How Smart is Momentum Strategy? An Empirical Study for Indian Equities
Smart Beta Investing has revolutionized Investment Management with the ability to offer higher returns with lower costs. The Momentum factor in the Smart Beta Universe has outperformed other popular factors, besides being well documented in the literature, it is found to be pervasive across different geographies and asset classes. In this paper, we practically implemented a long-only Momentum based Investment strategy for the Indian Equity Markets that deliver superior risk-adjusted performance, derived upon comparing multiple strategies across time frames. The results of these backtest demonstrate the clear dominance of Momentum Investing over the Contrarian Style Investing. We also tested a related phenomenon called the Accelerated Momentum, which is described as the change in Momentum for the Indian Equity Markets. We found that the Accelerated Momentum underperforms the traditional Momentum both on an absolute and risk-adjusted basis. We have also tested the excess returns produced by our chosen strategy for asset pricing tests using the CAPM Model, the Fama-French 3 factor model, and the Carhart Model. We found that none of these models was able to fully account the excess returns produced by the strategy and hence we seek Behavioural explanation for this observed phenomenon. The empirical testing for behavioural models for the Indian Equity Markets and transaction cost analysis deserves further attention.
To illustrate the working of the strategy, we show how the strategy would select its constituents based on the latest market data till 31st August 2020 (last available month-end). We assume that we have Rs. 1000 on 31st August 2020, and we will illustrate how this strategy would allocate this amount into chosen stocks.
Step-1: We extract the daily data for Adjusted Closing prices (BSE) for all constituents of the S&P BSE-100 index for the period 1st January 2020 till 31st August 2020.
Step-2: Convert the daily data to monthly data by taking the Adjusted Closing Price as on the last trading day for each month.
Step-3: For each stock, we calculate the Momentum rank based on the Lagged 6 Months' compounded returns, described by the below equation.
In this example, the Momentum return as on 31st August 2020 would be as follows:
Step-4: Rank the stocks based on the above indicator and select the top 10 stocks.
Step-5: The strategy equally weights all the stocks, since we assume that we have Rs. 1000 as on 31st August 2020, we will allocate Rs.100 for each stock. The highest-ranked stocks and the number of shares of each constituent will be as follows:
We see that all the shares are infractions, this is because of the total notional of Rs. 1000 being quite small but in practice, the Notional amount is much higher.
Step-6: Once the constituent stocks are selected and invested for 3 Months (which is the rebalancing Frequency), the return of the strategy will be the average return of all constituents, and the same process is repeated with the new notional (Value of the strategy after three months). E.g. if we assume the randomly generated returns for these stocks for the three months (Aug 31,2020-Nov 30,2020), the New Notional would be computed as follows:
Based on new Notional, the newly selected constituent will have a notional of 96.650 (966.50/10), and the top 10 stocks momentum ranked stocks on 30th November 2020 will be the new constituents calculated as per the described process.