About Kristal
Global Technology ETFs is a genetic algorithmic strategy focussed specifically on the Technology sector. The algorithm takes a tactical approach to portfolio construction based on the prevailing market conditions as it assesses a universe of global technology ETFs for various risk and return parameters. The objective of the strategy is to optimise the utility derived from higher returns subject to volatility constraints. The strategy is overseen by an experienced investment committee who reviews the performance of the Kristal on a regular basis and assesses the appropriateness of the risk constraint in any given market scenario.Global Technology ETFs invests to take tactical positions at a more targeted level than traditional style-based investing. It enables investors to acquire a low-cost exposure to technology sector via ETFs that have been characterised by solid risk-adjusted returns. To diversify, a proportion of the holdings provide exposure to international companies.
Why Invest in Global Technology ETFs?
Ideal for medium-high risk profile investors looking for Technology exposure via an actively managed strategy that delivers growth.
When to Invest in Global Technology ETFs?
The Kristal performs best in a growing economic environment.
Returns
| Returns | 1Y | 3Y |
|---|---|---|
| Global Technology ETFs | 142.19% | 37.74% |
Similar Kristals
Projected Returns
Returnsbased on historic SIP returns of 23.40% per annum
Scheme Information
| Sharpe | 1.48 |
| Volatility | 23.05% |
| Max drawdown | -17.70% |
Dealing Information
Strategy - How is this Kristal managed ?
The strategy is dynamically rebalanced on a monthly basis with discretionary oversight of an experienced investment committee. ETFs are carefully chosen using an algorithm in order to deliver optimal risk-adjusted returns based on a medium-high risk growth portfolio. Individual allocation is further driven by model-driven discretionary tactical trades.
References
Genetic algorithm is a concept that is inspired by Charles Darwin’s theory of the process of natural selection. Such algorithms imitate natural biological processes like mutation, inheritance, crossover and selection. Genetic algorithms are especially good at solving optimisation problems. This makes them highly applicable in the field of finance. They can solve the problem of portfolio rebalancing optimization efficiently.
