Impact of COVID-19 policy on stock prices of listed property companies

The research of foreign scholars using the event study to analyse trends of changes in stock prices and other financial market fluctuations is relatively mature. Dolly uses the event study to study the stock price of the stock split in the past 10 years in the 1920s. By further observing the changes of the stock price in the process of stock split, Dolly deeply studies and discusses the effect of stock price in the process of stock split [9]. Fama explicated the event study method in 1969, and this study eventually laid the foundation for a complete theoretical system. Fama found that the data of daily average return is an important indicator to measure the ability of stock price volatility to respond to events. Fama further submit that, the existence of special cases in which stock prices cannot quickly reflect event information makes the conclusion of efficiency research still controversial [10]. Ball's innovative research focuses on the delayed response of stock prices to events [11]. The new breakthrough in this field has triggered a great discussion in various academic circles. Many scholars have devoted themselves to the study of a series of problems, such as ‘why delay exists’ and ‘the relationship between earnings volatility and technical errors’. The study of Craig MacKinlay [12] examines the practical application and performance of the event study approach in economics and finance. Based on the event study method, Khotari and Warner [14] summarised the research review of the event study method in detail, and concluded that: ‘The form of event study method can change with the change of events, and the nature of event study method can change with the change of the characteristics of sample companies’. Summarising the research progress and changes of relevant research pertaining to the event study method in the past decade, Charles [15] made a detailed review. While foreign scholars largely agree that the event study method has been applied to many situations, it does not mean that a single research method can be applied to all research contexts.

The application research of event study method can be elaborated from two perspectives: event study within a country and cross-event study across many countries.

As an example for a study of events within a country, Caliskan and Najand's [16] study of the impact of metal price movements on capital markets may be mentioned. The following further examples can be quoted: The Zhang et al. [17] study examines the impact of domestic policy changes on the stock market, and makes a comparative analysis of the two stages before and after the changes. Aris Andriyani and Aryani [18] used the event study method to study the capital markets' reaction to the 2016 US presidential election. Yogi Makino [19] used the event study method to study the reaction of the Japanese stock market to domestic chemical accidents.

With regard to the multinational cross-event study, Aizenman et al. [20] focussed on the reaction of developing countries' securities markets to the European debt crisis using the event study method; Ruffino et al. [21] studied the impact of the US presidential election on the Indonesian stock market; Dimpfl [22] used event studies to analyse, among other things, the impact of US news on the German stock market.

The relevant research on the impact of policy information on stock price changes began in the 1970s. Together with an increasing number of scholars paying attention to the impact of policy information on stock price changes, many scholars also use the event study method to study the impact of relevant policy information, transmitted by the national macro-level policy authority, on the stock market. Further analysis of the impact of relevant policy information on stock price changes, fluctuations and trends can be found in the study of Fama [23], whose research confirmed that there was a significant negative correlation between the price return of stocks and the level of inflation. In 2002, Morelli used GARCH and VAR models to study the stock market and found that it was different from the American stock market. The information of exchange rate policy in the macro-economy of the United Kingdom has a significant impact on the price changes of stocks.

China's stock market is often referred to as the ‘policy’ market, and many scholars focus on the impact of policy information release events on the stock market; and most of the research results show that these events have an important impact on stock price changes. Han and Tang [24] also used the event study method, and the conclusion reached is that the multiple objectives and responsibilities of the government in issuing policy information will certainly increase the amount of financing in the stock market and thus exert a significant impact on the stock market. Xiao [25] believes that in a short period of time, the release of policy information has a more significant impact on the stock prices of listed companies in China, but the release of policy information does not always have a significant positive relationship with the change in stock price. When domestic scholars study the impact of policy information release on stock prices, they usually choose the policy information release events with typical social characteristics of China as the main object of study. Based on the event study method, Yang and Yang [26] selected the tax policies issued from 2016 to 2018 as the research events. The study finds that these events have had a significant positive effect on the stock prices of SMEs and GEM enterprises in China.

