Assessing Commonality in Liquidity: Evidence from an Emerging Market’s Index Stocks

Abstract

This study investigates the degree to which movements in stock liquidity is determined by common underlying factors in a large emerging market, India. This degree is called commonality. Commonality has been measured for NIFTY50 stocks using high frequency data across a variety of liquidity measures. This study empirically verifies the relative strength of market- and industry-wide liquidity in explaining commonality. Furthermore, the study analyses the impact of industry-wide liquidity on the liquidity of individual stocks belonging to the key industries of Indian economy, viz. consumer goods and pharma, energy, financial services, infrastructure, information technology (IT) and telecom, manufacturing and natural resources. Among all the sectors studied infrastructure, IT and telecom, manufacturing and natural resources sectors possess higher degree of Industry-wide commonality. This means fund managers find it difficult in altering a portfolio having greater exposure to these sectors. Studying the behaviour of commonality will also assist regulators in monitoring abnormal market fluctuations. The study contributes to the understanding of commonality on an emerging order driven market like India.

Keywords

Industry commonality market microstructure NSE liquidity

Introduction

Stock liquidity is the ability to buy or sell stocks without impacting the price. A highly liquid stock is one which can stock that can be sold quickly. When it is sold at a discounted value or takes longer to sell, it is considered to be less liquid or illiquid. A company’s stock liquidity is adjudged using the difference between the buying price and selling price, that is, bid–ask spread. On other hand, market liquidity refers to market’s ability to facilitate buy and sale of stock without significantly changing its price. A liquid market is associated with less risk and attracts a large pool of market participants. Liquid market encourages the savers to invest in stock markets because they have the opportunity to sell stocks quickly and cheaply. A sudden dry up of the liquidity from the stock markets is the risk for market participants. Market crashes such as international stock market crash (October 1987) and Asian Financial Crises (1997) are not caused because of any major news. These crashes were characterized by sudden evaporation of aggregate market liquidity. This sheds light on the importance of systematic liquidity which is transmitted across financial assets and investors.

The phenomena of stock’s liquidity getting impacted by market liquidity is known as commonality in liquidity (Chordia et al., 2000). Commonality is a kind of non-diversifiable risk or systematic risk which has its implication on portfolio construction. Market may become highly illiquid in severe market conditions and it may jeopardize market participant’s ability to change their positions. Recently, commonality is found to be a priced risk factor in asset pricing models (Acharya & Pedersen, 2005). This calls for a greater understanding of commonality in liquidity. When liquidity of an individual stock is impacted by industry-wide liquidity, the phenomenon is known as industry commonality. Studying industry-wide commonality on Indian stock exchange will help investors in formulating trading strategies and industry and stock market regulators in taking measures to avoid market crashes due to sudden evaporation of liquidity in a particular industry.

As per World Economic Situation and Prospects report (2019), the global economy is facing many risks, which could severely disrupt economic activity and inflict significant damage on long-term development prospects. National and multilateral policy actions are vital to contain the systematic risk, particularly in emerging markets. Global investors seek out emerging markets to take advantage of the high growth rates and opportunities. The main emerging market powerhouses are China and India. In 2017, the combined economic output of these two economies (US$32.6 trillion) was greater than the European Union (US$20.9 trillion) and the USA (US$19.4 trillion). However, emerging markets like India are highly illiquid, volatile and possess high commonality risks. Therefore, it is vital to study commonality phenomena on emerging markets. Commonality results of developed markets cannot be replicated to emerging markets as these markets are undergoing constant changes. So, one should not utilize historical information to draw correlations between returns and events.

As per PricewaterhouseCoopers (PwC)¹ and Ernst & Young (EY)² reports, India is an attractive emerging market destination for foreign portfolio investors. Post liberalization, India has been on an economic growth trajectory. Industries like services, information technology (IT) and information technology enabled services (ITeS) have boomed because of cost advantages. The boosting economy is improving the income distribution which in turn has benefitted the industries like fast moving consumer goods (FMCG), banking and automobiles. Turbulent macroeconomic conditions and US subprime crises impacts the productivity gains of banking industry (Sharma & Sharma, 2015). Also, certain industries like pharmaceuticals and IT have more exposure to the foreign markets, and thus they are badly impacted during financial crises. The performance of energy industry gets impacted because of price fluctuations in crude oil or gulf crises. Industry expansion and deceleration impacts the trading of stocks of particular industries. Since each industry has unique characteristics, investment analyst should understand how industry-wide economic forces impact the co-movement of stocks.

This study focuses on analysing market- and industry-wide commonality risk on a key emerging market, India. The sample of the study comprises of NIFTY50 stocks listed on National Stock Exchange (NSE), India. These stocks are called as large capitalization, or large cap stocks. Large cap stocks are known to be most liquid, highly traded and draws high demand from both individual and institutional investors. Therefore, these stocks form a core of the investment portfolios. Drawing on the high frequency dataset on NSE comprising over 11 million intraday transactions, we report strong support for market-wide commonality, and a greater impact of industry-wide commonality in explaining the liquidity of individual stocks. Moreover, the study analyses the strength of industry-wide commonality across seven sectors of Indian economy: consumer goods and pharma, energy, financial services, infrastructure, IT and telecom, manufacturing and natural resources. Among all the sectors manufacturing, infrastructure, IT and telecom and natural resources are found to be significant in explaining the liquidity of stocks. The article contributes to the existing literature in the following ways: first, unlike the existing literature which focuses more on market-wide commonality, this article also includes industry-wide commonality and does a comparison of both. Thus, this article is quite comprehensive in terms of estimating and comparing commonality in liquidity. Second, existing literature on commonality is focused on the developed markets. This article focuses on a key emerging market, India, where systemic risk like commonality is more pervasive than the developed markets.

The rest of the article is organized as follows: the second section of the article discusses the literature and specifies research objectives. It is important to understand the market microstructure of the stock market as it defines the structural factors which drives the price and volume preferences of the participants (CFA Institute, 2009). Market microstructure further determines the liquidity in the stock market. The study on liquidity and commonality requires the understanding of underlying market microstructure. The third section provides insights into the market microstructure of Indian stock markets. The fourth section describes the data, how it was processed for analysis, defines the liquidity measures employed along with the explanation of methodology to examine commonality in liquidity. The fifth section 5 discusses the empirical results on commonality evidence, comparison between market- and industry-wide commonality and the strength of industry-wide commonality across the sectors. The sixth section summarize our findings, briefly discusses the managerial implications and provides the direction for future research.

Literature Review

One of the prominent functions of the financial markets is to provide liquidity to stocks as well as to the market (Syamala et al., 2014). Demsetz (1968) and Tinic (1972) propounded that trading activity is the determinant of liquidity along with other factors such as firm size and volatility. Amihud and Mendelson (1986) further defined the concept of liquidity as the ease at which a stock is traded in the stock markets. Early strands of literature make independent study of stock liquidity and market liquidity. Negative relationship is found between stock returns and associated liquidity (Amihud & Mendelson, 1991; Brennan & Subrahmanyam, 1996; Fiori, 2000).

