De Bondt and Thaler (1985) and Brown et al. (1988) proposed the overreaction hypothesis, which states that individual stocks tend to place too much weight on recent information. As a result, stock prices decline until the uncertainty surrounding a sudden financial event is resolved. Hong and Stein (1999) proposed the gradual information hypothesis, asserting that the gradual dissemination of news initially causes an underreaction, but that subsequent momentum leads to a correction that is too strong. A firm experiencing a fraud outbreak will exhibit a gradual reaction, but the post-announcement period will witness a significant overreaction.

H1: Fraud outbreak generates significant negative abnormal returns for the victim bank.

H2: Fraud outbreak in one bank leads to negative abnormal returns in other banks, indicating sectoral spillover effects.

In his 2018 study, Kan examined and evaluated market manipulation in Malaysia’s securities market. He found that those involved in stock market manipulation were mainly individuals in privileged positions with access to relevant information. Greed, dishonesty and the desire for quick profit were some of the causes of such manipulation. Akgün et al. (2021) investigated potential accounting irregularities and fraud in the US banks in relation to financial reporting, considering the use of either IFRS or GAAP. They found that banks using US GAAP demonstrated superior operating performance in certain variables compared to those using IFRS. Palmrosea et al. (2004) examined how the market reacted to restatement announcements on the US stock exchange. They found that the average abnormal return (AAR) fell within the two-day announcement window. The decline was particularly severe when restatements involved fraud and allegations against auditors and management, or when there was a decline in income. Similarly, Mun et al. (2010) studied investor behaviour in response to allegations of accounting irregularities and discovered that positive news had no significant impact, whereas adverse news prompted a delayed short-term response. They also found that the market reacted more quickly to favourable news than to adverse news. Song and Han (2017) examined the impact of various types of crime on the security market and found that corporate crime elicited a more significant response than individual crime. Furthermore, financial crimes have a greater impact on the stock market than operational crimes within organisations. Sharma & Verma (2020) observed abnormal losses in Indian banking stocks and indicated that fraudulent activities can erode investor confidence. For the U. S. stock exchange, Ge et al. (2020) found negative short-term reactions. Moreover, U. S. investors react more negatively than investors from other countries. Griffin et al. (2004) studied the news of securities fraud litigation and found no systematic price momentum except on the dates of announcements. Later, Eryigit (2019) found that the announcement of financial irregularities of companies by the Capital Market Board of Turkey (CMBT) Bulletin had no statistically significant influence on the security market performance. On the other hand, Hu (2014) observed that neither negative nor neutral reputational information relating to the auditor affects shareholders’ perceptions. Therefore, no changes were witnessed in the market around such announcements. Smith et al. (2023) evaluated the impact of cyber terrorism on the market value of publicly traded firms, finding that news of cyber terrorism had a negative effect on the stock prices of targeted firms. Jain (2022) estimated how stock prices reacted to regulatory action against listed Indian firms involved in economic malpractice, defaults or non-compliance with laws and regulations. The study found that regulatory action had a negative impact on firm stock prices. More severe charges caused a greater negative reaction than minor violations. Nguyen (2022) examined opportunity as a factor contributing to fraudulent behaviour in Vietnam’s stock market and identified opportunity factors that contribute to fraud. The factors include insider information, lack of internal control, influence of foreign investors, weak deterrence and limited regulatory authority. Mousavi et al. (2022) studied the role of corporate governance in dealing with fraud and money-laundering cases, and found that the right type of board and audit committees can significantly reduce fraudulent activity. Ebaid (2023), on the other hand, examined the relationship between board characteristics and financial statement fraud, and found a negative relationship between fraud likelihood and board independence, while a positive relationship exists between large board size and fraud.

This literature review clearly shows that infamous activities have a significant impact on a country’s stock market performance. Among these activities, accounting irregularities and securities market fraud are the most prevalent in developed and emerging economies. Considering Indian banking stocks, minimal research has been conducted, although banks are an easy target for fraud. Since their establishment, Indian banks have fallen victim to fraud numerous times and in various ways. Such activities not only affect the financial performance of the victim banks, but also their stock market performance.. Therefore, in this study, an attempt is made to examine the impact of the outbreak of fraudulent activity in the banking sector on the stocks of victim banks and other banks. The paper also seeks to identify possible factors that may have affected the stock prices of victim banks around the time of the fraud.

In this study, we consider entire banking firms (public sector and private banks) and observe the stock reaction to the outbreak of major fraud. Initially, the study considered five bank frauds, but, because of event clustering and also to achieve uniformity, the final sample comprises four major banking frauds. The BSE 30 index is used as a proxy for the Indian stock market and stock price data were collected from the BSE India website. Leading newspapers such as The Hindu, Hindustan Times and Financial Express were consulted for event dates. Details of the fraud cases considered in the present study are given in the table below.

