Commonality in Liquidity of UK Equity Markets: Evidence from the Recent Financial Crisis

Commonality in Liquidity of UK Equity Markets: Evidence from the Recent Financial Crisis Andros Gregoriou Bournemouth Business School University of Bournemouth BH12 5BB Christos Ioannidis * Economics Department University of Bath Bath, BA2 7AY Kelly Zhu Economics Department University of Bath Bath, BA2 7AY Abstract In this paper we investigate the empirical association between correlated movements in liquidity for the UK equity market, over the time period of 2005-2009. We find overwhelming evidence of commonality in liquidity between individual firms and the market portfolio. Commonality exists over the entire sample period, but is significantly stronger after the financial crises. This could be due to the tightening of liquidity in equity markets as a result of the credit crunch. We attribute the movements in liquidity to correlations in trading activity between individual firms and the London Stock Exchange. JEL Classifications: G23, D82 Keywords: Liquidity, Bid-Ask Spreads, UK Equity Markets * Corresponding Author : Email:c.ioannidis@bath.ac.uk, Tel: +44(0)1225383226

Introduction As recent events in financial markets have demonstrated, liquidity appears to be one of the most important concepts in finance. Liquidity is defined as the ability to trade stock rapidly with little price impact. A vast majority of the market microstructure literature is focused upon the repeated trading of a single homogeneous security. However, over the last decade there has been a body of an emerging literature on commonality in liquidity. These studies find an empirical relationship between common factors across individual stocks, which determine the time series movements in liquidity. There is evidence of commonality in liquidity in US equity markets (Chordia et al, 2000), between US and UK equity markets (Galariotis & Giouvris, 2007), the Australian Stock Exchange (Fabre and Frino, 2004), and over a number of international stock exchanges (Hasbrouck, 2001). Commonality in liquidity could arise from several sources. Trading displays market wide responses to changes in individual equity prices. Given that trading volume is a major determinant of dealer inventory, its variation may induce co-movements in optimal inventory levels of market makers, which in turn lead to co-movements of the most common measure of liquidity for each individual asset, the bid-ask spread. In addition, the inventory cost component of market makers depends upon the volatility of an asset, which often has a market component. Furthermore, the covariation in liquidity could be induced by asymmetric information amongst a small number of traders with privileged information about broader market movements. In this paper we contribute to the existing literature on commonality in liquidity in the following ways. First, we are the only study to investigate liquidity comovements between individual assets and the market portfolio in the UK equity market. It is vital that 1

we research this area given that UK equity markets are the most heavily traded in the world with the exception of the USA. Second, we empirically examine liquidity co-movements between individual financial companies and the market portfolio in the UK equity market before and after the recent financial crises. Fernando et al (2008) show in a theoretical framework that common liquidity shocks resulting from a financial crisis are permanent and non diversifiable. This is because in an order driven market liquidity becomes scarcer as a result of a negative shock, due to a vast proportion of market makers withdrawing from the exchange as a result of large order imbalances. Our empirical findings provide strong support for the Fernando et al (2008) model, because even though we find overwhelming evidence of commonality of in liquidity in the London Stock Exchange (LSE), this is more apparent after the credit crunch that began in August 2007. Finally, after providing evidence of the existence of commonality in liquidity in the LSE, we shed some light on its determinants. Using the number of trades and pound trading volume as proxies for trading activity, we confirm that the comovements in liquidity between financial companies and the market portfolio in the UK equity market are driven by trading activity, before and after the recent credit crunch. The remainder of the paper is organized in the following way. Section 2 describes the data. Section 3 explains the econometric methodology. Section 4 presents the empirical results. Finally, Section 5 concludes. 2