Recently, research interest has moved from studying individual characteristics of the liquidity to the common characteristics (Chordia et al., 2000; Hasbrouck & Seppi, 2001; Huberman & Halka, 2001). Chordia et al. (2000) studied transactions data from New York Stock Exchange (NYSE) and opined that liquidity is not just the characteristic of individual stock, rather it is the systematic characteristic. Liquidity of individual stock co-moves with the liquidity of the market. This co-movement is a systematic risk known as commonality in liquidity. Inventory risks and asymmetric information are identified as the reasons for the existence of such a co-movement. Hasbrouck and Seppi (2001) does not provide significant support regarding commonality phenomenon on the basis of principal component analysis for 30 Dow stocks. Brockman and Chung (2002) take account of the commonality in an order-driven market structure by examining intraday data on Hong Kong stock exchange (or The Stock Exchange of Hong Kong [SEHK]). Their study argued that a quote-driven system imposes barriers on entry and exit in market participation while order-driven systems have free existed perspective. This makes order-driven systems more susceptible to commonality. Also, there is no market maker to maintain liquidity in the market. This study documented that market-wide commonality is more pronounced than industry-wide commonality on spread- and depth-based liquidity measures. Dicle and Mukherjee (2011) provide evidence in favour of strong commonality in liquidity in the first and last hour of trading. They argued that common intraday trading patterns, as well as commonality, is caused by common factors, that is, inventory risk and information asymmetry. Rösch and Kaserer (2014) moved deeper into the order book and observed that commonality becomes stronger during crises. Co-variation in supply side liquidity and demand side liquidity are reported to be two sources of commonality.

In the context of emerging markets, Pukthuanthong-Le and Visaltanachoti (2009) reconfirmed commonality evidence on Stock Exchange of Thailand (SET) over 8-year period and reports that the explanatory power of industry-wide commonality is stronger than market-wide commonality. However, the strength of commonality on SET is lesser in comparison to NYSE (Chordia et al., 2000). Day-of-the-week; stock returns and tick size are identified as possible determinants of liquidity. Kumar and Shah (2012) investigated the existence of commonality on NSE, India, using one proxy and four timestamps in a day. Their study suggests the strength of commonality is more during the bearish phase of the market. Investment managers find it difficult to alter the portfolio during downturns. The study also shows that commonality follows weekly and monthly effects. Brockman et al. (2009) shows that local factor drives commonality more in comparison to the global factors. Macroeconomic announcements are found to be stronger source of commonality in emerging economies. Both Karolyi et al. (2012) and Brockman et al. (2009) noted that emerging markets are more vulnerable to the commonality risk. Wang’s (2013) empirical model tests the strength of global, local and regional factors in commonality across 12 Asian markets. Wang also finds evidence on increasing strength of commonality over a decade from 2000 to 2010.

There have been an increasing number of studies on market-wide commonality in recent years. However, few studies have examined industry-wide commonality. Narayan et al. (2015) examines the influence of industry-wide liquidity in explaining the variation in liquidity of stocks listed on Chinese stock exchanges. The strength of industry-wide commonality is found to be highest in industrial sector.

Previous literature also extensively discusses the role of liquidity and commonality in asset pricing. Fama and French (1993) argued that the non-diversifiable risk factor in asset pricing model is explained by market-wide factors. Amihud (2002) and Pastor and Stambaugh (2003) found negative relationship between expected stock returns and market liquidity. Liu (2006) extends two factor Fama–French Capital Asset Pricing Model (CAPM) model with liquidity factor and reports better results in explaining returns. Other, studies re-examine asset pricing by including stock’s liquidity relative to market-wide liquidity, that is, commonality risk. Acharya and Pedersen (2005) formulated a liquidity-adjusted capital asset pricing model to understand how various channels of liquidity risk affect asset prices in US stock markets. Lee (2011) further tested this model on international financial markets to show pricing of liquidity risks. The priced liquidity risks are covariance of individual stocks’ liquidity with the local market liquidity and the covariance of individual stocks’ liquidity with local and world market returns. Global liquidity risk factors are found to be more important for developed markets than local liquidity risk factors. Amihud et al. (2015) found high illiquidity premiums in emerging markets.

Rationale of the Study

The increasing globalization of financial markets has increased the interest of international investors in emerging markets. The empirical literature has thus far focused on developed markets and overlooked the commonality of liquidity phenomenon on key emerging economies like India. For instance, Chordia et al. (2000) reported commonality evidence on NYSE (USA), which is a quote-driven market consisting of market specialists. However, India’s NSE is the order-driven market where such market specialists are absent. Thus, specialist inventory holding costs are less relevant on NSE. These differences in the market microstructure of NSE and NYSE could result in differences in the strength of commonality (Brockman & Chung, 2002). Finally, research relating to commonality is of prime relevance to market participants as systematic variations in liquidity is a priced risk factor. Fernando and Herring (2003) shows that shocks to commonality can even trigger financial crises. In fact, the simultaneous decline in systematic liquidity across several markets was a major contributory factor in the Asian and Russian crises in 1997–1998. This variation in systematic liquidity is systematic risk. This risk is more pervasive in emerging markets because of their interconnectedness (Rao, 2000). Thus, it is imperative to study commonality phenomenon on emerging markets like India.

Objectives of the Study

Our study contributes to the published literature by testing for the existence of market- and industry-wide commonality in liquidity on all 50 stocks belonging to benchmark NIFTY50 index of NSE. We use detailed order and transaction data which is difficult to obtain for emerging markets. Specifically, we examine liquidity co-movement of individual stocks with market liquidity. Moreover, we investigate the relative strength of commonality across market and across industry. After noticing that the strength of industry-wide commonality is more than the market commonality, we further investigate the strength of commonality across seven industries.

Market Microstructure: Indian Stock Market

As per Chan et al. (1995), the institutional features of the markets can have material effects on the differences in intraday patterns. Hence it is prominent to study the market microstructure of Indian stock markets. Indian stock market comprises of two main trading avenues viz. NSE and Bombay Stock Exchange (BSE). The trading system of these two is identical, having the same features. Because of the development in IT and financial market reforms, electronic limit order book and order driven market structures have become popular trading designs. NSE and BSE, both have electronic limit order book into their order driven market structure. In order driven market structure there is no designated market makers and all limit orders submitted to the screen-based electronic trading system (National Exchange for Automated Trading [NEAT]) is displayed. This trading system operates on price–time priority. The limit orders are first sorted by price and the best price buy-order gets priority in matching with the best sell order. The orders having a similar price are sorted on the basis of time priority, and the earlier orders get priority over the later orders. An unmatched order remains in the system for a day until the fresh arrival of the order, or cancellation or modification of the earlier order. The order matching is done electronically, thus keeping trading system transparent and fair. Brockman and Chung (2002) reasoned that the order-driven market structures are more susceptible to commonality because of the absence of market makers. The market makers are obliged to maintain a balance of liquidity in quote driven markets.

