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ORIGINAL PAPER
DOI: 10.26794/2587-5671-2022-26-6-104-114 JEL E44, C52, C58
(CO ]
Do Exogenous Shocks in Macroeconomic Variables Respond to Changes in Stock Prices?
H. Srivastavaa, P. Solomon", S. P. Singhc
a' b Amity University, Noida, Sector-125, India; c ALLiance University, Bangalore, India
ABSTRACT
The research aims to examine the unexpected changes in stock prices due to external shocks given to the macroeconomic variables to forecast future stock market returns. The study applies two econometric models such as "Variance Decomposition" (VDC) and "Impulse Response Function" (IRF) for examining the exogenous shocks in macroeconomic variables respond to changes in stock prices. Monthly time series data of five significant macroeconomic variables Real Exchange Rate, Interest Rate, Consumer Price Index (CPI), Crude Oil Prices, and Trade Openness, taken as independent variables and BSE SENSEX as a dependent variable. The research period is from Jan 2009 to Dec 2019. The study has taken the responsibility to reveal a few strong evidences for changes in stock prices due to exogenous shocks in Exchange Rate, Trade Openness, Inflation, and Interest rate along with crude oil prices. According to the results, changes in the stock market are due to external factors like changes in dividend policy or capital loss, and some changes in the stock market are due to its own innovative shocks. This study suggests to reduce unexpected changes in stock prices frequently, companies should control capital loss and focus on stable return/dividend policies. There are divergent views in the literature review in the context of measures of these variables, however no research has been done on exogenous shocks in macroeconomic variables to BSE SENSEX for the Indian stock market with this particular data set and duration. Keywords: BSE Sensex; exchange rate; interest rate; consumer price index; crude oil prices; trade openness; variance decomposition; impulse response function
For citation: Srivastava H., Solomon P., Singh S. P. Do exogenous shocks in macroeconomic variables respond to changes in stock prices? Finance: Theory and Practice. 2022;26(6):104-114. DOI: 10.26794/2587-5671-2022-26-6-104-114
ОРИГИНАЛЬНАЯ СТАТЬЯ
Реагируют ли экзогенные шоки в макроэкономических переменных на изменение цен на акции?
X. Шривастава, П. Соломон", С. П. Сингхс
a b Университет Амити, Нойда, Сектор-125, Индия; c Университет Альянса, Бангалор, Индия
АННОТАЦИЯ
Цель исследования - изучить неожиданные изменения цен на акции в результате внешних шоков, вызванных макроэкономическими переменными, для прогнозирования будущих доходов фондового рынка. Авторы применяют две эконометрические модели, такие как «Variance Decomposition» (VDC) и «Impulse Response Function» (IRF) для изучения того, как внешние шоки в макроэкономических переменных влияют на изменения цен на акции. Ежемесячные временные ряды данных пяти значимых макроэкономических переменных: реального обменного курса, процентной ставки, индекса потребительских цен (ИПЦ), цен на сырую нефть и открытости торговли, взяты в качестве независимых переменных, и BSE SENSEX - в качестве зависимой переменной. Период исследования - с января 2009 по декабрь 2019 г. Выявлено несколько убедительных доказательств изменения цен на акции в результате экзогенных шоков, связанных с обменным курсом, открытостью торговли, инфляцией и процентной ставкой наряду с ценами на сырую нефть. Согласно полученным результатам изменения на фондовом рынке вызывают внешние факторы, такие как изменения в дивидендной политике или потери капитала, а некоторые изменения на фондовом рынке вызваны собственными инновационными шоками. Авторы рекомендуют компаниям контролировать потери капитала и сосредоточиться на стабильной доходности/дивидендной политике, чтобы снизить частоту неожиданных изменений цен на акции. В обзоре литературы представлены различные мнения в контексте мер этих переменных, но исследования экзогенных шоков макроэкономических переменных на BSE SENSEX для индийского фондового рынка с таким конкретным набором данных и продолжительностью не проводились.
© Srivastava H., Solomon P., Singh S. P., 2022
BY 4.0
Ключевые слова: BSE Sensex; обменный курс; процентная ставка; индекс потребительских цен; цены на сырую нефть; открытость торговли; дисперсионное разложение; функция импульсного отклика
Для цитирования: srivastava H., solomon P., singh s.P. Do exogenous shocks in macroeconomic variables respond to changes in stock prices? Финансы: теория и практика. 2022;26(6):104-114. DOI: 10.26794/2587-5671-2022-26-6104-114
INTRODUCTION
An appreciation factor in the context of the stock market investigated is the ability of the Various communities to improve the financial important indicators, mostly cash inflows [1]. The share market is always susceptible to additional price-shaping information, which can be relevant for market developments and future trends. The factors related to price formulation generally include macroeconomic variables but sometimes inner and subjective factors impact the investor's behavior which may affect the entire conduct of the whole stock market and its volatility (which grows along with the growing numbers of market participants), the developments of technologies and impact of globalization [2]. In determining the share price, macroeconomic factors such as inflation, money supply, and commodity prices always play a significant role. "The monetary policy represents the most efficient instrument in different countries" [3]. Because the overall development of the monetary policy is the essential instrument for influencing the business and economy, now this is essential to comprehend the impact of the variations on the Indian capital market in macroeconomic factors. Stock markets always have an essential role in allocating resources to their most productive applications [4]. As per the Arbitrage Pricing Theory, macroeconomic factors like unexpected inflation, industrial production, and interest rate highly affect the stock market [5]. It is widely accepted in financial economics that macroeconomic variables have a strong effect on stock markets. The association between the stock market and macroeconomic variables creates attention for researchers and policymakers [6]. It is also attracted, economists and financial investors. In some developed economies, the nature of the stock market and macroeconomic variables may be different from the developing economy.