The study employs event study methodology, developed, enhanced and popularised by Fama (1970) and Brown & Warner (1980), to examine how stocks reacted to the outbreak of banking fraud. Under this method, the actual return (R) around the time of the announcement is compared with the expected return (ER), with the difference being known as the abnormal return (AR). These abnormal returns are then aggregated to find the average abnormal return (AAR), which is further aggregated to observe the reaction over a shorter time period. The CAAR and AAR calculated in this way are tested for significance using a t-test.

The expected return is calculated using the parameters estimated during the estimation period by employing the Risk-Adjusted Model of Sharpe (1964). The estimation period is the period immediately before the event day. It is assumed that no similar events under study occurred during this period, which typically lasts 120 days, starting before the event window (Peterson, 1989; Dahal & Das, 2022). In the present study, the standard duration of the event window is 41 days (±10 days) (Peterson, 1989). Figure 1 shows the graphical presentation of the event study timeline.

Figure 2 shows the security behaviour of the victim bank (IDBI Bank), the PSU banks (PSUBNK) and the private bank (Pvt. BNK). It demonstrates that security fluctuated between 0.02 and 0.06 for the victim bank, between -0.001 and 0.015 for the private bank, and between -0.02 and 0.02 for the PSU bank. On the day of the event, the victim bank and PSU bank were on an upward trend, while the private bank was on a downward trend.

The t-test (Table 2) confirms the significance of all return series. Observing the abnormal returns of the victim bank reveals that none of the returns are significant, either during the pre- or post-announcement periods. On the event day, the return is also non-significant; therefore, it can be concluded that the fraud outbreak has not affected the victim bank’s returns.

During the pre-announcement period, the PSUBNK exhibited significant negative returns on days -10, -4, and -2, indicating that the spread of rumours had a negative impact due to the fraud. During the post-announcement period, the stock continued to fluctuate with significant positive and negative returns on days 11 and 30 and days 13, 18 and 23 respectively, while on the event day the return was negative and insignificant. From PSUBNK’s reaction, it can be inferred that the stock continued to react to the IDBI Bank fraud outbreak, although the reaction on the day of the event was insignificant. However, it would be incorrect to assume that the outbreak did not impact the market. Rather, it can be inferred that, due to rumours, the market was aware of the information before it was officially declared. Furthermore, it can be inferred that fraud in private banks does have an impact on PSU banks as a whole.

The reaction of Pvt. BNK was observed and found to be significant, with positive and negative reactions occurring during the pre-announcement period. The same pattern continued during the post-announcement period, with positive returns on days 3, 11, 16, 17 and 30, and negative returns on days 4, 6, 19 and 26. However, on the event day itself, the reaction was positive but insignificant. It can therefore be inferred that there was initially a shift in investment from the victim bank to other private banks. However, the significant negative returns show that the outbreak has not spared the other private banks either.

The behaviour of the CAAR (see Table 3) showed that none of the CAAR windows were significant, indicating that there was no cumulative effect of the outbreak.

Figure 3 shows the security behaviour of the victim bank (PNB), PSU banks and private banks, and shows that security fluctuated between -0.12 and 0.07 for victim banks, between -0.002 and 0.02 for private banks, and between -0.03 and 0.02 for PSU banks. On the day of the event, the trend for victim banks and private banks was positive, while the trend for PSU banks was negative.

The significance of all the return series is confirmed by the t-test (see Table 4). It can be seen that the abnormal returns of the victim bank (PNB) during the pre-announcement period are significant and negative on day -2, which suggests that rumours about the outbreak had already emerged. On the day of the announcement, the return is positive but insignificant. However, on days 1 and 2, the return is positive and significant, indicating a positive reaction from the stock market in the post-announcement period. Furthermore, as time passed, the stock continued to trend positively, The PSUBNK showed mixed reactions, with both positive and negative results on days -10, -8, and -6, respectively, indicating that rumors were spreading and causing a mixed effect on the stock price. On the day of the event, the result was negative but insignificant. After the announcement, the stock showed mixed responses, with significant positives and negatives on days 11 and 30. It can be concluded that the stock continues to fluctuate due to the PNB fraud outbreak, although the response on the day of the incident was insignificant. Nevertheless, it is incorrect to assume that the outbreak did not affect the market, as it can be seen that rumors about the situation were already known before the official announcement. Moreover, it can also be inferred that fraudulent activities in PSU banks affect other PSU banks collectively.

