Air pollution, individual investors, and stock pricing in China

International Review of Economics and Finance 67 (2020) 267–287

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International Review of Economics and Finance journal homepage: www.elsevier.com/locate/iref

Air pollution, individual investors, and stock pricing in China Qinin Wu a, Jing Lu b, c, * a

Accounting School, Chongqing University of Technology, China School of Economics and Business Administration, Chongqing University, China c Innovation Institute of Corporate Finance and Accounting Governance, Chongqing University, China b

A R T I C L E I N F O

A B S T R A C T

JEL classification: G12

We construct a firm-level index for measuring the mood of individual investors induced by air pollution in the Chinese stock market. In a setting where individual investors dominate the stock market, we examine how the individual investor mood affects stock pricing. We find that the investor mood only affects individual investors’ trading behaviors, thereby validating the individual investor mood indices. Further, a pessimistic individual investor mood decreases liquidity and volatility, eventually causing a decline in stock returns. The mispricing caused by the individual investor mood cannot be eliminated by using risk-factor models, thereby implying the limited efficiency of the Chinese stock market.

Keywords: Air pollution Asset pricing Individual investors Investor mood

1. Introduction A growing body of literature provides evidence from the behavioral finance perspective that natural conditions are associated with stock prices. Through their adverse effects on investors, severe natural conditions lead to considerable declines in stock market returns (Cao & Wei, 2005; Hirshleifer & Shumway, 2003; Kamstra, Kramer, & Levi, 2003; Saunders, 1993). Specifically, Goetzmann, Kim, Kumar, and Wang (2015) construct a weather-induced mood index for institutional investors and explore how mood affects the trading behaviors of these investors in the stock market. However, individual investors are more likely to be the ideal unit to examine this mood effect because they are more irrational than institutional investors (Kumar, Page, & Spalt, 2013). In this respect, Schmittmann, Pirschel, Meyer, and Hackethal (2015) provide empirical evidence that weather conditions affect individuals’ moods and, in turn, their trading activities and risk tolerance. Huang, Xu, and Yu (2019) is one of the first studies to explore the relationship between air pollution and individual investors’ trading behaviors. Nevertheless, an air-pollution-induced mood index, especially for individual investors, has not yet been constructed. In this study, we construct a firm-level index that directly measures individual investors’ mood as induced by air pollution and explore its effects on investors’ trading behaviors and stock pricing in the Chinese stock market. As we examine how the air-pollution-induced individual investor mood affects the Chinese financial market, there are advantages to conducting this study in this context. First, individual investors have dominated the Chinese stock market since the establishment of the Shanghai Stock Exchange (SSE) and Shenzhen Stock Exchange (SZSE). According to the 2017 Annual Statistics of the SSE, individual investor trading accounted for 82.01% of the total market trading volume, which provides an ideal setting to test the effects of air pollution on mood, because individual investors are more susceptible to mood. Second, the air pollution issue in China is serious and has resulted in a considerable number of deaths annually. He and Liu (2018) state that the public environmental awareness has been increasing gradually in China, resulting in air pollution effects in the stock market. Therefore, this setting is an opportunity to investigate

* Corresponding author. Department of Finance, School of Economics and Business Administration, Chongqing University, China. E-mail address: [email protected] (J. Lu). https://doi.org/10.1016/j.iref.2020.02.001 Received 19 July 2019; Received in revised form 2 February 2020; Accepted 6 February 2020 Available online 10 February 2020

Q. Wu, J. Lu

International Review of Economics and Finance 67 (2020) 267–287

Table 1 Individual and institutional investors’ trading volumes during sudden downturns in investor mood. Average trading volumes during days t-5 to t-1 Panel A: Buying volume value in thousand Yuan (RMB) Individual 48,498.0290 investors Institutional 21,314.7520 investors Panel B: Number of buy orders Individual 4401.6652 investors Institutional 13.3596 investors Panel C: Selling volume value in thousand Yuan (RMB) Individual 45,257.4980 investors Institutional 30,860.6290 investors Panel D: Number of sell orders Individual 3934.0340 investors Institutional 18.7314 investors

Average trading volumes on day t

Average trading volumes on day tþ1

Average trading volumes on day tþ2

46,318.6500

48,664.5210

47,691.2240

21,685.2840

21,376.1800

21,413.1470

4159.5887

4405.0470

4271.1011

13.5486

13.4378

13.4039

45,503.3370

46,344.5860

46,279.1330

30,269.3540

31,142.5630

30,303.2620

3958.3833

4042.2615

4010.5790

18.2535

18.8118

18.5278

Notes: This table reports the average individual and institutional investors’ buying and selling volumes during sudden downturns in investor mood. The event windows are when AQI_svol is greater than 100 on day t, but lower than 100 during days t-5 to t-1 and days tþ1 to tþ2.

how air pollution affects financial markets. Third, referring to the World Federation of Exchanges’ statistics at the end of 2017, the total market value of the SSE and SZSE was USD 8.70 trillion, making the Chinese stock market the largest order-driven market worldwide. Therefore, our study can identify investor mood effects in an order-driven stock market and also facilitate the understanding of limited market efficiency. Da, Engelberg, and Gao (2011) show that Google search volumes can directly measure the attention of individual investors. We conjecture that individual investors are more likely to trade a particular stock if it receives increased attention. Similarly, the Baidu Search Index can measure the focus of Chinese individual investors’ attention. We thus follow previous studies and employ air pollution levels as a proxy for investor mood. Accordingly, the individual investor mood is measured as the average air pollution of 364 Chinese cities and is weighted by the Baidu Search Index of the corresponding cities. Using daily firm-level data, we first test whether the individual investor mood indices can capture individual investors’ trading behaviors. After examining individual and institutional investors’ trading volumes during sudden downturns in investor mood, we observe a significant decline in individual investors’ buying volume, while the change in institutional investors’ buying volume is insignificant. Additionally, from the regression results, the air-pollution-induced individual investor mood indices only reduce the individual investors’ preference for buying stocks and have no significant effect on institutional investors’ trading activities. As a result, our individual investor mood indices effectively capture individual investors’ mood. Next, we examine the influence of individual investor mood indices on trading activities. The results indicate that the more pessimistic the individual investor mood is, the lower stock liquidity and volatility are. These findings are consistent with the opinion that a more negative investor mood promotes risk-averse behaviors and dampens overconfidence (Gervais & Odean, 2001), which induces investors not to trade risky assets. Meanwhile, air-pollution-induced mood indices only have a short-term effect on stock liquidity, implying that air pollution affects investors’ trading activities mainly through its effect on investor mood. Subsequently, we find that, through the air pollution effect on behavior bias, depressed individual investors markedly decrease stock returns. These outcomes confirm the extant research results. Particularly, a one standard deviation change in the investor mood index corresponds to a decline of 0.0046% in daily stock returns, which is equal to an annualized return of 1.15%, based on 250 trading days. Therefore, the air-pollution-induced individual investor mood plays a crucial role in the restriction of market efficiency, which eventually results in mispricing in the Chinese stock market. Additionally, after using the Fama–MacBeth regression method, employing de-seasonalized investor mood proxies, air pollution proxies detrended with the moving average over the past two weeks and excluding small firms, firms headquartered in Shanghai and Shenzhen, and financial firms, the results still hold. Finally, we focus on the relationship between individual investor mood and risk-adjusted returns. If the relationship is not remarkable, we expect that the stock returns caused by individual investor mood are attenuated or eliminated by an efficient asset pricing model. Otherwise, limited market efficiency is implied. First, we form 10 portfolios by ranking individual investor mood proxies. The results show that the alphas with respect to the risk-factor model are monotonically reduced from the optimistic to the pessimistic portfolios. Specially, the most optimistic portfolio earns an average return 0.0851% higher than of the most pessimistic portfolio. Second, we construct optimistic-minus-pessimistic (OMP) portfolios and explore whether the returns of these portfolios may be completely captured by risk-factor models. We find that OMP portfolios still earn positively abnormal returns, supporting the hypothesis of limited market efficiency. We obtain similar results after excluding the influences of industry, market environment, and week effects.

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International Review of Economics and Finance 67 (2020) 267–287

Table 2 t-test for investors’ trading behaviors. Optimistic Pessimistic Difference t-statistics Panel A: Observations with AQI_svol below 100 are in optimistic groups, while observations with AQI_svol above 150 are in pessimistic groups. BSI_indvol Buy_indvol Sell_indvol BSI_indorder Buy_indorder Sell_indorder BSI_insvol Buy_insvol Sell_insvol BSI_insorder Buy_insorder Sell_insorder

0.4158 0.7909 1.5502 0.3514 1.1806 1.9013 4.0270 4.3169 4.8122 2.3474 1.6961 6.2700

0.2504 3.9385 3.3363 0.0833 4.6742 3.5469 4.0476 5.3667 3.6249 2.3092 2.6898 5.1741

0.1654 3.1476 1.7861 0.2681 3.4936 1.6456 0.0206 1.0498 1.1873 0.0382 0.9937 1.0959

4.0153*** 13.3819*** 6.9964*** 5.6719*** 14.8228*** 6.4326*** 0.1302 1.7014 1.8047 0.2494 1.8040 1.8043

Panel B: Observations with AQI_svol below its mean are in optimistic groups, otherwise observations are in pessimistic groups. BSI_indvol Buy_indvol Sell_indvol BSI_indorder Buy_indorder Sell_indorder BSI_insvol Buy_insvol Sell_insvol BSI_insorder Buy_insorder Sell_insorder

0.3796 0.8728 1.4639 0.3172 1.2655 1.8104 3.9449 4.3052 4.3946 2.2776 1.7248 5.9018

0.3558 2.0037 2.3070 0.2222 2.4266 2.4115 4.0842 4.6845 4.4850 2.3622 1.8339 6.2205

0.0238 1.1309 0.8431 0.0950 1.1611 0.6011 0.1393 0.3792 0.0903 0.0846 0.1091 0.3187

1.6617 13.6894*** 9.4055*** 5.7806*** 14.0440*** 6.6932*** 2.4835 1.7420 0.3864 1.5547 0.5608 1.4713

Panel C: Observations with AQI_svol below its 67th percentile are in optimistic groups, otherwise observations are in pessimistic groups. BSI_indvol Buy_indvol Sell_indvol BSI_indorder Buy_indorder Sell_indorder BSI_insvol Buy_insvol Sell_insvol BSI_insorder Buy_insorder Sell_insorder

0.4076 0.8700 1.5841 0.3462 1.2451 1.9273 4.0138 4.4349 4.6143 2.3358 1.8076 6.0917

0.2917 2.2642 2.2444 0.1400 2.7308 2.3012 3.9707 4.4975 4.0439 2.2590 1.6846 5.8932

0.1159 1.3942 0.6603 0.2062 1.4857 0.3739 0.0431 0.0626 0.5704 0.0768 0.1230 0.1985

7.7299*** 16.1355*** 7.0436*** 11.9965*** 17.1821*** 3.9814*** 0.7345 0.2751 2.3305 1.3485 0.6047 0.8761

Notes: This table reports the average excess buy-sell measures of individual and institutional investors. The investor mood groups are sorted by individual investor proxy. BSI_indvol and BSI_insvol are the excess buy-sell measures of individual and institutional investors based on trading volume value, respectively. BSI_indorder and BSI_insorder are the excess buy-sell measures of individual and institutional investors based on the number of trading orders, respectively. Buy_indvol and Buy_insvol are the values of individual and institutional investors’ buying volumes deviated from the annual means, respectively. Buy_indorder and Buy_insorder are the numbers of individual and institutional investors’ buy orders deviated from the annual means, respectively. Sell_indvol and Sell_insvol are the values of individual and institutional investors’ selling volume deviated from the annual means, respectively. Sell_indorder and Sell_insorder are the numbers of individual and institutional investors’ sell orders deviated from the annual means, respectively. *, **, and, *** represent significance at the 10%, 5%, and 1% levels, respectively.