Since the outbreak of COVID-19, although more than 100 pieces of policy information have been released by the state, ministries and local governments – in order to control the spread of the COVID-19 epidemic and prevent the number of infections from crossing the manageable limit, given scarce healthcare resources, and also to support large, medium and small enterprises – multiple factors were considered in the selection of policy information related to COVID-19: Firstly, policy information with high relevance to enterprises were emphatically selected; secondly, it was ensured that the time distribution of policy information related to the COVID-19 situation was reasonable, and the policy information releases selected were such that each was maintained at a certain time difference to avoid mutual influence; thirdly, every effort was made to avoid the interference of different policy information pieces related to COVID-19 that could arise owing to their nature, content and intensity; and finally, it was ensured that the selection of policy information related to the epidemic situation of COVID-19 met the objective criteria [8]. Based on these factors, we selected three COVID-19 policies as the research event of this paper, the relevant policy announcement day as the event day, excluding holidays, extended to the next trading day. The research events are shown in Table 1.

Since this study focuses on the impact of policy information related to COVID-19 on the short-term fluctuation of stock prices of listed companies of property service enterprises in China, the impact of research events released by policy information related to COVID-19 will be relatively short. Therefore, the first 10 trading days and the last 10 trading days of the event day (T = 0) studied in this paper are selected as the event windows (−10, 10). Considering that the long time period of the estimation window will be affected by the overlap of some other factors, and the short time period of the estimation window will lead to non-representative normal returns, this paper finally selects the event window in the 10 trading days before and after the event day (T = 0), and selects the estimation window from the 120 trading days before the event day (T = 0) to the 10 trading days before the event day (−120, −10), a total of 110 days.

As shown in Table 2, we combed the event date, event window and estimation window corresponding to the policy information disclosure events of COVID-19.

Since the event time defined in Table 2 for the release of policy information related to COVID-19 is 20 January 2020, in order to obtain sufficient public data, the selected listed property service companies should have been listed before 31 December 2019. Therefore, this study excludes the listed companies of property service enterprises that were not listed before 31 December 2019.

Due to the different opening times of Shanghai Stock Exchange, Shenzhen Stock Exchange and Hong Kong Stock Exchange during the Spring Festival of 2020, in order to make the data comparable, the listed companies of property service enterprises listed in Shanghai and Shenzhen are excluded.

Therefore, 17 H-share listed property service companies were finally selected as the sample for the study, as shown in Table 3. The following is a list of companies selected: Color Life Services, China Overseas Property Management, Zhongao Dajia, Greentown Property Services, Clifford Modern Living, Pujiang China, A-Living Services, Country Garden Services, S-Enjoy Services, Kaisa Prosperity, Ever Sunshine Lifestyle Services, Binjiang Property Management, Aoyuan Healthy Life, Hehong Service, Xinyuan property management, Languang Justbon Property Services and Yincheng Life Services.

The market return model is an improvement of the mean return model, which carries the consequence of enhancing the effect of detecting events. Therefore, this research finally decided to adopt the market return model – the modified CAPM model as a tool to predict the normal return rate. The model equation is as follows: Rit=αi+βiRmt+εit, E(εit)=0, Var(εit)=σi2 {R_{it}} = {\alpha _i} + {\beta _i}{R_{mt}} + {\varepsilon _{it}},\quad E\left({{\varepsilon _{it}}} \right) = 0,\quad Var\left({{\varepsilon _{it}}} \right) = \sigma _i^2 where R_it represents the real return of study sample i on day t and R_mt is the real return of the capital market m on day t, and ɛ_it is a random disturbance term. Based on the linear relationship between the daily return of individual stocks and the daily return of market index, this model takes the actual return R_it and the market index return R_mt in the same period of stock i in the estimation window (−120, −10) and the event window (−10, 10) as the research samples, respectively, for the calculation data.

The market return model is established for parameter estimation, and the actual return rate of individual stocks and market indexes in the same period in the estimation window is substituted. The estimated parameters in the study period are obtained, and the expected return rate of the company's stock on the day of the event period is obtained.

For each property company, the actual return of the stocks in the estimation period on the actual return rate of the capital market was regressed by using the market model to obtain the estimated parameters of α_i and β_i. They are α^i {\hat \alpha _i} and β^i {\hat \beta _i} , respectively. Therefore, the expected return of the stocks of the property company i on day t during the event window represents R^it {\hat R_{it}} , and the formulation can be expressed as follows: R^it=α^i+β^iRmt {\hat R_{it}} = {\hat \alpha _i} + {\hat \beta _i}{R_{mt}}

The actual return rate of market index in the event window is substituted into this model equation as R_mt, and the normal return rate R^it {\hat R_{it}} predicted in the event window can thus be calculated.

