This study examines the impact of white-collar scams on the Indian stock market using a quantitative research approach, analyzing risk, return, and volatility patterns before and after major financial frauds. It focuses on three significant cases: the Punjab National Bank (PNB) fraud, the Yes Bank crisis, and the Adani Group allegations. Findings indicate that all three scams led to sharp declines in cumulative average abnormal returns (CAAR), with Yes Bank experiencing the most severe impact but also the strongest recovery due to regulatory intervention. Adani’s stocks showed moderate post-scandal recovery, while PNB faced prolonged investor distrust and weaker rebounds. Yes Bank exhibited the highest pre-scam underperformance, whereas Adani experienced the greatest post-scam underperformance, signaling significant market disruption. Volatility analysis suggests that while Adani’s stock shifted from a strong sell position to uncertain fluctuations, Yes Bank and PNB continued to experience selling pressure after their respective scandals. The study highlights the crucial role of regulatory actions in restoring market confidence, as seen in Yes Bank’s recovery, while Adani and PNB faced sustained investor skepticism.
Contents
Abstract
1. Introduction
2. Literature Review
3. Data Analysis and Interpretation
4. Limitations of the Study
5. Conclusion
References
ABSTRACT
This study examines the impact of white-collar scams on the Indian stock market using a quantitative research approach, analyzing risk, return, and volatility patterns before and after major financial frauds. It focuses on three significant cases: the Punjab National Bank (PNB) fraud, the Yes Bank crisis, and the Adani Group allegations. Findings indicate that all three scams led to sharp declines in cumulative average abnormal returns (CAAR), with Yes Bank experiencing the most severe impact but also the strongest recovery due to regulatory intervention. Adani’s stocks showed moderate post-scandal recovery, while PNB faced prolonged investor distrust and weaker rebounds. Yes Bank exhibited the highest pre-scam underperformance, whereas Adani experienced the greatest post-scam underperformance, signaling significant market disruption. Volatility analysis suggests that while Adani’s stock shifted from a strong sell position to uncertain fluctuations, Yes Bank and PNB continued to experience selling pressure after their respective scandals. The study highlights the crucial role of regulatory actions in restoring market confidence, as seen in Yes Bank’s recovery, while Adani and PNB faced sustained investor skepticism.
Keywords: Stock market, Scams, Risk-return
1. INTRODUCTION
White-collar scams in the stock market refer to fraudulent activities carried out by individuals, corporations, or financial professionals who manipulate stock prices, deceive investors, or exploit insider knowledge for unlawful financial gains. These scams are typically non-violent but involve complex financial deceptions that can cause significant losses to investors, financial institutions, and the overall market. They often exploit gaps in regulatory oversight, investor trust, and market mechanisms.
The stock market is a financial marketplace where investors buy and sell shares of publicly traded companies. It serves as a vital component of the economy, enabling businesses to raise capital for growth while providing investors opportunities to earn returns through stock appreciation and dividends. The market operates through major exchanges such as the New York Stock Exchange (NYSE) and Nasdaq, facilitating the trade of stocks, bonds, and other securities. It is influenced by various factors, including economic conditions, corporate performance, interest rates, and investor sentiment. The stock market is divided into two segments: the primary market, where companies issue new shares through initial public offerings (IPOs), and the secondary market, where existing stocks are traded among investors. While investing in the stock market can offer significant financial growth, it also involves risks due to market fluctuations. Successful participation requires research, strategy, and a long-term perspective. The stock market is a financial marketplace where investors buy and sell shares of publicly traded companies. It serves as a vital component of the economy, enabling businesses to raise capital for growth while providing investors opportunities to earn returns through stock appreciation and dividends. The market operates through major exchanges such as the New York Stock Exchange (NYSE) and Nasdaq, facilitating the trade of stocks, bonds, and other securities. It is influenced by various factors, including economic conditions, corporate performance, interest rates, and investor sentiment. The stock market is divided into two segments: the primary market, where companies issue new shares through initial public offerings (IPOs), and the secondary market, where existing stocks are traded among investors. While investing in the stock market can offer significant financial growth, it also involves risks due to market fluctuations. Successful participation requires research, strategy, and a long-term perspective.