The investigations had practical significance because a deep understanding of the behavior of the stock market allows firms to work more efficiently [7]. By gaining knowledge about this relationship, governments can establish the economy and the stock market, attracting more investors and firms that can control the economic situation [8]. It can be the first step towards a strong economy. In 1965, Efficient Market Theory was introduced in the security market [9]. According to the definition of "A market where a large number of rational profit
maximizers actively competing with each other and try to predict the future market values of individual security and the current information of the stock market easily available for everyone." Although this definition provides an understanding of what the efficient market is? Fama (1991) renamed the three concepts as inner information, event studies, as well as predictability for semi-strong form, strong form, along with weak side [10]. Different additional knowledge about what is meant by the available data makes clear what the efficient market is.
The weak state of the market itself confines just one subset of public information, basically historical data, i.e., As a result, the current asset values integrate all relevant previous information, such as dividend payments, trading volume as well as prior asset prices [11]. The previous nature of the stock market has not given any future prediction — Asset prices are predicted to evolve in accordance with the EMH theory on the hypothesis that they do [12]. From the standpoint of an investor, whatever information is accessible in the market should not result in an unusual profit in the stock market, regardless of the source of the information [13]. An investor can not beat the market consistently. This is compatible with a financial concept, according to which the highest price that investors are ready to pay is equal to the present value of future cash flows. An investment's present value is often assessed using a discount rate that signifies the uncertainty degree related with the investment based on all available information [14]. As the BRICS nations' economic and social transformations have progressed over the past several decades, they have made a significant contribution to the development of the financial sector [15]. It is widely known that economic growth is the most powerful driver for reducing inequalities by reducing the disparities in economic growth positively [16]. An effective financial system is essential for enhancing economic growth and development [17]. Many empirical studies have shown that a scarce financial market can be a source of financial imperfection and income inequalities [18]. The Indian economy has demonstrated a remarkable performance on the international platform in the last decade [19]. According to the global monetary fund, in GDP terms, the economy of India is the third-largest economy as well as the seventh-largest economy in the world [19, 20]. Indian officials have declared that their country was classed as a Newly Industrialized Country by the Central Intelligence Agency [21].
LITERATURE REVIEW
An analysis with sectoral stock indices was conducted to investigate the impulse changes due to exogenous shocks in certain macroeconomic variables in the short as well as long run. Results were found as the sectoral stock indices fluctuated and responded with shocks to FDI, imports, and exports at a certain level of variation [22]. The financial integration had been investigated between four major stock markets in the SAARC region. Results confirmed that Bangladesh's market fluctuates more than other markets, and the stock prices are changing due to different innovative changes in macroeconomic variables [23]. The reactions of oil prices as well as the currency rate to inflation have been considerable, according to the results of the Impulse Response Function. Within the first few months, the relationship between inflation and labour costs became considerable. Economic policies, fiscal and monetary policies play a vital role in controlling inflation. The price of crude oil had been an external element in which it has been necessary to find alternate means of reducing the inflationary effect [24]. Liquidity in the stock market increases after the increased price of oil from the demand side. Imagine that the oil price shocks originate on both the demand and supply sides of the oil industry. As a result, the liquidity of the stock market is adversely correlated with oil prices in such circumstances [25]. After the 2008 Global financial crisis, the U.S. financial risk structure changed. After 0. E. (quantitative easing) announcements, This occurs as a result of the risk spillover shifting from purchased to non-purchased resources [26]. The impact of quantitative easing on real economy quantifying in the research filtered an unobservable propensity to 0.E. and delivered impulse responses to the 0.E. shocks. Other approaches reveal that the business cycle is studied in terms of unexpected policy shocks. 0.E. shocks lead to a decrease in interest rates and an increase in stock prices [27]. Based on the VAR model impulse response function results found strong evidence that FIIs destabilize the stock market, especially with selling activities, and significantly increase the share price volatility [28]. By estimating the VAR-BEKK model, in the Greater China Area evidence was found of return and volatility spillovers among stock markets. The results of the impulse response function are used to measure the influence of the financial crisis on unanticipated conditional stock price volatility in the short term. The Greater China capital market is highly responsive to exogenous shocks [29]. The research examined the association between key macroeconomic variables such as government policies, disposable income, inflation