The last decade (2000–2010) has witnessed major developments in Indian securities market regarding improved market microstructure, regulatory reforms and the introduction of new products. The introduction of an automated trading system, reduction of rolling settlement cycle from T + 5 to T + 2 and dematerialization of the stocks, reduces transaction time and enhances liquidity. Direct market access (DMA) provides direct access to the exchange trading system through brokers infrastructure but without manual intervention by the broker. This facility is introduced for institutional investors. Applications supported by blocked amount (ASBA) overcome the limitation of refund amount and empower investors to earn interest on blocked amount. Mini derivative contracts on Nifty and Sensex enhances retail participation and market liquidity in stock derivatives. Regulatory framework has been strengthened by mandating the corporation and demutualization of stock exchanges.

Methodology

Data Source and Sample

The Trade and Quotes (TAQ) dataset for NSE is collected from Bloomberg database. This dataset is difficult to obtain for emerging economies. The period for the study is from 1 to 30 April 2015. As per Chordia et al. (2000) infrequently traded stocks cannot provide reliable information. Næs and Bank (2004) argued that low-value stocks are problematic because they tend to have exaggerated returns. NIFTY50 stocks are continually traded throughout the year and not suffered from the problem of exaggerated returns. Therefore, the sample consists of all securities included in the NIFTY50 index as on 1 April 2015. The list of NIFTY50 stocks is given in Appendix I. The raw data were cleaned for errors, the same timestamp values and missing values. There are on an average 11,400,000 intraday transactions recorded in 950 files comprising of 50 stocks on 19 trading days. After computing the variables at each timestamp, the study aggregated them at 15-min intervals. The programmes for calculating intraday liquidity measures were written to work iteratively on one stock for one trading day. After computation on daily data, the data for each stock is kept in 50 different files. The sectoral composition of data is presented in Table 1.

Table 1.

Weight and Composition of Sector in the Sample

Sector	Weight (%)	Composition
Financial services	30.91	Banking and financial companies
IT	16.73	IT
Energy	10.43	Energy, oil
Consumer goods	9.99	Consumer goods
Manufacturing*	9.83	Automobile, industrial manufacturing
Infrastructure*	7.49	Cement, construction
Telecom and media*	3.1	Media, entertainment, telecom
Natural resources*	3.24	Metals, coal
Pharma	8.29	Pharmaceutical companies

Source: Fact sheet of CNX Nifty (www.nseindia.com).

Note: *Some sectors are combined by authors for the ease of computation.

Liquidity Measures

Liquidity is an elusive concept (Kyle, 1985). A single measure is not able to capture all dimensions of the liquidity. Therefore, the studies of liquidity are carried using multiple liquidity proxies or measures. We measure liquidity using proxies such as quoted spread (QS), effective spread (ES) and depth (DEP). The definition of these measures is provided in Table 2. Following Chordia et al. (2000), market liquidity ( ${Liq}_{m, t}$ ) is computed by taking an equally weighted average of all the firms (except stock j) belonging to NIFTY50 index. Thus, for an individual stock, market liquidity is computed separately. ${Liq}_{i, t}$ is the industry liquidity which is computed by taking an equally weighted average of all the firms belonging to an industry discussed in Table 1, except that particular stock j. $Δ {Liq}_{t}$ is the percentage change in liquidity in a time interval of 15 minutes.

{ÄLiq}_{t} = \frac{({Liq}_{t} - {Liq}_{t - 1})}{{Liq}_{t - 1}}

(1)

Table 3 (panel A) provides summary statistics of the variables, viz. QS, ES, DEP, mean volume (Mvol) traded and square returns (Sqrtn) of the stocks included in the sample. There is right skewness in the cross-section of liquidity proxies as the median is smaller than the mean values. This is consistent with the studies of Chordia et al. (2000), Fabre and Frino (2004) and Pukthuanthong-Le and Visaltanachoti (2009). Table 3 (panel B) reports that correlations among studied measures of liquidity are low. Also, there is a high correlation (0.8) among Mvol and DEP. These two variables tend to behave in the same way. Hence, between these two, DEP proxy is taken for analysis.

Table 2.

Liquidity Variables Definitions

Liquidity Measure	Acronym	Definition	Units
Effective spread	ES	$2 D_{t} ({PRICE}_{t} - \frac{({BID}_{t} + {OFR}_{t})}{2})$	₹
Depth	DEP	${OFRSIZ}_{t} + {BIDSIZ}_{t}$	Shares
Quoted spread	QS	${OFR}_{t} - {BID}_{t}$	₹

Notes: This table presents definition of each of liquidity measures along with the units. PRICE_t, BID_t and OFR_t are the trade-price, bid-price and ask-price, respectively, at time t. OFRSIZ_t and BIDSIZ_t are the size corresponding to ask-price and size corresponding to bid-price. $D_{t}$ is 1 (−1) if trade was buy (sell) (Bessembinder, 2003; Boehmer, 2005).

Table 3.

Summary Statistics and Correlations Among Variables of NIFTY50 Stocks

Panel A: Summary Statistics
Mean	Std. Dev.		Median
QS	0.217	6.571		0.185
ES	1.114	5.150		0.361
DEP	1213.988	16331.330		492.639
Mvol	488.405	11521.960		231.992
Sqrtn	0.000	0.000		0.000
	Panel B: Correlation Matrix
	QS	ES	DEP	Mvol
ES	−0.019
DEP	−0.069	0.050
Mvol	−0.103	0.085	0.824
Sqrtn	−0.121	0.281	0.234	0.339

Source: The authors.

Notes: QS, ES and DEP are the proxies for liquidity. Mvol is mean volume traded in 15 minutes interval. Sqrtn is squared return of 15 minutes.

Empirical Model: Market-wide Commonality

The study tests the presence of market-wide commonality following the empirical model (Chordia et al., 2000).

Δ {Liq}_{j, t} = α + β_{1} Δ {Liq}_{m, j, t} + β_{2} Δ {Liq}_{m, j, t - 1} + β_{3} Δ {Liq}_{m, j, t + 1} + δ {Sqrtn}_{m, j, t} + ε_{j, t}

(2)

where $Δ {Liq}_{j, t}$ the percentage change in individual stock (j) liquidity is, $Δ {Liq}_{m, t}$ is the percentage change in market liquidity. ${Liq}_{m, j, t - 1}$ and ${Liq}_{m, j, t + 1}$ are the lag and lead of the change in market liquidity variable, included as independent variables. Following Chordia et al. (2000), the study also included squared market returns as control variables. This is to control the spurious dependence between liquidity measures and returns.

