The strategy trades Indian stocks sector-wise, buying outperformers, shorting underperformers, using risk budgeting, maintaining 15% risk allocation, equally weighting sectors, and rebalancing every 30 minutes for intraday momentum.
ASSET CLASS: futures, stocks | REGION: India | FREQUENCY:
Intraday | MARKET: equities | KEYWORD: Momentum, India
I. STRATEGY IN A NUTSHELL
Trade Indian stocks sector-wise, capturing intraday cross-sectional momentum by going long on top 3-day risk-adjusted performers and short on underperformers. Sector weights are equal, and portfolio weights use risk budgeting with 15% total risk and 10% target risk. Rebalancing occurs every 30 minutes.
II. ECONOMIC RATIONALE
Momentum anomalies, driven by behavioral biases and risk factors, persist across markets. This intraday, sector-wise strategy demonstrates that risk-adjusted momentum exists in Indian equities, confirming the robustness and practical relevance of momentum effects for short-term and cross-sectional trading.
III. SOURCE PAPER
Momentum in the Indian Equity Markets: Positive Convexity and Positive Alpha [Click to Open PDF]
Sonam Srivastava, Wright Research; Gaurav Chakravorty, Qplum; Mansi Singhal, Qplum
<Abstract>
We present effective momentum strategies over the liquid equity futures market in India. We evaluate and determine the persistence of the returns at various look-backs ranging from quarterly and weekly to more granular look-backs. We look at a universe of the liquid equity instruments traded across the Indian markets
to evaluate this anomaly. We evaluate momentum across the two datasets based on frequency – daily data and intraday bar data. On the daily scale we compare momentum with other style factors. In the intraday scale we evaluate time series momentum or absolute momentum and cross-sectional momentum or relative momentum. We demonstrate that at the optimal horizon, momentum strategies on securities in India can be a source of uncorrelated alpha. We use active riskbudgeting at a given target risk for portfolio construction. We will show in a separate publication how it outperforms mean-variance optimization.
IV. BACKTEST PERFORMANCE
| Annualised Return | 27.46% |
| Volatility | 17.48% |
| Beta | N/A |
| Sharpe Ratio | 1.54 |
| Sortino Ratio | N/A |
| Maximum Drawdown | -16.4% |
| Win Rate | N/A |
