Bridge over ocean
14 February 2019 CFA Institute Journal Review

Air Pollution, Stock Returns, and Trading Activities in China (Digest summary)

  1. Clifford S. Ang, CFA

Studying the relationship between air pollution and stock trading characteristics in China, the authors find that severe air pollution results in high illiquidity as well as low returns, turnover, and volatility, primarily through a home bias. Such effects are based on investor mood bias rather than economic effects.

How Is This Research Useful for Practitioners?

The authors’ primary takeaway for practitioners is that, although air pollution could negatively affect stock returns, in such cases, these effects appear to be temporary, and the affected stocks subsequently bounce back. Specifically, they observe a statistically significant but economically small decline in stock returns on the measurement date with a reversal in stock returns on subsequent days. This finding suggests that air pollution does not affect fundamental values, and as a result, air pollution does not appear to have a permanent effect.

Another useful takeaway for practitioners is that the impact of air pollution is not reflected in the China-wide market indexes but rather affects mainly companies headquartered in the cities experiencing the worst pollution on any given day. The authors show that looking only at the relationship between the national market index and air pollution in the city where the stock exchange is located can lead to biased results, as pollution in Shanghai and Shenzhen affects mainly firms located in those cities. The authors find that air pollution has a significant negative effect on returns because of decreased volatility and trading. The stocks with the strongest negative effects are distressed stocks, extreme growth stocks, and those stocks with higher prior volatility.

How Did the Authors Conduct This Research?

The authors study firms on the Shanghai and Shenzhen stock exchanges and examine the relationship between air pollution, stock returns, and trading securities. They use Chinese firms because of China’s vast territory and the large distance between major cities, which means each city’s air quality is not fully synchronized with the air quality in other cities. The authors use daily stock returns, turnover, illiquidity, and volatility as dependent variables. The final sample includes 1,548 firms located in 33 Chinese cities. The baseline model includes the dependent variable, which is the Air Quality Index (AQI), on the left-hand side, and on the right-hand side, the lag of the dependent variable and the control variables.

The AQI is a comprehensive measure of China’s air pollution index because it includes a number of air pollution indicators. It is divided into six ranges, which correspond to six levels of air quality—from excellent to severely polluted. The authors conduct their study using data from 1 December 2013 to 31 December 2015. All firm-level data are from the Wind Information and China Stock Market & Accounting Research (CSMAR) databases, and the AQI data are from China’s Ministry of Environmental Protection.

The authors use several control variables that have been identified in prior studies, including cloud cover, temperature, humidity, and wind speed. Apart from the cloud cover ratio, these data are from the Weather Underground Corporation website. The authors calculate the cloud cover ratio by using an indicator variable that is equal to one when the weather condition is rain, snow, fog, or any other weather event that mostly or entirely covers the sky and a variable that is equal to zero otherwise.

They then test for home bias. In particular, they test whether air pollution effects are caused by local air pollution rather than by air pollution where the stock exchange is located. They find that the latter test may be biased, so they conduct a “placebo” experiment to confirm that home bias exists and that using firm-level data may be more appropriate to analyze the effects of air pollution on the Chinese stock market.

With respect to economic effects, the authors hypothesize that local air pollution affects local stock returns and trading activity through its impact on local investor moods. For example, when pollution increases quickly, daily volume in local stocks can decline as much as 10%. They also test whether air pollution affects fundamental values or whether there are mood effects that result in an initial decline in stock prices with subsequent bounce backs.

Abstractor’s Viewpoint

The authors’ results suggest potential temporary mispricing of securities occurs because of changes in investor moods caused by fluctuations in air pollution levels. The authors’ findings are consistent with the markets being efficient in general, but markets are not efficient all the time, and all stocks in a market do not reflect fundamental values all the time. Also, profiting from associated market opportunities is challenging because it involves being able to time the market by taking advantage of the temporary decline in price before the eventual bounce back.

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