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Investment Strategy based on Market Beta for S&P 500 Companies- SYSTEMATIC INVESTMENT STRATEGIES REPORT

SYSTEMATIC INVESTMENT STRATEGIES REPORT

EXECUTIVE SUMMARY

The growing fame of algorithmic trading can be justified by the obvious benefits it has over quantitative trading. In this report, I have presented a very simple algorithmic trading strategy based on Market Beta that exhibit some promising enough results to qualify for a full-fledged backtesting that I was unable to perform due to time constraints.

The idea behind my strategy is very simple and was conceived during my work on one of the assignments for the topics course. I found pretty convincing arbitrage opportunities for Overnight and Last 30 minutes returns based on Market Beta and VIX values. With this statistical arbitrage in mind, I thought of exploiting the tradeoff between risk and return.

A higher market beta implies that the volatility of asset compared to the market is more and vice versa. Therefore, I calculated rolling market beta based on the past quarter for each asset in S&P500 and divided the companies into quantiles based on their beta values. Afterwards, a simple long/short strategy was adopted by going long for the assets in the top quantile and going short for 2nd lowest quantile.

Although, the strategy seems pretty innocuous but it produces significant results in terms of annual returns (9.2%) and Sharpe/Sortino (1.47 & 2.58) ratios. The fundamental relationship that this strategy exploits is that higher risk generates higher return. Since volatility is generally conceived to be a measure of risk, which may not often be the case, the strategy goes long for the stocks that have high beta for the past quarter and sell short the stocks that have the low beta value. However, the beta values are calculated using a rolling regression, therefore, the strategy have significantly lower drawdowns with high positive annual returns. Given the fact that the strategy was tested for a period of past 14 years, it can be considered robust enough against the stress events.

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