72 july2021

Investors and Indian Stock Indices Reaction to News Announcements during Covid-19
Dr. Rama Krishna Yelamanchili
Associate Professor, Finance
ICFAI Business School, IFHE-Hyderabad
Dr. Sager Reddy Adavelli∗
Assistant Professor, Finance
ICFAI Business School, IFHE-Hyderabad
Abstract
Using Mahalanobis Distance process we identify 31 extreme values in daily return series of study variables span between
February 2020 and June 2021. These extreme values represent 10 percent of total observations (N=304). We gather news
announcements around those 31 trading days and quantified into four groups. We examine investors (Clients, FIIs, DIIs and
Proprietary) and stock indices (BSE Sensex, and BSE S&P 500) reaction to these news announcements. In addition, using
non-parametric statistical tests, we investigate randomness, equality of distribution and day of the week effect of study
variables. Results are mixed. We find statistically significant difference between two groups of news announcements in
stock indices. On the other side, there is no such difference in investor categories. We find the returns series of Sensex, BSE
S&P 500, FIIs, DIIs to be random and Clients, Proprietary to be non-random. We also find that the daily returns of all
variables are not statistically significant from their median return. Finally, we do not find any evidence of day of the week
effect on investors’ investment pattern. We conclude that impact of negative news surrounding Covid-19 is succumbing by
Government of India (GoI) interventions. The stock market movements are random and there is no day of the week effect in
net flow of investments. There is a visible trend in clients and proprietary investors net flow into the market.
Keywords: Investors’ reaction, news announcement, non-parametric tests, random walk theory, stock market, day of the
week effect.
I.INTRODUCTION
Investors and stock markets reactions to news announcements have received increasing attention in recent years.
Few researchers (Broadstoc and Zhang, 2019, Shi and Ho, 2020) tried to explain whether there exists any
relation between price movement in stock markets and news announcements with empirical and theoretical
studies. Similarly, a proposition in behavioral finance posits that in addition to the basic value of stocks,
emergencies will have an impact on investors' psychological and behavioral factors, which in turn will have an
important impact on stock prices (Pinglin He et. al., 2020). Lee and Jiang (2002) provide an empirical evidence
of this proposition and believe investor confidence reduces earnings volatility, while investor distrust increases
earnings volatility. In the recent years there is growing interest in event studies, especially during Covid-19
pandemic period. Several studies analyze the impact of news on stock markets (Caruso, 2019; Lyocsa, Molnar,
and Plihal, 2019). The consensus is that stock markets tend to adjust prices to news continuously. In the case of
the Covid-19 pandemic, the price adjustment process was being hindered by the enormous amount of news.
Even though investors agreed that the virus would lead to a decrease in current and future cash flows and
earnings and, thus, to a drop in stock prices, the extent of the drop was unclear. A subgroup of studies has
investigated the effect of macroeconomic announcements on stock markets (Jawadi et al., 2019; Ozatay et al.,
2009). Another strand of the literature investigates the effect of non-fundamental news, such as natural disasters
and terrorist attacks, on stock markets (Braun et al., 2019; Ammar, 2020). In this study, we aim to expand the
literature related to the impact of different categories of news announcements on stock markets during Covid-19
pandemic period. During this pandemic period, along with Covid related news, news related to other categories
like federal government interventions, economic activities, and foreign markets are on headlines of media. We
test whether these four news categories have any impact on stock market returns and investors investment
patterns.
In a different context there is a long debate on market anomalies with regard to day of the week effect. These
anomalies are in contrast to efficient market hypothesis. Day-of-the-Week Effect refers to the observations that
mean stock returns are differently distributed among different week days. The First day of the week is usually
considered as a week day because the market remains bearish, while on the last day of the week the market is
found buoyant. We posit that in present information age, information is disseminated in fraction of minute and
∗
Corresponding Author
Journal of Xi'an University of Architecture & Technology
Volume XIII, Issue 7, 2021
ISSN No : 1006-7930
Page No: 743

any anomaly surrounding to a stock or market get adjusted instantly. So, in such a context there is no place for
week of the day effect. We opine that this pandemic period is the right time to test this assumption. In this paper
we test our proposition.
