Concept:

AlphaMax Diversified is a long-term quantitative model based on cross-sectional momentum, designed to analyze the price performance of a diversified basket of assets spanning multiple market classes, subclasses, and sectors. The model focuses on identifying and emphasizing historically top-performing assets using selected measurement metrics, potentially broadening diversification by incorporating a higher number of such assets within the simulated framework.

This model has been back-tested over a 21-year period, during which it may have demonstrated consistent performance in simulations, including during severe market downturns and bear markets. The back-tested results suggest that its analytical framework may help highlight potential trends and performance patterns.

A sophisticated momentum metric forms the possible core contributor of this model's results, aiming to detect upward market trends in historical data. This metric is designed to attempt to optimize the model's allocations and highlight potential return opportunities in various market environments.

Rebalance Frequency and Process

To support the hypothetical objectives of long-term analysis with minimal intervention, the model uses adjusted daily closing prices in its calculations to assess performance metrics and simulate rebalancing of allocations. rebalance notifications are designed to occur monthly under normal conditions or more frequently during periods of heightened volatility.

Model Inception Date: 2003-10-31

Rebalance Regime:

• Monthly Rebalance

Under typical market conditions, AlphaMax simulates monthly rebalancing on the final trading day. This process adjusts allocations based on the top five selected assets within the diversified basket. Allocation percentages are optimized using mathematical models such as the efficient frontier, aiming to minimize volatility and possibly enhance overall portfolio metrics based on historical analysis.

• Emergency Rebalance

The model incorporates a monitoring system designed to track critical market signals. If bear market conditions are detected in simulations, emergency rebalancing is triggered. During such events, normal allocations are overridden, and the model adjusts to a specialized set of assets. When conditions stabilize, the model reverts to regular allocations, selecting the top three performing assets from its diversified basket.

Positioning and Allocation:

All positions in this model are long-only, meaning no shorting is utilized. During back-tested bear markets, the model may have allocated to inverse market ETFs or other defensive inverse assets that were taken as long positions, aiming to mitigate potential risks.

• Normal Market Allocations

In typical conditions, the model's monthly rebalancing focuses on the five highest-performing assets in its diverse asset framework. Allocation percentages are dynamically optimized using mathematical analysis in attempt to balance volatility and possibly enhance overall returns.

• Emergency Allocations

AlphaMax’s algorithm includes quantitative signals designed to closely monitor market conditions. When bear market triggers are detected in back-testing, emergency allocations replace standard ones until conditions stabilize. During recovery, the model resumes normal allocations, selecting the top five performers in its asset basket.

The emergency alert system employs a quantitative methodology, which has been tested in back-testing, to try and respond to major and minor bear market events. It is designed to transition seamlessly between standard and emergency allocations. Emergency assets may include inverse market ETFs, bond ETFs, or inverse bond/debt ETFs, highlighting the model's possible potential adaptability to varying historical market conditions.

Diversification

This quantitative model analyzes a diverse range of assets across multiple classes, including equities, bonds, commodities, and currencies. It considers potential exposure across various subclasses and market sectors, such as U.S. and international equities, corporate and government bonds, and sectors ranging from healthcare to energy and technology.

The model incorporates two primary sources of diversification in its analytical framework:

A) Horizontal Asset Diversification

This involves examining allocations across multiple assets spanning different classes, subclasses, and sectors. The aim is to explore how diversification may reduce volatility and potentially could mitigate substantial losses. In historical simulations under normal market conditions, the model selected the top five performing assets each month from distinct classes and subclasses within the financial market. A filtering mechanism is applied to avoid overrepresentation of similar subclasses or classes among the top selected performing assets, thereby attempting to enhance diversification and manage volatility.

B) Vertical Time Diversification

This refers to the dynamic rebalancing of assets over time. As the model adjusts its hypothetical positions, it transitions across various classes, subclasses, and sectors on a month-to-month basis. This ongoing rebalancing process is designed to explore how enhanced diversification over the model's analytical lifespan may contribute to reduced volatility and potentially limit the magnitude of losses.

This multi-dimensional approach to diversification, based on historical back-testing, may have indicated lower maximum drawdowns and reduced overall volatility, providing insights into how diversification can influence performance metrics.

Asset Classes Covered by this Model:

1. Equities

2. Bonds

3. Commodities

4. Real Estate

5. Inverse/Leveraged ETFs

Subclasses and Categories Covered by this Model:

1. Equities

   • U.S. Equities

     • Large-Cap (e.g., S&P 500, Dow Jones)

     • Mid-Cap

     • Small-Cap

     • Growth and Value

     • Dividend-Focused

   • International Equities

     • Developed Markets (e.g., Europe, Japan, Canada)

     • Emerging Markets (e.g., Asia, Latin America)

     • Region-Specific (e.g., Asia-Pacific, Europe, Middle East)

     • Sector-Specific International Equities (e.g., Japanese Equities, Chinese Equities)

