Summary of "How a Quant Fund Manager Manages Risk & Beats Nifty | Ft.Rishabh Nahar | MastersInOne | EP - 67"
How a Quant Fund Manager Manages Risk & Beats Nifty
Ft. Rishabh Nahar | MastersInOne | EP - 67
Key Finance-Specific Content
1. Quant vs Algo Trading
- Algo Trading: Fast-paced, high-frequency trades executed automatically without human intervention.
- Quant Trading: Structured, rule-based investing; may or may not be fully automated. Focuses on longer-term holding periods and incorporates fundamental and technical rules.
- Both approaches are system-driven and free the trader from screen time, enabling more research and strategic thinking.
2. Rishabh Nahar’s Background
- Started as a research analyst (2014-2016) focusing on company fundamentals and valuation models.
- Transitioned from discretionary trading (futures & options) to automation and quant strategies to reduce screen time and emotional bias.
- Emphasizes learning from losses and strictly following system rules rather than subjective calls.
3. Backtesting & First Principles
- Backtesting is necessary but not sufficient; understanding why a system works is critical.
- Example: Momentum strategies work because underlying companies show earnings growth over the long term.
- Market conditions (liquidity, monetary policy) heavily influence system performance.
- Systems that do not understand macro or fundamental drivers risk failure when environments change.
- Post-COVID market influx of traders relying solely on backtests without understanding fundamentals is risky.
4. Portfolio Construction & Strategies
Simple Quant Method Example
- Start with Nifty 50 (market-cap weighted, ~12% CAGR historically).
- Improve by adding fundamental factors:
- Rank NSE 500 companies by Return on Capital Employed (ROCE) and EV/EBITDA, each weighted 50%.
- Pick top 30 companies, equal weight, rebalance annually.
- Backtest from 2000-2017 showed 25-30% CAGR.
- Real portfolio examples: Tata Elxsi (7-8x returns), Page Industries, Eicher Motors.
All Weather Strategy
- Classify market states: risk-on, neutral, risk-off.
- Risk-off involves shifting to safe assets rather than just debt or fixed deposits.
- Portfolio example (2015-25):
- 100% Nifty 50: 11% CAGR, max drawdown 38.44% (COVID 2020).
- 100% Gold: 14% CAGR, max drawdown 22.31%.
- 50% Nifty + 50% Gold: 13.3% CAGR, max drawdown 21%, Calmar ratio doubled from 0.3 to 0.62.
- Adding a momentum ETF (e.g., Nifty 230 Momentum ETF) to the mix (33.33% each) increased returns to ~15% but increased drawdown to 25%.
- Protective puts are used to reduce drawdown further from ~20-25% to 10-12%.
- Strategy involves annual rebalancing; no intra-year churn.
Rotational Strategy (Combining Fundamentals + Technicals)
- Select top 30 companies based on ROCE.
- Monthly rank these 30 companies by average price momentum over last 30, 60, and 90 days.
- Hold top 15 companies each month.
- In bear markets, the system exits but does not enter new positions (sits in cash).
- Reduces drawdowns from 40-45% (buy and hold) to 15-20%.
- Emphasizes simplicity and robustness over complicated indicators.
5. Risk Management & Drawdowns
- Focus on capital preservation is critical.
- Avoid subjective decisions; rely on tested systems.
- Understand maximum drawdown tolerance; e.g., 40% drawdown can cause panic and system abandonment.
- Use diversification across uncorrelated assets (equity, gold, momentum ETFs).
- Use options (puts) for portfolio protection, triggered by simple rules like Nifty crossing below 10 or 20-day EMA.
6. Leverage and Realistic Return Expectations
- High returns (40-50% CAGR) often come from leveraged options trading, not pure alpha.
- Without leverage, realistic CAGR for robust systems is around 20-30%.
- Removing leverage from backtests drastically reduces expected returns.
7. Automation & Tools
- Automation frees up time and reduces emotional trading.
- Recommended backtesting platform: Amibroker (low-code, powerful, supports automation and trade execution).
- Python is good for developers but Amibroker is accessible for non-programmers.
- Data is available freely from NSE and other sources.