The paper proceeds as follows. Section II presents the literature review. Section III outlines the methodology.
Section IV presents the empirical results and analysis. Section V concludes the article.
II. LITERATURE REVIEW
In stock markets disasters and pandemic diseases affects investors’ behaviour towards the stock price influence.
Covid-19 has arisen as a curse in the capital markets with surprising levels of insecurity and high volatility.
Almost thirty per cent of world’s wealth had eroded within three months of emergence of this disease. Ali,
Alam, & Rizvi (2020) conducted a study on nine countries and find the pandemic situation has deteriorated
because of the global spread which was uncontrollable. This started impacting even the safer commodities like
gold. However, in comparison to the stock markets the commodities markets were less volatile.
The presence of calendar anomalies has been observing comprehensively for almost last five decades in capital
markets. Berument & Kiymaz (2001) by taking S&P 500 index for a quarter century, tested the presence of the
day of the week effect on stock market volatilities and find that the volatility and returns patterns across all the
days of the week are different. They also observed that the highest and lowest returns on Wednesday and
Monday, the highest and the lowest volatility on Friday and Wednesday correspondingly. In other countries like
Australia, Chiah & Zhong (2021) reported Tuesday’s stock returns were lower in Australia in comarison to other
days. They also report that speculative stock returns in Australia is more consistent with dynamics revealing of
current day domestic mood and previous day US mood. However, there were observable differences in US with
respect to day-of-the-week effect (Birru, 2018). The results of Birru (2018) were robust in different sub-
samples which are not explained by news releases. Investors are in soaring moods tend to fairly outstrip in
future when they are imagining the ascending mood and vice-versa and further, the role of mood beta observed
that, high mood beta stocks outperform during imminent climbing mood periods and disappoint during
imminent downward mood period (Hirshleifer, Jiang, & DiGiovanni, 2020).
Public news has also been an important influencing weapon on stock markets in recent years. Many research
studies have undertaken on understanding price movements in stock markets which are driven by any political
and economic news. Cepoi (2020) investigated the impact of Covid-19 related news on stock markets by
consindering a quite short term data of 50 trading days and observed the evidence of Covid-19 related news and
stock market returns has the relationship in the countries which are affected by the Covid-19 pandemic. Birru
(2018) find that their results of stock returns are not explained by news releases on economy, news of
companies. Anand, Basu, & Thampy, 2021 used a model developed by Anand et.al (2021) to measure sentiment
describes better the stock market returns and solidifies existing sentiment variables of Consumer confidence
index and Baker Wurgler index are insignificant in its incidence. The Covid-19 has affected the stock markets
across the globe as badly as compared to Spanish Flu. Baker et.al (2020) used text-based methods to observe the
effect of Covid-19 pandemic and government restrictions commercial activities, social distancing measures, and
other stringent interventions to control the pandemic on US stock markets and find that these actions impacted
the stock markets. Hussain & Omrane (2021) the impact of economic news related to US on the Canadian
benchmark index return and volatility considering five minute high frequecy data of every five minutes and find
that US news announcements have impact on the Canadian stock market return and volatility. Afees & Vo
(2020) evaluated the relevance of health related news during the covid-19 pandemic in predicting the stock
returns on twenty most affected countries and find health-news index made by us outstrips the benchmark index,
which indicates the health news searches as a good predictor of stock returns since during this Covid-19
pandemic period.
III. Methodology
Through this paper, we intend to investigate extreme reaction of investors and stock market indices to news
announcements during Covid-19 pandemic period. We also aim to test the randomness and normality of return
series surrounding extreme events during pandemic period. Additionally, we wish to explore whether there is
any day of the week effect in investors investment pattern. To reach our objectives, we sourced daily close
values of BSE Sensex, BSE S&P 500, and Client Category-wise net investment in to the market from Bombay
Stock Exchange of India’s official website. Further, we sourced FIIs net investment from National Securities
Depositary Limited (NSDL). The study period range between February 2020 and June 2021. This is the period
when Indian stock market witnessed impact of Covid-19 pandemic and this research is carried out. By the time
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we are writing this paper, concerns of Covid-19 still prevail and uncertainties persist. During study period there
were 305 trading days.