Our study contributes to this growing body of literature as follows. First, to the best of our knowledge, this is the first study to directly measure the individual investor mood induced by natural conditions. Although several studies, such as Goetzmann and Zhu (2005), Loughran and Schultz (2004), and Schmittmann et al. (2015), examine the weather effect on individual investors’ behaviors, they employ city- and weather station-level natural conditions, which affect the mood of both individual and institutional investors. Second, we verify that the air-pollution-induced individual mood causes a significant decrease in stock returns by reducing liquidity and volatility. This provides additional evidence that air pollution affects investors’ investment decisions and trading behaviors through its effect on mood, finally resulting in mispricing (Hu, Li, & Lin, 2014; Lepori, 2016; Levy & Yagil, 2011; Wu, Hao, & Lu, 2018a). Third, we find that the Fama–French asset-pricing models cannot eliminate the risk-adjusted returns of investor mood portfolios. This suggests the limited efficiency of the Chinese stock market due to a variety of arbitrage constraints. The remainder of this paper is organized as follows. Section 2 reviews the literature related to investor mood. Section 3 describes the data sources and defines the variables. Section 4 tests the validation of investor mood indices. Section 5 presents the empirical results on how individual investor mood affects stock returns. Section 6 analyzes the portfolios sorted by investor mood. Finally, section 7 concludes the paper.

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International Review of Economics and Finance 67 (2020) 267–287

Table 3 Effects of individual investor mood on investors’ trading behaviors. AQI_svol PM2.5_svol Panel A: The effects of individual investor mood on BSI_indvol

Control variables Month effects Firm-specific fixed effects Adj-R2 N

0.0018*** (0.0003) Yes Yes Yes 0.0819 1,050,008

0.0023*** (0.0004) Yes Yes Yes 0.0819 1,050,008

PM10_svol

SO2_svol

CO_svol

NO2_svol

O3_svol

0.0011*** (0.0003) Yes Yes Yes 0.0819 1,050,008

0.0089*** (0.0010) Yes Yes Yes 0.0820 1,050,008

0.1371*** (0.0376) Yes Yes Yes 0.0819 1,050,008

0.0050*** (0.0010) Yes Yes Yes 0.0819 1,050,008

0.0031*** (0.0004) Yes Yes Yes 0.0820 1,050,008

0.0044*** (0.0014) Yes Yes Yes 0.3691 1,050,911

0.0258*** (0.0050) Yes Yes Yes 0.3690 1,050,911

0.5092*** (0.1728) Yes Yes Yes 0.3690 1,050,911

0.0649*** (0.0069) Yes Yes Yes 0.1538 1,050,911

0.0035 (0.0085) Yes Yes Yes 0.1537 1,050,911

0.0037** (0.0016) Yes Yes Yes 0.3382 1,050,156

0.0013 (0.0057) Yes Yes Yes 0.3382 1,050,156

0.0742 (0.1993) Yes Yes Yes 0.3382 1,050,156

0.0308*** (0.0050) Yes Yes Yes 0.3608 1,050,156

0.0064*** (0.0023) Yes Yes Yes 0.3382 1,050,156

0.0015*** (0.0003) Yes Yes Yes 0.0940 1,050,067

0.0110*** (0.0012) Yes Yes Yes 0.0941 1,050,067

0.2236*** (0.0435) Yes Yes Yes 0.0940 1,050,067

0.0082*** (0.0011) Yes Yes Yes 0.0940 1,050,067

0.0030*** (0.0005) Yes Yes Yes 0.0941 1,050,067

0.0066*** (0.0014) Yes Yes Yes 0.3302 1,050,831

0.0365*** (0.0051) Yes Yes Yes 0.3302 1,050,831

0.8279*** (0.1797) Yes Yes Yes 0.3302 1,050,831

0.0111 (0.0153) Yes Yes Yes 0.3302 1,050,831

0.0042 (0.0035) Yes Yes Yes 0.3302 1,050,831

0.0010 (0.0017) Yes Yes Yes 0.3192 1,049,941

0.0120** (0.0058) Yes Yes Yes 0.3192 1,049,941

0.4775** (0.2039) Yes Yes Yes 0.3192 1,049,941

0.0274*** (0.0056) Yes Yes Yes 0.3192 1,049,941

0.0037 (0.0024) Yes Yes Yes 0.3192 1,049,941

0.0018 (0.0012) Yes Yes Yes 0.0338 873,042

0.0393*** (0.0051) Yes Yes Yes 0.0339 873,042

0.1129 (0.1555) Yes Yes Yes 0.0338 873,042

0.0031 (0.0041) Yes Yes Yes 0.0338 873,042

0.0084*** (0.0017) Yes Yes Yes 0.0338 873,042

0.0070 (0.0045) Yes Yes

0.1050*** (0.0225) Yes Yes

1.0732 (0.8993) Yes Yes

0.0430 (0.0284) Yes Yes

0.0246* (0.0131) Yes Yes

Panel B: The effects of individual investor mood on Buy_indvol

Control variables Month effects Firm-specific fixed effects Adj-R2 N

0.0046*** (0.0016) Yes Yes Yes 0.3690 1,050,911

0.0050** (0.0020) Yes Yes Yes 0.3690 1,050,911

Panel C: The effects of individual investor mood on Sell_indvol

Control variables Month effects Firm-specific fixed effects Adj-R2 N

0.0046 (0.0054) Yes Yes Yes 0.3382 1,050,156

0.0025 (0.0023) Yes Yes Yes 0.3382 1,050,156

Panel D: The effects of individual investor mood on BSI_indorder

Control variables Month effects Firm-specific fixed effects Adj-R2 N

0.0021*** (0.0004) Yes Yes Yes 0.0940 1,050,067

0.0033*** (0.0005) Yes Yes Yes 0.0940 1,050,067

Panel E: The effects of individual investor mood on Buy_indorder

Control variables Month effects Firm-specific fixed effects Adj-R2 N

0.0072*** (0.0017) Yes Yes Yes 0.3302 1,050,831

0.0087*** (0.0021) Yes Yes Yes 0.3302 1,050,831

Panel F: The effects of individual investor mood on Sell_indorder

Control variables Month effects Firm-specific fixed effects Adj-R2 N

0.0014 (0.0020) Yes Yes Yes 0.3192 1,049,941

0.0083*** (0.0024) Yes Yes Yes 0.3192 1,049,941

Panel G: The effects of individual investor mood on BSI_insvol

Control variables Month effects Firm-specific fixed effects Adj-R2 N

0.0022 (0.0015) Yes Yes Yes 0.0338 873,042

0.0055*** (0.0018) Yes Yes Yes 0.0338 873,042

Panel H: The effects of individual investor mood on Buy_insvol

Control variables Month effects

0.0078 (0.0059) Yes Yes

0.0278*** (0.0100) Yes Yes

(continued on next page)

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Table 3 (continued ) Firm-specific fixed effects Adj-R2 N

Yes 0.1367 1,054,681

Yes 0.1367 1,054,681

Yes 0.1344 1,054,681

Yes 0.1345 1,054,681

Yes 0.1367 1,054,681

Yes 0.1367 1,054,681

Yes 0.1367 1,054,681

0.0022 (0.0043) Yes Yes Yes 0.1414 1,055,952

0.0674*** (0.0162) Yes Yes Yes 0.1415 1,055,952

0.5183 (0.5295) Yes Yes Yes 0.1414 1,055,952

0.0223 (0.0146) Yes Yes Yes 0.1414 1,055,952

0.0177*** (0.0063) Yes Yes Yes 0.1414 1,055,952

0.0010 (0.0012) Yes Yes Yes 0.0282 869,487

0.0355*** (0.0048) Yes Yes Yes 0.0283 869,487

0.0096 (0.1495) Yes Yes Yes 0.0282 869,487

0.0000 (0.0039) Yes Yes Yes 0.0282 869,487

0.0085*** (0.0017) Yes Yes Yes 0.0282 869,487

0.0039 (0.0044) Yes Yes Yes 0.1455 1,054,550

0.0847*** (0.0200) Yes Yes Yes 0.1455 1,054,550

1.2992 (0.9025) Yes Yes Yes 0.1455 1,054,550

0.0007 (0.0150) Yes Yes Yes 0.1455 1,054,550

0.0041 (0.0063) Yes Yes Yes 0.1455 1,054,550

0.0072 (0.0047) Yes Yes Yes 0.1399 1,055,873

0.0278 (0.0196) Yes Yes Yes 0.1399 1,055,873

0.4280 (0.4907) Yes Yes Yes 0.1399 1,055,873

0.0367 (0.0255) Yes Yes Yes 0.1399 1,055,873

0.0069 (0.0058) Yes Yes Yes 0.1399 1,055,873

Panel I: The effects of individual investor mood on Sell_insvol

Control variables Month effects Firm-specific fixed effects Adj-R2 N

0.0140 (0.0094) Yes Yes Yes 0.1414 1,055,952

0.0090 (0.0098) Yes Yes Yes 0.1414 1,055,952

Panel J: The effects of individual investor mood on BSI_insorder

Control variables Month effects Firm-specific fixed effects Adj-R2 N

0.0019 (0.0014) Yes Yes Yes 0.0282 869,487

0.0047*** (0.0017) Yes Yes Yes 0.0282 869,487

Panel K: The effects of individual investor mood on Buy_insorder

Control variables Month effects Firm-specific fixed effects Adj-R2 N

0.0039 (0.0052) Yes Yes Yes 0.1455 1,054,550

0.0114 (0.0113) Yes Yes Yes 0.1108 1,054,550

Panel L: The effects of individual investor mood on Sell_insorder

Control variables Month effects Firm-specific fixed effects Adj-R2 N

0.0172* (0.0096) Yes Yes Yes 0.1399 1,055,873

0.0139 (0.0097) Yes Yes Yes 0.1399 1,055,873

Notes: This table reports the effects of individual investor mood on excess buy-sell measures. The estimated model is as per Equation (15). BSI_indvol and BSI_insvol are the excess buy-sell measures of individual and institutional investors based on trading volume value, respectively. BSI_indorder and BSI_insorder are the excess buy-sell measures of individual and institutional investors based on the number of trading orders, respectively. Buy_indvol and Buy_insvol are the values of individual and institutional investors’ buying volumes deviated from the annual means, respectively. Buy_indorder and Buy_insorder are the numbers of individual and institutional investors’ buy orders deviated from the annual means, respectively. Sell_indvol and Sell_insvol are the values of individual and institutional investors’ selling volumes deviated from the annual means, respectively. Sell_indorder and Sell_insorder are the numbers of individual and institutional investors’ sell orders deviated from the annual means, respectively. AQI_svol, PM2.5_svol, PM10_svol, SO2_svol, CO_svol, NO2_svol, and O3_svol are the individual investor mood proxies. The lagged values of the dependent variables, local air pollution, weather conditions, SAD effect, lunar phases, investor attention, local economic conditions, stock market returns, Monday and month effects are controlled for. All models are estimated by the cross-sectional fixed-effect method with robust standard errors clustered by firm as per Angrist and Pischke (2009). The robust standard errors are in parentheses. Due to space considerations, the coefficients on the controlling variables are not reported. *, **, and, *** represent significance at the 10%, 5%, and 1% levels, respectively.

Table 4 Pearson correlation coefficients between stock-level dependent variables and individual investor mood proxies.

Return Illiq Turn Vol

AQI_svol

PM2.5_svol

PM10_svol

SO2_svol

CO_svol

NO2_svol

O3_svol

0.0099*** 0.0564*** 0.0509*** 0.0398***

0.0027*** 0.0543*** 0.0227*** 0.0130***

0.0084*** 0.0536*** 0.0189*** 0.0105***

0.0056*** 0.0741*** 0.0223*** 0.0499***

0.0047*** 0.0311*** 0.0189*** 0.0044***

0.0235*** 0.0551*** 0.0763*** 0.0664***

0.0145*** 0.0050*** 0.0024** 0.0121***

Notes: This table reports the Pearson correlation coefficients between stock-level dependent variables and individual investor mood proxies. Return are the daily returns in percentage for each firm. Illiq are the absolute daily returns divided by the daily dollar trading volume, and then multiplied by 108. Turn is the daily trading volume divided by its outstanding shares. Vol is measured as variance of 5-min return over one day for each firm. AQI_svol, PM2.5_svol, PM10_svol, SO2_svol, CO_svol, NO2_svol, and O3_svol are the individual investor mood proxies. *, **, and, *** represent significance at the 10%, 5%, and 1% levels, respectively.