1.2 RESEARCH GAP
The existing literature on financial frauds and stock market reactions primarily focuses on large-scale scams such as the Harshad Mehta Scam, the Ketan Parekh Stock Market Scam, and the Satyam Scandal. While these studies provide valuable insights into market manipulation and financial irregularities, limited research has been conducted on the impact of recent white-collar scams post-2015. The Punjab National Bank Fraud, the Yes Bank Scam, and the Adani Group Allegations have had significant consequences on investor sentiment, market stability, and regulatory responses. However, there is a lack of empirical evidence assessing how these scams have influenced stock market risk, return patterns, and volatility during the pre-and post-event periods. Additionally, comparative analysis of these scams against benchmark indices remains underexplored, leaving a critical gap in understanding their broader implications on the equity market. This study aims to bridge this gap by conducting a comprehensive analysis of stock market behavior before and after these significant financial scandals.
1.3 NEED OF THE STUDY
White-collar scams have repeatedly posed challenges to market integrity, investor confidence, and economic stability. In recent years, financial frauds such as the Punjab National Bank Fraud (2018), the Yes Bank Scam (2020), and the Adani Group Allegations (2023) have highlighted vulnerabilities in India's banking and corporate governance frameworks. These scandals have led to regulatory interventions, market disruptions, and long-term effects on stock market volatility. Understanding how such scams impact stock returns, risk levels, and volatility trends is essential for investors, policymakers, and regulatory authorities. This study is crucial as it will help identify patterns in market behavior before and after major financial scandals, providing insights for better risk management and regulatory reforms. By addressing these aspects, this study will contribute to the existing body of knowledge and assist in strengthening financial transparency and market efficiency.
1.4 OBJECTIVES OF THE STUDY
1.4.1 To examine the risk and return dynamics of the stock market pre and post-selected white-collar scams affected stocks.
1.4.2 To evaluate the impact of selected white-collar scams affected stocks on the equity market during the pre-and post-event periods.
1.4.3 To compare the market performance of selected white-collar scam-affected stocks with benchmark indices in the pre-and post-event periods.
1.4.4 To analyze the volatility effect of selected white-collar scam-affected stocks volatility in the post-event period.
1.5 HYPOTHESIS OF THE STUDY
1.5.1 Null hypothesis (H02): The selected white-collar scams have no significant impact on the equity market during the pre-and post-event periods.
1.5.2 Null hypothesis (H03): There is no significant difference in the market performance of stocks affected by the selected white-collar scams compared to benchmark indices in the pre-and post-event periods.
1.5.3 Null Hypothesis (H04): The selected white-collar scam-affected stocks do not have a volatility effect during the pre-and post-event periods.
1.6 SCOPE OF THE STUDY
The present study, "Impact of White-Collar Scams on the Stock Market Concerning India," focuses on analyzing the financial and market implications of major white-collar scams that have occurred in India after 2015. Specifically, the study examines the impact of the Punjab National Bank Fraud (2018), the Yes Bank Scam (2020), and the Adani Group Allegations (2023) on the Indian stock market. This research aims to assess how these financial frauds have influenced stock returns, risk levels, and market volatility during the pre-and post-event periods. It investigates the volatility patterns and regulatory responses triggered by these scams, providing valuable insights for investors, policymakers, and financial analysts.
1.7 RESEARCH METHODOLOGY
1.7.1 Research Approach
This study adopts a quantitative research approach to analyze the impact of white-collar scams on the Indian stock market. The study utilizes statistical techniques to measure risk, return, and volatility patterns in the equity market before and after the occurrence of selected financial frauds.