To identify extreme movement in the market, we followed Mahalanobis Distance Process. We run two separate
multiple regression models. In first multiple regression model, we regress investor categories on BSE Sensex,
and in second multiple regression model, we regress investor categories on BSE S&P 500. We calculate Chi-
square value using more stringent statistical significance value (0.001) and use this Chi-Square value to filter the
Mahalanobis Distance values. We sorted Mahalanobis Distance values in descending order and considering Chi-
Square value as cutoff value we separated extreme values in each multiple regression equation. Further, we
combined extreme values of each multiple regression equation and retained only unique date values. Of the 305
daily observations, we notice 31 extreme values. Using the dates on which these events occurred, we collected
news announcements from various news media. We see similarity in these news announcements. So, we decided
to quantify this qualitative information. We classified these news announcements into four groups.
Group one contains news announcement related to Covid-19 (Covid-19 spread, vaccine development,
alternative medication etc.). Group two contains news announcement related to foreign markets (movement in
foreign stock exchanges, foreign exchange rate, crude oil prices, foreign economies, etc.). Group three contains
news related to Indian real economy factors (GDP, Inflation, Employment, Index of Industrial Production, and
Purchase Manager’s Index etc.). Group four includes Government of India’s (GoI) intervention measures in the
economy from time to time (Incentives to poor and needy, loan moratorium, policy changes, support to
business, monetary policy, infusion of funds in to market etc.).
Next, to understand roots and repercussions of extreme movements we sourced two preceding and succeeding
days’ data surrounding each extreme day. This data series contained 123 data points. Finally, we have 103
return series points, after excluding pre-post trading days of contingent extreme value days. In our analysis, we
used 31 extreme values to understand the impact of news announcements, 103 return series points to test
randomness and equality of distribution, and 123 data points to test day of the week effect. A preliminary view
of descriptive statistics of data series reveals that all the variables have extreme values, and there is no way to
transform these extreme data points and perform parametric statistical tests. In such context, we prefer to
analyze the data with robust non-parametric statistical tests. We use one way analysis of variance to find out the
difference in mean scores of different news groups. We use Runs test to test the randomness in the data series,
and one sample Wilcoxson Signed Rank Test to check equivalence of distribution of data from its median.
Finally, we use Chi-Square test to assess day of the week effect. In Runs test we define our variables in
dichotomous as positive (1) and Negative (0). In Wilcoxson Signed test we use return series in percentage and
compare it with Median return. In Chi-Square test we classified dependent variable into dichotomous, Net Buy
(1) and Net Sell (0). In next section, we present results of the study in a sequential order in concurrence with
study objectives.
IV. RESULTS
We start presentation of study results with descriptive statistics drawn on combination of two pre and post event
days along with event days. Table 1 displays the descriptive statistics. In all there are 123 trading days including
31 extreme value days. The Skewness and Kurtosis values for all the variables are high and evidently indicate
non-normality of the series. Furthermore, the range of each variable is also high with extreme minimum and
maximum values. Standard deviation values also support this high range with high volatility in data series. All
these statistics drives us to prefer non-parametric statistical tests to parametric statistical tests. We experimented
with different transformation to make this data normal. However, all our experiments fail. So, we influenced to
run non-parametric tests to meet study objectives.
