1. Introduction
In recent years, there have been several instances of infrastructure failures that have raised concerns about their impact on sustainability issues in our society. Incidents such as the power outage during the winter storm in Texas in 2021 [
1] and the blackouts in Bangladesh that affected 140 million people in 2022, as well as in Pakistan in 2023 [
2], have brought renewed attention to the importance of having reliable and resilient power grids.
On 14 August 2003, at approximately 4:10 p.m. EST, a blackout affected the northeastern and midwestern parts of the United States, leaving them without electricity. More than 500 generating units from 265 power stations in the U.S. and Canada went offline, resulting in reduced generation of around 61,800 MW. In Ontario, Canada, over 10 million people experienced disruptions to their daily lives, resulting in nearly 19 million lost work hours. The manufacturing industry faced an estimated Current Account Deficit (CAD) 2.3 billion decline in deliveries, and there was a 0.7% drop in Canadian GDP in just one month due to a power outage, which is unpredictable in terms of timing [
3].
The blackout that occurred in August 2003 led to significant economic impacts for various industries. Moreover, it has been observed that the blackout had a tangible effect on the values of firms within financial markets. Various attempts were made to examine the economic consequences of the blackout on the United States. In [
4], the impact of the blackout on electrical firms’ security values was investigated using the event approach. Reference [
5] summarizes many studies that evaluate the overall economic consequences associated with the blackout. Reference [
6] describes the typical cascading processes of major blackout events and provides insights on how the economic impact of such blackout events can be evaluated.
Blackouts can result in significant financial losses for individuals, businesses, and governments. Furthermore, a blackout might have an influence on liquidity because the financial market relies on sufficient liquidity for normal operation. Liquidity is closely related to financial market efficiency [
7], as the higher level of liquidity facilitates price discovery and lowers transaction costs, ultimately improving market quality. Recently, many studies have been carried out on the effect between liquidity and external shocks, such as the COVID pandemic [
8,
9,
10], economic policy uncertainty [
11,
12], and the business cycle [
13]. The uncertainty surrounding the future cash flows of firms may lead to investor concerns. The negative and positive effects on the firms can be attributed to increased information asymmetry and uncertainty surrounding their cash flows. Furthermore, information asymmetry and ambiguity in information quality can reduce market efficiency, lower liquidity, and increase information-based trading [
14]. Market makers require higher compensation for the heightened risk and uncertainty during a blackout, which results in wider bid–ask spreads [
15,
16,
17,
18]. Additionally, the combination of asymmetric information and herding behavior among traders further amplifies the decline in market liquidity during blackout events [
19]. However, no studies have explored the relationship between blackouts and their effect on liquidity and information asymmetry in financial markets.
This paper first examines the impact of the blackout on liquidity and information asymmetry within financial markets. It specifically examines the impact of the blackout that occurred in August 2003 on firms’ liquidity on the main stock exchange in U.S. The findings of this study provide valuable insights and contribute to the existing literature that has already identified exogenous shocks such as oil price or supply shock, COVID pandemics, economic policy uncertainty, and the business cycle as factors that negatively affect stock liquidity. Also, this paper examines how the blackout affected the information and information asymmetry of non-U.S. firms listed on the NYSE.
This paper is structured as follows: In
Section 2, a comprehensive overview of the various metrics of liquidity and information asymmetry is provided.
Section 3 presents the data employed in this study and provides a detailed description of the empirical analysis. Finally,
Section 4 serves as the conclusion of the paper, where the key findings and implication are summarized.
2. Measures of Liquidity and Information Asymmetry
This section will provide various procedures for calculating the numerous liquidity- and information-based trading metrics.
The quoted spread for firm “i” at time “t” is calculated by taking the difference between the ask and bid prices in Equation (1).
where Ask
i,t represents firm i’s ask price at time t and Bid
i,t represents firm i’s bid price at time t. The time-weighted average quoted spread for each firm is then calculated during each day in August 2003. The quoted spread represents the discrepancy between the bid price and the ask price of a security. It is calculated as the difference between these two prices. It represents the implicit cost of trading market orders at the quoted price without any price improvement.