   • Thematic/Sector-Specific Equities

     • Technology and Innovation

     • Energy

     • Clean Energy and Infrastructure

     • Consumer

     • Financials

     • Dividend and High Yield

2. Bonds

   • U.S. Bonds

     • Aggregate Bonds

     • Treasury Bonds

     • Investment-Grade Corporate Bonds

     • Municipal Bonds

     • Short-Term Bonds

   • International Bonds

     • Developed Market Sovereign Bonds

     • Emerging Markets Bonds

     • High-Yield Bonds

3. Commodities

   • General Commodity Exposure

   • Precious Metals

     • Gold

   • Energy Commodities

     • Natural Gas

4. Real Estate

   • U.S. Real Estate

   • Global Real Estate

5. Inverse/Leveraged ETFs

   • Inverse ETFs

   • Leveraged Inverse ETFs

   • Leveraged Bond and Treasury ETFs

Market Sectors Covered by this Model:

1. Technology

   • Innovation and internet-focused funds

2. Healthcare

   • Healthcare momentum-focused funds

3. Financials

   • Insurance-focused funds

4. Energy

   • Traditional energy, renewable energy, and MLPs

5. Utilities and Infrastructure

   • Infrastructure-focused funds, including smart grid technology

6. Consumer Discretionary

   • Consumer-focused funds, with emphasis on emerging markets

7. Industrials

   • Industrial-focused funds, including sectors such as steel production

8. Materials

   • General exposure through diversified funds

9. Dividend and Income-Focused Sectors

   • High-dividend yield-focused funds, including funds that emphasize U.S. dividends and international dividends

Model’s Benchmark

The benchmark for this quantitative model is constructed using a combination of two U.S.-traded Exchange Traded Funds (ETFs) that are focused on equity and bond markets. This benchmark is designed to serve as a reference for analyzing the model's hypothetical performance in historical back-testing.

• SPY - SPDR S&P 500 ETF Trust (75%): This ETF tracks the performance of the Standard & Poor's 500 (S&P 500) index, which is widely regarded as an indicator of the overall health of the U.S. economy and equity market.

• AGG - iShares Core US Aggregate Bond ETF (25%): This ETF tracks the Bloomberg US Aggregate Bond Index, offering exposure to U.S. investment-grade bonds, including government, corporate, and mortgage-backed securities. Historically, AGG has been associated with income and stability within bond markets.

The benchmark aims to provide a representation of general market performance, serving as a point of comparison for evaluating historical data and performance metrics associated with the quantitative model.

Results Review :

This quantitative model provides users with updated hypothetical allocations and positions at each simulated rebalance date. These updates specify potential actions, including closing previous positions, opening new ones, and adjusting the percentage allocations of current positions. Any adjustments in allocations, whether increases or decreases, are based on historical market conditions as part of the back-testing process.

Compounded Annual Growth Rate (CAGR):

Over a 21-year back-tested period, this model may have demonstrated performance exceeding that of its benchmark. This potential result is attributed to two key principles: a disciplined momentum-based approach that attempts to leverage strong price trends and a strategic approach during simulated bear markets and downturns. In such scenarios, the model employs a vigilant and potentially conservative or opportunistic stance while maintaining diversification as a central analytical focus. Based on back-testing, the model delivered an average annual CAGR of 18.71%.

During the same back-tested period, the model's cumulative return was 3563.14%, compared to the benchmark's cumulative return of 498.76%. These results are derived solely from historical simulations and do not guarantee future outcomes.

Risk-Adjusted Ratios: Sharpe and Alpha

The model's back-tested Sharpe Ratio, which exceeds 1, suggests that the simulated returns may have surpassed the historical volatility and drawdowns, reflecting a favorable balance of reward over risk in the historical simulations analyzed.

The risk-adjusted Sharpe Ratio, which evaluates annual growth relative to return volatility in the back-tested data, was measured at 1.19 over the 21-year period. Additionally, the model's back-tested Alpha ratio, indicating potential outperformance compared to the benchmark, was calculated at 13.42.

Volatility, Drawdowns, and Beta Insights

While delivering higher cumulative returns than the benchmark during back-testing, the model demonstrated lower volatility and smaller maximum drawdowns in the simulations. The maximum drawdown was approximately -18.51%, compared to the benchmark’s -39.32%. This simulated performance may reflect the impact of the model's emergency allocation system, which attempt to adjusts allocations during periods of historical market downturns and uncertainty.

The annual return volatility in back-testing was 15.54%, lower than the benchmark. Despite the high simulated returns, the model's approach attempted to balance growth with controlled volatility. The Beta, measured at 0.59, indicates the model may have been 41% less volatile than the broader market benchmark during the analyzed period.

Based on these back-tested results, the model achieved an Alpha of 13.42, suggesting hypothetical outperformance of 13.42 percentage points over the benchmark. This analysis highlights the model's potential to combine reduced volatility, a smaller maximum drawdown, and a higher Sharpe ratio within the tested historical data set.

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 AlphaMax Diversified 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 AlphaMax 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.