1.7.2 Sample Selection
The study focuses on major white-collar scams that occurred in India after 2015. The selected scams and their respective announcement dates are:
1.7.1. Punjab National Bank (PNB) Fraud – February 14, 2018
o A ₹11,000 crore fraud involving Nirav Modi and Mehul Choksi, was officially disclosed by Punjab National Bank through a regulatory filing.
1.7.2. Yes Bank Crisis – March 5, 2020
o The Reserve Bank of India (RBI) imposed a moratorium on Yes Bank, restricting withdrawals to ₹50,000 per account and taking control of the bank’s board due to financial mismanagement.
1.7.3. Adani Group Allegations – January 24, 2023
o Hindenburg Research released a report accusing Adani Group of stock manipulation and financial fraud, leading to a significant decline in Adani stocks and market turbulence.
1.8 Data Collection and Sources
This study relies on secondary data obtained from reputable financial platforms, including Investing.com and Yahoo Finance. The data comprises stock prices, index values, and historical market performance.
1.9 Event Analysis
The study employs an event study methodology to assess the stock market's reaction to white-collar scams. The analysis considers a 90-day pre-event period and a 90-day post-event period for each selected scam to evaluate its effect on market returns and volatility.
1.9.1 Variables Used
1.9.1.1.Independent Variables
● PNB Stock Price (Punjab National Bank)
● Yes Bank Stock Price
● Adani Enterprises Stock Price
1.9.1.2 Dependent Variable
● Nifty 50 Benchmark Index (Market Returns)
1.9.2. STATISTICAL TOOLS
1.9.2.1 Return Calculation
● Daily Stock Return (Rt):
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Risk (Volatility) Calculation
● Standard Deviation (σ) – Measures stock risk:
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1.9.2.2. Cumulative Average Abnormal Return (CAAR)
The Cumulative Average Abnormal Return (CAAR) is used in event studies to measure the overall impact of an event (e.g., a white-collar scam) on stock returns over a period. It aggregates the Average Abnormal Returns (AAR) over multiple event windows to assess market reaction.
Step-by-Step Calculation of CAAR
Step 1: Define the Event Window
● Identify the event date (t = 0) – the scam announcement date.
● Define pre-event (-90 to -1 days) and post-event (+1 to +90 days) periods.
Step 2: Collect Stock and Market Data
● Gather daily closing prices of scam-affected stocks (PNB, Yes Bank, Adani Enterprises) and the Nifty 50 index (benchmark).
● Compute daily returns using:
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Step 3: Calculate Expected Returns (ERt)
● Estimate expected returns using the Market Model:
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Where α and β are estimated from historical data.
Step 4: Compute Abnormal Returns (ARt)
● Measure deviation of actual return from expected return:
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Step 5: Calculate Average Abnormal Return (AARt)
● Compute AAR across the selected stocks:
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Step 6: Compute Cumulative Average Abnormal Return (CAAR)
● The sum of AAR over the event window:
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1.9.2.3 Pair t-test
A Paired T-test is used to compare the means of two related groups, such as measurements taken from the same subjects under different conditions. For example, it can be used to analyze the effect of a treatment by comparing measurements taken before and after the treatment for the same group of individuals. This test helps determine if there is a statistically significant difference between the paired observations, accounting for the correlation between them.
1.9.2.4 Autoregressive Conditional Heteroskedasticity (ARCH)
The Autoregressive Conditional Heteroskedasticity (ARCH) model is designed to model and analyze time series data with volatility clustering—periods of high and low volatility. Unlike traditional models that assume constant variance, ARCH accounts for changing variance over time by allowing current volatility to depend on past error terms. This feature makes the ARCH model particularly useful in finance and economics, where asset returns often exhibit periods of fluctuating volatility. By capturing this dynamic volatility, ARCH models provide a more accurate representation of risk and can improve forecasts in markets or datasets with volatility effects.
2. REVIEW OF LITERATURE
2.1. Vividha Gurung and Chander Mohan Gupta analyze the $2 billion Satyam scam, highlighting flaws in India's corporate governance and auditing. They stress the need for stricter laws, enforcement, and transparency to prevent future fraud.