The reaction of PVT.BNK has also been observed, and a mixed reaction was found, both positive and negative, during the pre-annoucement period. It continued in the post-announcement period with positive returns on days 3, 11, 16, 17 and 30, and negative returns on days 4, 6, 19 and 26. The reaction on the event day was positive but insignificant. It can be inferred from this that initially there was a shift in investments from the victim bank towards other private banks. However, the significant negative returns show that the outbreak has not spared other private banks either.

Further analysis of CAAR (Table 5) shows that the CAAR window (0, 2); (1, 5); (1, 10) is significant and positive for the victim bank (PNB), while none of the windows are significant for others, indicating no cumulative long-term impact.

Figure 4 shows the security behaviour of the victim Bank (BOI), PSU banks and private banks. It is evident that security fluctuated between -0.08 and 0.03 for Victim Bank, between -0.002 and 0.02 for Private Banks, and between -0.03 and 0.02 for PSU Banks. On the event day, all the series show a positive trend.

The significance of the return series is confirmed by the t-test (Table 6), and it is observed that for the victim bank (BOI), none of the returns during the pre-announcement period is significant. On the event day the return is positive but insignificant, and the same continued in post-announcement days except for day 28.

For PSUBNK, the return was positively significant on day -1, while on the day of the event the return was positive but insignificant. During the post-announcement period, the return showed mixed reactions, with positive returns on days 11 and 30 and negative reactions on days 13; 19 and 23. Similarly, for Pvt.BNK, none of the return was significant in the pre-announcement period. On the event day the return was positive but insignificant; however, on day 3 it became positive and significant, but on day 4 the return was negative and significant. Such fluctuation continued till day 30, indicating an initial shift in investment from the victim bank to private banks; however, the significant negative return shows that the outbreak has not spared the other private banks.

Further CAAR (Table 7) shows that none of the windows for victim banks (BOI) and PSU banks is significant.

Figure 5 shows the security behaviour of the victim bank (Canara Bank), PSU banks and private banks. It is evident that the security fluctuated between -0.08 and 0.15 for the victim bank, between -0.02 and 0.02 for private banks and between -0.02 and 0.07 for PSU banks. On the event day, the trend for the victim bank, PSU banks and private banks was negative.

The significance of the return series is confirmed by the t-test (Table 8), which showed that the abnormal returns of the victim bank (Canara Bank) were significant and positive on days -9 and -8 and negative on days -7 and -3. On the event day the return was negative but insignificant; however, on the next day, i.e. on day 1, the return became negative and significant. Again, on day 2 and 3 the return was significant and positive. On the days following the announcement, the return continued to fluctuate with both significant positive and negative reactions. Similarly, the PSUBNK during the pre-announcement period showed a mixed reaction with both significant positive reactions on days -9, -8 and -4 and negative reactions on days -6, -3 and -2. On the event day the return was significant and negative, implying a negative impact of the outbreak on other PSU banks. During the post-announcement period, the return was positive on days 11 and 30, but negative on days 13, 19 and 23. From Pvt.BNK it can be seen that the return was significant and positive on days -8 and -7 in the pre-announcement period, while on the event day the return was significant and negative. This implies the negative impact of the announcement on private banks. During the post-announcement period, the stock shows a mixed reaction with a positive return on day 3, 11, 16, 17 and 30 and a negative reaction on day 4, 6, 19 and 26.

To gain further insight into the behaviour of the securities, the CAAR (see Table 9) is calculated. It is found that for the victim bank (Canara Bank), the CAAR window of (0,1) shows a significant negative return, while for the PSU banks, none of the CAAR windows is significant. For the private banks, the CAAR window of (0, 2) shows a significant positive return.

Given the fluctuations and stability of the stock in the event of fraud, further analysis was conducted to determine the possible reasons for the stock’s stability. In this regard, the present study considered the following regression model:

AAR_it = α + β₁COVID 19 + β₂FirmValue + β₃Age + β₄No.of Shares + β₅No.of Trades + + β₆Total Turnover + β₇Delivery Quantity + ε_i

Here,

AAR is taken for -10 to +30 days.

The dummy variable of COVID-19 is taken; before the COVID-19 outbreak it is set to ‘0’, and after, to ‘1’.

All the other data, namely firm value, age, number of shares; number of trades; total turnover and delivery quantity, is taken from the official website of BSE India.

Table 10 presents the regression results with VCE ( Variance–Covariance Estimator), which show that the factors like age and total turnover are significant and positive, which means that these two are major contributors to the stability of the stock. In other words, the greater the age and turnover, the higher the stability of the stock. In the case of COVID-19, although a negative coefficient is observed, it is not significant. This could be because the time gap between the outbreak of COVID-19 and fraud is around one, and so the market has already absorbed the information related to COVID-19, which resulted in an insignificant coefficient. In the case of all other factors, a positive coefficient is observed, but it is also insignificant.