rate, stock prices, and exchange rate. A strong longrun cointegration relationship had been seen between stock market returns along with macroeconomic variables. Increases in the inflation rate eroded the panorama of the positive performance of the Sensex, but it was not significant [30]. The study employed certain macroeconomic factors and analysed their influence on stock market returns in order to create a (CCA) canonical association analysis model for ZSE "Zimbabwe stock exchange". Most of the movements in the money supply, exchange rate, consumer price index, unemployment rate, mining along with the industrial index, and other economic indicators have an impact on stock returns. A key contribution to the development of the CCA model came from the stock returns of Barclays, Bindura, Hwange, Ariston, and Falcon [30, 31]. The findings of the multivariate analysis demonstrated a long-run causality between stock indexes and the T-bill rate, but no short-run relationship was discovered. The findings of the variance decomposition suggest that the stock index has a high degree of relative homogeneity, and the impulse response function demonstrates that the stock market responds positively to shocks in macroeconomic variables that are innovative [32]. The Nepalese monetary authority was only able to impact the stock market in the short term, and not in the long term [33]. Bank stock prices have been significantly affected by the changes in the inflation rate, interest rate, along with Exchange rate in the duration from 2000 to 2015. In this duration, the government ensured a stable macroeconomic environment and moderated policy-making for monetary policy. Banks are also having the responsibility for not engaging the speculations in foreign currencies. The fluctuations in currency value also affect bank stock prices [34]. Money supply, T-bill rate, exchange rate, and federal fund rate were suitable targets for the government for stabilizing the Islamic capital market as well as inspiring more capital flows into the market. When the interest rate rises at either the national or international level, the investors more will buy compliant stocks [35]. A long-run relationship and a substantial short-run relationship had been found between stock prices as well as macroeconomic variables in the stock market of Malaysia. The exchange rate has a negative association with the stock price value. Money supply papers have immediate favourable liquidity impacts and negative long-run effects on stock prices as a result of money supply expansion [35, 36]. Some macroeconomic factors, for example, treasury bill rate, customer pricing index, and money supply have a considerable impact on the stock market variables when the variables are measured in the past. The stock market, on the other
hand, has no effect on the macroeconomic indicators that have been picked. After analyzing with Variance Decomposition and impulse response function only a minority of the forecast variance error of the market index. Three principles were identified through factor analysis such as interest rate, exchange rate, as well as inflation. A statistically substantial association has been discovered between stock market indicators as well as macroeconomic factors. It is discovered that there is a one-way causation from the stock market to the actual economy. It was discovered that there were around five cointegration linkages between macroeconomic factors along with stock market indicators. Maybe diverse macro dimensions are responsible for stock price movements [36]. It has been discovered that there are significant disparities between the portfolios when the OLS approach is applied. In the serial connection problem discussed by utilizing Durbin-Watson statistics a big difference had been found among the market portfolios against macroeconomic variables against variation of R. No evidence was suggested in other market portfolios [1, 36]. Both long-run and short-run links between macroeconomic factors and stock prices have been established in this study. For the Sri Lankan stock market, the validity of the semi-strong form of the EMH theory was demonstrated, as was the existence of an opportunity for investors to make investments in both the domestic and foreign equity markets. Several methodological weaknesses were found during the research [37].
statement of the Problem
After reviewing a lot of research papers from the last decades, it was found that no research has been done on exogenous shocks in macroeconomic variables to BSE Sensex for the Indian stock market with this particular data set and period. Thus, the study has taken the responsibility to reveal strong evidence for changes in stock prices due to exogenous shocks in macroeconomic variables.
Need of the study
In the last two decades, Global financial markets, particularly the capital market, have undergone a significant transformation. The successive changes in macroeconomic variables have been the underlying reasons for these shifts in the economy. These modifications resulted in a major rise in the volatility of the stock market and the number of trades. In the context of measurements of these variables, there are a variety of points of view expressed in the literature review. The Indian economy has undergone
tremendous changes in the last decades. The present study is expected to add some key contributions to the present literature. This study will examine the level of changes in macroeconomic variables due to innovative external shocks, which may cause a change in share prices in stocks listed in BSE Sensex. This study is expected to offer some acumens to policymakers, investors, and portfolio managers.
objectives
To examine the unexpected changes in stock prices due to the external shocks given to the macroeconomic variables (i.e., Exchange Rate, Inflation, Interest Rate, Crude Oil Prices, and Trade Openness) to forecast the future stock market returns.
hypothesis
H0: Stock prices do not respond to an external shock from any of the macroeconomic variables.
H1: Stock prices respond to an external shock from any of the macroeconomic variables.
research methodology
sample selection
The present study includes the estimation findings for the unexpected changes in stock prices due to external shocks given to macroeconomic variables using the monthly frequency data. We have taken five macroeconomic variables, i.e., Exchange Rate (EXR), Crude Oil Prices (C.O.), Interest Rate (I.R.), Consumer Price Index (CPI), and Trade Openness and BSE SENSEX (B.S.) for stock prices.
source of Data
The source of data is https://fred.stlouisfed.org/ & www. bseindia.com.