2. Data Transaction data for the LSE stocks were obtained from DataStream over the time period, from October 10 th 2005 to June 10 th 2009. The dataset consists of daily share prices, trading volume and bid and ask prices quoted by the exchange. We implement four alternative measures of liquidity costs to capture commonality in liquidity. First we use the relative spread defined as the ask price minus the bid price, divided by the mid-price (the average of the bid and ask prices). As Lee & Ready (1991) point out, the problem with the relative spread is that it can be regarded as an inaccurate measure of liquidity because many trades occur at prices within the bid and ask price. Therefore, in order to obtain a more accurate measure of the market liquidity, we follow the methodology in Heflin & Shaw (2000) and Hegde & McDermott (2003) and compute the effective bid-ask spread as our second measure. The effective bid-ask spread is computed as twice the absolute value of the difference between the transaction price and the mid-price in effect at the time of the trade. In addition to the relative and effective bid-ask spread we also the use number of shares traded and trading volume as alternative measures to encapsulate commonality in liquidity. This is because, as Chordia et al (2000) suggest, trading activities are related to co-movements in liquidity. Such suggestion is plausible given that trading activities reflect concurrent changes in market wide price fluctuations in equity markets. More specifically Chordia et al (2000) discover that trading volume is a key determinant of dealer inventory. Hence variations in trading volume appear to stimulate the concurrent changes in optimal inventory levels, which result in concurrent changes in the relative and effective bid-ask spread for individual securities. 3

We define the event date of the financial crisis as the 9 th August 2007. This is because on that date the USA, European and Japanese central banks all injected funds into their banking systems to try to ease the subprime credit crisis. Therefore, the pre event period is defined as 10/10/05-08/08/07 and the post event period is defined as 09/08/07-10/06/09. 1 The objective of this study is to examine co-movements in liquidity between financial companies and the LSE before and after the financial crises. We use a value weighted FTSE100 index to represent the LSE. 2 Approximately, 80% of the entire trading volume in the LSE is undertaken by the FTSE100, therefore this seems an approximate proxy for the UK equity market. The use of the FTSE100 ensures that all stocks in our sample are heavily traded providing reliable observations and empirical estimates. Our sample consists of all stocks that our continually listed on the FTSE100 throughout the entire sample period. This is because as pointed out by Gregoriou and Ioannidis (2006) share prices, trading volume and transaction prices exhibit significant changes as a result of index composition in the FTSE100. Therefore, inclusion of added and or deleted firms from the FTSE100 would bias our results. In addition, we eliminate companies which were taken over and where insufficient data is available. The final sample includes 89 companies, 15 of which are financial companies. The complete lists of firms that are eliminated from the sample and the directory of financial companies that are include in our sample are displayed in Tables 1 and 2. [INSERT TABLES 1 AND 2 HERE] 1 In addition, Jason Cox and Laurie Glapa at the Center for International Finance & Development at the University of Iowa, constructed a detailed list of banking and finance industry events related to the financial crisis which was collated from the Credit Crisis Timeline. Over 90% of these events were between the time period of 09/08/07 until 10/06/09. Therefore, 9 th August 2007 seems an appropriate event date for the financial crises. 2 For further details on the construction of value weighted FTSE100 indices see Gregoriou et al (2007). 4

In Table 3 we observe the summary cross-sectional statistics for the liquidity and trading activity measures. We witness substantial variations between liquidity and trading activity, this could be because bid-ask spreads are discrete variables and their nature contributes in attenuating volatility. For all the liquidity measures, the mean is larger than the median, implying excess skewness in the cross sectional means of daily spreads. We find that the effective bid-ask spread is smaller than the relative spread. This implies that large proportions of transactions are undertaken at the quoted bid and ask prices, over the duration of our sample period. Table 4 provides overwhelming evidence of positive comovement amongst all the liquidity measures. [INSERT TABLES 3 AND 4 HERE] Figures 1 and 2 below provide a graphical representation of changes in liquidity and trading activity before and after the financial crisis. From Figure 1 we see that relative bid-ask spreads sharply decline during the credit crunch, whereas the effective bid-ask spreads remain relatively stable, but are always lower than the relative spread at all time intervals. It shows that liquidity on the whole is tighter and that most of the trading occurs within the bid and asks quotes. Furthermore it is indicative of investors enjoying preferential treatment from market makers in the period of reduced liquidity caused by the credit crunch. Trading activity, Figure 2, exhibits an upward trend during the financial crisis. This is to be expected given the uncertainty in the economy as a result of the credit crunch. [INSERT FIGURES 1 AND 2 HERE] 5

