5 Equity and ETF Scripts Long Term Growth Model Portfolio | Theme – AIPioneers | Rebalancing Monthly | Future Risk may Exceed 40% | Commodity ETFs Composition is 65% | 12 Months Subscription Plan

₹20,000.00 for 1 year

Telegram Bot Command for @ShubhaangBot : /ai_5_800000_c_65

Click here: “Portfolio Research Report & Factsheet”.

Click here: “Instructions & Standard Disclosures”.

Stock Group Title : Theme – AIPioneers

Portfolio Size : 5 Equity and ETF Scripts

ETFs Composition Percentage : Commodity ETFs Composition is 65%

Portfolio Risk : Future Risk may Exceed 40%

Minimum Annual Investment : ₹ 8,00,000

Optimal Investment Tenure : 8 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 45%

Backtest – Annualised Risk : Hist. Annualised Risk Exceeding 25%

Backtest – Max. Drawdown : Hist. Max. Drawdown Exceeding 20%

Backtest – Max. Recovery Time : Hist. Max. Recovery Time Exceeding 8 Quarters

Simulation – Lumpsum Multiple : More than 49.5 times in 20 yrs with 99.9% probability

Simulation – Lumpsum Multiple : More than 125 times in 20 yrs with 99% probability

Simulation – Lumpsum Multiple : More than 173 times in 20 yrs with 98% probability

Simulation – Lumpsum Multiple : More than 287 times in 20 yrs with 95% probability

Simulation – Lumpsum Multiple : More than 454 times in 20 yrs with 90% probability

“Investment Objective:
Invest in companies actively leveraging artificial intelligence to drive innovation, efficiency, and competitive advantage across sectors.

Investment Rationale:
Front-Runners in Innovation: AI pioneers are at the forefront of technological disruption, offering substantial growth opportunities driven by their investments in AI across various business verticals.

Enhanced Efficiency and Competitive Edge: Adoption of AI enables these companies to optimize operations, reduce costs, and improve service delivery, translating into superior profit margins and valuation premiums.

Long-term Growth Potential: As AI transforms industries globally, these companies are well-positioned to capitalize on industry-wide shifts, offering higher growth and earnings potential compared to traditional sectors or assets.

Investment Methodology:
1. The broad universe for securities comprises of listed companies within the NSE 500 Index and all ETFs, which are meticulously filtered qualitatively in accordance with the predefined investment objective and investment rationale.
2. Proprietary algorithms, advanced quantitative models, and technical analysis are applied to systematically identify securities with the highest likelihood of delivering superior returns.
3. These models, extensively validated across various asset classes and time horizons, incorporate customized proprietary indicators of momentum, volume, volatility, and trend, ensuring that portfolio construction is entirely driven by objective data and high-quality signals, free from biases, prejudices, or emotional influence.
4. The selection of securities and their respective weightings within the model portfolio is further optimized based on factors including the target portfolio size, ETF allocation, average daily trading turnover, available investment capital, rebalancing frequency, as well as the number of quantitative and technical criteria met by each security.”

Stocks Group Title

Scripts Types

Portfolio Size

ETFs Composition Percentage

Portfolio Risk

Ideal Annual Investment

Optimal Investment Tenure

Rebalancing Frequency

Subscription Tenure

Subscription Plan Fee

Backtest - Sharpe Ratio

Backtest - Annualised Returns

Backtest - Annualised Risk

Backtest - Max. DrawDown

Backtest - Max.Recovery Time

Simulation Lumpsum Multiple

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 and simulation outputs are provided solely as factual data to support comparative assessment of subscription plans.
  3. Simulation results are derived from 100,000 trials and represent potential future scenarios based on normalised inputs (average returns and return volatility) obtained from backtesting.
  4. Users are strongly advised to exercise extreme caution when using this information.
  5. "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.

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 1,00,000 simulation trials using the Monte Carlo method.
  4. Simulation results are derived for probability levels (confidence levels) of 99.9%, 99%, 98%, 95% and 90%.
  5. Simulation values do not predict or reflect future market conditions; they represent only possible future outcomes.
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