Period Taken for the study
The period of the study is taken from January 2009 to December 2019
Tools & Models used in the study
E-views 11 is used for the analysis. For converting time series data into stationary series Augmented Dicky Fuller Test had been applied. The Impulse Response Function has been used to examine the external shocks Variance Decomposition Model and check the impulse responses.
Model specification
LBS = a0 + a1LCO + a2CPI + a3LEXR + a4LIR + + a5LTO + St.
Table 1
Results of Variance Decomposition
Variance Decomposition of DBS:
Period S.E. DBS DCO DCA DEXTER DIR DTO
1 1089.165 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000
2 1106.142 97.29432 1.533761 0.324406 0.094107 0.752060 0.001350
3 1131.913 96.16228 1.785321 0.319205 0.232665 0.722288 0.778235
4 1134.004 95.85114 1.780334 0.380576 0.231825 0.833000 0.923124
5 1134.731 95.79291 1.787323 0.381532 0.247423 0.850064 0.940752
6 1134.849 95.77335 1.787888 0.386212 0.247406 0.850327 0.954817
7 1134.875 95.77037 1.788449 0.386194 0.247860 0.852088 0.955042
8 1134.877 95.77007 1.788473 0.386296 0.247865 0.852238 0.955059
9 1134.878 95.77002 1.788510 0.386298 0.247866 0.852246 0.955064
10 1134.878 95.77000 1.788510 0.386298 0.247866 0.852249 0.955073
Variance Decomposition of DCO:
Period S.E. DBS DCO DCPI DEXTER DIR DTO
1 4.968296 0.064685 99.93531 0.000000 0.000000 0.000000 0.000000
2 5.350178 2.228039 96.26406 0.107869 0.291521 1.019707 0.088805
3 5.381352 2.493096 95.72546 0.295593 0.348509 1.036288 0.101059
4 5.392125 2.739793 95.34631 0.318969 0.351754 1.066785 0.176387
5 5.393157 2.739539 95.33409 0.321746 0.355290 1.066781 0.182553
6 5.393622 2.742391 95.32288 0.322281 0.359785 1.068995 0.183664
7 5.393652 2.742428 95.32201 0.322507 0.359849 1.069003 0.184202
8 5.393663 2.742443 95.32178 0.322514 0.359875 1.069156 0.184236
9 5.393664 2.742457 95.32175 0.322515 0.359876 1.069160 0.184238
10 5.393664 2.742459 95.32175 0.322515 0.359877 1.069160 0.184238
Variance Decomposition of DCPI:
Period S.E. DBS DCO DCPI DEXTER DIR DTO
1 0.728475 6.635239 0.105598 93.25916 0.000000 0.000000 0.000000
2 0.742328 7.966931 0.145100 91.51259 0.215895 0.044889 0.114593
3 0.760494 8.309864 1.186148 87.22171 0.478072 1.087939 1.716266
4 0.763803 8.337282 1.382817 86.46902 0.521916 1.183103 2.105863
5 0.764073 8.333694 1.382311 86.40814 0.564171 1.198845 2.112840
6 0.764276 8.329917 1.381626 86.36283 0.564088 1.215673 2.145863
7 0.764309 8.329448 1.382427 86.35523 0.564866 1.216641 2.151393
8 0.764321 8.329352 1.382581 86.35277 0.564853 1.219111 2.151328
9 0.764322 8.329331 1.382600 86.35239 0.564851 1.219167 2.151657
10 0.764323 8.329320 1.382600 86.35230 0.564854 1.219210 2.151714
Variance Decomposition of DEXR:
Period S.E. DBS DCO DCPI DEXTER DIR DTO
1 1.330815 14.14855 0.004007 0.057641 85.78980 0.000000 0.000000
2 1.433250 16.10615 0.212214 3.652014 76.23171 3.625078 0.172833
3 1.474954 16.71446 1.051329 5.545052 71.99604 4.501969 0.191147
Table 1 (continued)
Variance Decomposition of DEXR:
Period S.E. DBS DCO DCPI DEXTER DIR DTO
4 1.483369 17.20902 1.118085 5.486907 71.18695 4.489730 0.509303
5 1.484276 17.20873 1.147853 5.507854 71.11378 4.498001 0.523780
6 1.484882 17.21150 1.152670 5.504787 71.06012 4.528039 0.542879
7 1.484952 17.21107 1.152585 5.506950 71.05356 4.527995 0.547839
8 1.484976 17.21106 1.152683 5.506868 71.05122 4.530236 0.547940
9 1.484979 17.21102 1.152679 5.506894 71.05097 4.530295 0.548148
10 1.484980 17.21102 1.152682 5.506909 71.05087 4.530376 0.548148
Variance Decomposition of DIR:
Period S.E. DBS DCO DCPI DEXTER DIR DTO
1 0.400924 4.30E-05 1.255395 0.064041 0.614634 98.06589 0.000000
2 0.402214 0.170434 1.261692 0.064925 0.881465 97.58133 0.040153
3 0.409696 0.169739 1.228133 0.727353 1.251220 96.58273 0.040822
4 0.410004 0.299136 1.232075 0.728171 1.249815 96.44587 0.044937
5 0.410367 0.310222 1.242527 0.779892 1.267747 96.32801 0.071604
6 0.410461 0.332449 1.247422 0.780643 1.267593 96.28405 0.087845
7 0.410478 0.333234 1.247884 0.782902 1.267504 96.27977 0.088709
8 0.410488 0.334407 1.248111 0.782961 1.267505 96.27587 0.091150
9 0.410489 0.334425 1.248106 0.783074 1.267512 96.27569 0.091192
10 0.410489 0.334454 1.248108 0.783072 1.267511 96.27559 0.091263
Variance Decomposition of DTO:
Period S.E. DBS DCO DCPI DEXTER DIR DTO
