Exchange Traded Funds (ETFs) focused on the equity and bond markets. The benchmark is constructed as follows:

• SPY - SPDR S&P 500 ETF Trust (30%): This ETF tracks the performance of the Standard & Poor's 500 (S&P 500) index, widely regarded as an indicator of the overall health of the U.S. equity market.

• AGG - iShares Core US Aggregate Bond ETF (70%): This ETF tracks the Bloomberg US Aggregate Bond Index, providing exposure to the U.S. investment-grade bond market, including government, corporate, and mortgage-backed securities. Historically, it has been associated with income generation and stability.

This benchmark is employed as a reference point for analyzing the model’s historical performance. It aims to represent typical market trends, helping to contextualize the historical back-tested results of the model relative to broader market conditions.

Results Review :

he VolatilityShield Model provides users with updated simulated allocations or positions at each hypothetical rebalance date. These updates detail potential actions such as closing previous positions, opening new ones, and adjusting percentage allocations of existing positions based on historical market conditions. This analysis includes scenarios where allocations may be increased or decreased, reflecting changes in historical market conditions.

The model’s historical Sharpe Ratio, which is above 1, suggests that its back-tested returns may have exceeded its historical volatility and drawdowns (the inherent risks considered in any quantitative analysis).

Compounded Annual Growth Rate (CAGR)

Over the past 21 years, the VolatilityShield model has demonstrated historical results that may indicate outperformance of the market benchmark in back-testing. This outcome may be attributed to two key factors: controlled, diversified price momentum, where positive prices trends possibly encouraged to unfold, and an attempted focus on low volatility and diversification during periods of historical market downturns. Based on back-tested data, the model achieved an average annual CAGR of 6.59% over the 21-year period.

The cumulative return generated by the model over the back-tested period is 282.08%, compared to the benchmark’s 203%. These results reflect hypothetical performance based on historical simulations.

Risk-Adjusted Metrics: Sharpe and Alpha Ratios

The historical Sharpe Ratio of 1.29 reflects the model’s back-tested possible ability to balance returns against annualized volatility over the 21-year period. The Alpha Ratio, a measure of the model’s relative performance against the benchmark, is 3.63 in back-tested simulations, indicating hypothetical outperformance during the analyzed period within the model's conditions and the selected benchmark.

Volatility, Maximum Drawdown, and Beta Ratio

While delivering higher cumulative returns than the benchmark in back-testing, the VolatilityShield model demonstrated lower annual volatility and drawdowns. The maximum drawdown during the back-tested period is approximately -6.66%, compared to the benchmark’s -14.70%. This result may be associated with the model’s emergency allocation system, which adjusts hypothetical allocations during simulated periods of market uncertainty.

The annualized volatility of returns is 5.00%, lower than the benchmark. Additionally, the model’s Beta is measured at 0.53 in back-testing, suggesting it may have been 47% less volatile than the broader market benchmark. These results provide insights into how the model may have reduced the impact of bear markets in hypothetical scenarios, delivering an Alpha of 3.63, with lower volatility, smaller drawdowns, and a higher Sharpe Ratio compared to the benchmark over the same 21-year period.

Important Considerations

The VolatilityShield Quantitative Model is designed as an informational tool, offering insights based on historical back-testing data. While historical analyses indicate strong performance, past results are not indicative of future outcomes. The model employs a monthly rebalancing approach, during which market fluctuations may lead to short-term variations in performance. It is important to view this model as part of a long-term analytical framework, as reacting to short-term performance shifts may impact the model’s potential as observed in historical simulations.

Key Points to Remember

• Market Volatility: Short-term market volatility is a natural component of long-term analysis and should be anticipated.

• Consistency Matters: Staying aligned with the model's framework provides a consistent basis for understanding its historical performance over time.

• Independent Evaluation: Users are encouraged to thoroughly review all provided data and consult professional financial advisors to make decisions suited to their unique goals and risk tolerance.

Important Declaration

AlgoMart does not recommend or endorse specific Quantitative Model or analyze individual risk profiles for users. The VolatilityShield and other content provided on this platform are for informational purposes only. Subscribers and users are strongly encouraged to consult a qualified financial advisor to determine their own investment needs and assess risk tolerance. AlgoMart’s role is to offer data, statistics, and analysis without making personalized recommendations or offering financial advice.