3 Equity Scripts Long Term Growth Portfolio | Mixed Sectors 6 - Hotels & Resorts - Investments - Media and Publication - Aero&Defence - Food Products - Household Appliances - Transport Services - Ferrous Metals - Iron and Steel Products | Rebalancing Monthly | ETFs Composition is 0% | 12 Months Subscription Plan
3 Equity Scripts Long Term Growth Portfolio | Mixed Sectors 6 - Hotels & Resorts - Investments - Media and Publication - Aero&Defence - Food Products - Household Appliances - Transport Services - Ferrous Metals - Iron and Steel Products | Rebalancing Monthly | ETFs Composition is 0% | 12 Months Subscription Plan - Image 2

3 Equity Scripts Long Term Growth Portfolio | Mixed Sectors 6 – Hotels & Resorts – Investments – Media and Publication – Aero&Defence – Food Products – Household Appliances – Transport Services – Ferrous Metals – Iron and Steel Products | Rebalancing Monthly | ETFs Composition is 0% | 12 Months Subscription Plan

₹7,375.00 for 1 year

Telegram Bot Command for @ShubhaangBot : /ms6_3_295000_n_0

Click here: “Portfolio Research Report & Factsheet”.

Click here: “Instructions & Standard Disclosures”.

Portfolio Parameters

Parameter Value
Scripts Group Title Mixed Sectors 6 – Hotels & Resorts – Investments – Media and Publication – Aero&Defence – Food Products – Household Appliances – Transport Services – Ferrous Metals – Iron and Steel Products
Portfolio Size 3 Equity Scripts {3 Equities and 0 All ETFs}
ETFs Composition Percentage ETFs Composition is 0%
Portfolio Risk Future Risk may Exceed 75%
Minimum Annual Investment Capital ₹ 2,95,000
Optimal Investment Tenure 15 or more years.
Rebalancing Frequency Rebalancing Monthly
Subscription Tenure 12 Months Subscription Plan
Backtest – Sharpe Ratio Hist. Sharpe Ratio above 1.6
Backtest – Annualised Returns Hist. Annualised Returns Under 80%
Backtest – Annualised Risk Hist. Annualised Risk Exceeding 40%
Backtest – Max. Drawdown Hist. Max. Drawdown Exceeding 45%
Backtest – Max. Recovery Time Hist. Max. Recovery Time Exceeding 7 Quarters
Simulation – Lumpsum Capital Growth Multiple ** More than 2100 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 ** 2,100.96 161.03 14.08 1.61 0.81 0.53
99% Probability 8,208.11 523.67 37.40 3.25 1.39 0.73
98% Probability 13,450.72 803.59 52.91 4.16 1.68 0.82
95% Probability 28,335.75 1,530.96 89.51 6.03 2.24 0.97
90% Probability 55,722.22 2,742.76 143.60 8.40 2.89 1.13
80% Probability 1,31,597.10 5,741.25 260.00 12.69 3.97 1.35
75% Probability 1,86,485.44 7,733.96 330.24 14.95 4.50 1.45
50% Probability 22,10,701.67 57,337.97 1,489.78 38.60 8.95 2.08

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

Investment Objective:

  • Invest in hospitality, media, aerospace, defence, food, household appliances, transport, and steel sectors poised for growth driven by consumption and infrastructure expansion.

Investment Rationale:

  1. Strong Growth Driven by Increasing Domestic and International Travel: The hospitality, transport, and aerospace sectors are benefiting from rising tourism, business travel, and defence modernization, offering significant long-term growth and earning potential.
  2. Urbanization and Rising Incomes Fuel Consumer and Infrastructure Demand: Growth in household appliances, food products, and household innovation is supported by income growth and urban development, providing stable revenue streams and expansion opportunities.
  3. Policy Support and Investment in defence and Infrastructure: Government initiatives like “Make in India”, defence modernization, and infrastructure projects boost demand for aerospace, iron, and steel, creating a conducive environment for sustained profitability and higher returns compared to other industries or asset classes.

Investment Methodology:

  1. Universe Definition: The investment universe encompasses securities from listed companies within the NSE 500 Index and all exchange-traded funds (ETFs), which are rigorously filtered based on qualitative measures aligned with predefined investment objectives and rationales.
  2. Systematic Identification: Proprietary algorithms, advanced quantitative models, and technical analysis are utilized to systematically identify securities with a high probability of delivering superior returns.
  3. Data-Driven Framework: These models, thoroughly validated across diverse asset classes and various time horizons, integrate customized proprietary indicators such as momentum, volume, volatility, and trend analysis. This approach ensures that the portfolio construction process is entirely based on objective data and high-quality signals, devoid of biases, prejudices, or emotional factors.
  4. Optimisation of Security Selection: The selection of securities and their corresponding weightings within the Equity/ETF portfolio are further refined based on critical factors, including target portfolio size, ETF allocation, average daily trading turnover, available investment capital, rebalancing frequency, and the number of quantitative and technical criteria met by each security.

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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