1 4.452876 0.391262 0.010824 0.266691 1.022135 0.082661 98.22643
2 5.092322 4.809630 0.628899 0.205265 1.022690 0.063329 93.27019
3 5.209225 5.463493 1.128258 0.323462 1.176100 2.747543 89.16114
4 5.246499 5.439944 1.142569 0.323952 1.159757 3.169740 88.76404
5 5.256885 5.452165 1.138108 0.344805 1.155294 3.197613 88.71201
6 5.259247 5.447832 1.137124 0.348671 1.155373 3.274045 88.63696
7 5.259513 5.447286 1.137199 0.349876 1.155623 3.277364 88.63265
8 5.259602 5.447222 1.137341 0.350509 1.155620 3.278131 88.63118
9 5.259619 5.447187 1.137366 0.350507 1.155620 3.278582 88.63074
10 5.259621 5.447236 1.137367 0.350526 1.155623 3.278586 88.63066
Cholesky Ordering: DBS DCO DCPI DEXR DIR DTO
Source: E-Views 11.
DATA ANALYSIS AND DISCUSSION
Variance Decomposition Results
The Variance Decomposition analysis provides the percentage values of forecasted error variance in a single variable, based on the forecasted error variance. The empirical results are illustrated in
Table 1, which shows that 95.77% of stock prices changed by innovative shocks. The crude oil prices have a more significant impact, around 1.78%, whereas trade openness has contributed a .95% impact on the BSE. However, the analysis explains the maximum percentage of variation after the prices of crude oil
Response to Cholesky One S.D. (d.f. adjusted) Innovations ± 2 S.E. Response of DCO to DBS Response of DCPI to DBS
1.5 1.0 0.5 0.0 -0.5 -1.0
23456789 10 Response of DEXR to DBS
123456789 10 Response of DIR to DBS
.08
-.04
\ \ * V""
23456789 10 Response of DTO to DBS
123456789 10
-1
12 3 4
9 10
Fig. Combined Graph of Impulse Response Function Analysis
Source: E-Views 11.
which affect the stock market. The contribution of other variables is very minimal.
Impulse Response Functions
As a result of an external change, the impulse response function depicts the reaction of some dynamic system. Using this method, you may figure out how sensitive each of the dependent variables in the VAR is when a shock is applied to the variable in question. Graph No. 2 shows the estimation from the impulse response function of the stock market index as against the "own shocks" along with shocks of crude oil prices, interest rate, exchange rate trade openness, and inflation. Results in Fig. are represented in graphs. Results show the impulse response in crude
oil prices to BSE SENSEX in the first graph that shows the value of crude oil prices is rising from the first period. After a short period, it will be declined, and after the sixth period, it is stable. Results show in the second graph, the impulse response in CPI (Consumer price index) to BSE SENSEX, which shows the value of CPI is declining, and after a short period, it was rising. Results show in the third graph the impulse response in Exchange Rate to BSE SENSEX, which shows the value of the Exchange Rate is declining and after a short time period it is rising. Results in Table 2 show the impulse response in the Inflation Rate to BSE SENSEX in the fourth graph, which shows the Inflation Rate is rising from the first period. After a short period, it will be declined, and after the sixth
1
1
1
0
Table 2
Results of Impulse Response Function
Period DCO DCPI DEXTER DIR DTO
1 0.126360 0.187647 0.500580 -0.000263 0.278532
(0.43736) (0.06307) (0.11295) (0.03530) (0.39167)
2 0.788540 0.093221 0.283325 0.016603 -1.081499
(0.48197) (0.06718) (0.12858) (0.03656) (0.45473)
3 -0.290192 0.064486 -0.181019 0.003031 0.485133
(0.48039) (0.06773) (0.13128) (0.03657) (0.46710)
4 -0.273168 -0.024064 -0.122651 -0.014763 0.121700
(0.23717) (0.03276) (0.07617) (0.01861) (0.31048)
5 0.015201 -0.003674 0.021367 -0.004423 -0.096495
(0.12394) (0.02331) (0.05335) (0.01461) (0.19312)
6 0.031102 0.001920 0.019263 0.006139 0.012481
(0.07820) (0.01283) (0.03155) (0.00751) (0.12448)
7 0.004399 -0.001244 -0.005076 0.001171 0.001280
(0.03382) (0.00636) (0.01778) (0.00417) (0.06703)
8 -0.002762 0.000954 -0.003524 -0.001415 0.005733
(0.01591) (0.00383) (0.01034) (0.00231) (0.03571)
9 -0.002106 0.000307 0.000673 -0.00018 0.000333
(0.00824) (0.00177) (0.00515) (0.00118) (0.01798)
10 0.000762 -8.28E-05 0.000741 0.000225 -0.003882
(0.00331) (0.00097) (0.00280) (0.00070) (0.00872)
Cholesky Ordering: DBS DCO DCPI DEXR DIR DTO
Standard Errors: Analytic
Source: E-Views 11.