2.2. Insider Trading and Market Integrity – Sreekumar Ray examines the impact of insider trading on India’s stock market, citing cases like Harshad Mehta and Ketan Parekh. He calls for stricter enforcement and reforms to ensure transparency and protect investor confidence. a. 4o
2.3. Banking Frauds in India – Deeksha Upadhyay (2019) examines the rise of banking frauds, including internet banking, ATM scams, and identity fraud, citing cases like Nirav Modi and Vijay Mallya. The article stresses the need for stricter regulations and vigilance to protect economic stability and public trust.
2.4. Corporate Governance and the Satyam Scandal – Nishant Chaturvedi & Karandeep Makkar (2011) analyze the Satyam fraud, exposing ethical lapses, weak auditing, and regulatory failures. They call for stricter governance measures, inspired by global frameworks, to prevent future corporate scandals in India. b. 4o
2.5. Securities Fraud in India – A Criminological Examination of Insider Trading (Journal of Informatics Education and Research, 2024) analyzes insider trading’s impact on market integrity and investor trust. It calls for stronger enforcement and regulatory reforms to curb securities fraud in India.
2.6. The Evolution of Insider Trading in India – Ayan Roy (2010) traces insider trading’s shift from a once-accepted practice to a criminal offense, highlighting SEBI’s role in regulation. The report calls for stronger legal frameworks and stricter enforcement to align with global standards.
2.7. Identifying White-Collar Crimes in India – Aarav Singhania (2024) examines financial crimes like fraud, corruption, and money laundering, highlighting detection methods such as AI and forensic accounting. The study emphasizes the need for a tech-driven, integrated approach to combat these crimes effectively. c. 4o
2.8. Banking Frauds in India: A Conceptual Overview – Prakash Kumar Pradhan & Miss Goutami Bai (2018) analyze major banking frauds like those involving Vijay Mallya and Nirav Modi, highlighting their economic impact. The study calls for stricter monitoring, enhanced security, and stronger regulations to restore public trust.
2.9. The Nirav Modi-PNB Fraud: A Case Study – Fehmina Khalique & Smriti Srivastava (2024) analyze the Punjab National Bank scam, exposing corporate governance failures and weak internal controls. The study calls for stricter regulations, stronger oversight, and proactive intervention to prevent future fraud.
3. DATA ANALYSIS & INTERPRETATION
3.1 Adani Risk and Return Pre and post-Scam Analysis
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3.2. YES Bank Risk and Return Pre and Post-Scam Analysis
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3.3 PNB Risk and Return Pre and post Scam Analysis
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3.4. CAAR of Adani Pre and post
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3.5. CAAR of YES Bank Pre and post
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3.6 CAAR of PNB Pre and post
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3.7 Paired Samples Test of Pre-Adani with pre benchmark
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3.8 Paired Samples Test of Post Adani with Post Benchmark
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3.9 Paired Samples Test of Pre YES Bank with pre-benchmark
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3.10. Paired Samples Test of Post YES Bank with post benchmark
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3.11. Paired Samples Test of Pre PNB with pre-benchmark
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3.12. Paired Samples Test of Post PNB with post-benchmark
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3.13. ARCH model of Adani Pre scam
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3.14. Adani arch model for post
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3.15 Yes bank arch model for pre
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3.16 Yes bank arch model for post
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3.17 PNB Arch model for pre
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3.18 PNB arch model for post
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4 LIMITATIONS OF THE STUDY
4.1 Government interventions, regulatory actions, and corporate restructuring after a scam may influence stock performance, making it difficult to isolate the exact impact of the scam itself.
4.2 Stock market movements are influenced by multiple factors such as global events, economic policies, and investor sentiment, which may overshadow the impact of the scams being studied.
4.3 The study compares stock movements with the Nifty 50 index, which may not always be the best representation of broader market reactions, especially for sector-specific frauds.