Empirical Model: Market-wide and Industry-wide Commonality

The study also examined the relative impact of industry-wide commonality on individual stock liquidity, using the following empirical model (Chordia et al., 2000).

\begin{array}{l} Δ {Liq}_{j, t} = α + β_{1} Δ {Liq}_{m, j, t} + β_{2} Δ {Liq}_{m, j, t - 1} + β_{3} Δ {Liq}_{m, j, t + 1} + γ_{1} Δ {Liq}_{i, j, t} + γ_{2} Δ {Liq}_{i, j, t - 1} \\ + γ_{3} Δ {Liq}_{i, j, t + 1} + δ {Sqrtn}_{m, j, t} + ε_{j, t} \end{array}

(3)

$Δ {Liq}_{i, t}$ is the concurrent percentage change in industry liquidity. ${Liq}_{i, j, t - 1}$ and ${Liq}_{i, j, t + 1}$ are the lag and lead of the industry liquidity variable, included as independent variables. $Δ {Liq}_{m, t}, {Liq}_{m, j, t - 1}$ and ${Liq}_{m, j, t + 1}$ are concurrent, lag and lead of the change in market liquidity variable.

Empirical Model: Industry-wide Commonality

The study tests the presence of industry-wide commonality following the empirical model (Chordia et al., 2000).

{Liq}_{j, t} = α_{j} + β_{1} Δ {Liq}_{i, j, t} + β_{2} Δ {Liq}_{i, j, t - 1} + β_{3} Δ {Liq}_{i, j, t + 1} + δ {Sqrtn}_{i, j, t} + ε_{j, t}

(4)

where $Δ {Liq}_{j, t}$ is the percentage change in individual stock liquidity, $Δ {Liq}_{i, t}$ is the percentage change in industry liquidity. ${Liq}_{i, j, t - 1}$ and ${Liq}_{i, j, t + 1}$ are the lag and lead of the industry liquidity variable. These are included as independent variables. Following Chordia et al. (2000), the study also included squared market returns as control variable. This is to control the spurious dependence between liquidity measures and returns. The statistical significance of the sum of ( $β_{1} + β_{2} + β_{3}$ ), indicates the existence of industry-wide commonality in liquidity.

Analysis and Discussion

Market-wide Commonality in Liquidity

The study empirically tests for the presence of commonality along three liquidity proxies. The results reported in Table 4 reports the regression results corresponding to Equation (2). Squared market returns are nuisance variables, and thus not reported (Chordia et al., 2000). The statistical significance of the concurrent market liquidity variable (β₁) indicates the presence of commonality. Cross-sectional t-tests are used to test if the cross-sectional average of β₁ equals to 0. The average coefficient on concurrent market liquidity variable is 0.86, 0.92 and 0.85, respectively, on QS, ES and DEP proxies. Approximately 90 per cent of these individual concurrent market coefficients are positive from 50 regressions. The t-stat of the liquidity measures ranges from 7.256 to 13.973. The results report that ES shows the highest degree of commonality with a mean coefficient of concurrent market liquidity variable (β₁) equals to 0.925.

The study provides strong evidence of commonality when compared with the previous studies. At 5 per cent significance levels, Narayan et al. (2015) reported that 37–88 per cent of the stocks listed on Chinese stock markets have positive and significant concurrent coefficients. Chordia et al. (2000) reported the significance of 14–34 per cent of the stocks on NYSE. Moreover, Pukthuanthong-Le and Visaltannachoti (2009) reported that 17–59 per cent of the stocks listed on SET have positive and significant concurrent coefficients.

The comparative analysis shows that commonality in liquidity is much pervasive in Indian stock markets than on other order driven markets such as China and Thailand, and also quote driven markets like the USA.

Market-wide and Industry-wide Commonality in Liquidity

The study further investigated the effect of industry-wide liquidity on the liquidity of the individual stocks taking market liquidity as a control variable. The results presented in Table 5 reports results corresponding to Equation (3). Chordia et al. (2000) argued that asymmetric information about a particular industry causes co-variation in the liquidity of the stocks belonging to the same industry.

The cross-sectional mean of the concurrent coefficient on industry liquidity (γ₁) is found to be greater than market liquidity (β₁). This is also true for sum coefficients (β₁ + β₂ + β₃, γ₁ + γ₂ + γ₁). ‘Sum’ variable captures the combined effect of concurrent, lead and lad coefficients on stock liquidity. This means that explanatory power of the change in industry-wide liquidity is stronger than the explanatory power of the change in market-wide liquidity along all proxies. The study points out that the proportion of positively significant beta (β₁) and concurrent coefficient (β₁) decreases after controlling for the industry effect (on comparing Tables 4 and 5). This implies that quantum and significance of industry-wide commonality emerges while market-wide commonality decreases reflecting greater industry effects.

In consistence with the results in other markets (Chordia et al., 2000; Narayan et al., 2015), the study reports evidence of industry-wide commonality by significance the of industry and market concurrent coefficients. However, the findings are inconsistent with the studies of Brockman and Chung (2002) and Sujoto et al. (2005).

Table 4.

Market-wide Commonality in Liquidity

	ΔLiq (QS)	ΔLiq (ES)	ΔLiq (DEP)
Concurrent	0.863	0.925	0.853
t-Stat	8.359	7.256	13.973
Median	0.670	0.738	0.738
Percentage+	92%	90%	100%
Percentage + significant (5%)	80%	78%	90%
Percentage + significant (10%)	86%	82%	90%
Lead	0.015	0.041	0.207
t-Stat	1.894	4.577	6.418
Median	0.008	0.036	0.191
Percentage+	58%	72%	82%
Percentage + significant (5%)	0%	2%	22%
Percentage + significant (10%)	2%	6%	26%
Lag	−0.016	−0.020	0.075
t-Stat	−1.661	−1.738	2.204
Median	−0.004	−0.021	0.073
Percentage+	46%	42%	68%
Percentage + significant (5%)	2%	4%	8%
Percentage + significant (10%)	4%	12%	10%
Sum	0.862	0.947	1.134
t-Stat	8.004	7.130	12.216
Median	0.569	0.569	0.569
p-Value	0.000	0.000	0.000
Mean Wald test (χ²)	50.436	25.446	11.241
Percentage + significant (5%)	78%	74%	70%
Percentage + significant (10%)	80%	82%	80%
Adjusted R² mean	0.371	0.353	0.060
Adjusted R² median	0.360	0.342	0.053
DW mean	2.066	2.088	2.329
DW median	2.049	2.073	2.341

Source: The authors.

Notes: The table reports regression results corresponding to the model (2) for NIFTY50 market. In addition to the change in market liquidity, lag of change in market liquidity, a lead of change in market liquidity; squared market return is included as an additional regressor (coefficients not reported). Equally weighted cross-sectional means of time-series slope coefficients are reported with the t-statistics. ‘Percentage + significant’ reports the percentage of positive and significant slope coefficients at 5% and 10% levels of significance. DW is Durbin–Watson test statistic. Wald test indicates the joint significance of β1 + β2 + β3 in explaining commonality.

Table 5.