Table 1: Pre-post event Descriptive Statistics
Descriptive Sensex BSE500 FIINet DIINet Clients Net Proprietary Net
Mean 41289.44 16060.89 457.73 298.00 -608.74 100.06
Median 38900.80 15088.37 188.08 -120.35 -131.96 81.94
Standard Deviation 7643.35 3264.04 3980.43 1749.53 2853.10 208.22
Kurtosis -1.42 -1.27 23.83 2.83 91.92 0.25
Skewness 0.06 0.19 3.72 1.09 -9.12 0.24
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Range 24659.73 10897.23 37034.34 12590.06 30872.06 1236.07
Minimum 28265.31 10666.93 -8295.17 -4968.90 -29822.91 -526.86
Maximum 52925.04 21564.16 28739.17 7621.16 1049.15 709.21
Count 123 123 123 123 123 123
*Index values are in points. Investors’ values are in Rupees (Rs.) Crores.
Next, we proceed with analysis of event days. Descriptive statistics of event days level values are presented in
Table 2 and descriptive statistics of event days return series are presented in Table 3. In level series and in return
series, the market indices are normally distributed with Skewness and Kurtosis values close to zero. On the other
hand, both the data series of investor categories have high Skewness and Kurtosis values indicating non-
normality. We also observe low volatility in indices values and exorbitant volatility in investor categories. From
descriptive statistics it appears that both the indices report negative daily mean return during these 31 event
days. In contrast, we notice positive daily mean return values in DIIsNet, Clients Net, and Proprietary Net. FIIs
do have negative mean return value. Descriptive statistics indicate that during extreme event days, domestic
investors pumped money into the stock markets and foreign investors were net sellers. To statistically validate
these preliminary observations, we run inferential statistics for each variable. We use one way ANOVA test to
determine whether there are any statistically significant differences between the means of news announcements
groups of each variable. First, we run ANOVA test on investor categories and then on market indices. Results of
ANOVA tests and post-hoc tests and presented in Table 4a and 4b. From ANOVA analysis we fail to find any
statistically significant differences in the mean returns of investor categories. The ANOVA results fail to reject
the null hypothesis at 5% significance level. In divergence, we find statistically significant difference in mean
returns of at least two groups with regard to market indices. For both the BSE Sensex and BSE S&P 500 we
reject the null hypothesis at 5% significance level. To know which two or more groups are different we run two
post-hoc tests namely Tukey HSD test and LSD test (see Table 4b). The post-hoc tests indicate that there is a
significant mean difference between Covid News and Government of India interventions. For Sensex the mean
difference is negative (-2.96) and statistically significant (p = 0.04 and 0.03). Similarly, for BSE S&P 500 the
mean difference is negative (-2.81) and statistically significant (p = 0.03 and 0.00). Results indicate that
negative news related to Covid-19 is followed by positive or supportive news from government agencies.
Similarly, when there is good news about Covid-19 combat the GOI relaxed stringency norms related to
lockdown and people movement.
Table 2: Events descriptive statistics at level
Mean 41151.17 15967.70 2303.95 608.63 -1994.94 122.06
Median 38854.55 15006.09 1004.11 -90.46 -303.02 95.13
Kurtosis -1.47 -1.25 7.98 2.92 23.38 0.52
Skewness 0.13 0.26 2.42 1.56 -4.61 0.08
Range 22734.07 10366.64 37034.34 10045.77 30872.06 1236.07
Minimum 29815.59 11106.47 -8295.17 -2424.61 -29822.91 -526.86
Maximum 52549.66 21473.11 28739.17 7621.16 1049.15 709.21
Count 31 31 31 31 31 31
*Index values are in points. Investors’ values are in Rupees (Rs.) Crores.
Table 3: Events descriptive statistics of Returns in percentage
Mean -0.13 -0.15 -1018.56 32.02 1506.69 58.52
Median -0.35 -0.17 -54.31 -18.74 19.18 -22.07
Kurtosis 0.55 0.23 25.46 10.66 14.47 9.08
Skewness 0.51 0.18 -4.85 2.63 3.67 2.71
Range 8.80 7.82 28237.64 3075.35 32306.40 1790.14
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Minimum -3.80 -3.66 -24566.06 -925.51 -3350.57 -424.51
Maximum 5.00 4.16 3671.58 2149.83 28955.84 1365.63
Count 31.00 31.00 31.00 31.00 31.00 31.00
Table 4a: One Way Analysis of Variance result
ANOVA
df F Sig.
