Concept

StableUp Diversified is a long-term quantitative model based on cross-sectional momentum, designed to analyze a curated selection of assets spanning various market classes, subclasses, and sectors. The model aims to emphasize historically best performing assets based on certain calculated metrics while maintaining a balanced allocation across equity and debt/bonds, with an attempt to enhance diversification by including a higher proportion of assets that may have historically demonstrated performance that fulfill certain standards.

This model has been back-tested over a 21-year period, with historical simulations suggesting it may have achieved consistent performance, particularly during market downturns and bear markets, where its impact was possibly minimized. An advanced dynamic momentum metric drives the model's analytical framework, attempting to capture trends in rising markets to highlight potential opportunities. This performance indicator is designed to identify historical trends and adjust allocations accordingly.

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. Rebalancing is structured to be straightforward, with users receiving email notifications regarding adjustments in those theoretical positions, typically at the end of each month during regular conditions, or occasionally multiple times per month during emergency scenarios.

Rebalance Regime

Under normal market conditions, the model simulates monthly rebalancing, typically aligning with the last trading day of each month. However, hypothetical rebalancing may occur at other times if emergency signals are triggered. The model exclusively analyzes long positions and does not involve shorting. During back-tested bear market conditions, the model occasionally allocated to certain Bond ETFs as part of a simulated strategy to attempt to manage volatility and maintain stability.

Normal Market Positions/Allocations

During regular market conditions, the model simulates rebalancing with the five best selected assets from its diversified basket. These hypothetical allocations are dynamically adjusted using a mathematical approach that seeks to minimize portfolio volatility and optimize returns by recalibrating the composition to assets historically demonstrated performance that fulfill certain standards.

Emergency Allocations

The model’s algorithm attempts to monitor market conditions using rigorously tested quantitative signals. When back-tested bear market conditions were detected, triggering specific alerts, the model transitioned to emergency allocations in simulations, closing all normal allocations. Once recovery signals were identified, the model exited emergency mode and resumed its simulated normal operations with the top five ranked assets from its diversified basket based on the assigned performance metric.

The emergency alert system employs a quantitative method designed to try detect both major and minor bear market events. This approach enables the model to simulate transitions between normal and emergency allocations. In most cases, emergency allocations in simulations included short-term bond ETFs, reflecting a trial of a defensive posture during challenging periods.

Diversification

StableUp is designed to analyze a diversified asset basket, including equities, bonds, and commodities, providing potential exposure across various subclasses and market sectors. The model evaluates U.S. large-cap blend and value equities, corporate and government bonds of varied durations, and commodities such as gold to examine asset allocation strategies. Additionally, it considers sector-specific equities, such as consumer staples and utilities, aiming to provide insights into diversification across financial instruments and market segments.

The model applies two complementary approaches to diversification:

A) Horizontal Asset Diversification:
This refers to allocation analysis across multiple assets, spanning different classes, subclasses, and sectors. The approach attempts to highlight how diversification may reduce volatility and minimize the risk of significant losses. During simulations of normal market conditions, the model selects the top five performing assets from different financial market categories at each month-end for analysis.

B) Vertical Time Diversification:
This involves dynamic rebalancing of assets over time. As the model adjusts its hypothetical positions, it transitions between various classes, subclasses, and sectors on a monthly basis. This process aims to enhance diversification over the model’s analytical lifetime, possibly contributing to reduced volatility and mitigating potential losses.

This multi-dimensional approach to diversification, as reflected in historical back-testing, may have indicated lower maximum drawdowns and reduced overall volatility, offering insights into how diversification might influence portfolio performance over time.

• Asset Classes covered by this strategy:

  • Equity

  • Bond

  • Commodities

  • Inverse Debt

  • Inverse Equity

• Sub-Classes covered by this strategy:

  • Equity:

    • US Large Cap Growth Equities

    • US Large Cap Value Equities

    • US Large Cap Blend Equities

    • US All Cap Equities

  • Bond:

    • US Total Bond Market

    • US Short Term Government Bonds

    • US Long Term Government Bonds

    • Corporate Bonds

  • Commodities:

    • Commodities - Gold

    • Commodities (General)

  • Inverse Debt:

    • Inverse Bonds

  • Inverse Equity:

    • Inverse Equities

• Sectors covered by this strategy:

  • Technology

  • Consumer Staples

  • Utilities

Model’s Benchmark

StableUp uses a blended benchmark for comparative analysis, reflecting historical performance of U.S.-traded Exchange Traded Funds (ETFs) focused on equity and bond markets. The benchmark is constructed as follows:

• SPY - SPDR S&P 500 ETF Trust (60%): This ETF tracks the performance of the Standard & Poor's 500 (S&P 500) index, widely regarded as an indicator of the U.S. equity market's performance.

• AGG - iShares Core US Aggregate Bond ETF (40%): 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, this ETF has been associated with income generation and stability.

This benchmark aims to provide a representation of typical market performance, serving as a reference point for analyzing the model's historical results relative to broader market trends.

Results Review :

The StableUp Model provides users with updated hypothetical allocations or positions at each simulated rebalance date. These updates detail potential adjustments such as closing previous positions, opening new ones, and modifying the percentage allocations of existing positions based on historical market conditions. These changes include increasing or decreasing allocations in response to historical data.

The model’s historical Sharpe Ratio, which is above 1, suggests that the returns from back-testing may have exceeded the historical volatility and drawdowns, which are inherent risks in any quantitative analysis.

Compounded Annual Growth (CAGR)

Over a back-tested period of 21 years, the StableUp model may have demonstrated performance that outpaced its benchmark. This potential result could be attributed to two primary factors: the use of controlled, diversified price momentum to allow trends to unfold and a focus on maintaining diversification during historically challenging periods, such as bear markets and downturns. Based on historical analysis, the model achieved an average annual CAGR of 11.44% over this period.

The cumulative return generated during back-testing was 872.01%, compared to the benchmark’s 385.26%. These results reflect hypothetical performance based on historical simulations.

Risk-Adjusted Metrics: Sharpe and Alpha Ratios

The Sharpe Ratio, which evaluates risk-adjusted returns relative to annualized volatility, was 1.38 during the 21-year back-tested period. The Alpha Ratio, which measures the hypothetical outperformance of the model compared to the benchmark, was 8.21, indicating potential possible relative returns during historical scenarios.

Volatility, Maximum Drawdown, and Beta Ratio

While delivering higher cumulative returns than the benchmark in historical back-testing, the model demonstrated lower volatility and drawdowns. The maximum drawdown during back-testing was approximately -11.13%, compared to the benchmark’s -31.60%. This performance may be attributed to the model’s emergency allocation system, which hypothetically adjusted allocations during periods of market uncertainty.

The annualized volatility of returns was 8.16%, lower than the benchmark, suggesting that the model’s framework may have controlled volatility while achieving back-tested returns. The Beta, measured at 0.40, indicates that the model was 60% less volatile than the broader market benchmark during the analyzed period. These results provide insights into how the model may have achieved lower volatility, smaller drawdowns, and potentially higher risk-adjusted returns based on historical 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

This model is designed as an informational tool, offering insights based on historical back-testing data. While historical analyses indicate good 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 StableUp 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.