Market-wide and Industry-wide Commonality

	ΔLiq (QS)		ΔLiq (ES)		ΔLiq (DEP)
	Market	Industry	Market	Industry	Market	Industry
Concurrent	0.170	0.724	0.218	0.810	0.062	0.859
t-Stat	1.377	5.764	1.385	6.737	0.470	6.031
Median	0.229	0.514	0.239	0.540	0.305	0.564
Percentage+	60%	72%	60%	88%	64%	94%
Percentage + significant (5%)	52%	66%	44%	74%	42%	82%
Percentage + significant (10%)	54%	68%	48%	76%	44%	82%
Lead	0.002	0.000	0.008	0.017	0.106	0.028
t-Stat	0.412	0.119	0.635	1.913	2.642	1.126
Median	0.003	0.003	−0.003	0.003	0.107	0.024
Percentage+	52%	56%	50%	52%	66%	64%
Percentage + significant (5%)	0%	0%	0%	0%	10%	6%
Percentage + significant (10%)	0%	0%	0%	0%	10%	12%
Lag	−0.003	−0.002	−0.012	0.000	0.068	−0.015
t-Stat	−0.329	−0.488	−0.714	0.044	1.756	−0.696
Median	0.000	−0.007	−0.009	−0.005	0.072	−0.028
Percentage+	48%	38%	48%	46%	66%	38%
Percentage + significant (5%)	0%	0%	2%	2%	8%	8%
Percentage + significant (10%)	2%	0%	4%	2%	14%	8%
Sum	0.170	0.722	0.214	0.828	0.237	0.872
t-Stat	1.317	5.614	1.282	6.722	1.394	5.251
Median	0.219	0.515	0.164	0.548	0.302	0.523
p-Value	0.148	0.000	0.153	0.000	0.009	0.000
Mean Wald test (χ²)	21.422	96.764	12.611	62.026	5.640	52.537
Percentage + significant (5%)	76%	68%	64%	64%	42%	62%
Percentage + significant (10%)	82%	72%	68%	72%	48%	62%
Adjusted R² mean	0.474		0.464		0.206
Adjusted R² median	0.479		0.411		0.139
DW mean	2.069		2.103		2.329
DW median	2.045		2.090		2.341

Source: The authors.

Notes: The table reports regression results corresponding to the model (3) for NIFTY50 market and constituent industries. In addition to the change in market liquidity, lag of change in market liquidity, a lead of change in market liquidity, change in industry liquidity, lag of change in industry liquidity, a lead of change in industry liquidity; squared market return is included as an additional regressor (coefficients not reported). Equally weighted cross-sectional means of time-series slope coefficients are reported with the t-statistics. ‘Percentage + significant’ reports the percentage of positive and significant slope coefficients at 5% and 10% levels of significance. DW is Durbin–Watson test statistic. Wald test indicates the joint significance of β1 + β2 + β3 and γ1 + γ2 + γ3 in explaining commonality.

Industry-wide Commonality in Liquidity

This section presents empirical findings corresponding to the empirical model (4) across the seven industries. This model is estimated using ordinary least squares (OLS) for each stock. The selected industries are consumer goods and pharma (CGP), energy (ENGY), financial services (FIN), infrastructure (INFRA), IT and Telecom (ITT), manufacturing (MANF) and natural resources (NATR).

Table 6 reports the results of industry-wide commonality on spread-based QS proxy of liquidity. The highest mean is 0.996 on NATR industry while it is lowest on CGP industries with a value of 0.390. This implies that the strength of commonality is highest on NATR and lowest in CGP on the basis of QS liquidity measure. The highest proportion of stocks with positive and concurrent coefficient significant (5%) are part of ENGY, INFRA, ITT, MANF and NATR industries. The ENGY industry remains volatile throughout 2015–2016, because of the sluggish global demand and sharp decline in oil prices. These reasons may be attributing to the high commonality in this industry during the studied time period. Similarly, high commonality is observed in MANF industries. During April–December 2015–2016, the Index of Industrial Production (IIP) indicates a growth rate of 3.1 per cent. The policies like make in India are providing up thrust to this industry and thus may be causing commonality. The other coefficients like lead coefficient and lag coefficient have limited explanatory power in explaining the movements of individual stock liquidity. Most of these coefficients are not positive and not significant. ‘Sum’ variable captures the combined effect of concurrent, lead and lad coefficients on stock liquidity. Mean Wald test also test the combined robustness of the three coefficients in explaining the commonality. The result show that more than 57 per cent of the stocks in all the industries are influenced by industry-wide commonality. The mean and median R² is lies between 5 and 59 per cent. This indicates that individual regressions do not have much explanatory power and there are other variables that effects the movement in individual stock liquidity (Chordia et al., 2000).

Table 6.

Industry-wide Commonality for QS Proxy

	CGP	ENGY	FIN	INFRA	ITT	MANF	NATR
Concurrent	0.390	0.776	0.742	0.938	0.959	0.968	0.996
t-Stat	1.531	2.408	4.362	2.739	3.332	5.713	3.03
Median	0.08	0.411	0.801	0.932	0.81	0.966	0.738
Percentage+	86%	100%	91%	100%	100%	100%	100%
Percentage + significant (5%)	86%	100%	91%	100%	100%	100%	100%
Percentage + significant (10%)	86%	100%	91%	100%	100%	100%	100%
Lead	0.001	−0.009	0.007	0.026	−0.001	0.004	−0.001
t-Stat	0.56	−0.98	0.97	0.94	−0.102	0.249	−0.097
Median	0.002	−0.013	0.008	0.021	−0.006	0.004	0
Percentage+	86%	25%	55%	60%	38%	67%	40%
Percentage + significant (5%)	0%	0%	0%	0%	0%	0%	0%
Percentage + significant (10%)	0%	0%	0%	0%	0%	0%	0%
Lag	−0.004	0.001	−0.013	−0.034	0.001	−0.019	0.002
t-Stat	−1.254	0.042	−1.779	−1.456	0.184	−0.809	0.205
Median	−0.002	−0.005	−0.013	−0.036	0.005	−0.004	−0.003
Percentage+	29%	50%	36%	40%	63%	50%	40%
Percentage + significant (5%)	0%	0%	0%	0%	0%	0%	0%
Percentage + significant (10%)	0%	0%	0%	0%	0%	0%	0%
Sum	0.387	0.767	0.736	0.93	0.959	0.952	0.997
t-Stat	1.509	2.324	4.187	2.588	3.216	5.399	2.98
Median	0.069	0.391	0.807	0.934	0.803	0.969	0.736
p-Value	0.016	0.016	0.003	0.063	0.008	0.031	0.063
Mean Wald test (χ²)	371.487	185.63	86.109	99.065	201.323	96.933	160.601
Percentage + significant (5%)	57%	75%	82%	80%	88%	100%	100%
Percentage + significant (10%)	57%	75%	91%	100%	100%	100%	100%
Adjusted R² mean	0.243	0.379	0.435	0.448	0.506	0.45	0.458
Adjusted R² median	0.054	0.301	0.462	0.458	0.597	0.478	0.453
DW mean	2.081	2.08	2.055	2.07	2.062	2.029	2.119
DW median	2.078	2.046	2.06	2.053	2.042	2.029	2.05

Source: The authors.

