Purpose The novel coronavirus (COVID-19) leaves Indian business teetering on the edge of survival. This paper aims to set out to assess the impact of the pandemic shocks on the small and medium business segments in India. The research also explores the strategies that potentially take the segments back to recovery and growth. Design/methodology/approach The findings draw on the perspectives of academic and business people, and the authors use linear and nonlinear regression modelling under three recovery scenarios to support our arguments. Findings Evidence suggests that the shocks to business are manifold and the severity of most of the issues will aggravate as the recovery prolongs. Practical implications The paper explains the rationale of realistic strategies and compares its effects across potent recoveries. The findings are useful for both academics and business and relates to the strategic decisions that would be taken by small and medium enterprises to expedite recovery from the crisis. Originality/value The research is unique in surveying the academics and entrepreneurs about the impact of COVID-19 on Indian business.
This paper searches further evidence for the relation between the time varying macroeconomic conditions and stock returns in India using monthly data during the post 2000 period. Unlike other research in the area, the study uses industry level stock price data on six sectors namely Banking, Energy, FMCG, Information Technology, Pharmaceuticals and Automobiles. Data availability and diverging business cycle sensitivity constitute the rationale behind the selection of industry groupings. Empirical methodology involves a multi-factor modeling using Generalized Auto Regressive conditional Heteroskedasticity (GARCH) model. The results of the study proved that the expected premium on stock market investments in India was time varying and has been affected by the time varying conditional volatilities of macroeconomic factors. The impact of economic changes found different across the industries and the sectoral variations in stock returns confirm the potentials of industry allocation for the diversification of investment risks.
Purpose Research on price extremes and overreactions as potential violations of market efficiency has a long tradition in investment literature. Arguably, very few studies to date have addressed this issue in cryptocurrencies trading. The purpose of this paper is to consider the extreme value modelling for forecasting COVID-19 effects on cryptocoin markets. Additionally, this paper examines the importance of technical trading indicators in predicting the extreme price behaviour of cryptocurrencies. Design/methodology/approach This paper decomposes the daily-time series returns of four cryptocurrency returns into potential maximum gains (PMGs) and potential maximum losses (PMLs) at first and then tests their lead–lag relations under an econometric framework. This paper also investigates the non-random properties of cryptocoins by computing the incremental explanatory power of PML–PMG modelling with technical trading indicators controlled. Besides, this paper executes an event study to identify significant changes caused by COVID-19-related events, which is capable of analysing the cryptocoin market overreactions. Findings The findings of this paper produce the evidence of both market overreactions and trend persistence in the potential gains and losses from coins trading. Extreme price behaviour explains volatility and price trends in crypto markets before and after the outbreak of a pandemic that substantiate the non-random walk behaviour of crypto returns. The presence of technical trading indicators as control variables in the extreme value regressions significantly improves the predictive power of models. COVID-19 crisis affects the market efficiency of cryptocurrencies that improves the usefulness of extreme value predictions with technical analysis. Research limitations/implications This paper strongly supports for the robustness of technical trading strategies in cryptocurrency markets. However, the “beast is moving quick” and uncertainty as to the new normalcy about the post-COVID-19 world puts constraint on making best predictions. Practical implications The paper contributes substantially to our understanding of the pricing efficiency of cryptocurrency markets after the COVID-19 outbreak. The findings of continuing return predictability and price volatility during COVID-19 show that profitable investment opportunities for cryptocoin traders are prevailing in pandemic times. Originality/value The paper is unique to understand extreme return reversals behaviour of cryptocurrency markets regarding events related to COVID-19 breakout.
This research, under Engle-Granger Co-integration framework, examines the hedging efficiency of Indian rubber future markets during the period 2004-2017. The essence of this study is to seek evidence for the effects of global financial crisis of 2008 on the efficiency of rubber futures in hedging price risks of spot rubber in India. The study proved the hedging efficiency of rubber futures during both pre and post recession periods. However, increased price volatility of Indian rubber after recession heightened risk exposure to market participants that eventually lead to unexpected changes in the hedging efficiency of rubber futures. The research concludes with a suggestion that writing of rubber futures in India allows traders to hedge risk exposures in spot market along with the potentials of arbitrage gains.
PurposeEquity research in experimental psychology reveals investors' overreactions to bad news events. This study of asymmetric price structures in equity markets investigates whether such behavior predicts stock returns in an emerging market of India.Design/methodology/approachThe research decomposes Bombay Stock Exchange (BSE) Sensex returns into Extremely Positive Returns (EPR) and Extremely Negative Returns (ENR) based on extreme values at first and then tests their lead–lag relations.FindingsThe empirical finding is consistent with the existing evidence of asymmetric news effects on stock returns in India. In precise, ENR robustly predicts one-month-ahead EPR for the sample period from January 1991 to March 2020. This predictive power persists even in the presence of popular valuation ratios and business cycle variables.Practical implicationsThe paper explains the rationale of extreme value modeling in price forecasting. Investors can find additional utility gains from market cycle information while predicting extreme returns in Indian stock market.Originality/valueThe paper is unique to understand business cycle effects in extreme return reversals in emerging markets.
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