Air pollution within the US decreases by 6.75% while long-short returns from mispricing decrease by 2.87% in the two years following the implementation of CAIR programs. Second, we investigate whether attention to air pollution is associated with air quality and mispricing in the stock markets. We follow Li et al. 2019) and use the natural logarithm of the AQI as our air pollution measure.
We further examine the relationship between air pollution and the long-short return anomalies and mispricing scores using a regression framework. We regress long-short value-weighted returns of each anomaly and error price score on lagged 1-month air pollution and report results for these tests in Table 3. The result is also economically significant, as a one percent increase in lagged air pollution is associated with 3.502% per month of additional long-short return of error price score.
Air pollution in the US decreases by 6.75%, while long-short returns on miscalculation estimates decrease by 2.87%, two years after the introduction of CAIR programs. We further examine whether attention to air pollution is associated with air quality and financial market mispricing. Overall, the results of Table 4 confirm the existence of a causal relationship between air pollution and the behavioral biases observed in stock markets.
Overall, the results presented in Table 6 are consistent with previous findings and confirm that the relationship between air pollution and long-short mispricing returns is not driven by weather conditions.
Further analysis
We report the results for these tests in Table 6. Overall, the results in Table 6 are consistent with previous findings and confirm that the relationship between air pollution and the long-short returns of the price mispricing are not driven by weather conditions. differences in long-short returns between high and low pollution periods range from 0.926%. up to 1.296% per month for an incorrect price score built with the availability of 8 to 16 deviations. Panel B of Table 6 reports the results for the relationship between air pollution and long-short returns of the erroneous price score based on the availability of 8 to 16 nonmissing anomalies, using a regression framework. Overall, the results in Table 7 are consistent with the findings in Table 2 and Table 3, further confirming that mispricing is more pronounced after periods of high pollution, as air pollution amplifies cognitive biases in financial markets.
For now, we use the market-wide AQI index as a measure of air pollution, which is compiled by averaging air pollution data across all metropolitan areas over a month. In this section, we test whether our findings remain robust when we consider alternative measures of air pollution. 2014) and use a three-month moving average of AQI as a measure of air pollution to mitigate potential intertemporal effects of mispricing.
Finally, we use the proportion of polluted days per month as an alternative measure of air pollution. We calculate a proportion of polluted days in a given month for each US metropolitan area and then aggregate this measure into the market-wide air pollution measure (denoted as %Polluted). Consistent with previous findings, we find long-short returns from the composite mispricing score to be positive and statistically significant only after high pollution periods.
The difference in the long-short return of the price error between high and low pollution periods ranges from 0.858% to 1.477% per month for different measures of air pollution. In order to conduct a full investigation of the impact of severe air pollution on incorrect price scores, in this section we examine out-of-sample evidence. Similar to the US sample, we calculate the monthly air quality index for each city in China based on the daily air quality data and then aggregate it to the market-wide air pollution by averaging the air pollution data of all cities in a month.
According to the US setting, air pollution is classified as high (low) based on the sample median air quality index. We find that our results hold when we use alternative definitions of the aggregate mispricing outcome. 2019), prior to 2014, the Chinese government monitored only three air pollutants (i.e., SO2, NO2, and PM10) that were used to construct the Air Pollution Index (API), so the API and AQI (based on five major air pollutants ) are not directly comparable (Zheng, Cao, & Singh, 2014; Dong et al., 2019). Specifically, we find that the long-short returns of the composite mispricing result are positive and statistically significant only after periods of high pollution.
Conclusion
The Impact of Air Pollution on Infant Mortality: Evidence from Geographic Differences in Recession-Induced Pollution Shocks. Smog in our brains: gender differences in the impact of exposure to air pollution on cognitive performance. Estimates and 25-year trends in the global burden of disease attributable to ambient air pollution: analysis of data from the Global Burden of Disease Study 2015.
Fine-scale damage assessments of particulate air pollution reveal opportunities for site-specific emissions mitigation. Impact of short-term exposure to ambient air pollution on cognitive performance and human capital formation. Association between school traffic-related air pollution and cognitive development in elementary school children: a prospective cohort study.