Notes: The table reports regression results corresponding to the model (4) for NIFTY50 constituent industries. The regressors are change in concurrent industry liquidity, lag of change in industry liquidity, a lead of change in industry liquidity. Equally weighted cross-sectional means of time-series slope coefficients are reported with the t-statistics. ‘Percentage + significant’ reports the percentage of positive and significant slope coefficients at 5% and 10% levels of significance. DW is Durbin–Watson test statistic. Wald test indicates the joint significance of β1 + β2 + β3 and γ1 + γ2 + γ3 in explaining commonality.

Table 7 reports the results of industry-wide commonality on spread-based ES proxy of liquidity. The highest mean is 1.067 on ENGY industry while it is lowest on CGP industries with a value of 0.567. This implies that the strength of commonality is highest on ENGY and lowest in CGP on the basis of ES liquidity measure. The highest proportion of stocks with positive and concurrent coefficient significant (5%) are part of INFRA and NATR industries. The other coefficients like lead coefficient and lag coefficient have limited explanatory power in explaining the movements of individual stock liquidity. ‘Sum’ coefficient is observed to be in the range of 0.570 (CGP) to 1.062 (ENGY). The median R² is lies between 31 and 61 per cent.

Table 7.

Industry-wide Commonality for ES Proxy

	CGP	ENGY	FIN	INFRA	ITT	MANF	NATR
Concurrent	0.567	1.067	0.843	0.704	0.819	0.948	0.954
t-Stat	2.256	2.883	3.473	2.996	2.784	3.209	4.138
Median	0.217	0.675	0.48	0.564	1.041	1.204	1.075
Percentage+	100%	88%	91%	100%	75%	83%	100%
Percentage + significant (5%)	71%	88%	91%	100%	75%	83%	100%
Percentage + significant (10%)	71%	88%	91%	100%	75%	83%	100%
Lead	0.015	0.026	0.02	0.009	−0.001	0.051	0
t-Stat	2.779	1.297	1.97	0.429	−0.22	1.727	0.021
Median	0.018	0.033	0.024	0.002	0	0.054	0.006
Percentage+	86%	63%	73%	60%	50%	67%	60%
Percentage + significant (5%)	0%	0%	0%	0%	0%	17%	0%
Percentage + significant (10%)	0%	0%	0%	0%	0%	17%	0%
Lag	−0.012	−0.031	−0.006	−0.01	0.004	−0.009	−0.001
t-Stat	−1.713	−1.155	−0.474	−0.339	0.538	−0.29	−0.053
Median	−0.006	−0.03	−0.005	0.021	0.007	−0.014	0.007
Percentage+	14%	13%	45%	60%	63%	33%	60%
Percentage + significant (5%)	0%	13%	9%	20%	0%	17%	0%
Percentage + significant (10%)	0%	13%	9%	20%	0%	17%	0%
Sum	0.57	1.062	0.856	0.703	0.822	0.99	0.953
t-Stat	2.242	2.729	3.46	2.837	2.777	3.174	3.918
Median	0.214	0.705	0.48	0.577	1.045	1.325	0.994
p-Value	0.016	0.039	0.003	0.063	0.055	0.063	0.063
Mean Wald test (χ²)	136.803	90.211	123.758	162.519	263.807	85.94	63.311
Percentage + significant (5%)	57%	88%	82%	100%	100%	100%	80%
Percentage + significant (10%)	71%	88%	82%	100%	100%	100%	80%
Adjusted R² mean	0.336	0.413	0.456	0.457	0.624	0.46	0.411
Adjusted R² median	0.325	0.386	0.443	0.318	0.612	0.477	0.325
DW mean	2.095	2.155	2.097	2.216	2.051	2.154	2.06
DW median	2.101	2.166	2.092	2.242	2.047	2.169	2.055

Source: The authors.

Table 8 reports the results of industry-wide commonality on depth-based DEP proxy of liquidity. The highest mean is 1.301 on NATR industry while it is lowest on CGP industries with a value of 0.605. This implies that the strength of commonality is highest on NATR and lowest in CGP on the basis of DEP liquidity measure. The lowest proportion of stocks with positive and concurrent coefficient significant (5%) are part of CGP and MANF industries. The other coefficients like lead coefficient and lag coefficient have limited explanatory power in explaining the movements of individual stock liquidity. ‘Sum’ coefficient is observed to be in the range of 0.659 (INFRA) to 1.38 (NATR). Across all industries, the median R² is found to be in between 3 and 16 per cent.

From the above discussion, it is evident that industry-wide commonality exists for the studied industrial sectors of the Indian economy. These findings are consistent with the studies of Narayan et al. (2015), where industry-wide commonality is reported across the sectors on Chinese stock exchanges, viz. industrial sector, resources sector and financial sector. We report that, the strength of industry-wide commonality is higher in Infrastructure, IT and Telecom, Manufacturing and Natural Resources. As per Chordia at al. (2000), the difference in magnitude of commonality could be attributed to inventory levels and asymmetric information.

Table 8.

Industry-wide Commonality for DEP Proxy

	CGP	ENGY	FIN	INFRA	ITT	MANF	NATR
Concurrent	0.605	0.738	0.821	0.645	1.219	0.922	1.301
t-Stat	3.328	4.736	9.762	2.241	2.355	2.571	2.234
Median	0.674	0.655	0.86	0.344	0.481	0.662	0.73
Percentage+	86%	100%	100%	100%	100%	100%	100%
Percentage + significant (5%)	71%	100%	100%	100%	100%	83%	100%
Percentage + significant (10%)	71%	100%	100%	100%	100%	100%	100%
Lead	0.055	0.032	0.095	0.046	0.093	0.066	0.071
t-Stat	1.532	0.657	1.818	2.184	2.135	1.402	1.182
Median	0.041	0.012	0.069	0.066	0.037	0.05	0.007
Percentage+	86%	50%	73%	80%	100%	83%	60%
Percentage + significant (5%)	14%	0%	18%	0%	13%	17%	0%
Percentage + significant (10%)	14%	25%	27%	0%	25%	17%	0%
Lag	0.003	0.021	0.018	−0.032	0.032	−0.047	0.008
t-Stat	0.106	0.48	0.473	−1.245	0.91	−0.665	0.108
Median	−0.007	0.044	0.013	−0.045	0.004	−0.063	−0.024
Percentage+	43%	63%	64%	20%	50%	33%	40%
Percentage + significant (5%)	14%	0%	9%	0%	0%	0%	0%
Percentage + significant (10%)	14%	13%	9%	20%	13%	17%	0%
Sum	0.663	0.791	0.934	0.659	1.344	0.941	1.38
t-Stat	3.373	4.287	7.911	2.119	2.279	3.201	1.961
Median	0.913	0.8	0.876	0.291	0.565	0.661	0.623
p-Value	0.031	0.016	0.001	0.063	0.008	0.031	0.063
Mean Wald test (χ²)	64.739	30.817	18.718	163.365	110.288	25.81	25.049
Percentage + significant (5%)	71%	75%	100%	80%	100%	67%	100%
Percentage + significant (10%)	71%	75%	100%	80%	100%	100%	100%
Adjusted R² mean	0.251	0.169	0.122	0.216	0.237	0.144	0.182
Adjusted R² median	0.116	0.142	0.086	0.039	0.117	0.089	0.168
DW mean	2.369	2.339	2.323	2.264	2.293	2.317	2.264
DW median	2.333	2.361	2.348	2.318	2.259	2.32	2.3

Source: The authors.