- YouTube is a valuable resource for learning backtesting and automation.
8. Investor Psychology & Conviction
- Compounding returns require patience and conviction.
- System returns are not linear; expect flat years and big gains in others.
- Motivation often fades during drawdowns; conviction is key to staying invested.
- Keep improving strategies incrementally (1% better every day).
9. Macro Outlook
- Rishabh expects Nifty to reach 60,000 in 10 years (from ~26,000) due to fiat currency expansion.
- The journey will have volatility; hence the need for quant systems and risk management.
Assets, Instruments, and Sectors Mentioned
- Equity Indices: Nifty 50, Nifty 200, Nifty Low Volatility Index.
- ETFs: Nifty Bees, Gold Bees, Momentum ETFs (Nifty 230 Momentum ETF).
- Stocks: Tata Elxsi, Page Industries, Eicher Motors.
- Options: Put options for portfolio protection.
- Commodities: Gold.
- Sectors: Small cap, mid cap (mentioned in context of risk and returns).
Methodologies / Frameworks Shared
-
Quant Strategy Development:
- Define universe (e.g., NSE 500).
- Rank stocks by fundamental metrics (ROCE, EV/EBITDA).
- Select top N (e.g., 30) stocks, equal weight.
- Rebalance annually.
- Overlay technical momentum filters (30, 60, 90-day price moves).
- Monthly rotate top performers (e.g., top 15 out of 30).
- Sit in cash during bear markets (no new entries if momentum signals absent).
- Use options (puts) to hedge drawdowns triggered by EMA crossovers.
-
All Weather Portfolio Construction:
- Allocate across uncorrelated assets (equity, gold, momentum ETFs).
- Rebalance annually.
- Use protective puts to reduce drawdown.
- Adjust allocations based on risk-on, risk-off environment.
-
Risk Management:
- Measure Calmar ratio (CAGR / Max Drawdown).
- Understand psychological impact of drawdowns.
- Avoid subjective, intuition-based decisions.
- Backtest thoroughly with 20+ years of data.
- Understand macroeconomic drivers behind system performance.
Key Numbers & Performance Metrics
- Nifty 50 CAGR (last 10 years): ~11%, max drawdown ~38.4% (COVID 2020).
- Gold CAGR (2015-now): ~14%, max drawdown ~22.3%.
- 50% Nifty + 50% Gold: CAGR ~13.3%, max drawdown ~21%, Calmar ratio improved from 0.3 to 0.62.
- Adding Momentum ETF: CAGR ~15%, drawdown ~25%.
- Rotational strategy reduces drawdown from ~40-45% to ~15-20%.
- Backtested fundamental + momentum strategy: 25-30% CAGR (2000-2017).
- Portfolio drawdown protection with puts can reduce drawdown to 5-12%.
- Realistic unleveraged returns: 20-30% CAGR.
- Leverage inflates returns but increases risk.
Recommendations & Cautions
- Always understand why a strategy works before deploying.
- Avoid subjective decisions; rely on system rules and backtested data.
- Diversify across uncorrelated assets to smooth returns and reduce drawdowns.
- Use options strategically for downside protection.
- Be patient and build conviction; expect uneven returns.
- Automate trading/investing to reduce emotional bias and free time.
- Start backtesting with accessible tools like Amibroker.
- Focus on capital preservation as much as on returns.
- Understand macroeconomic context—liquidity, money printing, and valuation gaps impact performance.
- Avoid overfitting or relying on short-term backtests without fundamental understanding.
- Small investors should focus on increasing capital through savings and investing systematically.
Disclaimers
The speaker emphasizes this is not direct financial advice but sharing of methods and mindset. Backtesting results are not guaranteed future performance. Leverage increases risk and can distort perceived returns. Investors must develop conviction and risk tolerance before following any system.
Presenters / Sources
- Rishabh Nahar: Quant Fund Manager, SEBI-registered PMS manager, managing ~350 crores INR.
- Vijay Thakkar: Host of MastersInOne podcast, interviewer.
This summary encapsulates the key finance concepts, strategies, and practical insights shared by Rishabh Nahar on quant investing, risk management, portfolio construction, and system development.
Category
Finance
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