2 Mutual Funds Long Term Growth Portfolio | MutualFunds_Hybrid_MultiAssetAllocation | Rebalancing Quarterly | 12 Months Subscription Plan

2 Mutual Funds Long Term Growth Portfolio | MutualFunds_Hybrid_MultiAssetAllocation | Rebalancing Quarterly | 12 Months Subscription Plan

₹625.00 for 1 year

Telegram Bot Command for @ShubhaangBot : /mfhbma_2_25000_n_0

Click here: “Portfolio Research Report & Factsheet”.

Click here: “Instructions & Standard Disclosures”.

Portfolio Parameters

Parameter Value
Scripts Group Title MutualFunds_Hybrid_MultiAssetAllocation
Portfolio Size 2 Mutual Funds {2 Mutual Funds and 0 Equities or ETFs}
ETFs Composition Percentage Equities and ETFs Composition is 0%
Portfolio Risk Future Risk may Exceed 15%
Minimum Annual Investment Capital ₹ 25,000
Optimal Investment Tenure 3 or more years.
Rebalancing Frequency Rebalancing Quarterly
Subscription Tenure 12 Months Subscription Plan
Backtest – Sharpe Ratio Hist. Sharpe Ratio above 0.9
Backtest – Annualised Returns Hist. Annualised Returns Under 15%
Backtest – Annualised Risk Hist. Annualised Risk Exceeding 5%
Backtest – Max. Drawdown Hist. Max. Drawdown Exceeding 5%
Backtest – Max. Recovery Time Hist. Max. Recovery Time Exceeding 3 Quarters
Simulation – Lumpsum Capital Growth Multiple ** More than 4.7 times in 20 yrs with 99.9% probability

Simulation Results :- Lumpsum Capital Growth Multiples over Future Years at varying Probability Levels

Probability Levels (%) 20 Years 15 Years 10 Years 5 Years 3 Years 1 Year
99.9% Probability ** 4.76 2.77 1.67 1.08 0.93 0.87
99% Probability 6.39 3.59 2.07 1.25 1.05 0.93
98% Probability 7.11 3.94 2.23 1.32 1.10 0.95
95% Probability 8.34 4.53 2.50 1.43 1.17 0.99
90% Probability 9.63 5.13 2.77 1.54 1.23 1.02
80% Probability 11.49 5.97 3.14 1.68 1.32 1.06
75% Probability 12.32 6.34 3.29 1.73 1.35 1.07
50% Probability 17.67 8.62 4.20 2.05 1.54 1.15

Refer to the “Backtests & Simulations” tab for further details on their operational settings.

Investment Objective:

  • To achieve diversified returns by investing in hybrid multi-asset allocation mutual funds that balance across various asset classes for optimal risk management.

Investment Rationale:

  1. Comprehensive Diversification: Hybrid multi-asset allocation funds invest across various asset classes, including equities, fixed income, gold, and other securities. This diverse approach helps spread risk by not relying on a single asset class for returns, thereby enhancing portfolio stability and reducing volatility.
  2. Dynamic Asset Allocation: These funds employ a flexible investment strategy, allowing fund managers to adjust the allocation among different asset classes based on market conditions and economic trends. This dynamic asset allocation can help capitalize on growth opportunities while protecting the portfolio during adverse market conditions, potentially leading to better overall performance.
  3. Balanced Risk and Return: By combining different asset classes, hybrid multi-asset allocation funds aim to provide a balance between risk and returns. They can offer investors the growth potential of equities along with the stability of fixed-income investments, catering to a wide range of investment objectives, from conservative to aggressive growth strategies. This balanced approach can be particularly appealing for investors looking for resilience in their portfolio.

Investment Methodology:

  1. Universe Definition: The investment universe comprises all open-ended equity-based mutual funds, providing a comprehensive range of options for portfolio construction.
  2. Systematic Identification: Proprietary algorithms and advanced quantitative models are employed to rigorously identify mutual funds with the highest potential for superior returns. This assessment is grounded in historical performance and various fund characteristics.
  3. Data-Driven Framework: The models, extensively validated across multiple fund types and time horizons, integrate customized proprietary indicators such as momentum, volatility, and trend analysis. This ensures that the portfolio construction process is strictly based on objective data and high-quality signals, devoid of biases, prejudices, or emotional influences.
  4. Optimisation of Fund Selection: The selection of mutual funds, along with their respective weightings within the mutual fund portfolio, is optimized based on essential factors, including target portfolio size, expense ratio, minimum investment requirements, exit load, assets under management (AUM), and available investment capital.

Backtests and Simulation Results:

  1. Backtest models are constructed using a limited set of assumptions about market conditions and portfolio characteristics; therefore, backtest results should not be interpreted as indicative of actual past performance in real-world conditions.
  2. Backtest results and Simulation results should never be used as a basis for comparing the actual performance of other intermediaries.
  3. Backtest and simulation outputs are provided solely as factual data to support comparative assessment of my subscription plans. They highlight the model’s potential in relation to the portfolio’s investment rationale and parameters under the stated investment methodology.
  4. Simulation results are derived from 10,00,000 trials and represent potential future scenarios based on normalised inputs (average returns and return volatility) obtained from backtesting.
  5. Users are strongly advised to exercise extreme caution when using this information. 
  6. These back-test results and simulation have not been verified by PaRRVA (Past Risk and Return Verification Agency) or any other SEBI-recognized agency.
  7. The impact of periodic portfolio rebalancing and transaction costs has been factored into these back-test results.
  8. "Backtesting and simulation results do not serve as indicators of future performance or guarantees of returns and they should not be interpreted as an endorsement or advertisement."

Simulation Assumptions:

  1. Monthly Portfolio Returns are assumed to follow a normal distribution over a 20-year horizon, with mean returns and volatility presumed to mirror historically observed levels.
  2. Portfolios are rebalanced once monthly.
  3. Portfolio risk level and Sharpe ratio are assumed to remain unchanged over the 20-year period.
  4. Other than the initial lump sum investment, no additional contributions or withdrawals are made over the 20 years period.
  5. Portfolio rebalancing is assumed to be unconstrained by liquidity over the 20‑year period: required quantities of each security can be bought or sold at competitive prices on every rebalancing, and transaction volumes are assumed to have a negligible impact on market prices.
  6. Earnings from dividends are ignored.
  7. Simulation results are not adjusted for inflation rates.
  8. Effects of taxes on the capital appreciation (Capital Gains Tax & Taxes on Dividends) are not accounted.

Simulation Settings:

  1. Transactional costs are included in the Sharpe ratio calculations.
  2. Risk free rate is assumed to be 6.5% p.a.
  3. Each simulation value is resulted from 10,00,000 simulation trials using the Monte Carlo method.
  4. Simulation results are derived for probability levels (confidence levels) of 99.9%, 99%, 98%, 95%, 90%, 80%, 75%, and 50%.
  5. Simulation values do not predict or reflect future market conditions; they represent only possible future outcomes.
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