Effect of long-term exposure to air pollution on anxiety and depression in adults: a cross-sectional study. The table reports the value-weighted returns for 16 anomalies and mispricing based on the combination of all anomalies after months of high and low air pollution. The table reports the monthly regression estimates of the value-weighted returns for 16 anomalies and a composite mispricing score based on the combination of all anomalies on the lagged market air quality index.
Panel A of the table reports results for the relationship between air pollution and long-short value-weighted returns from a composite mispricing score using the Clean Air Interstate Rule (CAIR) as an exogenous shock to local air quality. Panel B of the table reports results for the relationship between attention to air pollution and a composite mispricing in the financial market. The table reports the estimates of the monthly regression of the value-weighted returns for a composite mispricing score based on the combination of all deviations on lagged market air quality index after controlling for a range of weather conditions.
Panel A reports the value-weighted returns after months of high and low air pollution for a mispriced score based on the combination of all anomalies with at least 8 to 16 nonmissing anomalous variables available to construct the mispriced score. Periods of high (low) pollution are defined based on the sample median of the air quality index. Panel B reports the monthly regression estimates of the value-weighted returns of a composite mispricing on the lagged market air quality index.
Panel A reports the value-weighted returns for a composite mispricing score based on the combination of all anomalies after months of high and low air pollution, while Panel B reports the estimates from the monthly regression of the value-weighted returns of a composite mispricing score on lagged market air quality index. High (low) pollution periods are defined based on the sample median of each air pollution measure. Air Pollution and Misvaluation Scores: Evidence from the Chinese Stock Market The table reports the value-weighted returns for a misvaluation score based on the combination of twelve anomalies in the Chinese stock market after months of high and low air pollution.
High (low) pollution periods are determined based on the sample mean of the air quality index in China.
Online Appendix for
Air Pollution and Behavior Biases: Evidence from Stock Market Anomalies”
The Online Appendix discusses several alternative explanations for the link between air pollution and stock market mispricing. First, we consider whether our documented link between air quality and stock mispricing is driven by negative sentiment caused by bad mood and anxiety as Kaplanski and Levy (2010) find that negative sentiment affects asset prices. Specifically, we follow Kaplanski and Levy (2010), Dai, Rau, Stouraitis, and Tan (2019), and Wang and Young (2019) and control for negative sentiment caused by terrorist attacks and aviation disasters.
We obtain information on terrorist attacks from the Global Terrorism Database32 and aviation disaster data from the Bureau of Transport Statistics33. We follow Antoniou, Kumar, and Maligkris (2019) and consider only terrorist attack events that caused human casualties. Next, we repeat our baseline regression after controlling for the number of terrorist attacks and aviation accidents and report the results for this test in Table A.1 in the Online Appendix.
Second, we control for sport-induced sentiment as Edmans et al. 2007) shows a relationship between sports sentiment and stock returns. 2007), we use a dummy variable, Sports Sentiment, which equals 1 if there are World Cup matches in a given month and zero otherwise.34. Third, we control for analyst coverage as Engelberg et al. 2019) find that analysts' recommendations contribute to mispricing in the stock market. We control for total analyst coverage, measured as the equally weighted value of analyst coverage across all firms, as an additional control variable in our baseline regression.
Fourth, we consider earnings announcements as an important source of corporate information, as Nguyen and Truong (2018) show that earnings announcements drive extreme stock returns and Engelberg et al. (2018) find that abnormal returns are higher on earnings release days. We collect earnings announcement data from Compustat and control for the number of corporate earnings announcements in a given month in our basic regression. 34 Because our focus is on the US stock market, we only consider matches in which the US national soccer team qualified for the FIFA World Cup.
Finally, we consider investor attention since Li and Yu (2012) and Chen, Tang, Yao, and Zhou (2019) show that investor attention is related to market returns while Jiang et al. 2019) find investor attention issues for equity anomalies. To ensure that our findings are not driven by investor attention, we control for investor attention in our baseline regression. We take the aggregate investor attention data from Chen et al. 2019) constructed based on twelve individual measures of investor attention.35.