period, it is stable. Results show the impulse response in Trade Openness to BSE SENSEX in the fifth graph, which shows the value of Trade openness is declining. After a short period, it rises, and after the fifth period, it is stable.
Findings
1. According to the results, the Null Hypothesis H0 is rejected.
2. In order to predict the short and long-run shocks, variance decomposition is utilized for the study; VDC analysis results show that a significant unexpected change in stock prices is due to external factors like changes in dividends, and also most of the changes are due to exogenous shocks in macroeconomic variables
which have been taken for the study in-sample period and some changes in the stock market are due to its own innovative shocks.
3. VDC Analysis results show that Crude oil prices have a more significant effect on BSE SENSEX, whereas other macroeconomic variables have moderate effects on the BSE SENSEX.
4. The results of IRF show that all macroeconomic variables are changing due to impulse changes in BSE SENSEX due to exogenous shocks.
SUGGESTIONS
1. To reduce these unexpected changes frequently, companies should control the capital loss and focus on some stable policies regarding dividends.
2. Changes in stock market returns are more affected by changes in crude oil prices in India. The reason could be, the companies in Energy, Industrial and Material sectors raise the value of shares to rise in oil prices. Raising prices in oil is a positive signal to the investors in India to increase the amount of investment.
3. A hike in oil prices will have a positive impact on other commodity prices like Gold prices.
4. And if there is a hike in share prices, commodities prices will go down in the coming years.
conclusion
An attempt was made in this research work to investigate the impact of macroeconomic factors on the Indian stock market. The empirical study began with testing stationarity of the time-series data, and lag length criteria were decided. Finally, the model's short-run and long-run shocks were predicted using the Impulse Response Function as well as Variance Decomposition analysis, respectively. According to the findings, crude oil prices have a greater influence on the BSE Sensex than other factors, with other variables having a small impact. The results of VDC analysis discover that stock prices change due to their innovative shocks. The macroeconomic variables
were selected based on the existing literature and the availability of the resources.
limitations of the study
The study has considered a limited number of macroeconomic variables, i. e., Exchange Rate, Consumer Price Index, Interest Rate, Crude Oil Prices, along with Trade Openness and stock index i. e BSE Sensex. Other macroeconomic variables could have a considerable impact on the movement of the stock market. In contrast, Other stock indexes are just as important for assessing the overall influence of macroeconomic factors on stock market performance as the Dow Jones Industrial Average.
scope of further research
Further study might re-examine the difficulties raised in this work using a more complete data collection that includes more macroeconomic solid factors and stock indexes, in addition to the data set used in this paper. Research can be enhanced by including the study of a structural break in the time series data due to the Covid-19 pandemic. The Indian market also can be examined by comparing it with the stock market of developed countries at the international level.
