Concept

SereneYield is a long-term quantitative model rooted in price momentum, designed to analyze a curated selection of low-volatility assets (ETFs) across specific equity and bond subclasses. The model focuses on identifying historically top-performing assets within its chosen group, maintaining balanced diversification while emphasizing allocations to assets that historically demonstrate performance within selected boundaries for stability and growth in back-testing scenarios.

Based on over 21 years of back-testing, the SereneYield model has demonstrated consistent performance under varying market conditions, including bear markets and downturns. This historical results is driven by a a complex momentum detection metric that identifies and leverages assets with certain momentum trends. The model is designed to focus on minimizing historical volatility while maintaining long-term simulated stability.

Rebalance Framework

The model operates on a low-frequency rebalancing approach, focusing on long-term analytical gains. It uses adjusted daily closing prices for hypothetical calculations, performance tracking, and allocation adjustments. Users analyzing the model can review its rebalancing mechanics, typically simulating monthly adjustments at the end of each month under regular market conditions or more frequent updates during market stress scenarios.

Inception Date: 2003-10-31

Rebalance Regime

Under normal market conditions, the SereneYield model simulates rebalancing monthly, typically aligning with the last trading day of each month. During market distress, an emergency system may hypothetically trigger rebalancing at any time. The model exclusively analyzes long positions, avoiding shorting scenarios. In back-testing simulations, bear market conditions have prompted the model to shift allocations to extremely low-volatility bond ETFs, attempting to maintain portfolio stability.

• Normal Market Positions/Allocations:
  During regular conditions, the model identifies and evaluates the four top-performing assets from a selected basket of assets. Allocations are dynamically optimized using mathematical models to minimize volatility and enhance returns by adjusting the composition of historically top assets measured by the defined by model's momentum detection metric.

• Emergency Allocations:
  The model employs tested quantitative signals to monitor market conditions. When bear market indicators are triggered, it transitions to emergency allocation simulations, closing all normal positions. Emergency allocations typically consist of low-volatility, short-term bond ETFs, reflecting a defensive posture attempt during downturns. When hypothetical recovery signals are identified, the model transitions back to normal operations, focusing on the top-performing assets from its selected basket.

The emergency alert system is a quantitative mechanism that attempt to detects both major and minor bear market events, enabling the model to simulate seamless transitions between normal and emergency allocations. This approach highlights how the model aims to remain defensive during market volatility while positioning itself for recovery during improved conditions.

Diversification

The SereneYield Model analyzes a diversified asset basket that includes equities and bonds, providing insights into exposure across various subclasses and market sectors. The model focuses on low-volatility equities and a range of bond types, including government, corporate, and municipal bonds, to evaluate asset allocation frameworks that highlight diversification and balance.

The model incorporates two complementary approaches to diversification:

A) Horizontal Asset Diversification:
This approach evaluates allocations across multiple assets, spanning diverse classes, subclasses, and sectors. The analysis highlights how diversification can historically reduce volatility and mitigate negative returns. Based on historical data, the model selects the top four assets measured by the defined by model's momentum detection metric from different financial market classes and subclasses at the end of each month under normal market conditions.

B) Vertical Time Diversification:
This approach examines the dynamic rebalancing of assets over time, transitioning between various classes, subclasses, and sectors on a monthly basis. By continuously simulating adjustments to its positions, the model illustrates how diversification can evolve across its lifecycle, possibly contributing to historically reduced volatility and drawdowns.

This multi-dimensional diversification framework, as shown in historical back-testing, has been associated with reduced maximum drawdowns and lower overall volatility, demonstrating the passible benefits of a diversified analytical approach to portfolio evaluation.

Model's Assets Basket Classifications:

Classes Covered by this model:

• Equities: Low-volatility equities and specific equity sectors.

• Fixed Income: Broad bond coverage, including government, corporate, municipal, and inflation-protected securities (TIPS).

Subclasses Covered by this 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.

