Concept:

AlphaPulse is a quantitative framework based on cross-sectional momentum analysis of prices from a selected, diversified basket of model assets (tickers). These assets are ETFs spanning various market classes, subclasses, and sectors. The model aims to identify top-performing assets within the dataset while incorporating diversification for a balanced analytical approach.

The model has been back-tested over more than 21 years, during which historical simulations suggest potential performance during varied market conditions, including challenging periods and bear market scenarios. It employs an sophisticated and complex momentum-measuring metric designed to analyze historical price trends and attempt to detect upward market movements, aiming to explore potential opportunities within these trends.

To support low-frequency rebalancing with a focus on long-term analytical insights, the model utilizes adjusted daily closing prices for calculations, performance tracking, and allocation updates. Users are notified of rebalancing actions via email, typically at the end of each month under normal conditions or more frequently during periods of market volatility.

Strategy's Inception Date: 2003-10-31

Rebalance Regime:

• Normal Market Allocations:

Under normal conditions, the model is designed to rebalance monthly on the last trading day, focusing on the three top-performing assets from the diversified basket. Allocation percentages are dynamically optimized using mathematical methods to potentially reduce volatility and explore higher returns by managing the composition of top-performing assets.

• Emergency Allocations:

The model’s algorithm incorporates rigorously tested quantitative signals to monitor market conditions. If bear market conditions are identified through specific triggers, an emergency allocation process is activated, replacing normal allocations with a predefined set of assets. Once market conditions stabilize, the model aim to transitions back to its standard allocation approach, focusing on the three top-performing assets from the diversified basket.

Emergency allocations may include inverse market ETFs or bond ETFs, which are selected based on their potential to mitigate adverse market conditions. While the model may incorporate inverse ETFs, they are included as long positions within the allocation framework.

Diversification

The quantitative model analyzes a diverse basket of assets encompassing multiple classes, including equities, bonds, commodities, and currencies. It provides exposure across various subclasses and market sectors, featuring U.S. and international equities, corporate and government bonds, and a wide range of industries, from healthcare to energy and technology.

The model incorporates two key methods for diversification:

A) Horizontal Asset Diversification:

This process evaluates multiple assets across different classes, subclasses, and sectors to analyze the potential for reduced volatility and minimized risk. Each month, under normal market conditions, the model identifies the top three performing assets from distinct classes and subclasses within the analyzed dataset. A filtering mechanism is applied to ensure assets from similar subclasses or classes are not repeatedly selected, enhancing diversification and potentially reducing volatility.

B) Vertical Time Diversification:

This refers to the model’s dynamic rebalancing of asset positions over time. As the model adjusts its allocations, it transitions across various classes, subclasses, and sectors on a month-to-month basis. This ongoing adjustment aims to enhance diversification over the model’s time horizon, contributing to the mitigation of volatility and the attempt reduction of significant losses.

This multi-dimensional approach to diversification is designed to provide insights into lower maximum drawdowns and reduced overall volatility, as observed in the model’s back-tested performance data.

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 ETFs (Exchange Traded Funds) that focus on equity and bond markets:

• SPY - SPDR S&P 500 ETF Trust (90%): 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.

• AGG - iShares Core US Aggregate Bond ETF (10%): This ETF tracks the performance of the Bloomberg US Aggregate Bond Index, providing broad exposure to the U.S. investment-grade bond market, including government, corporate, and mortgage-backed securities. It is designed to offer income and stability with relatively lower risk.

This benchmark is intended to provide a reference point for evaluating the model's analytical framework relative to broader market performance. It allows for comparisons based on historical data and does not indicate future results or outcomes.

Results Review :

The quantitative model provides users with updated allocation data at each rebalance date, outlining actions such as closing previous positions, opening new ones, and adjusting the percentage allocations of existing positions. These adjustments are based on market conditions at the time and aim to reflect analytical outcomes.

Compounded Annual Growth Rate (CAGR)

Based on historical simulations over a 21-year period, the model demonstrates potential performance outcomes under various market conditions. Two key factors have contributed to the observed results: the ability to leverage controlled, diversified price momentum, allowing trends to persist, and the application of adaptive allocation adjustments during bear market phases. Over the simulated period, the model has achieved an average annual CAGR of 22.05%, compared to approximately 9.93% for the benchmark.

The historical analysis indicates a cumulative return of 6461.89% for the model over the same 21-year period, compared to 630.52% for the benchmark. These outcomes are derived from historical data and do not guarantee future results.

Risk-Adjusted Metrics : Sharpe and Alpha Ratios

The model’s Sharpe Ratio, which exceeds 1 in back-tested hypothetical scenarios, suggests that its returns may have exceeded volatility and drawdowns (an inherent element of any financial analysis).

The model’s risk-adjusted Sharpe Ratio, which measures compounded annual growth relative to return volatility, was observed at 1.22 during the historical back-testing period of 21 years. The Alpha Ratio, reflecting relative performance compared to the benchmark, was observed at 17.24, suggesting outperformance in the analyzed dataset.

Volatility, Maximum Drawdown, and Beta Ratio

The model maintained lower observed volatility and maximum drawdowns compared to the benchmark during the back-tested period. The maximum drawdown was approximately -18.90%, compared to the benchmark’s -46.41%. These results are attributed to the emergency allocation system, which adjusts allocations during periods of market uncertainty. Annual volatility of returns was 17.24%, close to the benchmark but paired with historically higher returns.

The model’s Beta Ratio was observed at 0.48, indicating lower volatility compared to the broad market benchmark by at least 52%. While achieving lower observed volatility and drawdowns, the model demonstrated hypothetical outperformance in terms of Alpha, Sharpe Ratio, and cumulative returns in the back-tested period.

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 AlphaPulse 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 AlphaPulse Model 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.