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Table 5 t-test for stock returns and trading activities. Investor mood

Return

Illiq

Turn

Vol

Optimistic Pessimistic Difference t-statistics

0.1020 0.0004 0.1024 7.2492***

2.0044 2.2933 0.2889 22.1749***

2.6729 2.2893 0.3836 27.5783***

0.2040 0.1675 0.0365 27.2027***

Notes: This table reports the average daily returns and trading activities of the groups sorted by individual investor proxy. Return are the daily returns in percentage for each firm. Illiq are the absolute daily returns divided by the daily dollar trading volume, and then multiplied by 108. Turn is the daily trading volume divided by its outstanding shares. Vol is measured as variance of 5-min return over one day for each firm. Observations are in optimistic groups if AQI_svol is smaller than 100, while observations are in pessimistic groups if AQI_svol is larger than 150. *, **, and, *** represent significance at the 10%, 5%, and 1% levels, respectively.

Table 6 Effects of individual investor mood on stock trading activities. AQI_svol PM2.5_svol Panel A: The effects of individual investor mood on illiquidity

Control variables Month effects Firm-specific fixed effects Adj-R2 N

0.1948*** (0.0112) Yes Yes Yes 0.1544 1,008,598

0.1863*** (0.0140) Yes Yes Yes 0.1543 1,008,598

PM10_svol

SO2_svol

CO_svol

NO2_svol

O3_svol

0.1588*** (0.0097) Yes Yes Yes 0.1544 1,008,598

0.5144*** (0.0572) Yes Yes Yes 0.1544 1,008,598

7.0492*** (1.3572) Yes Yes Yes 0.1542 1,008,598

1.2220*** (0.0341) Yes Yes Yes 0.1557 1,008,598

0.1015*** (0.0129) Yes Yes Yes 0.1542 1,008,598

0.0889*** (0.0053) Yes Yes Yes 0.6703 1,031,297

0.0704** (0.0293) Yes Yes Yes 0.6702 1,031,297

9.3959*** (0.7191) Yes Yes Yes 0.6703 1,031,297

0.5733*** (0.0174) Yes Yes Yes 0.6705 1,031,297

0.0311*** (0.0076) Yes Yes Yes 0.6702 1,031,297

0.0206*** (0.0008) Yes Yes Yes 0.4030 1,024,792

0.0400*** (0.0043) Yes Yes Yes 0.4026 1,024,792

2.5404*** (0.1089) Yes Yes Yes 0.4030 1,024,792

0.0910*** (0.0025) Yes Yes Yes 0.4032 1,024,792

0.0171*** (0.0010) Yes Yes Yes 0.4026 1,024,792

Panel B: The effects of individual investor mood on turnover

Control variables Month effects Firm-specific fixed effects Adj-R2 N

0.1463*** (0.0058) Yes Yes Yes 0.6704 1,031,297

0.1401*** (0.0078) Yes Yes Yes 0.6703 1,031,297

Panel C: The effects of individual investor mood on volatility

Control variables Month effects Firm-specific fixed effects Adj-R2 N

0.0338*** (0.0008) Yes Yes Yes 0.4034 1,024,792

0.0347*** (0.0011) Yes Yes Yes 0.4032 1,024,792

Notes: This table reports the effects of individual investor mood on stock trading activities. The estimated model is as per Equation (16). Illiq are the absolute daily returns divided by the daily dollar trading volume, and then multiplied by 108. Turn is the daily trading volume divided by its outstanding shares. Vol is measured as variance of 5-min return over one day for each firm. AQI_svol, PM2.5_svol, PM10_svol, SO2_svol, CO_svol, NO2_svol, and O3_svol are the individual investor mood proxies. The lagged values of the dependent variables, local air pollution, weather conditions, SAD effect, lunar phases, investor attention, local economic conditions, stock market returns, Monday and month effects are controlled for. All models are estimated using the cross-sectional fixed-effect method with robust standard errors clustered by firm as per Angrist and Pischke (2009). The robust standard errors are in parentheses. Due to space considerations, the coefficients on the controlling variables are not reported. *, **, and, *** represent significance at the 10%, 5%, and 1% levels, respectively.

2. Literature review Investor mood is considered an erroneous belief regarding future cash flows and investment risks (Baker & Wurgler, 2006). De Long, Shleifer, Summers, and Waldmann (1990) is one of the early studies that proposes an asset pricing model considering investor mood, whose setting includes rational and irrational noise traders. Noise traders are more vulnerable to mood and trade in concert, thus causing stock prices to deviate from their fundamental values and deterring the arbitrage of rational traders. Baker and Wurgler (2006) are the first to construct a composite investor mood index using principal component analysis. Since then, a growing number of studies have followed this approach to construct investor mood indices (Baker, Wurgler, & Yuan, 2012; Firth, Wang, & Wong, 2015; Hu & Wang, 2012). Employing the partial least squares method, Huang, Jiang, Tu, and Zhou (2015) introduce a new composite investor mood index, which has more predictive power than the Baker and Wurgler (2006) index. However, these are investor mood indices for the stock market as a whole, not for market participants. Institutional investors have a stronger ability to acquire and process information, as well as better professional skills than individual investors, resulting in more rational behaviors. Many scholars prove that individual investors are prone to being noise traders, who cause and facilitate mispricing 272

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International Review of Economics and Finance 67 (2020) 267–287

Fig. 1. Trading activities during the event windows. Notes: This figure reports the average illiquidity, turnover, and volatility during the event windows. Illiq are the absolute daily returns divided by the daily dollar trading volume, and then multiplied by 108. Turn is the daily trading volume divided by its outstanding shares. Vol is measured as the variance of 5-min return over one day for each firm.

and prevent price discovery, whereas institutional investors arbitrage and attenuate mispricing (Chou, Wang, & Wang, 2015; Li, 2014; Nam, Wang, & Zhang, 2008; Qian, 2014; Wang, Chiao, & Chang, 2012; Wei, 2018). Therefore, an individual investor mood index should be more accurate because the market mood index has been adjusted by institutional investors’ behaviors. Individuals are inevitably influenced and restricted by natural conditions in the course of their long-term evolution and development. Therefore, their investment decisions in the financial market are also affected by natural conditions. In an earlier study, Saunders (1993) finds that a high cloud cover in New York City significantly lowers market returns. This research has attracted extensive attention and, in its aftermath, other weather conditions such as including temperature (Cao & Wei, 2005), wind speed (Keef & Roush, 2002), and relative humidity (Yoon & Kang, 2009) have gained consideration as investor mood proxies. Furthermore, Goetzmann et al. (2015) construct weather-induced institutional investor mood indices. Kamstra et al. (2003) note that depression is associated with seasonal affective disorder (SAD) and individuals are more significantly affected during the seasons with relatively fewer hours of daylight. Moreover, individuals who suffer from SAD are more pessimistic and risk averse. Air pollution is a typical natural condition caused by human productive activities. In the following, we review the psychology literature related to air pollution and individual mood to clarify how air pollution affects investor mood and behavior. First, severe air pollution leads directly to a depressed mood for individuals because of its psychological and toxic effects (Lim et al., 2012; Lundberg, 1996; Mokoena, Harvey, Viljoen, Ellis, & Brink, 2015). The effects hold even under short-term exposure to air pollution (Szyszkowicz, Rowe, & Colman, 2009). Second, the awareness that air pollution is harmful to physical and mental health also induces depression, even when air quality is excellent (Claeson, Liden, Nordin, & Nordin, 2013). He and Liu (2018) note that increased public environmental awareness has resulted in air pollution effects in the financial markets. Third, severe air pollution causes high cortisol levels, making investors more risk averse (Rosenblitt, Soler, Johnson, & Quadagno, 2001). Fourth, air pollution undermines individual cognitive abilities (Mohai, Kweon, Lee, & Ard, 2011; Weuve et al., 2012) and further impairs investors’ ability to acquire and process information. Therefore, investors are likely to buy attention-grabbing stocks, which are generally overpriced (Huang et al., 2019). Overall, air pollution induces depression and risk aversion in investors and also undermines their cognitive abilities, which results in being less willing to buy stocks. Ultimately, severe air pollution is negatively related with stock returns. Recent studies identified a negative relationship between air pollution and market returns in the US (Lepori, 2016; Levy & Yagil, 2011), Chinese (Hu et al., 2014), and Finnish (F€ orsti, 2017) markets. Zhang, Jiang, and Guo (2017) further confirm that air pollution decreases stock returns using firms headquartered in Beijing. Wu, Chen, Guo, and Gao (2018b) and Huang (2017) find that the relationship between air pollution and stock pricing varies according to whether the firms belong to polluted industries. Wu et al. (2018a) consider that air-pollution-induced local investor mood adversely affects the stock returns of locally headquartered firms, mainly through the home bias, because the Chinese stock market adopts an order-driven system. This study differs from those of Huang et al. (2019) and Li, Massa, Zhang, and Zhang (2019), who explore how air pollution affects investors trading behaviors, in the following two aspects. First, it uses air pollution to construct a firm-level mood index for individual investors, whereas Huang et al. (2019) and Li et al. (2019) only employ city-level air pollution variables. Therefore, our air pollution 273

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International Review of Economics and Finance 67 (2020) 267–287

Table 7 Effects of individual investor mood on stock returns. Model 1 AQI_svol AQI_local

Model 2

Model 3

Model 4

Model 5

Model 6

0.0466*** (0.0127) 0.0435*** (0.0077)

PM2.5_svol PM2.5_local

0.0373*** (0.0089) 0.0453*** (0.0056)

PM10_svol PM10_local

0.6371*** (0.0365) 0.0240 (0.0162)

SO2_svol SO2_local

10.6660*** (1.1347) 3.0240*** (0.5340)

CO_svol CO_local

0.3888*** (0.0280) 0.0924*** (0.0182)

NO2_svol NO2_local O3_svol O3_local Svol Cloud Temp Wind Pressure Hum Visibility Rm SAD Fall Fullmoon Newmoon Gdp Ggdp Pop gpop Monday Returnt-1 Constant Month effects Firm-specific fixed effects Adj-R2 N

Model 7

0.0177* (0.0102) 0.0293*** (0.0060)

321.7649*** (2.5689) 0.7472 (0.5395) 0.5384*** (0.0747) 0.0961** (0.0438) 2.2167*** (0.4993) 5.9060*** (1.8157) 0.4384*** (0.0526) 90.5816*** (0.6162) 1.2021 (0.9778) 5.6129*** (0.8858) 4.5932*** (0.4793) 3.1392*** (0.4731) 49.3852*** (2.7583) 0.3891*** (0.0940) 33.1999*** (8.4463) 0.2616 (0.1641) 17.3161*** (0.5993) 0.0639*** (0.0014) 424.7378*** (68.9836) Yes Yes 0.2652 1,015,721

321.6834*** (2.5684) 0.9554* (0.5396) 0.5436*** (0.0745) 0.0749* (0.0437) 2.2808*** (0.4990) 5.4497*** (1.7763) 0.4894*** (0.0537) 90.5983*** (0.6163) 1.6600* (0.9763) 5.4589*** (0.8834) 4.6354*** (0.4796) 3.1186*** (0.4729) 53.0627*** (2.8476) 0.3646*** (0.0939) 35.0653*** (8.5021) 0.2708* (0.1642) 17.3095*** (0.5992) 0.0639*** (0.0014) 455.0569*** (69.4816) Yes Yes 0.2652 1,015,721

321.7115*** (2.5703) 1.1732** (0.5403) 0.5707*** (0.0743) 0.0666 (0.0430) 2.5680*** (0.4993) 10.0089*** (1.8601) 0.5550*** (0.0522) 90.5775*** (0.6164) 1.8945* (0.9721) 5.3802*** (0.8799) 4.6700*** (0.4794) 3.1056*** (0.4736) 53.2228*** (2.8418) 0.3614*** (0.0937) 34.7030*** (8.4578) 0.2560 (0.1641) 17.3648*** (0.5991) 0.0639*** (0.0014) 493.1704*** (69.1822) Yes Yes 0.2652 1,015,721 274

