Testing event-based day of the week anomaly and trading opportunities: Evidence from Indian sectoral indices

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The study is an attempt to examine the day-of-the-week anomaly of fourteen Indian sectoral indices and identify profitable opportunities, considering multiple positive and negative events. The aim of this study is to analyze the day-of-the-week effect on fourteen Indian sectoral indices and find profitable opportunities while considering multiple events that have positive and negative impacts. The study takes into consideration event-based anomalies, both national and global, and provides timing for trading to generate abnormal returns from the market. At first, dummy variable regression analysis was used to understand the initial anomalies. Later, time-varying symmetrical and asymmetrical volatility models, such as Generalized Autoregressive Conditional Heteroscedasticity (1, 1) and Exponential Generalized Autoregressive Conditional Heteroscedasticity (1, 1) were applied to determine the short-term and long-term volatility persistence. These models capture the leverage effect from various events that occurred during the study. The results showed mixed outcomes during multiple positive and negative shocks. After the recession, anomalies were observed across all sectoral indices, except for commodities, energy, and information technology. During the scam period, anomalies occurred in all sectors, except for consumer durables, financial services, and information technology. However, after the new government took over, anomalies persisted in all sectors. During the pandemic, anomalies persisted in all sectors except for finance, IT, pharmaceuticals, and services. Hence, national and global events have shown varied impacts on the Indian markets. The study provides investors with implications on strategies and timing techniques for planning their investments in different sectors of the Indian economy.

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    • Table 1. Time periods under consideration
    • Table 2. Descriptive statistics (mean average)
    • Table 3. Unit root results
    • Table 4. OLS results
    • Table A1. GARCH results for selected periods
    • Table A2. EGARCH results for selected periods
    • Conceptualization
      Parul Bhatia, Sudhi Sharma, Vaibhav Aggarwal
    • Formal Analysis
      Parul Bhatia, Sudhi Sharma, Niyati Chaudhary
    • Investigation
      Parul Bhatia, Vaibhav Aggarwal
    • Methodology
      Parul Bhatia
    • Validation
      Sudhi Sharma
    • Visualization
      Sudhi Sharma, Niyati Chaudhary
    • Writing – review & editing
      Sudhi Sharma, Vaibhav Aggarwal, Niyati Chaudhary
    • Resources
      Vaibhav Aggarwal
    • Supervision
      Vaibhav Aggarwal
    • Data curation
      Niyati Chaudhary
    • Writing – original draft
      Niyati Chaudhary