10 Equity and ETF Scripts Long Term Growth Portfolio | Theme - Soil & Land (Forest - Agricultural Products - Food Products - Realty - Metals & Mining) | Rebalancing Monthly | Commodity ETFs Composition is 40% | 12 Months Subscription Plan
10 Equity and ETF Scripts Long Term Growth Portfolio | Theme - Soil & Land (Forest - Agricultural Products - Food Products - Realty - Metals & Mining) | Rebalancing Monthly | Commodity ETFs Composition is 40% | 12 Months Subscription Plan - Image 2

10 Equity and ETF Scripts Long Term Growth Portfolio | Theme – Soil & Land (Forest – Agricultural Products – Food Products – Realty – Metals & Mining) | Rebalancing Monthly | Commodity ETFs Composition is 40% | 12 Months Subscription Plan

₹13,000.00 for 1 year

Telegram Bot Command for @ShubhaangBot : /soil_10_520000_c_40

Click here: “Portfolio Research Report & Factsheet”.

Click here: “Instructions & Standard Disclosures”.

Portfolio Parameters

Parameter Value
Scripts Group Title Theme – Soil & Land (Forest – Agricultural Products – Food Products – Realty – Metals & Mining)
Portfolio Size 10 Equity and ETF Scripts {6 Equities and 4 Commodity ETFs}
ETFs Composition Percentage Commodity ETFs Composition is 40%
Portfolio Risk Future Risk may Exceed 50%
Minimum Annual Investment Capital ₹ 5,20,000
Optimal Investment Tenure 10 or more years.
Rebalancing Frequency Rebalancing Monthly
Subscription Tenure 12 Months Subscription Plan
Backtest – Sharpe Ratio Hist. Sharpe Ratio above 1.3
Backtest – Annualised Returns Hist. Annualised Returns Under 55%
Backtest – Annualised Risk Hist. Annualised Risk Exceeding 30%
Backtest – Max. Drawdown Hist. Max. Drawdown Exceeding 30%
Backtest – Max. Recovery Time Hist. Max. Recovery Time Exceeding 6 Quarters
Simulation – Lumpsum Capital Growth Multiple ** More than 65 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 ** 65.98 13.57 3.10 0.89 0.61 0.53
99% Probability 209.80 36.41 6.92 1.56 0.95 0.69
98% Probability 316.14 51.94 9.25 1.91 1.11 0.76
95% Probability 587.18 88.77 14.28 2.59 1.41 0.88
90% Probability 1,029.29 144.07 21.13 3.41 1.75 0.99
80% Probability 2,097.01 264.76 34.53 4.81 2.27 1.15
75% Probability 2,793.21 338.40 42.05 5.50 2.52 1.22
50% Probability 19,105.49 1,618.45 137.72 11.74 4.38 1.64

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

Investment Objective:

  • Capitalize on land and resource assets in sectors like forestry, agriculture, real estate, and mining with long-term value appreciation.

Investment Rationale:

  1. Fundamental Foundation for Sustainable Growth: Soil and land resources underpin core sectors like agriculture, forestry, and real estate, which are critical to India’s economic expansion. The increasing focus on sustainable land use and food security drives long-term demand, offering high growth potential.
  2. Rising Domestic Consumption and Export Opportunities: The expanding middle class and global demand for agricultural, food, and metal products create robust markets for these stocks, supporting consistent revenue streams and higher earnings compared to more cyclical or less resource-dependent industries.
  3. Value Appreciation from Land and Resource Realignment: With global urbanization, infrastructure development, and policies favoring land use improvements, land and resource-based stocks are poised for significant value appreciation. They offer inflation protection and portfolio diversification advantages over other asset classes, with the potential for substantial long-term earnings.

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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