321.9598*** (2.5665) 0.7164 (0.5370) 0.4228*** (0.0736) 0.0345 (0.0432) 2.2793*** (0.5009) 11.0952*** (1.8334) 0.4425*** (0.0496) 90.5149*** (0.6156) 1.6197 (1.0027) 4.9114*** (0.8815) 4.3805*** (0.4793) 2.8956*** (0.4730) 80.4155*** (3.5939) 0.2097** (0.0929) 33.0108*** (8.6095) 0.1474 (0.1660) 17.4168*** (0.5990) 0.0639*** (0.0014) 746.9504*** (70.3030) Yes Yes 0.2654 1,015,721

321.9109*** (2.5689) 0.5726 (0.5377) 0.5848*** (0.0755) 0.0333 (0.0432) 2.6084*** (0.5017) 2.1624 (1.7417) 0.4503*** (0.0506) 90.6158*** (0.6161) 3.5271*** (1.0030) 4.5317*** (0.8848) 4.4788*** (0.4790) 2.9845*** (0.4733) 56.1440*** (2.8485) 0.3570*** (0.0943) 29.7394*** (8.4004) 0.1931 (0.1651) 17.3829*** (0.5996) 0.0639*** (0.0014) 561.1983*** (69.4560) Yes Yes 0.2652 1,015,721

321.1087*** (2.5701) 1.0289* (0.5404) 0.5774*** (0.0738) 0.1720*** (0.0490) 2.9093*** (0.4991) 9.7296*** (1.7939) 0.4864*** (0.0507) 90.6524*** (0.6165) 2.6011*** (0.9719) 4.9446*** (0.8777) 4.6672*** (0.4792) 2.6082*** (0.4742) 46.6099*** (2.7535) 0.3633*** (0.0926) 29.3322*** (8.3659) 0.2048 (0.1633) 16.7509*** (0.6002) 0.0638*** (0.0014) 519.0027*** (68.7620) Yes Yes 0.2653 1,015,721

0.2854*** (0.0125) 0.0059 (0.0070) 323.1092*** (2.5798) 0.0721 (0.5411) 0.2979*** (0.0759) 0.2923*** (0.0428) 1.8159*** (0.5016) 8.8386*** (1.9697) 0.1920*** (0.0500) 90.5748*** (0.6161) 0.7003 (0.9677) 7.8316*** (0.8802) 4.4868*** (0.4794) 3.5225*** (0.4738) 60.2675*** (2.8563) 0.3621*** (0.0956) 29.8534*** (8.4556) 0.2096 (0.1662) 17.2963*** (0.5989) 0.0644*** (0.0014) 476.4665*** (68.8864) Yes Yes 0.2655 1,015,721

Q. Wu, J. Lu

International Review of Economics and Finance 67 (2020) 267–287

Notes: This table reports the effects of individual investor mood on stock returns. The estimated model is as per Equation (16). The dependent variable are the returns (Return), measured as the daily returns for each firm as a percentage. AQI_svol, PM2.5_svol, PM10_svol, SO2_svol, CO_svol, NO2_svol, and O3_svol are the individual investor mood proxies. The lagged values of the dependent variables, local air pollution, weather conditions, SAD effect, lunar phases, investor attention, local economic conditions, stock market returns, Monday and month effects are controlled for. All models are estimated by the cross-sectional fixed-effect method with robust standard errors clustered by firm as per Angrist and Pischke (2009). The robust standard errors are in parentheses. *, **, and, *** represent significance at the 10%, 5%, and 1% levels, respectively.

variables could be better matched with firm-level independent variables. Second, Huang et al. (2019) and Li et al. (2019) focus on investors trading behaviors using a unique dataset that contains account-level stock trading information. However, we not only investigate the different effects of air pollution on individual and institutional investors’ trading behaviors for testing the validity of the air-pollution-induced mood indices, but also explore how air pollution affects stock pricing in the Chinese market. The recent literature on air pollution and stock pricing explores in detail the influence of air pollution on aggregate stock market returns, but fewer studies focus on firm-level returns. Moreover, the existing research has not yet proposed an individual investor mood index induced by natural conditions. Therefore, it is necessary to investigate the effects of air pollution on trading activities and stock pricing from the individual investor mood perspective. 3. Data and variable construction 3.1. Sample and dependent variables We first test whether the investor mood induced by air pollution affects individual investor trading behaviors directly, thereby validating our proposed investor mood indices. Next, we examine how this mood affects firm-level trading activities and stock returns and then study the role of the individual investor mood in asset pricing by constructing investment portfolios. At the end of 2017, there were 3530 A-share firms listed on the SSE and SZSE. Here, we focus on firms headquartered in 34 major Chinese cities, comprising approximately 1995 firms because of the availability of weather condition data. Additionally, 339 firms were excluded from the analysis because the Baidu Search Index does not provide data for search volumes when stock codes are used as search terms. Therefore, our final sample includes 1656 firms, analyzed from January 2014 to December 2017. The market values of these firms accounted for 68.12% of that of all A-shares. The Wind Info. (Wind Information Co., Ltd) database provides buying and selling volumes. It divides the trading statistics of each stock into four categories by order size: (1) less than RMB 40 thousand, (2) RMB 40 thousand to 200 thousand, (3) RMB 200 thousand to 1 million, and (4) more than RMB 1 million. This database considers that the smallest order size category (less than RMB 40 thousand) is more likely to capture retail investors’ trading, whereas the largest order size category (more than RMB 1 million) seems mainly to capture institutional investors’ trading. Meanwhile, Da et al. (2011) state that trade size is traditionally employed to identify investor trading. Therefore, we respectively calculate individual and institutional investors’ trading activities using trading statistics from the smallest and largest order size categories. First, following Schmittmann et al. (2015), we use two excess buy-sell measures, which are the deviations from the annual averages. The first measure is shown in Equations (1) and (2), which are based on the value of buying and selling volumes in RMB. The second measure is based on the number of buy and sell orders, shown in Equations (3) and (4). Generally, investors are unwilling to buy or hold stock assets when they are pessimistic. Therefore, we expect that the excess buy-sell measures are lower if severe air pollution affects investors’ mood more adversely. Second, we distinguish buying and selling volumes and examine investors’ trading behaviors. Equations (5)–(8) employ the value of trading volumes to calculate individual and institutional investors’ buying or selling behaviors, whereas Equations (9)–(12) employ the number of orders to calculate these measures. vol ind buy i;t

BSI indvol i;t ¼

BSI insvol i;t ¼

vol

indbuy i;t

þ vol

indsell i;t

vol insbuy i;t vol

¼ BSI indorder i;t

¼ BSI insorder i;t

Buy ind vol i;t ¼

insbuy i;t

þ vol

inssell i;t





vol indbuy i;y

vol insbuy i;y

order

þ order

(2)

sell vol insbuy i;y þ vol insi;y

order ind buy i;t ind buy i;t

(1)

sell vol ind buy i;y þ vol ind i;y

indsell i;t



order indbuy i;y sell order ind buy i;y þ order ind i;y

order insbuy i;y  sell þ order inssell order insbuy i;y þ order insi;y i;t

order insbuy i;t order

insbuy i;t

buy vol ind buy i;t  vol ind i;y

(3)

(4)

(5)

vol ind buy i;y

275

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International Review of Economics and Finance 67 (2020) 267–287

Table 8 Robustness check. AQI_svol

PM2.5_svol

PM10_svol

SO2_svol

CO_svol

NO2_svol

O3_svol

N

0.9920*** (0.1900) 1.8523*** (0.1470) 0.1861*** (0.0151) 0.1159 (0.1472)

0.9874*** (0.0993) 0.7789*** (0.0783) 0.1321*** (0.0088) 0.2612*** (0.0901)

14.4796*** (0.4569) 4.4535*** (0.2568) 0.1836*** (0.0297) 0.9689*** (0.2991)

250.3763*** (11.0292) 46.4686*** (8.1025) 13.5044*** (1.1032) 13.4561 (9.9368)

15.6134*** (0.4470) 8.2117*** (0.2196) 0.2405*** (0.0251) 0.0546 (0.3024)

0.3371** (0.1581) 0.8438*** (0.1208) 0.0674*** (0.0113) 0.0816 (0.0854)

1,054,314

0.1641*** (0.0099) 0.0665*** (0.0056) 0.0153*** (0.0008) 0.0253*** (0.0094)

1.1184*** (0.0645) 0.2233*** (0.0349) 0.0646*** (0.0049) 0.6331*** (0.0407)

10.1108*** (1.4669) 6.6085*** (0.7884) 2.2452*** (0.1173) 10.8644*** (1.2358)

0.7334*** (0.0317) 0.2054*** (0.0165) 0.0397*** (0.0024) 0.1631*** (0.0310)

0.0698*** (0.0136) 0.0625*** (0.0082) 0.0194*** (0.0011) 0.2742*** (0.0135)

Panel A: Fama–MacBeth method Illiq Turn Vol Return

0.8735*** (0.1381) 1.5213*** (0.0992) 0.1445*** (0.0101) 0.1360 (0.0959)

1,053,954 1,049,720 1,054,419

Panel B: Deseasonalized air pollution proxies Illiq Turn Vol Return

0.1304*** (0.0112) 0.1203*** (0.0060) 0.0260*** (0.0008) 0.0711*** (0.0106)

0.1257*** (0.0143) 0.1129*** (0.0082) 0.0250*** (0.0012) 0.1289*** (0.0132)

1,008,598 1,031,297 1,024,792 1,015,721

Panel C: Air pollution proxies detrended with the moving average over past two weeks Illiq Turn Vol Return

0.0168* (0.0090) 0.0060 (0.0050) 0.0016** (0.0007) 0.0064 (0.0102)

0.0855*** (0.0109) 0.0015 (0.0061) 0.0033*** (0.0009) 0.0370*** (0.0126)

0.0202*** (0.0077) 0.0021 (0.0042) 0.0028*** (0.0006) 0.0826*** (0.0088)

0.0879** (0.0420) 0.2393*** (0.0224) 0.0827*** (0.0032) 1.2518*** (0.0456)

5.3465*** (1.0444) 0.9865* (0.5833) 0.4993*** (0.0861) 6.9392*** (1.1778)

0.3208*** (0.0275) 0.0277* (0.0142) 0.0403*** (0.0022) 0.5854*** (0.0292)

0.1531*** (0.0130) 0.0551*** (0.0070) 0.0102*** (0.0009) 0.3471*** (0.0128)

1,008,598

0.2276*** (0.0214) 0.1245*** (0.0117) 0.0340*** (0.0017) 0.0667*** (0.0186)

0.1903*** (0.0148) 0.0807*** (0.0079) 0.0204*** (0.0011) 0.0323** (0.0130)

0.6468*** (0.0911) 0.1093*** (0.0394) 0.0262*** (0.0063) 0.6840*** (0.0532)

0.2342 (2.0569) 10.0523*** (1.0286) 2.8162*** (0.1593) 11.7589*** (1.7067)

1.2476*** (0.0536) 0.5399*** (0.0264) 0.0875*** (0.0038) 0.4406*** (0.0421)

0.0899*** (0.0204) 0.0243** (0.0109) 0.0148*** (0.0016) 0.2943*** (0.0194)

412,126

0.5464*** (0.0696) 0.0062 (0.0343) 0.0262*** (0.0050) 0.5852*** (0.0428)

4.8628*** (1.6890) 9.5982*** (0.8864) 2.6988*** (0.1317) 10.0973*** (1.3764)

1.1918*** (0.0409) 0.5684*** (0.0204) 0.0820*** (0.0028) 0.4065*** (0.0336)

0.0903*** (0.0156) 0.0267*** (0.0089) 0.0148*** (0.0012) 0.2901*** (0.0145)

0.5354*** (0.0590) 0.0779*** (0.0300) 0.0431*** (0.0044) 0.6488*** (0.0371)

7.2161*** (1.4065) 9.4019*** (0.7348) 2.5840*** (0.1114) 10.7236*** (1.1594)

1.2452*** (0.0350) 0.5717*** (0.0177) 0.0913*** (0.0025) 0.3931*** (0.0288)