Conclusion

This article extends the literature on market-microstructure by examining commonality in liquidity phenomenon on one of the key emerging market, India. High frequency trade and quote data for NSE’s NIFTY50 is used for the analysis.

The study finds strong evidence in favour of market-wide liquidity commonality. In over 80 per cent of the cases, the concurrent coefficient on market liquidity is found to be positive and significant. The quantum of the concurrent coefficient on NSE is more than those found in other markets such as NYSE (USA), SET (Thailand), etc. This indicates that the strength of market-wide commonality is more in Indian stock markets. The study also reports that the explanatory power of industry-wide commonality is more than the market-wide commonality. Moreover, the study tests industry-wide commonality across seven industries. The results suggest that commonality is pervasive across the key industries of the Indian economy. The stocks belonging to infrastructure, IT and telecom, manufacturing and natural resources industries, strongly co-moves with the liquidity of their corresponding industry.

Managerial Implications

Commonality poses the challenge to the diversification strategies, according to which the return characteristics of the stocks in a portfolio should not correlate with each other (Domowitz et al., 2005). The stocks belonging to the industries having high degree of commonality are more sensitive to systematic liquidity shocks. Investors demand higher expected returns in holding the stocks belonging to such industries. This makes findings important to fund managers, as liquidity and commonality risks are priced (Acharya & Pederson, 2005). Regulators should make efforts in order to make market stable by ensuring the adequacy of liquidity Geithner (2007). This empirical evidence will assist the stock market regulators such as SEBI in improving the market design (Coughenour & Saad, 2004). The results obtained have important implications for making investment strategies in the studied sectors and in understanding the market microstructure of Indian stock market.

Limitation

Availability of high frequency data is one of the key limitations of the study on market microstructure in emerging economies. To take a robust investment and policy decision regarding commonality in liquidity, one has to extend this study on a large sample of stocks and over a larger period.

Future Research

Studying the causes of market-wide commonality and industry-wide commonality on NSE will be an interesting area for future research.

Appendix I

NIFTY50 Stocks and Associated Industry Used in the Study

Bloomberg Code	Company Name	Industry
APNT IS Equity	Asian Paints Ltd.	Consumer goods
HUVR IS Equity	Hindustan Unilever Ltd.	Consumer goods
ITC IS Equity	ITC Ltd.	Consumer goods
BPCL IS Equity	Bharat Petroleum Corporation Ltd.	Energy
CAIR IS Equity	Cairn India Ltd.	Energy
GAIL IS Equity	GAIL (India) Ltd.	Energy
NTPC IS Equity	NTPC Ltd.	Energy
ONGC IS Equity	Oil and Natural Gas Corporation Ltd.	Energy
PWGR IS Equity	Power Grid Corporation of India Ltd.	Energy
RIL IS Equity	Reliance Industries Ltd.	Energy
TPWR IS Equity	Tata Power Co. Ltd.	Energy
AXSB IS Equity	Axis Bank Ltd.	Financial services
BOB IS Equity	Bank of Baroda	Financial services
HDFC IS Equity	HDFC Bank Ltd.	Financial services
HDFCB IS Equity	Housing Development Finance Corporation Ltd.	Financial services
ICICIBC IS Equity	ICICI Bank Ltd.	Financial services
IDFC IS Equity	IDFC Ltd	Financial services
IIB IS Equity	IndusInd Bank Ltd.	Financial services
KMB IS Equity	Kotak Mahindra Bank Ltd.	Financial services
PNB IS Equity	Punjab National Bank	Financial services
SBIN IS Equity	State Bank of India	Financial services
YES IS Equity	Yes Bank Ltd.	Financial services
ACC IS Equity	ACC Ltd.	Infrastructure
ACEM IS Equity	Ambuja Cements Ltd.	Infrastructure
GRASIM IS Equity	Grasim Industries Ltd.	Infrastructure
UTCEM IS Equity	UltraTech Cement Ltd.	Infrastructure
LT IS Equity	Larsen & Toubro Ltd.	Infrastructure
HCLT IS Equity	HCL Technologies Ltd.	IT
INFO IS Equity	Infosys Ltd.	IT
TCS IS Equity	Tata Consultancy Services Ltd.	IT
TECHM IS Equity	Tech Mahindra Ltd.	IT
WPRO IS Equity	Wipro Ltd.	IT
BJAUT IS Equity	Bajaj Auto Ltd.	Manufacturing
HMCL IS Equity	Hero MotoCorp Ltd.	Manufacturing
MM IS Equity	Mahindra & Mahindra Ltd.	Manufacturing
MSIL IS Equity	Maruti Suzuki India Ltd.	Manufacturing
TTMT IS Equity	Tata Motors Ltd.	Manufacturing
BHEL IS Equity	Bharat Heavy Electricals Ltd.	Manufacturing
Z IS Equity	Zee Entertainment Enterprises Ltd.	Other services
BHARTI IS Equity	Bharti Airtel Ltd.	Other services
IDEA IS Equity	Idea Cellular Ltd.	Other services
CIPLA IS Equity	Cipla Ltd.	Pharma
DRRD IS Equity	Dr. Reddy’s Laboratories Ltd.	Pharma
LPC IS Equity	Lupin Ltd.	Pharma
SUNP IS Equity	Sun Pharmaceutical Industries Ltd.	Pharma
COAL IS Equity	Coal India Ltd.	Natural resources
HNDL IS Equity	Hindalco Industries Ltd.	Natural resources
NMDC IS Equity	NMDC Ltd.	Natural resources
TATA IS Equity	Tata Steel Ltd.	Natural resources
VEDL IS Equity	Vedanta Ltd.	Natural resources

Source: Bloomberg database.

Footnotes

Acknowledgement

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Notes

ORCID iDs

Gaurav Kumar

Molla Ramizur Rahman

References

Acharya

V. V.

, & Pederson

L. H.

(2005). Asset pricing with liquidity risk. Journal of Financial Economics , 77, 375–410.

Amihud

(2002). Illiquidity and stock returns: Cross section and time-series effects. Journal of Financial Markets , 5(1), 31–56.