3. Econometric Methodology Following Chordia et al (2000), we compute a time series regression of daily percentage changes in liquidity variables for an individual financial company, regressed on the market measure (FTSE100) of liquidity, i.e.: (1) Where DL j, t is the percentage change of stock j, from trading day t-1 to t in liquidity variable L (L=Relative (RS) and Effective (ES) Bid-Ask Spread), DL M, t is the contemporaneous change in the cross-sectional average of the whole market for the same trading day. Following the previous literature we examine percentage rather than level changes in liquidity to avoid possible problems with non stationarity. One lead and lag of the market average liquidity (i.e, DLM, t 1 and DL M, t + 1) plus the contemporaneous, leading and lagged market return and the contemporaneous change in the individual security squared return are included as additional explanatory variables. The leads and lags are intended to capture any lags in co-movements of liquidity, while the market return removes any possibility of spurious dependence between returns and bid-ask spreads. Finally, squared stock returns are included to act as a proxy of stock market volatility. 4. Empirical Results The empirical estimates of the β ' s in equation (1) are reported in Tables 5 and 6. Table j 5 (6) provides evidence on the commonality in liquidity between financial companies and the market portfolio pre (post) the recent financial crisis. The results in Table 5 display 6

evidence of commonality in liquidity before the crisis. We observe that the mean coefficient beta for concurrent market liquidity change ranges from 0.1030 (with a significant t-statistic of 3.94) to 0.905206 (with an insignificant associated t-statistic of 1.70). For all the measures, more than 80% of the coefficients are positive and significant. The average R-squared for effective spread (ES) and the relative spread (RS) are above 5%. Even-though the goodness of fit appears very low, this is common in market microstructure studies such as Chordia et al (2000). The regression results post the financial crises (Table 6) provide much stronger evidence of liquidity commonality. For instance, the change in the effective spread (ES) produces a beta of 0.8018, which is highly significant with an associated t-statistic of 21.8618. Compared to the results from Table 5 the pre-credit crunch period, the proportion of significant betas is substantially higher. For both liquidity measures the betas are significant in 100% of cases. Average R-squared measures have also soared to above 10%. To summarize the findings, our results for the UK equity market provide strong evidence for the existence of commonality in liquidity. Comparing our results to the US equity markets we find stronger evidence of co-movement. This is because Chordia et al. (2000) find R-squared measures for the regressions in the region of 2%. Whereas our results show an impressive average R-squared of around 45% for effective spread (ES) and relative effective spread (RS). This indicates the market co-movements are stronger for the FTSE100 shares than for NYSE shares. The reason could be that we only use shares with the largest market capitalisation, and those shares are most frequently traded. In 7

contrast, Chordia et al (2000) used a much bigger sample of 1169 stocks. Galariotis & Giouvris (2007) also pointed out that the normal market size in LSE is higher than NYSE OR NASDAQ. Hence, we should observe a stronger market commonality, with which there are less frequently traded shares or shares with low market capitalisations. When comparing regression results for two sub- samples, there is a substantial increase in commonality in the second sub-sample, which covers the credit crunch period. We can explain our findings using Brockman & Chung (2002) s proposition that the behaviour of investors can generate more commonality for large firms. From liquidity demander s perspective, when the market is under stress, we expect to find a higher commonality in liquidity for large firms, because investors seek liquidate their stocks at a lowest possible cost. For small firms with high level of asymmetric information, their share price will fall sharply during market downturns hence their liquidity costs are much higher. We have firmly established substantial evidence of commonality in liquidity in the UK equity market, before and after the recent financial crises. We now attempt to identify possible sources of the commonality. Following Chordia et al. (2000) we empirically examine whether trading activity may be a source of commonality in liquidity. We employ number of trades (NT) and pound trading volume (VOL) as proxies for trading activity. We proceed by looking at the co-movements of daily percentage change in number of trades (pound trading volume) for the 15 financial companies on the sum of that for all the 89 stocks 3 3 When we construct the market number of trades or pound trading volume, we exclude the dependent variable stock as we did for the liquidity measures. 8