0.1032*** (0.0134) 0.0326*** (0.0077) 0.0174*** (0.0010) 0.2807*** (0.0128)

1,031,297 1,024,792 1,015,721

Panel D: Excluding small firms Illiq Turn Vol Return

0.2216*** (0.0169) 0.1299*** (0.0089) 0.0337*** (0.0013) 0.0299* (0.0153)

417,509 422,747 423,262

Panel E: Excluding firms headquartered in Shanghai and Shenzhen Illiq Turn Vol Return

0.2089*** (0.0135) 0.1395*** (0.0070) 0.0326*** (0.0010) 0.0210* (0.0123)

0.2104*** (0.0174) 0.1329*** (0.0095) 0.0324*** (0.0013) 0.0526*** (0.0156)

0.1772*** (0.0119) 0.0762*** (0.0064) 0.0184*** (0.0009) 0.0359*** (0.0109)

745,087 764,254 760,020 752,588

Panel F: Excluding firms belonging to finance industry Illiq Turn Vol Return

0.1990*** (0.0116) 0.1483*** (0.0059) 0.0343*** (0.0008) 0.0193* (0.0104)

0.1899*** (0.0145) 0.1410*** (0.0079) 0.0351*** (0.0012) 0.0474*** (0.0130)

0.1619*** (0.0101) 0.0899*** (0.0054) 0.0208*** (0.0008) 0.0361*** (0.0092)

971,964 998,095 988,580 976,999

Notes: This table reports the results of the robustness checks. The estimated model is as per Equation (16). The dependent variables are stock returns (Return), illiquidity (Illiq), turnover (Turn), and volatility (Vol). Illiq are the absolute daily returns divided by the daily dollar trading volume, and then multiplied by 108. Turn is the daily trading volume divided by its outstanding shares. Vol is measured as variance of 5-min return within one day for each firm. AQI_svol, PM2.5_svol, PM10_svol, SO2_svol, CO_svol, NO2_svol, and O3_svol are the individual investor mood proxies. The lagged values of the dependent variables, local air pollution, weather conditions, SAD effect, lunar phases, investor attention, local economic conditions, stock market returns, Monday and month effects are controlled for. The models in Panel A are regressed using the Fama–MacBeth method in 1973. The other models are estimated by the cross-sectional fixed-effect method with robust standard errors clustered by firm as per Angrist and Pischke (2009). The robust standard errors are in parentheses. Due to space considerations, the coefficients on the controlling variables are not reported. *, **, and, *** represent significance at the 10%, 5%, and 1% levels, respectively. 276

Q. Wu, J. Lu

Sell ind vol i;t ¼

Buy insvol i;t ¼

Sell insvol i;t ¼

International Review of Economics and Finance 67 (2020) 267–287 sell vol indsell i;t  vol ind i;y

(6)

vol indsell i;y buy vol insbuy i;t  vol insi;y

(7)

vol insbuy i;y sell vol inssell i;t  vol insi;y sell vol insi;y

¼ Buy ind order i;t

¼ Sell ind order i;t

¼ Buy insorder i;t

¼ Sell insorder i;t

(8)

buy order indbuy i;t  order ind i;y

(9)

order indbuy i;y sell order ind sell i;t  order ind i;y

(10)

order indsell i;y buy order insbuy i;t  order insi;y

(11)

order insbuy i;y sell order inssell i;t  order insi;y sell order insi;y

(12)

sell where vol indbuy i;t (vol indi;t ) is the value of the buying (selling) volume for stock i on trading day t under the smallest order size category, buy sell sell whereas vol indbuy i;y (vol indi;y ) is the average value of the buying (selling) volume for stock i in year y. order indi;t (order indi;t ) is the sell number of buy (sell) orders for stock i on trading day t under the smallest order size category, whereas order indbuy i;y (order indi;y ) is the sell average number of buy (sell) orders for stock i in year y. vol insbuy i;t (vol insi;t ) is the value of buying (selling) volume for stock i on trading buy buy sell sell sell day t under the largest order size category. The definitions of order insbuy i;t , order insi;t , vol insi;y , vol insi;y , order insi;y , and order insi;y

are similar for institutional investors. We investigate trading activities from the liquidity and volatility perspectives. Illiquidity in Amihud (2002) and turnover are employed to measure liquidity. Illiquidity (Illiq) is calculated as the absolute daily returns, divided by the corresponding daily trading volume, which is multiplied by 108. Turnover (Turn) is measured as the percentage of the daily trading volume to the number of outstanding shares. We calculate volatility (Vol) as the variance of 5-min returns within one trading day, following the method of Andersen, Bollerslev, Diebold, and Ebens (2001). We measure daily stock returns (Return) in percentage as the closing price on day t minus the closing price on day t-1 and then divide it by the closing price on day t-1. The closing price is adjusted by dividend and share splitting. All the data on dependent variables are obtained from the Wind Info. and the China Stock Market and Accounting Research (CSMAR) databases. 3.2. Air-pollution-induced individual investor mood index We construct an air-pollution-induced mood index for individual investors in the Chinese stock market. Da et al. (2011) believe that the Google Trends Search Volume Index directly captures the focus of individual investors’ attention. Accordingly, most Chinese individual investors collect information about the stocks they are interested in via the Baidu Search Index. Therefore, the aggregate search frequency in the Baidu Search Index measures the direct attention of Chinese individual investors. The more attention is paid to a stock, the larger the probability that individual investors will trade it. Goetzmann et al. (2015) use the cloud cover ratio weighted by holding shares of institutional investors to proxy institutional investor mood. Similarly, we employ the Baidu search volume to weight air quality, thus obtaining proxies for the air-pollution-induced individual investor mood. The Baidu Index (http://index.baidu.com) provides the daily search volumes for terms from 364 Chinese cities. To capture the focus of the direct attention of individual investors in different cities for a particular stock, we explore the daily search volume from those cities by the stock code. For example, “601398” represents the Industrial and Commercial Bank of China (ICBC), and “600519” represents Kweichow Moutai. Therefore, the individual investor mood induced by air pollution is measured as search volume weighted average air pollution proxies across different Chinese cities: AQ svoli;t ¼

364 X AQc1;t ⋅ svolc1;i;t 364 P c1¼1 svolc2;i;t

(13)

c2¼1

where AQc1,t (i.e., AQI, PM2.5, PM10, SO2, CO, NO2, and O3) is the air quality in city c1 on day t and svolc1,i,t is the search volume for firm i 277

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Table 9 Portfolios sorted by AQI_svol. P1

P2

P3

P4

P5

P6

P7

P8

P9

(Low) Excess return CAPM alpha Three-factor alpha Five-factor alpha MRK 278

SMB HML RMW

Sharpe Ratio Adj-R2 N

P1–P10

0.1288*** (2.5556) 0.0607*** (3.1572) 0.0603*** (3.2771) 0.0561*** (3.1421) 0.8808*** (41.4719) 0.0839 (0.9594) 0.0943 (1.1075) 0.0681 (-0.7000) 0.3061*** (-2.9355) 1.2932 0.8668 977

0.1304** (2.4604) 0.0581*** (3.1287) 0.0538*** (2.9016) 0.0569*** (3.0955) 0.8988*** (45.5862) 0.0562 (1.2481) 0.0098 (-0.1475) 0.1591** (-2.3326) 0.0093 (0.0984) 1.2455 0.8887 977

0.1322** (2.3906) 0.0563*** (3.0589) 0.0527*** (2.9046) 0.0574*** (3.2477) 0.9450*** (49.0003) 0.0045 (0.0807) 0.0196 (-0.2905) 0.1767* (-1.8537) 0.1076 (1.0508) 1.2100 0.8980 977

0.0677 (1.2046) 0.0099 (-0.5497) 0.0092 (-0.5120) 0.0056 (-0.3084) 0.9801*** (57.1985) 0.0579 (-0.8700) 0.0535 (-0.7864) 0.1199 (-1.3737) 0.0701 (0.8287) 0.6097 0.9013 977

0.0572 (1.0070) 0.0210 (-1.1382) 0.0157 (-0.8438) 0.0112 (-0.6098) 0.9865*** (46.1072) 0.1310** (-1.9928) 0.1538** (-1.9642) 0.1458 (-1.6123) 0.0046 (-0.0469) 0.5095 0.8990 977

0.0976* (1.7274) 0.0191 (1.1217) 0.0160 (0.9592) 0.0191 (1.1479) 0.9863*** (60.4018) 0.0249 (0.3277) 0.0371 (-0.5469) 0.0813 (-0.8354) 0.1057 (1.4976) 0.8732 0.9148 977

0.0942* (1.8081) 0.0226 (1.2910) 0.0257 (1.4583) 0.0279 (1.5845) 0.9081*** (52.2354) 0.0582 (-0.9115) 0.1223** (-2.0578) 0.0332 (-0.3805) 0.0392 (0.4749) 0.9145 0.8937 977

0.0766 (1.4508) 0.0035 (0.2114) 0.0061 (0.3658) 0.0064 (0.3890) 0.9358*** (57.3775) 0.0215 (-0.3628) 0.0960 (-1.4263) 0.0159 (0.2061) 0.0191 (-0.2508) 0.7340 0.9086 977

0.0568 (1.1270) 0.0115 (-0.6079) 0.0086 (-0.4474) 0.0114 (-0.6033) 0.9014*** (38.2994) 0.0327 (-0.6167) 0.0861 (1.1873) 0.0230 (-0.2905) 0.2198** (-2.1579) 0.5697 0.8711 977

0.0851*** (2.6104) 0.0987*** (3.0955) 0.0942*** (2.9234) 0.0918*** (2.8758) 0.1941*** (-4.6792) 0.1598 (1.3963) 0.0437 (0.3658) 0.0576 (-0.3592) 0.1800 (-1.0338) 1.3202 0.1030 977

Notes: This table reports the excess returns, as well as the alphas with respect to the CAPM, three-factor model of Fama and French (1993), and five-factor model of Fama and French (2015), respectively. The portfolios are value-weighted each trading day and divided by ranking, AQI_svol. Additionally, the table reports the coefficients on risk factors for the five-factor model and the Sharpe ratio. All models are estimated by the OLS method with robust standard errors as per White (1980). The t-statistics are reported in parentheses. *, ** and, *** represent significance at the 10%, 5%, and 1% levels, respectively.

International Review of Economics and Finance 67 (2020) 267–287

CMA

0.1418*** (3.2374) 0.0873*** (3.6828) 0.0856*** (3.6075) 0.0804*** (3.4685) 0.7073*** (19.8606) 0.1271 (1.4582) 0.1298 (1.5074) 0.0806 (-0.6661) 0.3998*** (-3.3849) 1.6359 0.7415 977

P10 (High)

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International Review of Economics and Finance 67 (2020) 267–287

Table 10 Returns of OMP portfolios.