Amihud

, & Mendelson

(1986). Asset pricing and the bid-ask spread. Journal of Financial Economics , 17, 223–249.

Amihud

, & Mendelson

(1991). Volatility, efficiency and trading: Evidence from the Japanese stock market. The Journal of Finance , 46(5), 1765–1789.

Amihud

, Hameed

, Kang

, & Zhang

(2015). The illiquidity premium: International evidence. Journal of Financial Economics , 117(2), 350–368. https://doi.org/10.1016/j.jfineco.2015.04.005

Bessembinder

(2003). Issues in assessing trade execution costs. Journal of Financial Markets , 223–257.

Boehmer

(2005). Dimensions of execution quality: Recent evidence for US equity markets. Journal of Financial Economics , 78(3), 553–582.

Brennan

, & Subrahmanyam

(1996). Market microstructure and asset pricing: On the compensation for illiquidity in stock returns. Journal of Financial Economics , 41, 441–464.

Brockman

, & Chung

D. Y.

(2002). Commonality in liquidity: Evidence from an order-driven market structure. The Journal of Financial Research , 4, 521–539.

10.

Brockman

, Chung

D. Y.

, & Pérignon

(2009). Commonality in liquidity: A global perspective. Journal of Financial and Quantitative Analysis , 44(4), 851. https://doi.org/10.1017/s0022109009990123

11.

CFA Institute. (2009). Market microstructure: The impact of fragmentation under the markets in financial instruments directive . https://www.cfainstitute.org/-/media/documents/article/position-paper/market-microstructure-impact-of-fragmentation-under-the-markets-in-financial-instruments-directive.ashx

12.

Chan

K. C.

, Christie

W. G.

, & Schulz

P. H.

(1995). Market structure and the intraday pattern of bid-ask spreads for NASDAQ securities. The Journal of Business , 68(1), 35–60.

13.

Chordia

, Roll

, & Subrahmanyam

(2000). Commonality in liquidity. Journal of Financial Economics , 56(2), 3–28.

14.

Coughenour

J. F.

, & Saad

M. M.

(2004). Common market makers and commonality in liquidity. Journal of Financial Economics , 73, 37–69.

15.

Demsetz

(1968). The cost of transacting. The Quarterly Journal of Economics , 82(1), 33–53.

16.

Dicle

M. F.

, & Mukherjee

(2011). Intraday trading patterns and commonality in liquidity . http://ssrn.com/abstract=1925826

17.

Domowitz

, Hansch

, & Wang

(2005). Liquidity commonality and return co-movement. Journal of Financial Markets , 8, 351–376.

18.

Fabre

, & Frino

(2004). Commonality in liquidity: Evidence from the Australian stock exchange. Accounting and Finance , 44, 357–368.

19.

Fama

E. F.

, & French

K. R.

(1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics , 33(1), 3–56. https://doi.org/10.1016/0304-405x(93)90023-5

20.

Fernando

C. S.

, & Herring

R. J.

(2003). Commonality in liquidity and market collapse: Theory and application to the market for perps . (Working Paper, AFA 2002 Atlanta Meetings). Wharton Financial Center.

21.

Fiori

(2000). Liquidity premia in the equity markets: An investigation into the characteristics of liquidity and trading activity (Working Paper). University of Chicago.

22.

Geithner

T. F.

(2007). Liquidity risk and the global economy. International Finance , 10(2), 183–189. https://doi.org/10.1111/j.1468-2362.2007.00204.x

23.

Hasbrouck

, & Seppi

D. J.

(2001). Common factors in prices, order flows, and liquidity. Journal of Financial Economics , 59, 383–411.

24.

Huberman

, & Halka

(2001). Systematic liquidity. Journal of Financial Research , 24, 161–178.

25.

IIT Kanpur. (2018).

26.

IIT Madras. (2016).

27.

Karolyi

G. A.

, Lee

, & van Dijk

M. A.

(2012). Understanding commonality in liquidity around the world. Journal of Financial Economics , 105, 82–112.

28.

Kumar

, & Shah

(2012). Commonality in liquidity of an open electronic limit order book market . http://www.igidr.ac.in/conf/money/mfc_08/Commonality%20in%20Liquidity%20of%20an%20Open%20Electronic%20Limit%20Order%20Book%20Market_SantoshKumar.pdf

29.

Kyle

(1985). Continuous auctions and insider trading. Econometrica , 53(6), 1315–1335.

30.

Lee

K. H.

(2011). The world price of liquidity risk. Journal of Financial Economics , 99(1), 136–161.

31.

Liu

(2006). A liquidity-augmented capital asset pricing model. Journal of Financial Economics , 82(3), 631–671.

32.

Næs

, & Bank

(2004). Ownership structure and stock market liquidity . http://www.norges-bank.no/upload/import/publikasjoner/arbeidsnotater/pdf/arb-2004-06.pdf

33.

Narayan

P. K.

, Zhang

, & Zheng

(2015). Some hypothesis on commonality in liquidity: New evidence from the Chinese stock market. Emerging Markets Finance and Trade , 51, 5.

34.

Pastor

, & Stambaugh

(2003). Liquidity risk and expected stock returns. Journal of Political Economy , 111(3), 642–685.

35.

Pukthuanthong-Le

, & Visaltanachoti

(2009). Commonality in liquidity: Evidence from the stock exchange of Thailand. Pacific-Basin Finance Journal , 17, 80–99.

36.

Rao

V. V. B.

(2000). The East Asian crisis of 1997–98: Implications for India. Global Business Review , 1, 1–10.

37.

Rösch

C. G.

, & Kaserer

(2014). Reprint of: Market liquidity in the financial crisis: The role of liquidity commonality and flight-to-quality. Journal of Banking & Finance , 45, 152–170. https://doi.org/10.1016/j.jbankfin.2014.06.010

38.

Sharma

, & Sharma

A. K.

(2015). Influence of turbulent macroeconomic environment on productivity change of banking sector: Empirical evidence from India. Global Business Review , 16(3), 439–462. https://doi.org/10.1177/0972150915569932

39.

Sujoto

, Kalev

P. S.

, & Faff

R. W.

(2005). An examination of commonality in liquidity: New evidence, long-run effects and non-linearities (Working Paper). Department of Accounting and Finance, Monash University.

40.

Syamala

S. R.

, Chauhan

, & Wadhwa

(2014). Institutional investors and stock liquidity: Evidence from Indian stock market. Global Business Review , 15(3), 461–476. https://doi.org/10.1177/0972150914535133

41.

Tinic

S. M.

(1972). The economics of liquidity services. The Quarterly Journal of Economics , 86(1), 79–93.

42.

Wang

(2013). Liquidity commonality among Asian equity markets. Pacific-Basin Finance Journal , 21(1), 1209–1231.

43.

World Economic Situation and Prospects report. (2019). https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/WESP2019_BOOK-web.pdf