The abnormal return (AR) is the difference between the actual return and the predicted normal return of the sample. In the event window (t1, t2), the average abnormal return (AAR) AR_it (the AR) of the property company i on day t can be formulated as follows: ARit=Rit−R^it=Rit−α^i−βiRmt, t1<t<t2 A{R_{it}} = {R_{it}} - {\hat R_{it}} = {R_{it}} - {\hat \alpha _i} - {\beta _i}{R_{mt}},\quad \quad {t_1} < t < {t_2}

Using the above model equation, we can get the normal rate of return in the event window, and subtract the normal rate of return from the actual rate of return in the event window, that is, the abnormal rate of return.

The AAR is the arithmetic average of the daily ARs of all research samples in the event window, which can be formulated as follows: AARt=1n∑i=1nARit AA{R_t} = {1 \over n}\sum\limits_{i = 1}^n A{R_{it}}

N is the number of research samples. The specific application in this paper can be expressed as follows. In the period of (−10, 10), the AAR sample stocks of 17 H-share property service companies on a certain day are equal to the cumulative value of the excess return on sample stocks of the 17 H-share property service companies on that day divided by 17.

Cumulative abnormal return (CAR) is the sum of daily average excess returns of all research samples in the event window, which can be formulated as follows: CARi=∑t1t2AARt=1N∑t1t2∑i=1NARit CAR_{i} = \sum\limits_{{t_1}}^{{t_2}} AAR_{t} = {1 \over N}\sum\limits_{{t_1}}^{{t_2}} \sum\limits_{i = 1}^N {AR}_{it}

Since the 17 listed property companies in the research sample of this paper are all listed in the Hong Kong capital market, this study provides a detailed statistical analysis of the movement of the Hang Seng Index during the event window (−10, 10). We find the daily closing index data of the Hang Seng Index in the event window (−10, 10) using the Flush software, organise and save the data in an Excel table and then calculate the daily market returns of Event 1 (2020.01.20), Event 2 (2020.01.30) and Event 3 (2020.02.20) in the event window (−10, 10), individually. Finally, a total of 63 data sets were obtained and the data were stored in an Excel sheet.

Figure 1 reflects the Hang Seng Index daily market yield change trend and Table 4 shows Hang Seng Index daily market return. It can be seen from Figure 1 and Table 4 that the stock prices in the Hong Kong stock market fluctuated sharply during the period corresponding to the COVID-19 epidemic. From the data analysis of the daily market yield of Hang Seng index, it is found that the stock price of the Hong Kong stock market fluctuates sharply around event days for the release of policy information related to COVID-19 – Event 1 (2020.01.20), Event 2 (2020.01.30) and Event 3 (2020.02.20). Thus, the release of policy information related to the COVID-19 epidemic has had a significant impact on China's stock market.

Fig. 1

As shown in Table 4, Event 1 (2020.01.20) has a negative daily market return of −0.90% at event date T = 0, which is 0.3 percentage points lower compared to the daily market return of −0.60% at T = −1. Moreover, the daily market return of −2.82% at T = 1 on event day 1 (2020.01.20) remains negative, which is 1.9 percentage points lower compared to the daily market return of −0.90% at T = 0. This indicates that the announcement of Event 1 (2020.01.20) did not have an immediate positive effect on the Hong Kong stock market, which continues to be depressed. However, Event 1 (2020.01.20) has a positive daily market return of 1.27% at T = 2 event date, which is a rapid increase of 4.1 percentage points compared to the daily market return of −2.82% at T = 1, which is a large increase. This indicates that the announcement of Event 1 (2020.01.20) had a positive effect on the Hong Kong stock market, promoting higher stock prices. However, over time, Event 1 (2020.01.20) has a daily market return of −1.52% at event day T = 3, which is a rapid decrease of 2.8 percentage points compared to the daily market return of 1.27% at T = 2. This illustrates that although there is an upward trend in stock prices in the Hong Kong stock market, the overall general environment of the Hong Kong stock market remains depressed and is less affected by the release of policy information related to COVID-19. Faced with the public health emergencies of COVID-19, all industries were greatly challenged, and the Hong Kong stock market experienced dramatic fluctuations and a more pessimistic overall performance.

The daily market returns for Event 2 (2020.01.30) were −2.82%, −2.62% and −0.52% on event day T = −1, event day T = 0 and event day T = 1, respectively. Although Event 2 (2020.01.30) maintained an upward trend around the event date when the policy information related to COVID-19 was released, the daily market returns for all three event dates are negative. It can be seen that the Hong Kong stock market is more pessimistic after the outbreak of COVID-19; although there is an upward trend, the increase is small. The outbreak of the public health emergency caused by COVID-19 had a greater impact on the Hong Kong stock market, the introduction of policies related to COVID-19 at the national level and at the ministry level failed to better stimulate the Hong Kong stock market, and the overall environment of the Hong Kong stock market remained depressed.

