Journal of Investment Strategies

LETTER FROM THE EDITOR-IN-CHIEF

Ali Hirsa Managing Partner

Sauma Capital LLC & Professor, Columbia University

Welcome to the second issue of the ninth volume of The Journal of Investment Strategies.

In this issue, you will find three papers: one on using strangle strategies in the Indian stock market; one covering small drawdowns and high average and risk-adjusted returns for equity portfolios, using options and power-log optimization based on a behavioral model of investor preferences; and one on time series momentum and momentum trading strategies.

In “Strangle to resuscitate: evidence from India”, our first paper, Peeyush Bangur examines the performance of two strangle strategies at different legs to find the best strategy for consistent profit generation when trading on the Indian stock market index Nifty.The strategies were analyzed using data ranging from 2007 to 2019 on a monthly basis. Both long and short strangle strategies have been used in this analysis. Both strategies are carried out for 141 months in total. Using newly developed payoff formulas for both strangle strategies, the performance up to three different legs has been measured based on monthly success rate, profitability, risk and return. The author’s results show that the success rate, profitability, risk and return of the short strangle are better than those for the long strangle at every leg.

Bangur shows that the success rate of the short strangle option strategy is higher than that of the long strangle strategy at every leg. Further, in terms of points earned on the Nifty, the short strangle is a profitable strategy at every leg, while the long strangle is a loss-making strategy at every leg. The results also indicate that as the leg number increases, the profitability and success rate of the short strangle also increase. In addition, the risk and return analysis of the short strangle strategy shows that the third leg has the lowest risk and the highest return relative to the other two legs, and the regression analysis verifies these results. In short, the author concludes that the short strangle strategy gives the best performance in terms of achieving a higher monthly success rate and larger profits. 

The second paper in the issue is titled “Smaller drawdowns, higher average and risk-adjusted returns for equity portfolios, using options and power-log optimization based on a behavioral model of investor preferences”. In it, Jivendra K. Kale and Tee Lim use a power-log utility optimization algorithm based on a behavioral model of investor preferences, along with either a call or a put option overlay, to reverse the negative skewness of monthly Standard & Poor’s 500 (S&P 500) index returns and to produce portfolios with smaller drawdowns and far higher risk-adjusted returns than the S&P 500 index. All the optimal portfolios have positively skewed returns, which are preferred by investors.

Based on their results, the authors conclude that power-log optimization with option overlays adds to the traditional methods of portfolio construction. It can deliver better downside protection, along with far higher average and risk-adjusted returns, than pure equity portfolios.

Momentum-based investment strategies have attracted strong interest over the past few years, particularly with the growth in alternative risk premium investing, and they present a compelling research topic. In the issue’s third paper, “What’s so special about time series momentum?”, Haotian Cai and Anatoly B. Schmidt apply data-mining techniques and time series analysis to evaluate the historic long-term performance of time series momentum (TSM), to explain its sources of return, and to compare and contrast the TSM trading rule with a buy-and-hold (B&H) strategy and a simple moving average (SMA) strategy. The paper follows a rigorous data-driven approach to analyze the strategy’s performance and draws attention to its characteristics in a succinct form.

The statistics for all ten- and twenty-year periods within the range January 1950– April 2019 show that the SMA strategy outperformed TSM in the past, but since the late 1990s the performance of the two strategies has become very close. The profitability of B&H and the trend-following trading strategies TSM and SMA may be due to the fact that the optimal autoregressive moving average(ARMA) model of the monthly returns for January 1950–April 2019 has a positive mean.

On behalf of the editorial board, I would like to say that we hope you are doing well during the Covid-19 pandemic. We would like to thank our readers for their continued support and keen interest in this journal. We look forward to sharing with you our growing list of practical papers on a broad variety of topics in modern investment strategies that we continue to receive from both academics and practitioners.
 

Smaller drawdowns, higher average and risk-adjusted returns for equity portfolios, using options and power-log optimization based on a behavioral model of investor preferences

The authors use a power-log utility optimization algorithm based on a behavioral model of investor preferences, along with either a call or a put option overlay, to reverse the negative skewness of monthly Standard & Poor’s 500 (S&P 500) index returns…

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