REFERENCES
1. Abbas G., Bashir U., Wang S., Zebende G. F., Ishfaq M. The return and volatility nexus among stock market and macroeconomic fundamentals for China. Physica A: Statistical Mechanics and its Applications. 2019;526:121025. DOI: 10.1016/j.physa.2019.04.261
2. Antwi S., Ebenezer A. M., Zhao X. The effect of macroeconomic variables on stock prices in emerging stock market: Empirical evidence from Ghana. International Journal of Social Sciences Tomorrow. 2012;1(10):1-10. URL: https://www.researchgate.net/publication/272171255_The_effect_of_macroeconomic_variables_on_stock_prices_ in_emerging_stock_market_Empirical_evidence_from_ghana
3. Ba^arir Bicil i. M., Yilmaz O. The relationship between selected financial and macroeconomic variables with consumer confidence index. Journal of Ya§ar University. 2019;(14):173-183. URL: https://dergipark.org.tr/tr/ download/article-file/680681
4. Brahmasrene T., Huang J.-C., Sissoko Y. Crude oil prices and exchange rates: Causality, variance decomposition and impulse response. Energy Economics. 2014;44:407-412. DOI: 10.1016/j.eneco.2014.05.011
5. Choi C .-Y., Chudik A. Estimating impulse response functions when the shock series is observed. Economics Letters. 2019;180:71-75. DOI: 10.1016/j.econlet.2019.04.017
6. Colin-Jaeger N., Delcey T. When efficient market hypothesis meets Hayek on information: Beyond a methodological reading. Journal of Economic Methodology. 2020;27(2):97-116. DOI: 10.1080/1350178X.2019.1675896
7. Colombo V., Paccagnini A. Does the credit supply shock have asymmetric effects on macroeconomic variables? Economics Letters. 2020;188:108958. DOI: 10.1016/j.econlet.2020.108958
8. Bodla B. S., Kumar A. Foreign institutional investors and macroeconomic variables in India: a study of causal relation. Paradigm: A Management Research Journal. 2009;13(2):80-87. DOI: 10.1177/09718907200902
9. Iwayemi A., Fowowe B. Impact of oil price shocks on selected macroeconomic variables in Nigeria. Energy Policy. 2011;39(2):603-612. DOI: 10.1016/j.enpol.2010.10.033
10. Malini H. Efficient market hypothesis and market anomalies of LO 45 index in Indonesia stock exchange. Sriwijaya International Journal of Dynamic Economics and Business. 2019;3(2):107-121. DOI: 10.29259/sijdeb. v3i2.107-121
11. Maji S. K., Laha A., Sur D. Dynamic nexuses between macroeconomic variables and sectoral stock indices: Reflection from Indian manufacturing industry. Management and Labour Studies. 2020;45(3):239-269. DOI: 10.1177/0258042X20922076
12. Mukhtarov S., Aliyev S., Zeynalov J. The effect of oil prices on macroeconomic variables: evidence from Azerbaijan. International Journal of Energy Economics and Policy. 2020;10(1):72-80. DOI: 10.32479/ijeep.8446
13. Niroomand F., Metghalchi M., Hajilee M. Efficient market hypothesis: A ruinous implication for Portugese stock market. Journal of Economics and Finance. 2020;44(4):749-763. DOI: 10.1007/s12197-020-09514-8
14. Nuru N. Y. Monetary and fiscal policy effects in South African economy. African Journal of Economic and Management Studies. 2020;11(4):625-638. DOI: 10.1108/AJEMS-08-2019-0308
15. Rossi M., Gunardi A. Efficient market hypothesis and stock market anomalies: Empirical evidence in four European countries. Journal of Applied Business Research (JABR). 2018;34(1):183-192. DOI: 10.19030/jabr.v34i1.10111
16. Sánchez-Granero M.A., Balladares K. A., Ramos-Requena J.P., Trinidad-Segovia J. E. Testing the efficient market hypothesis in Latin American stock markets. Physica A: Statistical Mechanics and its Applications. 2020;540:123082. DOI: 10.1016/j.physa.2019.123082
17. Sharma A., Kumar A. A review paper on behavioral finance: Study of emerging trends. Qualitative Research in Financial Markets. 2020;12(2):137-157. DOI: 10.1108/QRFM-06-2017-0050
18. Spyrou S. Momentum return volatility, uncertainty, and energy prices: Evidence from major international equity markets. Review of Behavioral Finance. 2020;12(4):411-433. DOI: 10.1108/RBF-09-2019-0133
19. Tripathi V., Seth R. Stock market performance and macroeconomic factors: The study of Indian equity market. Global Business Review. 2014;15(2):291-316. DOI: 10.1177/0972150914523599
20. Wongbangpo P., Sharma S. C. Stock market and macroeconomic fundamental dynamic interactions: ASEAN-5 countries. Journal of Asian Economics. 2002;13(1):27-51. DOI: 10.1016/S 1049-0078(01)00111-7
21. Zhu X.-H., Chen J.-Y., Zhong M.-R. Dynamic interacting relationships among international oil prices, macroeconomic variables and precious metal prices. Transactions of Nonferrous Metals Society of China. 2015;25(2):669-676. DOI: 10.1016/S 1003-6326(15)63651-2
22. Bhuvaneshwari D., Ramya K. Can select macroeconomic variables explain long-run price movements of Indian stock indices? Indian Journal of Research in Capital Markets. 2018;5(1):35-53. DOI: 10.17010/ijrcm/2018/v5/i1/122907
23. Rafay A., Farid S. Financial integration in money markets: Evidence from SAARC region. DLSUBusiness and Economics Review. 2017;26(2):87-114. URL: https://www.researchgate.net/publication/318225174_Financial_ integration_in_money_markets_Evidence_from_SAARC_region