Event 3 (2020.02.20) has a negative daily market return of −0.17% at T = 0, which is 1.55 percentage points lower compared to the daily market return of 0.46% at T = −1. Also, Event 3 (2020.02.20) has a daily market return of −1.09% at T = 1, which is still negative, and has decreased by 1.9 percentage points compared to the daily market return of −0.90% at T = 0, which is a larger decrease. This indicates that the announcement of Event 3 (2020.02.20) did not have an immediate positive effect on the Hong Kong stock market, which continues to be depressed.

As can be seen, the Hong Kong stock market was more significantly affected by the release of policy information related to COVID-19. Overall, the Hong Kong capital market was depressed during COVID-19 and stock prices in the Hong Kong capital market declined. Stock prices remained lower under the stimulus of policy information events related to the new crown pneumonia epidemic at the national level or at the ministry level.

After analysing the change of Hang Seng Index in the event window, this paper makes an overall analysis of 17 listed property service enterprises, and studies the effect of COVID-19 policy information on the stock price index of listed companies of property service enterprises in China.

The overall impact of the release of policy information related to COVID-19 on listed property service companies in China is reflected in the changes of stock prices. As shown in Figure 2, we can observe that the stock prices of listed companies in China's property service enterprises have fluctuated greatly during the event period. The rise and fall of the stock prices of listed companies of property service enterprises are greater than 0 at T = 0, which is different from the daily market return of the Hang Seng Index in Hong Kong. The stock prices of listed companies of property service enterprises rose against the market. Under the influence of Events 1, 2 and 3 at T = 1, the stock price's fluctuation range of listed companies of property service enterprises is more than 0.

Fig. 2

Therefore, the above analysis can show that the release of relevant policy information on COVID-19 has a certain impact on the stock prices of listed companies in China's property service enterprises. However, unlike the trend of the daily market return rate of the Hang Seng Index, the stock prices of listed companies in China's property service enterprises during the event period of this paper are mainly reflected in the advantages over disadvantages. The release of the relevant policy information of COVID-19 has played a good role in boosting the stock prices of listed companies in China's property service enterprises, thereby enhancing the beliefs of institutional investors and small and medium investors in the stock market, and thus promoting the continuous rise of the stock price yield of listed companies in China's property service enterprises. However, some of the stock prices of listed property service companies fell. On the one hand, the relevant policy information of COVID-19 issued at the national level or at the ministerial level did not meet the expectations of institutional investors and small and medium investors, and thus institutional investors and small and medium investors have sold shares in the property service industry, making the stock prices of listed property service companies in China continue to fall. On the other hand, due to the impact of the macroeconomic situation and other factors in the event window of this study, the stock prices of listed companies in China's property service enterprises have had a negative impact.

The release of policy information related to COVID-19 has an impact on the changes in the stock price index of listed companies of China's property service enterprises during the event period. In the event window (−10, 10), we calculate the CAR of the stock price index of the chosen 17 listed companies of property service enterprises constituting the research sample.

As shown in Figure 3, overall, the event window of this research is 63 days totally. In these 63 days, the CAR value of 17 listed companies of property services listed in the Hong Kong stock market in China is positive in 46 days, accounting for 73%, which can indicate that the impact of the release of policy information on COVID-19 in this paper is good.

Fig. 3

Specifically, the CAR of Events 2 and 3 on the sample of 17 listed property service companies in the Hong Kong stock market is relatively stable, and the cumulative CAR of AR is basically in the range of −2% to 3.5%. On the date of the publication of the policy information related to COVID-19, the CAR values of the stock prices of the research samples are positive, which indicates that the policy information of COVID-19 has had a positive impact on the stock prices of the chosen 17 listed property service enterprises in the Hong Kong stock market. The CAR values are positive on the second day (t = 1) of the release date of the policy information related to COVID-19, which further shows that the release of the policy information related to COVID-19 has had a positive effect on the research samples of 17 listed property service enterprises in the Hong Kong capital market in China, and the stimulation of the positive effect reached the highest value in the event window on the third day (t = 2) of the release date of the policy information related to COVID-19, which can explain the cognitive degree of institutional investors and small and medium investors for the information of the stock market, as well as the fact that the investment enthusiasm of the stock market also reached a peak during the event period. Subsequently, although the stock prices of the listed companies of 17 property service enterprises listed in the Hong Kong capital market in China have declined to some extent, they are still positive in most cases, which can also reflect the positive effect of the release of policies related to COVID-19 on the stock prices of the research samples for a certain period of time.