FII Net 30 1.236 0.32
DII Net 30 1.445 0.25
Clients Net 30 0.767 0.52
Proprietary Net 30 0.980 0.42
Sensex 30 2.699 0.07
BSE S&P 500 30 2.956 0.05
Table 4b: post-Hoc test result
Test Dependent News Mean Diff SE Sig.
Tukey HSD Sensex Covid vs. GOI Interventions -2.96 1.07 0.04
LSD Sensex Covid vs. GOI Interventions -2.96 1.07 0.01
Tukey HSD BSE S&P 500 Covid vs. GOI Interventions -2.81 0.97 0.03
LSD BSE S&P 500 Covid vs. GOI Interventions -2.81 0.97 0
The mean difference is significant at the 0.05 level.
We next run one sample Runs test for each of the investor category and stock indices. Results are presented in
Table 5. When market closed in green, we labeled it as 1 and when market closed in red, we labeled it as 0.
Using these dichotomous values of each variable we run Runs test to test the randomness of returns series. We
retain the null hypothesis for Sensex, BSE S&P 500, FIIs Net, and DIIs Net. For these variables the Z scores are
low and Asymp. Sig. values are high and range between 0.08 and 0.88. On the other side, we reject the null
hypothesis for Clients Net and Proprietary Net at 5% significance level. These results indicate that return series
of four variables have randomness and two variables are not random. In case of Clients Net we find continuous
net selling and in case of Proprietary Net we find continuous net buying resulting to a trend.
Table 5: Runs Test for Randomness
Runs Test
Sensex BSE500 FIINet DIINet ClientsNet ProprietaryNet
Test Valuea
1.00 1.00 1.00 1.00 1.00 1.00
Total Cases 103 103 103 103 103 103
Number of Runs 53 47 55 58 64 67
Z 0.147 -0.417 1.720 1.579 2.422 3.084
Asymp. Sig. (2-
tailed)
0.883 0.676 0.085 0.114 0.015 0.002
Exact Sig. (2-tailed) 0.921 0.755 0.100 0.121 0.016 0.002
Point Probability 0.078 0.081 0.023 0.023 0.004 0.001
a. User-specified.
To test equality of distribution around median return of each study variable, we use Wilcoxson Signed Rank
Test. The null hypothesis of equality is tested to ascertain the probability of obtaining a number of observed
responses above and below the median. Result of one-sample Wilcoxon Signed Rank tests and their related
descriptive statistics are presented in Table 6a and 6b. It is visibly manifest that the test statistics are high and 2-
sided test Asymptotic Significance values are 0.50 and above for all the six samples. Relying on the test
statistics and significance values we retain the null hypotheses. These results indicate that the return series have
equally distributed data points around their median scores.
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Table 6a: Return Series Descriptive Statistics
Descriptive Sensex BSE500 FIINet DIINet ClientsNet ProprietaryNet
Mean -0.04 0.03 -182.44 -41.68 452.00 21.43
Median 0.03 0.25 -54.31 -30.32 -20.39 -2.70
Kurtosis 4.92 5.80 51.75 19.72 53.14 15.00
Skewness -1.27 -1.60 -4.19 -0.96 6.94 2.61
Range 13.18 12.83 39711.66 4868.21 32306.40 1939.29
Minimum -8.18 -8.32 -24566.06 -2718.38 -3350.57 -573.66
Maximum 5.00 4.51 15145.60 2149.83 28955.84 1365.63
Count 103 103 103 103 103 103
Table 6b: One-Sample Wilcoxon Signed Rank Test Summary
Sensex BSE S&P 500 FIINet DIINet ClientsNet ProprietaryNet
Total N 103 103 103 103 103 103
Test Statistic 2754.50 2537.50 2781.00 2564.00 2827.00 2696.00
Standard Error 295.19 299.56 299.56 299.56 299.56 299.56
Standardized Test 0.61 -0.30 0.52 -0.21 0.67 0.23
Asymptotic Sig. 0.54 0.77 0.61 0.83 0.50 0.82
We, next test the day of the week effect on investors net buying and selling pattern. We, first draw a summary of
day wise investment flow of each investor category. We present this data in Table 7a. It is clearly visible that
FIIs are net sellers on Friday and Monday. DIIs and Proprietary investors are net buyers on all day of the weeks.
