Raghavendra

The investment decisions are subjected to risk and return of the financial asset. Options strategies help employ a suitable strategy to balance the risk-return trade-off. The study analyzes the risk-return trade-off of the long straddle, long strangle, long call butterfly (LCB), short straddle, short strangle, and short call butterfly (SCB) strategies. Moreover, it measures the impact of strategy risk and options premiums on strategy return using panel data analysis. Additionally, the study evaluates the performance of options strategies using the excess returns to risk approach under neutral and volatile market conditions. This paper considered companies of top-six sector indices of the National Stock Exchange from 2009 to 2020 and collected data of 18,720 option contracts and 3,744 observations for each strategy (22,464 observations). The study revealed that risks of long straddle and long strangle strategies have a positive impact, and options premiums negatively influence their payoff. ATM call premiums positively affect LCB payoff, while OTM and ITM call premiums positively influence SCB payoff. However, the risks of butterfly strategy did not influence its payoff. The risk of short straddle and short strangle strategies negatively influenced the payoff and were considered riskier strategies. Moreover, short straddle and short strangle strategies enhanced excess returns under both market conditions. The results would help the investors in choosing the appropriate strategy by analyzing the impact of risk on the payoff and the ability to enhance excess returns to the risk of various options strategies to incorporate in their investment.

Personality traits are qualities that make a person distinctive and describe stable behavior patterns. Therefore, understanding the influence of personality traits on behavioral intention will help predict investors’ investment decisions. This study aims to assess the impact of personality traits, i.e., openness to experience, neuroticism, conscientiousness, agreeableness, and extraversion, on investors’ behavioral intentions. Moreover, it assesses the mediating effect of attitude, subjective norms, and perceived behavioral control between investors’ personality traits and behavioral intention. The study employed a structured questionnaire on a sample of 413 retail investors. Further, obtained data were empirically examined on Smart-PLS 3.3 using the PLS-SEM method. The study found that perceived behavioral control, subjective norms, and attitude positively affected behavioral intention. However, the personality traits did not influence the intention directly. Further, mediation analysis revealed that attitude and subjective norm fully mediated the relationship between extraversion, neuroticism, openness, and intention. In contrast, attitude and subjective norms did not exhibit a mediating relationship between agreeableness, conscientiousness, and intention. Finally, perceived behavioral control fully mediated the relationship between personality traits and intention, except for conscientiousness. The study contributes by extending the applicability of the theory of planned behavior by examining the impact of big-five personality traits on behavioral intention and mediating the role of the theory of planned behavior’s dimension between personality traits and intention.

The interrelationship between equity, bond, commodity and forex movements can provide investors with abundant trading opportunities regardless of whether one market is trending upward or downward. Hence, to understand the interlinkage between markets, this study examines the long-run and causal linkage between forex, G-sec bonds, oil prices, gold rates, foreign institutional investment (FII) flows, and equity market and sectoral index returns. Daily time-series data from August 2012 to August 2021 were considered for empirical analysis. Johansen’s cointegration test revealed that foreign exchanges like USD, Euro, GBP and Yen, oil and gold rates, G-bond returns and FII flows were significantly cointegrated with the stock market and sectoral indices in the long run. Further, Granger causality found a uni-directional relationship between forex rates (i.e., USD, Euro, Yen) and the market, as well as sectoral indices, except Nifty 50 and Nifty IT indices. Oil price movements were found to effectively predict future price changes of Nifty consumer durables, auto, IT indices. Gold prices are useful to predict Nifty-Auto, Bank, Financial Services, Oil & Gas and PSU. The study also found a bi-directional relationship from FII inflows to the stock market and sectoral indices. The findings suggest that forex rates, oil prices and FII flows significantly affect India’s stock market and sectoral performance. The study contributes to the existing literature by comprehensively examining the interlinkage between commodities such as oil and gold, foreign exchanges like USD, Euro, GBP and Yen, G-bond, FII flows and the stock market, and fourteen sectoral indices in the Indian context.