10 Equity and ETF Scripts Long Term Growth Portfolio | Theme – AIPioneers | Rebalancing Monthly | Commodity ETFs Composition is 30% | 12 Months Subscription Plan

₹11,125.00 for 1 year

Telegram Bot Command for @ShubhaangBot : /ai_10_445000_c_30

Portfolio Parameters

Parameter	Value
Scripts Group Title	Theme – AIPioneers
Portfolio Size	10 Equity and ETF Scripts {7 Equities and 3 Commodity ETFs}
ETFs Composition Percentage	Commodity ETFs Composition is 30%
Portfolio Risk	Future Risk may Exceed 55%
Minimum Annual Investment Capital	₹ 4,45,000
Optimal Investment Tenure	11 or more years.
Rebalancing Frequency	Rebalancing Monthly
Subscription Tenure	12 Months Subscription Plan
Backtest – Sharpe Ratio	Hist. Sharpe Ratio above 1.2
Backtest – Annualised Returns	Hist. Annualised Returns Under 45%
Backtest – Annualised Risk	Hist. Annualised Risk Exceeding 25%
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 32 times in 20 yrs with 99.9% probability

Probability Levels (%)	20 Years	15 Years	10 Years	5 Years	3 Years	1 Year
99.9% Probability	** 32.38	8.31	2.37	0.83	0.62	0.56
99% Probability	88.10	19.92	4.85	1.38	0.92	0.71
98% Probability	126.96	27.28	6.28	1.65	1.05	0.77
95% Probability	220.20	43.84	9.26	2.17	1.30	0.88
90% Probability	362.08	67.30	13.09	2.77	1.57	0.98
80% Probability	677.73	115.28	20.20	3.75	1.98	1.12
75% Probability	872.96	143.14	24.03	4.23	2.17	1.18
50% Probability	4,458.83	545.58	66.73	8.17	3.53	1.52

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

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

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.

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.
Systematic Identification: Proprietary algorithms, advanced quantitative models, and technical analysis are utilized to systematically identify securities with a high probability of delivering superior returns.
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.
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.

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.
Backtest results and Simulation results should never be used as a basis for comparing the actual performance of other intermediaries.
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.
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.
Users are strongly advised to exercise extreme caution when using this information.
These back-test results and simulation have not been verified by PaRRVA (Past Risk and Return Verification Agency) or any other SEBI-recognized agency.
The impact of periodic portfolio rebalancing and transaction costs has been factored into these back-test results.
"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."

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.
Portfolios are rebalanced once monthly.
Portfolio risk level and Sharpe ratio are assumed to remain unchanged over the 20-year period.
Other than the initial lump sum investment, no additional contributions or withdrawals are made over the 20 years period.
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.
Earnings from dividends are ignored.
Simulation results are not adjusted for inflation rates.
Effects of taxes on the capital appreciation (Capital Gains Tax & Taxes on Dividends) are not accounted.

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