We obtain statistically robust results and we present them in Tables 5 and 6. The relevant t-statistic ranges from 1.70 to 19.89. We report reasonably high R-squared measures of 31% and 17% for NT and VOL respectively before the credit crunch, which rises to 44% and 33% post the credit crunch. These provide additional evidence have of a substantial increase in commonality in liquidity during the financial crisis. More than 80% of the coefficients are significantly positive, suggesting that the individual number of trades and pound trading volume are co-moving with the market trading activity. The results are also consistent with the US equity findings of Chrodia et al (2000), where individual trading activity is strongly influenced by the market and industry. [INSERT TABLES 5 AND 6 HERE] 5. Conclusion In this paper we empirically examine whether co-movements in liquidity exist in the UK equity markets. Using the methodology employed by Chordia et al (2000) we establish that there is ample evidence of commonality in liquidity for FTSE100 shares. Our findings show that commonality is evident in international equity markets, and is not just a US phenomenon. We are able to show that commonality is important and significant for all the fifteen financial companies in our sample. We investigate the impact of the credit crunch on commonality in liquidity in the LSE by comparing co-movements in liquidity pre and post the recent financial crisis. We establish that commonality in liquidity is significant before and after the financial crises using both the relative and effective bidask spread as measures of liquidity. We also discover that the commonality in liquidity is more evident after the financial crises. This could be because as suggested by Brockman & Chung (2002) during a financial crisis share prices fall rapidly resulting in higher 9

liquidity costs. Finally, we attribute co-movements in liquidity to trading activity. This is because there is a positive empirical association between percentages changes in number of trades and pound trading volume for individual financial companies and the market portfolio in UK equity markets, before and after the recent financial turmoil. Possible avenues for further research could include looking at comovements in other international financial markets as well as corporate bonds. In addition, we could explore the implications of co-movements in liquidity for asset pricing. References BROCKMAN, P. and D.Y. CHUNG (2002) Commonality in Liquidity: Evidence from an order-driven market structure. Journal of Financial Research 25, 521-539. CHORDIA, T., R. W. ROLL and A. SUBRAHMANYAM (2000) Commonality in Liquidity. Journal of Financial Economics 56, 3-28. GALARIOTIS E, C. and E. GIOUVRIS (2007) Liquidity Commonality in the London Stock Exchange. Journal of Business Finance & Accounting 34, 374-374. FABRE, J. and A. FRINO (2004) Commonality in liquidity: Evidence from the Australian Stock Exchange. Accounting and Finance 44, 357-368. FERNANDO C, S., J. HERRING RICHARD and A. SUBRAHMANYAM (2008) Common liquidity shocks and market collapse: Lessons from the market for perps. Forthcoming Journal of Banking and Finance. GREGORIOU, A. and C. IOANNIDIS (2006) Information costs and liquidity effects from changes in the FTSE 100 list. The European Journal of Finance 12, 347-360. GREGORIOU, A, J.V. HEALY, and C. IOANNIDIS (2007) Hedging Under the influence of Transaction Costs: An Empirical Investigation on FTSE 100 Index Options. Journal of Futures Markets 27, 471-494. HASBROUCK, D. J. S. (2001) Common factors in prices, order flows, and liquidity. Journal of Financial Economics 59, 383-411. 10

HEFLIN. F. and K.W. SHAW. (2000). Blockholder Ownership and Market Liquidity, Journal of Financial and Quantitative Analysis 35, 621-633. HEDGE, S.P. and J.B. McDERMOTT. (2003). The Liquidity effects of revisions to the S&P 500 index: an empirical analysis. Journal of Financial Markets 6, 413-459. LEE, C. M. and M. J, READY. (1991). Inferring trade direction from intraday data. Journal of Finance, 46, 733-746. 11

Table 1 List of eliminated firms in our sample Deleted Name Reason for Elimination 1 3i No data available 2 Alliance & Leicester Delisted 06/08 acquired by Banco Santander Central Hispano 3 Bradford &Bingley Delisted 30/09/08 acquired by Banco Santander Central Hispano 4 British Energy Group Delisted 03/02/09 Energy 5 Daily Mail and General Not traded everyday Trust 6 Experian Parts of data not available 7 HBOS Delisted 01/09 8 Imperial Chemical Delisted 02/01/08 Industries 9 Resolution Delisted 06/05/08 10 Scottish & Newcastle Delisted 29/04/08 11 Standard Life No data before 07/07/07 Table 2 List of Financial Companies in our sample Financial Companies 1 Aviva 2 Barclays 3 Friends Provident 4 HSBC 5 ICAP 6 INVESCO 7 Lloyds TSB 8 Man Group 9 Old Mutual 10 Prudential 11 Reuters Group 12 Royal & Sun Alliance Insurance 13 Royal Bank of Scotland Group 14 Schroders 15 Standard Chartered Financials 12