Excess return CAPM alpha Three-factor alpha Five-factor alpha MRK SMB HML RMW CMA Sharpe Ratio Adj-R2 N

OMP_AQI

OMP_PM2.5

OMP_PM10

OMP_SO2

OMP_CO

OMP_NO2

OMP_O3

0.1514*** (5.4460) 0.1598*** (5.7421) 0.1512*** (5.4345) 0.1452*** (5.3550) 0.1095*** (-3.6481) 0.2526** (2.1119) 0.2139* (1.8647) 0.0312 (-0.2285) 0.2720* (-1.7070) 2.7598 0.0728 977

0.1173*** (4.3444) 0.1273*** (4.7948) 0.1206*** (4.5245) 0.1125*** (4.3276) 0.1176*** (-4.0465) 0.2471** (2.2588) 0.2126** (2.0809) 0.0532 (0.4018) 0.2840* (-1.9582) 2.1959 0.0922 977

0.1177*** (4.4082) 0.1314*** (5.0812) 0.1240*** (4.7600) 0.1138*** (4.5177) 0.1645*** (-5.5270) 0.3041*** (2.8246) 0.2463** (2.3422) 0.0416 (0.3250) 0.4191*** (-3.3776) 2.2287 0.1786 977

0.0506* (-1.8603) 0.0360 (-1.3930) 0.0316 (-1.2910) 0.0456** (-2.0294) 0.2113*** (-8.9645) 0.3106*** (4.3444) 0.1015 (-1.3131) 0.0413 (0.4103) 0.6925*** (-7.0944) 0.9394 0.3650 977

0.0253 (0.8815) 0.0292 (1.0052) 0.0309 (1.2594) 0.0222 (0.9622) 0.1177*** (-4.9788) 0.3330*** (4.2205) 0.2627*** (-3.3548) 0.0220 (-0.1932) 0.4519*** (-3.7674) 0.4455 0.3669 977

0.3243*** (11.1062) 0.3238*** (10.7879) 0.3097*** (11.7549) 0.3157*** (12.1588) 0.0679*** (2.6192) 0.0829 (-0.9155) 0.4787*** (5.1521) 0.0102 (0.0860) 0.3820*** (3.2779) 5.6187 0.2659 977

0.0522* (1.9121) 0.0583** (2.1098) 0.0606** (2.2034) 0.0587** (2.2345) 0.0764** (-2.5264) 0.0179 (0.1345) 0.0916 (-0.7220) 0.1322 (0.9264) 0.0275 (0.1567) 0.9688 0.0223 977

Notes: This table reports the excess returns, as well as the alphas with respect to the CAPM, three-factor model of Fama and French (1993), and five-factor model of Fama and French (2015), respectively. The OMP portfolios are the average returns of small optimistic and large optimistic firms from which we subtract the average returns of small pessimistic and large pessimistic firms. Additionally, the table reports the coefficients on the risk factors for the five-factor model and the Sharpe ratio. All models are estimated by the OLS method with robust standard errors as per White (1980). The t-statistics are reported in parentheses. *, ** and, *** represent significance at the 10%, 5%, and 1% levels, respectively.

from city c1 on day t, svolc2,i,t is the search volume for firm i from city c2 on day t. We obtain the air pollution proxies, including AQI and pollutant concentrations, from the website of the Chinese Air Quality Study Platform (www.aqistudy.cn). This website provides statistics for the daily air quality index (AQI) and the concentrations of fine particulate matter (PM2.5), particulate matter (PM10), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3) for more than 300 Chinese cities since December 2013. The AQI is a composite index constructed based on the six pollutants listed above.1 A high AQI level indicates poor air quality. Consequently, individual investor’s mood is more pessimistic when AQ_svol increases. As previously mentioned, severe air pollution may undermine individuals’ ability to acquire and process information (Mohai et al., 2011; Weuve et al., 2012). Therefore, if air pollution adversely affects individual investors’ mood, they would not search for information on Baidu. As a result, the Baidu search volume on the stocks from the cities with heavy air pollution would experience a decline. Therefore, the air pollution in cities with heavy air pollution seems underestimated in our investor mood index, likely resulting in a relatively optimistic mood index. However, if we still find the air pollution effect by employing this investor mood index, the index is confirmed to be effective. 3.3. Portfolios Our portfolio analysis is focused on investor-mood-sorted and OMP portfolios. To form the investor-mood-sorted portfolios, we divide all the stock into 10 groups according to the individual investor mood index on each trading day. We then calculate the valueweighted average returns for the decile portfolios. Regarding the OMP portfolios, we follow the method of Fama and French (1992, 1993). On each trading day, we assign the stocks to six portfolios by independently ranking the stock according to the market value and the individual investor mood index. The size breakpoint is the median market value. The three investor-mood-sorted portfolios employ the bottom 30%, middle 40%, and top 30% breakpoints, corresponding to the optimistic, neutral, and pessimistic groups, respectively. The daily value-weighted average returns are computed for the six portfolios. The OMP portfolio return is calculated as the equal-weighted average returns of the two optimistic groups minus the equal-weighted average returns of the two pessimistic groups: 1 OMP ¼ ðSmall Optimism þ Big OptimismÞ 2 1  ðSmall Pessimism þ Big PessimismÞ 2

(14)

Therefore, we construct seven OMP portfolios (including OMP_AQI, OMP_PM2.5, OMP_PM10, OMP_SO2, OMP_CO, OMP_NO2, OMP_O3)

1 The AQI ranges from 0 to 500 in China. The air quality is respectively excellent, good, lightly polluted, moderately polluted, heavily polluted, and severely polluted, when the AQI is less than 50, between 51 and 100, between 101 and 150, between 151 and 200, between 201 and 300, and over 300. The air quality is healthy if the AQI is below 100.

279

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Table 11 Returns of OMP portfolios after controlling for market environment. OMP_AQI

OMP_PM2.5

OMP_PM10

OMP_SO2

OMP_CO

OMP_NO2

OMP_O3

0.2154*** (3.9963) 0.1848*** (3.8896) 0.1745*** (3.5511) 0.1768*** (3.6085) 166

0.1933*** (3.5730) 0.1587*** (3.4709) 0.1488*** (3.1216) 0.1450*** (2.9463) 166

0.1231** (2.3314) 0.0918** (2.0125) 0.0776* (1.7113) 0.0657 (1.4114) 166

0.0807 (-1.4084) 0.0733 (-1.3133) 0.0937** (-2.0450) 0.0978** (-2.0349) 166

0.0109 (0.2324) 0.0239 (0.5168) 0.0145 (0.3718) 0.0151 (0.4015) 166

0.3308*** (5.5410) 0.2870*** (5.9218) 0.2848*** (5.9187) 0.2899*** (5.8268) 166

0.0534 (1.1435) 0.0630 (1.4153) 0.0727 (1.5737) 0.0750 (1.5544) 166

0.2134*** (2.8529) 0.2395*** (3.1663) 0.2188*** (2.8808) 0.2098*** (2.8408) 302

0.1466** (2.0943) 0.1769** (2.5689) 0.1619** (2.3192) 0.1531** (2.2512) 302

0.2312*** (3.3076) 0.2731*** (4.0331) 0.2615*** (3.8609) 0.2491*** (3.8572) 302

0.0286 (0.4109) 0.0906 (1.6053) 0.0992* (1.7504) 0.0842* (1.6701) 302

0.015 (-0.1995) 0.0168 (0.2268) 0.0173 (0.2676) 0.0066 (0.1089) 302

0.4223*** (5.4844) 0.4037*** (5.0983) 0.3819*** (6.0345) 0.3894*** (6.3037) 302

0.1479** (2.0150) 0.1774** (2.4240) 0.1702** (2.2912) 0.1701** (2.3496) 302

0.0621*** (3.1050) 0.0608*** (3.0033) 0.0492** (2.3354) 0.0507** (2.4171) 489

0.0632*** (3.2746) 0.0614*** (3.1854) 0.0475** (2.3902) 0.0487** (2.4804) 489

0.0768*** (-3.6226) 0.0806*** (-3.7709) 0.1073*** (-5.6622) 0.1045*** (-5.6516) 489

0.0626*** (2.8981) 0.0472** (2.2126) 0.0169 (0.8842) 0.0244 (1.3163) 489

0.2231*** (10.6746) 0.2189*** (10.4246) 0.2200*** (10.1722) 0.2185*** (10.0306) 489

0.0115 (-0.5610) 0.0096 (-0.4662) 0.0117 (-0.5402) 0.0145 (-0.6668) 489

0.1726*** (3.4382) 0.1827*** (3.7642) 0.1781*** (3.7995) 0.1671*** (3.6758) 488

0.1722*** (3.4578) 0.1860*** (3.9804) 0.1817*** (3.9599) 0.1663*** (3.7938) 488

0.0244 (-0.4851) 0.0096 (-0.2074) 0.0115 (0.2694) 0.0117 (-0.3010) 488

0.0121 (-0.2274) 0.0075 (-0.1411) 0.0294 (0.6769) 0.0138 (0.3394) 488

0.4259*** (7.8579) 0.4254*** (7.7214) 0.3824*** (8.4062) 0.3977*** (9.0727) 488

0.1161*** (2.3036) 0.1221** (2.4208) 0.1266*** (2.6487) 0.1203*** (2.6581) 488

Panel A: Bear market Excess return CAPM alpha Three-factor alpha Five-factor alpha N Panel B: Bull market Excess return CAPM alpha Three-factor alpha Five-factor alpha N

Panel C: Low volatility period Excess return CAPM alpha Three-factor alpha Five-factor alpha N

0.1009*** (5.0450) 0.0966*** (4.7747) 0.0808*** (3.8855) 0.0807*** (3.8816) 489

Panel D: High volatility period Excess return CAPM alpha Three-factor alpha Five-factor alpha N

0.2019*** (3.9052) 0.2105*** (4.1270) 0.2089*** (4.2038) 0.1998*** (4.1362) 488

Notes: This table reports the excess returns, as well as the alphas with respect to the CAPM, three-factor model of Fama and French (1993), and five-factor model of Fama and French (2015), respectively. The OMP portfolios are the average returns of small optimistic and large optimistic firms from which we subtract the average returns of small pessimistic and large pessimistic firms. The coefficients on the risk factors are not reported. A bull (bear) market is defined as cumulative returns of the Shanghai A-share Composite Index over the past 180 trading days above 10% (below 10%). A high (low) volatility period is when daily market volatility, measured as the difference between highest and lowest price of the Shanghai A-share Composite Index, and then divided by the average of highest and lowest price, is above (below) the median. All models are estimated by the OLS method with robust standard errors as per White (1980). The t-statistics are reported in parentheses. *, ** and, *** represent significance at the 10%, 5%, and 1% levels, respectively.

based on seven air-pollution-induced investor mood indices. We calculate the alphas using a capital asset pricing model (CAPM), threefactor model, and five-factor model. The explanatory variables include market excess return (MRK), size factor return (small-minus-big, SMB), value factor return (high-minus-low, HML), profitability factor return (robust-minus-weak, RMW), and investment factor return (conservative-minus-aggressive, CMA). These risk factors follow Fama and French (1992, 1993, 2015). The daily risk factors are obtained from the CSMAR database.

3.4. Other variables We examine the effect of air-pollution-induced individual investor mood on firm-level trading activities and stock returns. The controlling variables include air pollution in the cities where firms are headquartered, weather conditions, SAD effects, lunar phases, investor attention, local economic conditions, and stock market returns. Wu et al. (2018a) and Zhang et al. (2017) prove that local air pollution affects local investor mood adversely and also reduces the 280

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Table 12 Returns of OMP portfolios after controlling for the week effect. Monday

Tuesday

Wednesday

Thursday

Friday

0.1027 (1.3150) 0.1137 (1.4974) 0.1272 (1.5739) 0.1355* (1.7109) 189

0.1661** (2.9398) 0.1705*** (2.8840) 0.1736*** (2.6824) 0.1614*** (2.6647) 198

0.2331*** (3.6028) 0.2478*** (3.4703) 0.2084*** (3.3205) 0.1557*** (2.6556) 200

0.1435** (2.3525) 0.1324** (2.3394) 0.1269** (2.1573) 0.0970* (1.9664) 196

0.1075** (2.2970) 0.1148** (2.4769) 0.1176** (2.5416) 0.1206** (2.6015) 194

0.0064 (-0.0924) 0.0057 (0.0858) 0.0132 (0.2065) 0.0189 (0.2983) 189

0.135** (2.2500) 0.1471** (2.4110) 0.1508** (2.3209) 0.1364** (2.1545) 198

0.2378*** (3.8293) 0.2536*** (3.8239) 0.2168*** (3.6816) 0.1601*** (2.9411) 200

0.1475** (2.3487) 0.1370** (2.2901) 0.1377** (2.2704) 0.1065* (1.9076) 196

0.0651 (1.4662) 0.0760* (1.8297) 0.0786* (1.8904) 0.0821** (1.9844) 194

0.0471 (0.7336) 0.0593 (0.9479) 0.0629 (0.8880) 0.0673 (0.9674) 189

0.1508** (2.6181) 0.1753*** (3.0821) 0.1827*** (3.0445) 0.1557*** (2.7520) 198

0.2154*** (3.1676) 0.2397*** (3.2928) 0.1870*** (2.9720) 0.1321** (2.3040) 200

0.1094** (1.9641) 0.0946* (1.9566) 0.0948* (1.8380) 0.0624 (1.4602) 196

0.0603 (1.1754) 0.0758 (1.6090) 0.0798* (1.7193) 0.0851* (1.8377) 194

Panel A: OMP_AQI Excess return CAPM alpha Three-factor alpha Five-factor alpha N Panel B: OMP_PM2.5 Excess return CAPM alpha Three-factor alpha Five-factor alpha N Panel C: OMP_PM10 Excess return CAPM alpha Three-factor alpha Five-factor alpha N