5. CONCLUSION:
White-collar scams significantly impact the Indian stock market, causing volatility, affecting investor confidence, and influencing long-term stock performance. Among the cases analyzed, Yes Bank experienced the most severe market shock, marked by extreme fluctuations and a sharp decline, though it later rebounded strongly due to regulatory intervention. Adani’s scandal resulted in notable short-term losses and ongoing uncertainty, indicating a moderate impact. PNB faced the most prolonged investor skepticism, with weak post-scandal recoveries and persistent negative trends. Despite Yes Bank’s underperformance before the scam, it demonstrated the strongest recovery, highlighting the role of swift regulatory actions in restoring trust. In contrast, Adani and PNB struggled with prolonged volatility and inconsistent returns, showing that market confidence takes longer to rebuild without strong interventions. The study also notes that pre-scandal periods were marked by high selling pressure across all three stocks. While Adani’s post-scandal performance remained uncertain, Yes Bank and PNB continued to experience negative volatility. Overall, the findings suggest that white-collar scams cause immediate stock market disruptions, with recovery patterns varying based on regulatory responses, investor sentiment, and the financial stability of affected firms.
REFERENCES
1. Chaturvedi, N., & Makkar, K. (2011). Satyam scam: Lessons for corporate legislation. Social Science Research Network. https://doi.org/10.2139/ssrn.1878507
2. Debnath, B., Shantharam, S. A., Dwarampudi, A. R., & Vidya, D. S. (2020). A study on the causes of the financial crisis in the Indian aviation industry with special reference to Kingfisher Airlines.
3. Goel, A., & Saini, P. (2015). Major scams in the Indian capital market.
4. Goel, S. (2014). Protection of whistle-blowers in India: A corporate perspective. Social Science Research Network. https://doi.org/10.2139/ssrn.2530397
5. Gurung, V., & Gupta, C. M. (2019). A review on Satyam Computer failure: Lessons for corporate governance and the world. Social Science Research Network. https://doi.org/10.2139/ssrn.3370291
6. Khalique, F., & Srivastava, S. (2024). Nirav Modi: A case study on banking frauds and corporate governance.Lloyd Business Review, 3 (1). https://doi.org/10.56595/lbr.v3i1.19
7. Mall, A. (2024). Insider trading in India: A study of the emerging issues of insider trading with reference to securities laws. Social Science Research Network. https://doi.org/10.2139/ssrn.4700517
8. Pradhan, P. K., & Bai, M. G. (2018). A conceptual study on banking frauds in India.
9. Ray, S. (2015). Insider trading: A white-collar crime and its impact on the share market.International Journal of Business. https://doi.org/10.26643/think-india.v17i3.7806
10. Roy, A. (2010). Project report on insider trading in India. Social Science Research Network. https://doi.org/10.2139/ssrn.1620386
11. Securities fraud in India: A criminological examination of insider trading in India. (2024). Journal of Informatics Education and Research, 4 (3). https://doi.org/10.52783/jier.v4i3.1534
12. Sharma, P., Gupta, B., & Setiya, T. (2019). A critical analysis of the Punjab National Bank scam and its implications.
13. Singhania, A. (2024). White-collar crime identification in India: A critical study.Indian Journal of Law, 2 (3). https://doi.org/10.36676/ijl.v2.i3.31
14. Sree, V. D. (2019). Impact of white-collar scams on stock market behavior in India.International Journal for Research in Applied Science and Engineering Technology. https://doi.org/10.22214/ijraset.2019.9037
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- A. M. Joseph Kumar (Author), Atla Sai Vardhan (Author), Gouni Sai Anmol (Author), Kola Harsha Vardhan (Author), Maryala Hari Narayana (Author), M. Sunanda (Author), 2024, Impact of White-Collar Scams on the Stock Market Concerning India, Munich, GRIN Verlag, https://www.grin.com/document/1583310