24. Köse N., Ünal E. The effects of the oil price and oil price volatility on inflation in Turkey. Energy. 2021;226:120392. DOI: 10.1016/j.energy.2021.120392
25. Zheng X., Su D. Impacts of oil price shocks on Chinese stock market liquidity. International Review of Economics & Finance. 2017;50:136-174. DOI: 10.1016/j.iref.2017.03.021
26. Balcilar M., Ozdemir Z. A., Ozdemir H., Wohar M. E. Fed's unconventional monetary policy and risk spillover in the US financial markets. The Quarterly Review of Economics and Finance. 2020;78:42-52. DOI: 10.1016/j.qref.2020.01.004
27. Meinusch A., Tillmann P. The macroeconomic impact of unconventional monetary policy shocks. Journal of Macroeconomics. 2016;47(Pt.A):58-67. DOI: 10.1016/j.jmacro.2015.11.002
28. Dhingra V. S., Gandhi S., Bulsara H. P. Foreign institutional investments in India: An empirical analysis of dynamic interactions with stock market return and volatility. IIMB Management Review. 2016;28(4):212-224. DOI: 10.1016/j.iimb.2016.10.001
29. Jin X. Volatility transmission and volatility impulse response functions among the Greater China stock markets. Journal of Asian Economics. 2015;39:43-58. DOI: 10.1016/j.asieco.2015.05.004
30. Keswani S., Wadhwa B. An empirical analysis on association between selected macroeconomic variables and stock market in the context of BSE. The Indian Economic Journal. 2018 Mar;66(1-2):170-189. DOI: 10.1177/0019466219876492
31. Mazuruse P. Canonical correlation analysis: Macroeconomic variables versus stock returns. Journal of Financial Economic Policy. 2014;6(2):179-196. DOI: 10.1108/JFEP-09-2013-0047
32. Bhattarai R. C., Joshi N. K. Dynamic relationship among the stock market and the macroeconomic factors: Evidence from Nepal. South Asia Economic Journal. 2009;10(2):451-469. DOI: 10.1177/139156140901000208
33. Okech T. C., Mugambi M. Effect of macroeconomic variables on stock returns of listed commercial banks in Kenya. International Journal of Economics, Commerce and Management. 2016;4(6):390-418. URL: https://ijecm.co.uk/wp-content/uploads/2016/06/4622.pdf
34. Majid M. S., Yusof R. M. Long-run relationship between Islamic stock returns and macroeconomic variables: An application of the autoregressive distributed lag model. Humanomics. 2009;25(2):127-141. DOI: 10.1108/08288660910964193
35. Ibrahim M. H., Aziz H. Macroeconomic variables and the Malaysian equity market: A view through rolling subsamples. Journal of Economic Studies. 2003;30(1):6-27. DOI: 10.1108/01443580310455241
36. Gunasekarage A., Pisedtasalasai A., Power D. M. Macroeconomic influence on the stock market: Evidence from an emerging market in South Asia. Journal of Emerging Market Finance. 2004;3(3):285-304. DOI: 10.1177/097265270400300304
37. Rjoub H., Türsoy T., Günsel N. The effects of macroeconomic factors on stock returns: Istanbul Stock Market. Studies in Economics and Finance. 2009;26(1):36-45. DOI: 10.1108/10867370910946315
ABOUT THE AUTHORS / ИНФОРМАЦИЯ ОБ АВТОРАХ
Himadri Srivastava — Research Scholar, Amity University, Noida, Sector-125, India
Химадри Шривастава — исследователь, Университет Амити, сектор 125, Нойда, Индия
https://orcid.org/0000-0002-8218-9825
Corresponding author / Автор для корреспонденции
Priya Solomon — PhD, Assoc. Prof., ASB, Amity University, Noida, Sector 125, India
Прия Соломон — доктор философии, доцент, ASB, Университет Амити, Нойда, сектор 125,
Индия
https://orcid.org/my-orcid?orcid=0000-0002-9564-8373 [email protected]
Satyendra P. Singh — PhD, Prof., Alliance School of Business, Alliance University, Bangalore, India
Сатьендра П. Сингх — доктор философии, профессор, Школа бизнеса Альянс, Университет Альянса, Бангалор, Индия https://orcid.org/0000-0003-1251-2962 [email protected]
Author's declared contribution:
Himadri Srivastava — problem identified, developed the research framework, review of literature, collected data,
performed analysis, interpretation and concluded with suggestions.
Priya Solomon — discussed the research results and interpretation.
Satyendra P. Singh — discussed and suggested the analysis methods and implications.
Заявленный вклад авторов:
Химадри Шривастава — постановка проблемы, разработка концепции исследования, обзор литературы,
сбор данных, выполнение анализа, интерпретация и выводы с предложениями.
Прия Соломон — обсуждение результатов исследования и их интерпретация.
Сатьендра П. Сингх — обсуждение и предложение методов анализа и результатов исследования.
Conflicts of Interest Statement: The authors have no conflicts of interest to declare. Конфликт интересов: авторы заявляют об отсутствии конфликта интересов.
The article was submitted on 13.02.2022; revised on 28.02.2022 and accepted for publication on 27.04.2022. The authors read and approved the final version of the manuscript.
Статья поступила в редакцию 13.02.2022; после рецензирования 28.02.2022; принята к публикации 27.04.2022. Авторы прочитали и одобрили окончательный вариант рукописи.