Notes: This table reports the excess returns, as well as the alphas with respect to the CAPM, three-factor model of Fama and French (1993), and five-factor model of Fama and French (2015), respectively. The OMP portfolios are the average returns of small optimistic and large optimistic firms from which we subtract the average returns of small pessimistic and large pessimistic firms. The coefficients on the risk factors are not reported. All models are estimated by the OLS method with robust standard errors as per White (1980). The t-statistics are reported in parentheses. *, ** and, *** represent significance at the 10%, 5%, and 1% levels, respectively.

stock returns of firms headquartered locally. As mentioned in subsection 3.2, we collect data on the seven air pollution proxies (including AQI_local, PM2.5_local, PM10_local, SO2_local, CO_local, NO2_local, and O3_local) for locally situated firms from the website of the Chinese Air Quality Study Platform. The data on weather conditions are from the Weather Underground Corporation website (WUC: www.wunderground.com), which provides the hourly air pressure, relative humidity, temperature, wind speed, and visibility for 34 major Chinese cities. We average the hourly air pressure in kPa (Pressure), relative humidity in percentage (Hum), temperature in degrees Celsius (Temp), and visibility in kilometers (Visibility) for each trading day. We define a dummy variable for the cloud cover ratio (Cloud), which equals 1 if partly cloudy, mostly cloudy, or entirely cloudy, and 0 otherwise. The wind speed (Wind) is also a dummy variable, which takes 1 when the daily average wind speed exceeds 5 km/h, and 0 otherwise. Kamstra et al. (2003) confirm that the investors suffering from SAD effects avoid risky portfolios in the fall and resume trading in risky assets in winter, finally giving rise to a decline in returns in the fall and an increase in returns following the longest night of the year. We follow Kamstra et al. (2003) in defining the SAD and fall effects as follows. The SAD effect (SAD) is measured as the number of nighttime hours minus 12 for the city where the firms are headquartered. The fall effect (Fall) is set as a dummy, which takes 1 from September 21 to December 20 for each year, and 0 otherwise. The lunar phases in our study include the new moon and full moon. The findings of Yuan, Zheng, and Zhu (2006) show that stock returns are lower during the full moon than that during the new moon. The Cycle Tourist website (cycletourist.com/moon) provides the date and time of the new moon, first quarter, full moon, and last quarter each month. The full moon dummy (Fullmoon) equals 1 during the three days before and after each full moon, and 0 otherwise. By analogy, the value of the new moon dummy (Newmoon) is 1 during the three days before and after each new moon, and 0 otherwise. Investor attention (Svol) is represented by the search volume in the Baidu Search Index when using the stock code as the search term. It is calculated as the deviation from the average over the past 15 days. Additionally, we control for the economic conditions in the cities where firms are headquartered and stock market returns. The local economic conditions include local GDP (Gdp), growth of local GDP (Ggdp), local population (Pop), and growth of local population (Gpop). Stock market returns are measured by the average returns of the Shanghai A-share Composite Index over the past 30 trading days. 281

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As shown in Table A1, Panels A, B, C, and D report the summary statistics of investor trading measures, stock-level dependent variables, air-pollution-induced individual and local investor mood indices, and the control variables, respectively. 4. Air-pollution-induced individual mood and investor trading Here, we test the validation of our individual mood indices. In other words, we expect to observe individual mood indices to be significantly associated with individual investors’ trading activities rather than institutional investors’ trading activities. First, we examine the changes in firm trading volume for individual and institutional investors during sudden downturns in investor mood. Severe air pollution induces a negative investor mood and increases risk aversion, in turn causing investors to be unwilling to buy or hold risk assets. Therefore, if the previously constructed individual investor mood proxies are really effective, we expect to observe a significant decrease (increase) in individual investors’ buying (selling) volume during a sudden downturn in investor mood, whereas we do not expect a significant decrease (increase) in institutional investors’ buying (selling) volume for the same firms and during the same period. Because air quality is unhealthy when AQI is larger than 100, investor mood may be relatively pessimistic when AQI_svol is above this value. Therefore, we select event windows when AQI_svol is higher than 100 on day t, but lower 100 during days t-5 to t-1 and during days tþ1 to tþ2. Finally, we obtain 22,148 windows. Table 1 shows the average trading volumes for individual and institutional investors during sudden downturns in investor mood. Panel A of Table 1 reports the value of the average buying volume during the event windows. On the days when a downturn in investor mood occurs, the average individual investors’ buying volume decreases by RMB 2179.3790 (¼48,498.0290–46,318.6500) thousand, a decline of approximately 4.49% from the average trading volume of the previous five trading days. It then returns to normal levels during the following two days. However, the average institutional investors trading volume does not decrease but increases slightly by RMB 370.5320 (¼21,685.2840-21,314.7520) thousand. Panel B reports the average number of buy orders and similar results are obtained. Panels C and D report the average selling volume during sudden downturns in investor mood. Both individual and institutional investors’ trading volumes indicate an insignificant change on day t. Overall, when a sudden downturn in investor mood occurs, only individual investors’ buying volume decreases remarkably, thereby confirming the validity of our investor mood index. Second, we conduct t tests for individual and institutional investors’ trading behaviors. Table 2 shows the results. In Panel A of Table 2, we divide our observations into three types according to the individual investor mood, for which variable AQI_svol serves as proxy. The breakpoints are when AQI_svol equals 100 and 150. Accordingly, observations with AQI_svol below 100 and above 150 correspond to the optimistic and pessimistic individual investor mood groups, respectively. We examine the differences in investor trading behaviors between the two groups by employing a t-test, which shows significant differences in individual investors’ trading behaviors, whereas institutional investors’ trading behaviors do not present remarkable differences between the two groups. For example, the average excess buy-sell measure of retail investors based on trading volume value (BSI_indvol) in the optimistic group is 0.4158 and that in the pessimistic group is 0.2504. The difference between them is 0.1654, and the corresponding t-statistic is 4.0153 and statistically significant at the 1% level. This indicates that the poorer the individual investor mood is, the less stocks individual investors buy. More importantly, it provides preliminary evidence that our individual investor mood indices are only related with individual investors’ mood because the buy-sell imbalance is usually employed as an investor mood proxy. In Panels B and C of Table 2, the breakpoints are the mean of AQI_svol and its 67th percentile, respectively. We obtain similar results. Next, we conduct regression analysis and the estimated model is as follows: Tradei;t ¼ α0 þ α1 AQ svoli;t þ α2 Tradei;t1 þ αCtrol þ μi;t

(15)

where Tradei,t refers to trading behavior measures of individual and institutional investors, as mentioned in Equations (1)–(12), for stock i on trading day t. AQ_svol is the firm-level air-pollution-induced individual investor mood, including AQI_svol, PM2.5_svol, PM10_svol, SO2_svol, CO_svol, NO2_svol, and O3_svol. On the right-hand side of the model, we include the lagged values of the dependent variables, local air pollution, weather conditions, SAD effect, lunar phases, investor attention, local economic conditions, stock market returns, and Monday and month effects. We estimate the regression by the cross-sectional firm-specific fixed effects method with robust standard errors clustered by firm as per Angrist and Pischke (2009), and the results are shown in Table 3. Due to space considerations, we report only the coefficients on air-pollution-induced investor mood indices. Panel A of Table 3 reports the effects of the individual investor mood on BSI_indvol. We find that six of the seven coefficients on the investor mood indices are significantly negative, implying individual investors buy less stock when their mood is poor. Particularly, a one standard deviation change in AQI_svol (25.7626 from Table A1) would induce a decline of 0.0464 (0.0018  25.7626) in BSI_indvol. Panels B and C of Table 3 report the regression results for Buy_indvol and Sell_indvol, respectively. This shows that our investor mood indices are negatively associated with individual investors’ buying volume, whereas they are insignificantly related with selling volume. This reconfirms that the increase in the investor mood indices causes individual investors to buy less stock assets compared with selling stock assets. Panels D to F of Table 3 show the results of the trading behavior measures based on the number of the trading orders submitted by retail investors. It also indicates that a poorer investor mood markedly decreases individual investors’ preference for buying stock. Panels G–L report the effects of investor mood on institutional investors’ trading behaviors. However, we find that most investor mood indices do not significantly affect institutional investors’ trading activities. Although two or three of the coefficients on the mood indices are statistically important, their regression direction is not consistent; thus, we cannot obtain a consistent conclusion. Therefore, our airpollution-induced investor mood indices are more likely to capture individual investors’ mood rather than institutional investors’ mood, which validates the indices.

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5. Effects of air-pollution-induced individual investor mood in the Chinese stock market 5.1. Research specification The baseline model in this section is expressed as follows: Depi;t ¼ β0 þ β1 AQ svoli;t þ β2 AQ localc;t þ β3 Cloudc;t þ β4 Tempc;t þ β5 Windc;t þ β6 Pressurec;t þ β7 Humc;t þ β8 Visibilityc;t þ β9 SADc;t þ β10 Fallt þ β11 Fullmoont þ β12 Newmoont þ β13 Svoli;t þ β14 Gdpc;t þ β15 Ggdpc;t þ β16 Popc;t þ β17 Gpopc;t þ β18 Rmt þ β19 Mondayt þ β20 Depi;t1 þ

11 X

(16)

λj monthjt þ εi;t

j¼1

Where Dep is the firm-level dependent variables, including illiquidity, turnover, volatility, and stock returns. AQ_svol is the firm-level airpollution-induced individual investor mood, including AQI_svol, PM2.5_svol, PM10_svol, SO2_svol, CO_svol, NO2_svol, and O3_svol. AQ_local is the air pollution proxy in the city where the firm is headquartered, including AQI_local, PM2.5_local, PM10_local, SO2_local, CO_local, NO2_local, and O3_local. Cloud, Temp, Wind, Pressure, Hum, and Visibility represent the city-level cloud cover, temperature, wind speed, air pressure, relative humidity, and visibility in the city where firms are locally headquartered, respectively. SAD is the seasonal affective disorder effect. Fall, Fullmoon, and Newmoon are the autumn, full moon, and new moon dummy variables, respectively. Svol is the investor attention for each stock. Gdp, Ggdp, Pop, and Gpop are the local GDP, growth of local GDP, local population, and local population growth, respectively. Rm are the average market returns over the past 30 trading days. Monday and month effects are also included in the baseline model. We run all models using cross-sectional firm-specific fixed effects with robust standard errors clustered by firm as per Angrist and Pischke (2009). 5.2. Statistical analysis Table 4 shows the Pearson correlation coefficients of individual investor indices with stock returns and trading activities. The results indicate that six of the seven individual investor mood proxies are negatively related with stock returns and are statistically significant, implying that a pessimistic individual investor mood is accompanied by low stock returns. Regarding trading activities, a poorer individual investor mood is significantly associated with high illiquidity, low turnover, and low volatility. We then divide our observations into three types, according to AQI_svol based on the breakpoints of 100 and 150. Therefore, the observations with AQI_svol below 100 (above 150) are in the optimistic (pessimistic) individual investor mood groups. We examine the differences in stock returns and trading activities between the two groups by employing a t-test. Table 5 shows the results, which coincide with the findings in Table 4. 5.3. Overall results Table 6 reports the results of how the individual investor mood influences trading activities. The six coefficients on the individual investor mood in Panel A of Table 6 are positive, which means that a poorer individual investor mood significantly undermines stock liquidity. The results in Panel B of Table 6 indicate a negative effect of individual investor mood on turnover. According to Liu (2015), under short-sale constraints, a more positive investor mood gives rise to larger noise trading and thus increases market liquidity if combining the models of Kyle (1985) and De Long et al. (1990). The literature provides evidence that the majority of individual investors are noise traders (Kyle, 1985). Thereby, our findings support this opinion and also shows the important effects of individual investor mood on firm-level liquidity. As shown in Panel C, all individual investor mood proxies are negatively related with return volatility. Gervais and Odean (2001) and Statman, Thorley, and Vorkink (2006) note that optimism and overconfidence are usually related; thus, optimistic investors trade aggressively, further increasing return volatility, and our results are consistent with this viewpoint. However, if air pollution affects trading activities through its effects on investor mood rather than those on stocks’ fundamental value, we expect to observe a short-term individual investor mood effect on illiquidity, turnover, and volatility. That is, when the individual investor mood indices are larger, turnover and volatility will decrease, whereas illiquidity will increase and will revert over the subsequent days. To investigate this hypothesis, we choose the event windows for which the AQI_svol is above 100 on day t and below 100 during days t-2 to t-1 and tþ1 to tþ2. Next, we calculate the average illiquidity, turnover, and volatility during these windows. Fig. 1 shows the changes in the three trading activities from days t-2 to tþ2. The top graph indicates that illiquidity increases on day t and decreases in the following two days. The graph on the bottom left shows that turnover experiences a significant drop on day t and gradually recovers in the subsequent days, which is consistent with the illiquidity results. The graph on the bottom right indicates that volatility does not show an obvious pattern. Nevertheless, the effects of the air-pollution-induced mood indices on stock liquidity can be attributed to investor mood. From the above results, air pollution depresses individual investors and induces them to trade less actively and aggressively. Moreover, we explore whether a pessimistic individual investor mood affects stock pricing. The results are presented in Table 7 and indicate that almost all the individual investor mood proxies decrease stock returns significantly. In Model 1, the coefficient on AQI_svol

