Concept:

SmartVol is a long-term quantitative model built on cross-sectional momentum principles, analyzing a selection of assets (tickers) across various market classes, subclasses, and sectors. The model emphasizes identifying assets with historically strong performance while maintaining a balanced allocation framework across equity and debt/bonds ETFs basket. It focuses on diversified, high-volume traded assets to ensure robust analysis and representation of market trends.

This model has been subjected to back-testing over a historical period of 21 years, during which it demonstrated consistent and steady performance under varying market conditions, including periods of downturns and bear markets. The model's design aims to limit volatility impacts during challenging market phases by prioritizing historically stable allocations.

A core feature of this model is an advanced momentum detection metric. This quantitative indicator is designed to analyze trends within ETFs and systematically select ETFs that align with the identified trends. The model operates with a low-frequency rebalancing approach, focusing on historical long-term performance trends. Adjusted daily closing prices are used for calculations, performance evaluations, and simulated allocation adjustments.

Rebalancing Approach

Rebalancing within the model is informed by historical testing and simulated allocation adjustments. Users analyzing this model may explore rebalancing scenarios to observe the impacts of different market conditions on allocations. Subscribed users receive notifications regarding hypothetical rebalancing allocations based on historical data, typically reflecting monthly adjustments during regular conditions or more frequent adjustments under emergency conditions. The model provides insights into trades and allocations from historical data for independent analysis.

Model Inception Date: 2003-10-31

Rebalancing Frequency:

• During regular market conditions, hypothetical rebalancing simulations are conducted monthly, typically based on the last trading day of each month.

• Under emergency market scenarios identified through quantitative signals, the model adjusts simulated allocations to reflect defensive positions, transitioning from regular allocations to predefined emergency scenarios.

Emergency Allocations and Monitoring

The model employs quantitative algorithms to monitor market trends, leveraging rigorously tested signals to identify market conditions indicative of downturns. If historical testing suggests bear market conditions, the model simulates emergency allocation adjustments, shifting to defensive positions that prioritize stability and reduced volatility. These adjustments may include allocations to bond ETFs or other defensive asset classes. Once conditions normalize, the model transitions back to hypothetical regular allocations.

Diversification: An Analytical Overview

SmartVol Model analyzes a diversified asset basket that includes equities, bonds, and commodities, providing insights into historical performance and exposure across various subclasses and market sectors. This model evaluates U.S. large-cap blend and value equities, corporate and government bonds of varied durations, and commodities such as gold ETFs. Additionally, the model examines sector-specific equities, including Technology and Real Estate, for a well-rounded analysis of diverse financial instruments and market segments.

The model incorporates two approaches to diversification based on historical analysis:

1. Horizontal Asset Diversification:
   This aspect evaluates allocations across multiple assets spanning different classes, subclasses, and sectors. The analysis aims to illustrate how diversification can historically reduce volatility and minimize the impact of significant losses. The model identifies the top four performing assets from different classes and subclasses of the financial market at the end of each month under normal market conditions for analysis.

2. Vertical Time Diversification:
   This component assesses dynamic rebalancing over time. The model’s simulated adjustments transition between various classes, subclasses, and sectors month by month. This approach is designed to demonstrate how diversification evolves over time and contributes to reduced historical volatility and mitigated drawdowns.

By examining multi-dimensional diversification strategies, the SmartVol model highlights how lower maximum drawdowns and reduced overall volatility have been reflected in its historical performance metrics.

