Testing the genetic algorithm based portfolio optimisation strategy for passive funds: Evidence from India
This repository holds the data and jupyter notebooks used in the project.
Figure 1. Infographic abstract showing the various steps in the workflow
The recent boom in product innovations and technology assisted investment management strategies are strengthening the case for activeness in passive funds wherein either the passive funds exhibit activeness in their investment strategies or act as the building blocks in other active portfolios. We test a genetic algorithm (GA) based portfolio optimisation strategy for a passive fund designed to track the Nifty 50 index in the Indian financial markets. We demonstrate that indeed the GA based portfolio optimisation strategies can be employed in the Indian passive investing ecosystem to generate superior risk-adjusted returns compared to the benchmark. The strategy is able to beat the benchmark in the bullish market when compared on the basis of Sharpe ratio and M2 measure. However, it slightly underperforms the benchmark in the bearish market and consistently underperforms the benchmark on the Maximum Drawdown (MDD) risk measure in both bullish and bearish markets. Hence, the strategy may not be successful alone and a combination of GA and other technical or quantitative strategies may help with better downside protection while generating superior risk-adjusted returns in the medium- to long-term.
genetic algorithm, portfolio optimisation, passive funds, Sharpe ratio, M2 measure, maximum drawdown