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is 0.0177, implying that a one standard deviation change in AQI_svol (25.7626, given in Panel C of Table A1) would cause a decline of 0.0046% (0.000177  25.7626) in daily stock returns, corresponding to 1.15% of annualized returns based on 250 trading days. The air pollution proxies for local investor mood reduce stock returns remarkably. This result is in line with Wu et al. (2018a) and Zhang et al. (2017), who confirm that a poorer local investor mood induced by air pollution affects locally headquartered stocks through the home bias. Similarly, the coefficients on weather conditions provide obvious evidence of the weather effects on financial markets studied by previous literature (Keef & Roush, 2002; Lu & Chou, 2012; Yoon & Kang, 2009). According to the coefficients on the SAD effect and autumn dummy, we do not find that investors suffering from SAD avoid risky portfolios in the fall and resume risky assets in winter, as discussed by Kamstra et al. (2003). Investor attention increases stock returns, supporting Da et al. (2011) view that a higher level of attention increases stock prices. We find stock returns are low during both the full moon and new moon periods, but they decrease more during the full moon period. 5.4. Robustness check To verify the robustness of our results, we employ several models. First, as the residual error terms in the regressions may be crosssectionally related, we re-estimate our models using the Fama–MacBeth method to correct for bias (Fama & MacBeth, 1973), and Panel A of Table 8 reports the results. Due to space considerations, only the coefficients on individual investor mood are shown here. The effects of individual investor mood on illiquidity are not clear, but the effects on turnover are markedly negative. Meanwhile, a poorer individual investor mood gives rise to a decline in both volatility and stock returns. In summary, the results are consistent. Second, we employ de-seasonalized mood proxies instead of raw mood proxies, following Goetzmann et al. (2015) and Hirshleifer and Shumway (2003). Seasonal air pollution is measured as the average daily air pollution for the same calendar week over the entire sample. De-seasonalized air pollution is the raw air pollution after subtracting the seasonal air pollution. Next, we reconstruct the individual investor mood index and rerun our regressions, obtaining similar results, reported in Panel B of Table 8. Meanwhile, we also use air pollution proxies detrended with the moving average over the past two weeks and our results are still robust, as shown in Panel C of Table 8. Third, as proved in previous studies, it is difficult for small firms to value and perform arbitrage, so the stock prices of small firms are more vulnerable to investor mood (Baker et al., 2012; Baker & Wurgler, 2006). We speculate that our results depend significantly on including small firms. To exclude this potential question, we delete the firms listed on the SZSE due to being generally smaller than the ones listed in the SSE and then rerun our tests. As reported in Panel D of Table 8, a high level of individual investor mood accompanies low liquidity, volatility, and stock returns. Finally, we conjecture that our results may be attributed to the roles of investors located in the cities where the stock exchanges are situated. Therefore, we re-estimate the models after excluding the firms headquartered in Shanghai and Shenzhen. We obtain similar results, as reported in Panel E of Table 8. Additionally, we exclude financial firms and rerun our tests, the similar results being reported in Panel F of Table 8. 6. Analysis of individual investor mood-sorted portfolios The results in the above sections state that our air-pollution-induced mood proxies influence individual investors’ trading behaviors and also significantly decrease firm-level stock returns. However, the question is whether individual investor mood is related to the riskadjusted returns. If the relationship is insignificant, the relationship of individual investor mood with daily stock returns would be attenuated or even eliminated by an efficient asset pricing model. Otherwise, stocks with an optimistic individual investor mood are associated with higher risk-adjusted returns than those with a pessimistic individual investor mood due to limited market efficiency. This is the main issue we focus on in this section. 6.1. The returns of individual investor mood-sorted portfolios Here, we consider the returns of 10 portfolios sorted by the individual investor mood index. The regression specification is as follows: Rt ¼ α þ β1 MRKt þ β2 SMBt þ β3 HMLt þ β4 RMWt þ β5 CMAt þ μt

(17)

Table 9, A2, and A3 show the returns for the 10 groups sorted by ranking AQI_svol, PM2.5_svol, and PM10_svol, respectively. The rightmost column tests the return difference between the groups with the most optimistic and pessimistic individual mood. They report excess returns, as well as the alphas with respect to the CAPM, three-factor model of Fama and French (1993), and five-factor model of Fama and French (2015), respectively. Accordingly, these alphas are calculated by Equation (17) by including the first 1, 3, and 5 variables in the right-hand side, respectively. The coefficients on the explanatory variables in the five-factor model and the Sharpe ratio are also reported. As per Table 9, excess returns decrease almost monotonically as individual investor mood gradually becomes more pessimistic. The right-most column shows that the difference in excess returns between the most optimistic and the most pessimistic mood groups is 0.0851, with a corresponding t-statistic of 2.6104, implying that the portfolio with the most optimistic investor mood earns on average 0.0851% more than the portfolio with the most pessimistic investor mood on each trading day. After controlling for the risk factors, the alphas in the most optimistic four groups are significantly positive, whereas the others are insignificant. Moreover, the abnormal returns in the most optimistic group are exceedingly larger than the ones in the most pessimistic group, ranging from 0.0918% to 0.0987% 284

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(corresponding t-statistics range from 2.8758 to 3.0955). A possible reason is that the optimistic groups have lower loadings on market risk factors than the pessimistic groups. Tables A2 and A3 also show consistent results. 6.2. OMP portfolios Here, we explore the performance of OMP portfolios. An OMP portfolio is calculated as the average returns of big and small optimistic firms subtracting the average returns of big and small pessimistic firms. All OMP portfolios are constructed in this way by ranking market capitalization and the seven individual investor mood indices. Table 10 reports the returns of OMP portfolios with or without risk factors adjusting and the risk loadings for the five-factor model. Five of the seven OMP portfolios have importantly positive excess returns and abnormal returns, whereas abnormal returns perform better in terms of economic magnitude and statistical significance. Taking OMP_AQI as an example, an OMP portfolio that is long on optimistic stocks and short on pessimistic stocks would respectively earn abnormal returns with respect to the CAPM, three-factor model, and five-factor model of 0.1598%, 0.1512%, and 0.1452% per trading day, ignoring transaction costs and short-sale constraints. The exposure of OMP_AQI to the market risk factor is negative, whereas the exposures to the size and value factors are positive, indicating that OMP portfolios are long on low-beta small stocks and stocks with high book-to-market ratios and whereas short on highbeta large stocks and stocks with low book-to-market ratios. The loadings of the profitability factor are insignificant. The negative loadings of the investment factor imply that optimistic stocks appear to have aggressive investment because the investment factor is long on stocks with low investment. Fig. A1 plots the returns of the OMP portfolios over time. Particularly, Panel A of Fig. A1 shows the cumulative excess returns of OMP_AQI, while Panel B shows the OMP_AQI’s cumulative abnormal returns with respect to market risk, size, value, profitability, and investment factors. This indicates that the OMP portfolios earn positive excess and abnormal returns over almost the entire research period, implying our results are not possibly attributed to a subsample. 6.3. Robustness of OMP portfolios We conduct a series of tests to check the robustness of the OMP portfolio performance. First, we assign our sample to one of 18 industries according to the industry classification standard of the China Securities Regulatory Commission. After excluding industries with less than 30 firms, we obtain 1559 firms belonging to 10 industries. In each industry, the OMP portfolio is the value-weighted average return difference between the most optimistic 30% of firms and the most pessimistic 30% of firms. Fig. A2 shows the abnormal returns of OPM_AQI for each industry. Specifically, Panel A reports the alphas from the five-factor model and the corresponding t-statistics are reported in Panel B. The alphas are markedly positive in all industries except two. Therefore, we obtain consistent results. Second, we are concerned that our results only exist in a specific market environment. We thus divide the sample into two subsamples according to past market returns and market volatility, respectively. The bull (bear) market is defined as the cumulative returns above 10% (below 10%) of the Shanghai A-share Composite Index over the past 180 trading days. A high (low) volatility period is when daily market volatility, measured as the difference between the highest and lowest price of the Shanghai A-share Composite Index and then divided by the average of highest and lowest price, is above (below) the median. Table 11 reports the results for different market periods. This shows that the OMP portfolios earn significantly positive returns regardless of the market environment, coinciding with the prior findings. It is worthwhile noting that the OMP portfolios outperform the others in a bull market and over high volatility periods. We speculate that the OMP portfolios contain more irrational information not eliminated by the risk model in a bull market and over high volatility periods because noise trading is more frequent and aggressive, thus delivering larger abnormal returns. Third, our results could be likely driven by the specific day-of-the-week due to the week effects of Damodaran (1989). Therefore, we assign our sample into five groups by weekdays. Table 12 shows the OMP portfolios’ excess returns and alphas with respect to the CAPM, three-factor model, and five-factor model on weekdays. The results are similar, although the returns of the OMP portfolios on Mondays are less remarkable. 7. Conclusions A growing body of literature on psychology and behavioral finance proves that air pollution is a natural condition that adversely affects investor mood, leading to behavior bias in the financial market. Individual investors are more vulnerable to mood than institutional ones. Individual investors in the Chinese stock market play a dominant role, which provides a perfect setting to test how the individual investor mood affects the stock market. To this end, we construct a daily firm-level individual investor mood index by employing air pollution and investor attention data, for which the Baidu Search Index serves as proxy. We find that a poorer air-pollution-induced investor mood increases risk aversion, thus causing individual investors, but not institutional ones, to buy less stocks. Meanwhile, a pessimistic individual investor mood significantly lowers stock returns, liquidity, and volatility. However, we obtain robust results by using different regression methods, investor mood proxies, and samples, respectively. Finally, our results indicate that long optimistic and short pessimistic portfolios could earn abnormal returns, suggesting the limited efficiency of the Chinese market. Additionally, the results remain robust even after conducting a series of tests.

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Author statement QinqinWu: Conceptualization, Methodology, Software, Data curation, Visualization, Writing-Original draft preparation; Jing Lu: Supervision, Writing-Reviewing and Editing, Project administration, Funding acquisition. Acknowledgements Jing Lu gratefully acknowledges the financial support from the National Natural Science Foundation of China (No: 71973018) and the Fundamental Research Funds for the Central Universities (No: 2018CDJSK02PT10). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.iref.2020.02.001. References Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5, 31–56. Andersen, T. G., Bollerslev, T., Diebold, F. X., & Ebens, H. (2001). The distribution of realized stock return volatility. Journal of Financial Economics, 61, 43–76. Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist’s companion. 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