Table 5 presents the statistical results of the AAR and CAR corresponding T-tests on the impact of the release of relevant policy information on COVID-19 on the stock prices of 17 property service companies listed in the Hong Kong capital market. Then, the AAR of 17 listed property service enterprises in the Hong Kong capital market were tested by single sample T-test, and the significance level of T-test of AAR under the corresponding degree of freedom was found through the critical value table of T distribution. In the second step, the T-test of CAR was carried out, and the significance level of T-test of CAR value under the corresponding degree of freedom was found through the critical value table of T distribution. The event window (−10, 10) corresponding to the three events selected by the author is 21 days, amounting to a total of 63 days.

We further study the AAR values of the research samples of the 17 listed property service companies listed in the Hong Kong capital market in the event window of 63 days, and conduct the T-test. In the event window period (−10, 10) defined in this paper, the number of days whose AAR is a positive integer is greater than the number of days whose AAR is negative, and there are 20 days whose AAR is significant at the level of 1%, 13 days whose AAR is significant at the level of 5% and 5 days whose AAR is significant at the level of 10%. The release of relevant policy information on COVID-19 has played a good role in promoting the stock prices of the 17 listed companies of H-share property service enterprises in this paper to a certain extent, which provides a good institutional guarantee for listed companies of property service enterprises in China to respond to the emergencies of COVID-19.

By sorting out the data of the T-test results of AAR and CAR in detail, the AARs in the three event windows are added to obtain the cumulative AARs, which is used to verify the impact of different pieces of policy information related to COVID-19 in the study period on the stock prices of the research samples of the 17 listed property service companies listed in the Hong Kong capital market in China. As shown in Table 6, we obtain the result that during the 63 days of the research period, the CAR on stock prices of the research objects is mostly positive, and it is significant at the level of 1% for 13 days, significant at the level of 5% for 7 days and significant at the level of 10% for the other 13 days. The CAR of listed property enterprises in the event window is relatively more positive, which fully supports the understanding that the introduction of policy information related to COVID-19 has had a positive effect on the 17 chosen listed property service enterprises in the Hong Kong capital market. Thus, the policy effect of policy information related to COVID-19 exists in the short term.

The continuous spread of COVID-19 has exerted a far-reaching influence upon various industries, and the property service industry in China is facing the challenges imposed by public health emergencies, while also welcoming the associated unprecedented opportunities. In order to stabilise the social order, the state level or ministries and commissions have successively issued relevant policies aimed at benefitting the people and enterprises. In this study, the event study method is used to analyse the impact of the release of policy information related to COVID-19 on the stock price changes of listed companies of property service enterprises in China, and to verify whether the policy effect exists.

The conclusions are as follows:

Firstly, affected by the release of policy information related to COVID-19, Hong Kong's capital market has been significantly impacted. On the whole, during the COVID-19 epidemic, the stock market in Hong Kong's capital market was sluggish, and the stock price in Hong Kong's capital market declined. Under the stimulus of policy information events related to COVID-19 at the national or ministerial level, the stock price still declined.

Secondly, the publication of policy information related to COVID-19 has promoted research into the stock price movements of 17 listed companies of property service enterprises in China, which were chosen as research samples for the present study, corresponding to the release of information on the COVID-19 epidemic; and the research shows that the policy effect of the publication of policy information related to COVID-19 exists in the short term.

Thirdly, the effect of the announcement of policy information related to COVID-19 on the stock prices of listed companies of property service enterprises in China still exists in the short term, but the effect will change with the extension of time period.

Based on the research results, we put forward reasonable and targeted suggestions to all parties involved in the capital market, including investors, governments, property service enterprises and listed companies. First of all, for investors, they should identify valuable information and improve their investment judgement ability. We should make reasonable investment decisions and avoid blind investment. A good investment idea should be established. Secondly, for the government, it is imperative to reduce policy fluctuations; guarantee the timeliness and continuity of policy information; and improve the relevant laws and regulations and the operation mechanism of the stock market. Finally, for listed companies of property service enterprises, information should be disclosed to stakeholders in a timely manner, thus improving the quality and pace of development of listed companies in China's property service enterprises.