To calculate the effective spread of firm “i” at time “t”, which measures the cost of trading when transactions occur at prices within the bid and ask quotes, Equation (2) is used.
where D
i,t represents a binary variable which is assigned a value of one for buy orders and a value of negative one for sell orders. The value of D
i,t is estimated using the method given in reference [
20]. P
i,t represents firm i’s transaction price at time t, and M
i,t represents the midpoint derived from firm i’s bid and ask quotes posted most recently. The average effective spread for each firm is calculated by considering the trade weight each day. The market quality index (MQI) [
21] is used to measure the impact of ratings on liquidity, taking into account both the quoted spread and market depth. It is computed by dividing the quoted depth by the quoted spread, providing a direct assessment of liquidity, as shown in Equation (3).
It is important to note the market quality index cannot be accurately computed using the Trade and Quote (TAQ) data for NASDAQ, as the data only provide information on the size of the first inside dealer quote for NASDAQ. As a result, the market quality index is only reported for firms listed on NYSE/AMEX exchanges, where the necessary data are available to calculate the index.
The realized spread measures the profit earned by market makers through the difference between the selling and buying prices of securities. The realized spread considers the influence of trades executed by informed traders who may have more information about the security than the market maker, resulting in a price change that is not attributable to the market maker’s actions (manifested by the price impact of trades). The realized spread is formulated in Equation (4):
where i is the firm; t is the time interval; D
i,t represents the trade direction, with a value of 1 indicating a buy trade and −1 indicating a sell trade; P
i,t is the transaction price; and M
i,t+5 represents the mid-quote, which is obtained by computing the average of the bid and ask prices observed 5 min after the transaction. The trade-weighted average realized spread is then calculated for both the event and control periods for each firm during each 30 min interval. This allows for a comparison of the realized spread between the two periods and provides insight into the market maker’s revenue performance during each period.
It is important to recognize that bid–ask spreads primarily serve as indicators of market liquidity rather than direct measures of information asymmetry. However, it can be seen that information asymmetries can influence spreads to some extent [
22]. Therefore, bid–ask spreads partially reflect such information asymmetries. While there are more direct measures available for assessing information asymmetries in capital markets, such as the PIN measure [
23] that correlates with trading volume and the VPIN measure that provides insights into informed trading activity for a broader range of securities [
24], these measures are not used due to data limitations in this paper.
4. Conclusions
This paper examined the impact of the blackout on the liquidity and information asymmetry of firms listed in major financial markets. The results of this study show that the blackout had a negative impact on the financial market’s liquidity. It implies a significant decline in liquidity during the blackout, evidenced by a considerable widening of bid–ask spreads and a decrease in the market quality index. Also, the results revealed increased information asymmetry during the blackout, as indicated by wider realized spreads. This implies that the blackout caused uncertainty and made it more challenging for market participants to access accurate and timely information, exacerbating information asymmetry in the market.
Similarly to the results mentioned above, the results of this study on U.S. firms involved in the power sector are consistent with those of firms listed in major financial markets, although the significance level is lower due to the smaller sample size. However, the results of electric equipment manufacturing firms indicate little significant changes in liquidity, information asymmetry, and market quality surrounding the blackout.
Furthermore, the findings of this empirical study reveal that the blackout’s effects extended beyond U.S. borders, negatively impacting the liquidity of non-U.S. firms listed on the NYSE market, suggesting spillover effects. This suggests that the blackout had a broader and global impact on financial markets. Significantly, the findings of this empirical study reveal that the negative liquidity shock persisted even after two weeks, indicating that the blackout’s effects were not temporary but had a lasting impact on the financial market’s liquidity.
In this study, the findings have significant implications for various stakeholders, including policymakers, regulatory bodies, and market participants. The study highlights the critical need to invest in infrastructure, establish effective regulatory frameworks, and leverage technology to enhance collaboration and information sharing. By prioritizing these areas, stakeholders can mitigate the impact of blackouts on market liquidity and foster a more resilient and efficient market environment for all.