In contrast to this Clients are net sellers on all day of the weeks. To test whether there is any significant
statistical difference in these preliminary observations we apply Chi-Square test. We categorized outcome
variable into dichotomous “1” represents net buy, and “0” represents net sell. Day of the week wise counts are
presented in Table 7b, followed by Chi-square test results in Table 7c. Count of net buy and net sell of FIIs and
DIIs are almost similar. Whereas, count of net buy and net sell of clients and proprietary investors varied. Chi-
square test statistic values range between 1.91 and 3.51 with p-values range between 0.47 and 0.75. For all the
four investor categories we retain the null hypothesis that there is no significant difference in investment pattern
over different day of the weeks. Clients are net sellers and other three categories of investors are net buyers in
the market and they don’t differentiate each day of the week with the other.
Table 7a: Week Day Net Buy / Sell (Rs. Crores)
Row Labels Sum of FIINet Sum of DIINet Sum of Clients Net Sum of Proprietary Net
Mon -4611.58 6168.71 -3010.05 1429.71
Tue 24109.22 3707.59 -11968.41 4660.79
Wed 29243.24 2503.26 -44971.55 2766.81
Thu 17843.25 7344.70 -6175.05 1771.02
Fri -10282.99 16929.79 -8749.94 1679.12
Grand Total 56,301.14 36,654.05 -74,875.00 12,307.45
Table 7b: Week Day Net Buy / Sell Count
FIIs Net DIIs Net Clients Net Proprietary Net
Week Day Net Sell Net Buy Net Sell Net
Buy
Net Sell Net
Buy
Net Sell Net Buy
Mon 11 11 13 9 17 5 7 15
Tue 8 15 12 11 18 5 4 19
Wed 14 13 15 12 21 6 9 18
Thu 11 16 15 12 19 8 11 16
Fri 11 13 9 15 15 9 9 15
Total 55 68 64 59 90 33 40 83
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Table 7c: Week Day Net Buy / Sell Chi-Square Test
Variable Chi=Square Value df p-value
FII Net 1.907a
4 0.75
DII Net 2.739a
4 0.60
Clients Net 2.284a
4 0.68
Proprietary Net 3.515a
4 0.47
a. 0 cells (0.0%) have expected count less than 5.
V. CONCLUSION
In this paper we aim to assess reactions of stock market indices and investor investment patterns to news
announcements during pandemic period. We desire to test the randomness of return series of study variables.
Furthermore, we examine whether return series follow Gaussian distribution. We also measure whether there is
any presence of day of the week effect. To achieve our objectives, we identified 31 extreme trade days using
Mahalanobis Distance process. In addition to these 31 extreme trade days’ data, we also gather data of two
previous and subsequent trading days. We apply non-parametric statistical tests. Based on the results of the
study, we conclude that different groups of news announcements do not have any significant impact on
investors’ investment patterns. For different news announcements, each category of investors reacts differently,
resultant to harmonizing of investment flows. But news announcements influence stock market movements.
Importantly, Covid-19 related news and GOI interventions drive the market movements. The stock indices
follow randomness because any sudden surge or shrink is adjusted quickly with net buying or selling by
investors. Furthermore, market indices have Gaussian distribution, and Clients and Proprietary investors’
investments follow down trend and uptrend respectively. Friday and Monday witness net selling activity by FIIs,
but there is no statistically significant evidence to prove this descriptive number. Investment flow into the
market does not differ from one day of the week to the other day of the week. Clients are short sighted and reap
benefits from short-term trading activities, and other three investors are investing in the market.
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