• Market Classes covered by this Model:

  • Equity

  • Bond

  • Commodity

• Market Subclasses covered by this Model:

  • U.S. Aggregate Bonds

  • Short-Term U.S. Government

  • Intermediate-Term Treasury

  • Long-Term Treasury

  • Corporate Bonds

  • U.S. Large Cap

  • U.S. Mid Cap

  • U.S. Small Cap

  • U.S. Total Market

  • International Developed

  • Emerging Markets Inverse

  • U.S. Large Cap Growth

  • U.S. Large Cap Value

  • U.S. Sector

  • Precious Metals

  • Real Estate Inverse

  • Long-Term Treasury Inverse

  • U.S. Small Cap Inverse

• Market Sectors covered by this Model:

  • Multi-Sector

  • Technology

  • Treasury

  • Investment Grade

  • Real Estate

  • Energy

  • Financials

  • Health Care

  • Gold

Model's Benchmark:

The benchmark used for the SmartVol Model is constructed using two widely recognized U.S.-traded Exchange Traded Funds (ETFs), reflecting a blend of equity and bond market exposure. This benchmark serves as a reference point for evaluating historical performance based on back-testing analysis:

• SPY - SPDR S&P 500 ETF Trust (50%):
  This ETF tracks the performance of the Standard & Poor's 500 (S&P 500) index, a widely recognized indicator of the overall health of the U.S. equity market.

• AGG - iShares Core US Aggregate Bond ETF (50%):
  This ETF tracks the performance of the Bloomberg US Aggregate Bond Index, which represents the U.S. investment-grade bond market, including government, corporate, and mortgage-backed securities. It is often analyzed for its potential to provide income and stability in historical performance reviews.

By combining these two ETFs, the benchmark reflects a balance of equity and bond markets, offering a reference for assessing the historical performance of the quantitative model relative to typical market trends.

Results Review :

Results Review: Informational Overview

SmartVol provides users with updated hypothetical allocations and positions at each simulated rebalance date. These updates detail changes taken by the model such as the closing of prior positions, opening of new positions, and adjustments to the percentage allocations of existing positions, based on historical market conditions.

Sharpe Ratio and Risk-Adjusted Performance

The historical Sharpe Ratio of the model, exceeding 1, indicates that historically observed returns have significantly outweighed associated volatility and drawdowns, which are inherent in any Quantitative model.

Compounded Annual Growth Rate (CAGR): Historical Insights

Over a 21-year back-tested period, the SmartVol model has demonstrated an average annual CAGR of 13.91%, reflecting historical performance. This result is attributed to two primary factors:

1. Controlled, diversified price momentum, allowing strong trends to run their course.

2. Adaptive allocation adjustments during historical bear markets and downturns, maintaining diversification as a core principle of the model’s framework.

The historical cumulative return over the back-testing period stands at 1440.19%, compared to the benchmark’s cumulative return of 318%.

Risk-Adjusted Metrics: Sharpe and Alpha Ratios

The Sharpe Ratio, which measures historical risk-adjusted returns relative to annualized volatility, was 1.13 over the 21-year back-testing period. The Alpha Ratio, measuring relative performance compared to the benchmark, has a lifetime back-tested value of 8.07, highlighting the model’s historical ability to exceed benchmark returns.

Volatility, Drawdowns, and Beta Ratio

Despite achieving higher returns than the benchmark during back-testing, the SmartVol model has maintained lower annual volatility and maximum drawdowns. Key metrics from historical analysis include:

• Maximum Drawdown: Approximately -16.81%, compared to the benchmark’s -26.06%.

• Annual Volatility: 12.12%, lower than the benchmark.

• Beta Ratio: Measured at 0.82, indicating that the model has historically been 19% less volatile than the selected broader market benchmark.

These results reflect the model's historical ability to adjust allocations dynamically during periods of market uncertainty in simulations.

Note : At AlgoMart, all simulation results—including statistics, allocations, chart data, and trades—for all models are generated by our proprietary engine and backtester system. This unified software forms the core of the AlgoMart Model Simulator, ensuring consistency and reliability across all Models simulations. Furthermore, all asset prices are sourced from a single, centralized data source provided by the AlgoMart engine. This centralization unifies and standardizes all inputs and outputs of the model simulations, ensuring they are exclusively powered by AlgoMart.

Important Considerations

The SmartVol 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 SmartVol 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.