Table 3.Cross-sectional statistics for time series means over the entire sample period Relative bid-ask spread (RS) is defined as the ask price minus the bid price divided by the quoted midprice. Effective bid-ask spread (ES) is defined as twice the absolute value of the difference between the transaction price and the midprice in effect at the time of the trade., NT is the number of shares traded throughout the day and VOL is daily trading volume. Each measure is used for 89 FTSE 100 companies daily transactions from 10 th Oct, 2005 to 10 th Jun, 2009. Total trading day is 927 for each measure and each company. MEASURE MEAN MEDIAN STD. DEVIATION RS 0.012876 0.011348 0.006443 ES 0.005675 0.004789 0.008707 NT 1540273 1512410 147576.8 VOL 320027.5 300995 Table 4. Cross-sectional means of time series correlations between liquidity measures Relative bid-ask spread (RS) is defined as the ask price minus the bid price divided by the quoted midprice. Effective bid-ask spread (ES) is defined as twice the absolute value of the difference between the transaction price and the midprice in effect at the time of the trade., Each measure is used for 89 FTSE 100 companies daily transactions from 10 th Oct, 2005 to 10 th Jun, 2009. Total trading day is 927 for each measure and each company. ES RS ES 1.00 0.8397 RS 0.938317 1.00 13

Table 5. The market-wide commonality in liquidity for 89 stocks, for Pre-9 th August 2007 period, 463 daily observations Daily percentage changes of liquidity measures are regressed against concurrent equally weighted average market liquidity measures for fifteen financial companies in the sample.the liquidity measures are: Relative bid-ask spread (RS) is defined as the ask price minus the bid price divided by the quoted midprice. Effective bid-ask spread (ES) is defined as twice the absolute value of the difference between the transaction price and the midprice in effect at the time of the trade. NT is the number of shares traded throughout the day and VOL is daily trading volume. D denotes a proportional change in the liquidity or trading activity variable, L, across successive trading days. (L = RS, ES, NT, VOL) DL t = (L t -L t-1 )/L t-1 for trading day t. The liquidity measure for market concurrent change excludes the dependent variable company. Table below reports the cross sectional means of time series slope coefficients and t-statistics in parentheses. % positive shows the percentage of positive signed slope coefficients. On the other hand, % + significant reports the percentage with t-values that are greater than + 1.64, which is the 5% critical value in a one-tailed test. Concurrent 0.1030 ES RS NT VOL (3.94) 0.5177 (5.83) 0.9052 (1.70) 1.3439 (19.79) % positive 100 86.7 93.3 86.7 %+significant 93.6 86.7 93.3 80.0 R squared mean R squared median 0.0519 0.1277 0.3133 0.1633 0.0446 0.0396 0.3022 0.5399 14

Table 6. The market-wide commonality in liquidity for 89 stocks, for Aft-9 th August 2007 period, 463 daily observations Daily percentage changes of liquidity measures are regressed against concurrent equally weighted average market liquidity measures for fifteen financial companies in the sample.the liquidity measures are: Relative bid-ask spread (RS) is defined as the ask price minus the bid price divided by the quoted midprice. Effective bid-ask spread (ES) is defined as twice the absolute value of the difference between the transaction price and the midprice in effect at the time of the trade. NT is the number of shares traded throughout the day and VOL is daily trading volume. D denotes a proportional change in the liquidity or trading activity variable, L, across successive trading days. (L = RS, ES, NT, VOL) DL t = (L t -L t-1 )/L t-1 for trading day t. The liquidity measure for market concurrent change excludes the dependent variable company. Table below reports the cross sectional means of time series slope coefficients and t-statistics in parentheses. % positive shows the percentage of positive signed slope coefficients. On the other hand, % + significant reports the percentage with t-values that are greater than + 1.64, which is the 5% critical value in a one-tailed test. Concurrent 0.8018 ES RS NT VOL (21.86) 1.0799 (20.92) 1.3439 (19.89) 1.1333 (15.37) % positive 100 100 86.7 100 %+significant 100 100 80.0 100 R squared mean R squared median 0.4671 0.4445 0.4433 0.3311 0.6236 0.5557 0.5399 0.3401 15

Figure 1. Plot of Daily Average Relative (QSPR) and Effective (ESPR) Bid-Ask Spreads Figure 2. Average daily number of trades (NT) and pound trading volume (VOL) 16