  • Investment-grade corporate bonds.

• Municipal Bonds: Tax-advantaged national municipal bond ETFs.

• High-Yield Bonds: Short-term high-yield ("junk") bond strategies.

• Inflation-Protected Bonds (TIPS): Bonds that adjust for inflation over time.

• Aggregate Bonds: Comprehensive U.S. bond market exposure, including investment-grade government and corporate debt.

• Equities:

  • Defensive low-volatility equity strategies.

  • Sector-specific equities, including Consumer Staples and Consumer Discretionary.

Sectors Covered by this model:

• Consumer Staples: Defensive equities tied to essential goods and services (e.g., food, beverages, household products).

• Consumer Discretionary: Cyclical equities linked to non-essential consumer spending (e.g., retail, travel, luxury goods).

• Gold and Commodities: Investments in gold and a diversified range of commodities to hedge against inflation and diversify risk.

• Treasuries and Fixed Income: Broad exposure to government-issued securities across various maturities.

• Corporate Bonds: Investment-grade and high-yield corporate debt.

• Municipal Bonds: Tax-efficient bonds issued by local governments.

Model’s Benchmark:

SereneYield uses a blended benchmark for comparative analysis, reflecting the historical performance of U.S.-traded Exchange Traded Funds (ETFs) that primarily focus on equity and bond markets. This benchmark combines the following components:

• SPY (10%): The SPDR S&P 500 ETF Trust tracks the S&P 500 index, widely recognized as a barometer of the U.S. equity market's performance.

• AGG (90%): The iShares Core US Aggregate Bond ETF tracks the Bloomberg US Aggregate Bond Index, offering exposure to U.S. investment-grade bonds, including government, corporate, and mortgage-backed securities. This ETF is historically associated with income generation and stability.

This benchmark is employed as a reference point to evaluate the historical performance of the model. Comparisons to the benchmark attempt to provide insights into how the model’s back-tested results align with the selected broader market.

Results Review :

The SereneYield Model provides users with updates based on simulated rebalancing dates, outlining adjustments such as hypothetical closure of positions, opening of new ones, and percentage allocation changes derived from historical market data. These updates aim to illustrate the model's approach to portfolio alignment, including adjustments to allocations based on historical conditions.

Compounded Annual Growth (CAGR)

Over a back-tested 21-year period, the SereneYield model has demonstrated consistent results, supported by two core principles. First, the model leverages diversified price momentum, attempting to get trends to naturally unfold within its simulations. Second, it employs a historical focus on low volatility and diversification during market downturns. These principles may have potential correlation to the model’s average annual CAGR of 5.68%, based on historical back-testing. During the same period, back-testing results suggest a cumulative return of 218.81%, compared to the benchmark’s cumulative return of 121%.

Risk-Adjusted Metrics: Sharpe and Alpha Ratios

The model’s historical Sharpe Ratio, which evaluates risk-adjusted returns relative to volatility, is 1.20 over the 21-year period. Additionally, the Alpha Ratio, reflecting the model’s historically simulated outperformance against the selected benchmark, stands at 2.57 based on back-tested data. These metrics highlight the model’s possible ability to balance performance and volatility within its analytical framework.

Volatility, Maximum Drawdown, and Beta Ratio

The model achieves back-tested results with lower historical volatility and drawdowns compared to its specific benchmark. Key findings include:

• Maximum Drawdown: Approximately -6.40%, compared to the benchmark’s -13.74%.

• Annual Volatility: 4.61%, significantly lower than the benchmark.

• Beta Ratio: Measured at 0.80, suggesting the model has historically been 20% less volatile than the broader market.

These metrics highlight the model's historical performance in attempting to maintain stability and manage risks, as demonstrated through back-tested scenarios. The model's simulated emergency allocation mechanism illustrates how transitions to lower-volatility assets may have contributed to reducing drawdowns and stabilizing returns during historically challenging periods.

Important Considerations

The SereneYield 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 SereneYield 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.