International Journal of Business and Social Science Vol. 5 No. 2; February 2014 Relations between Market Liquidity and New Listings: Istanbul Stock Exchange (BIST) Evidence Alp Polat Anadolu University Open Education Faculty 26470 Yunusemre Campus Eskisehir, Turkey Dr. GuvenSevil Anadolu University Open Education Faculty 26470 Yunusemre Campus Eskisehir, Turkey Abstract The study aims to discover the interactions between new listings and market liquidity in Turkey. While market liquidity can help the process of an initial public offering, new listings can induce the liquidity.The proof of these two way relationship is beneficial for economic and investment decisions.The fact that empirical researches are very limited in the related literature suggests the importance of the study. The Vector Autoregressive methodology is chosen due to its endogeneity bias and other advantages. The data collected from Istanbul Stock Exchange (BIST) is analysed for the period 2000-2012. Granger causality tests and impulse response functions are utilized. Market liquidity conditions are found useless in new issues of financial markets. It indicates that individuals have other interests than following the ease of trading. The analysis also detects that listings have an effect upon liquidity. Key Words: Listings, Liquidity, Trading Volume, Hot Market, BIST. Introduction The purpose of the study is to discover the relationship between new listings and market liquidity in Turkey. A liquid market has significant contributions in the process of initial public offerings, such as reducing underpricing, transaction costs and volatility (Corwin et al., 2005). Reversely, it is expected that new listings, with the help of growing market size, foster the liquidity (O’Hara, 2001). Different aspects of market liquidity and new listingshave received important attention. Ibbotson and Jaffe (1975), introducing hot issues phenomenon, showcyclical behaviour of the initial public offerings.This fact makes us to think that cyclical fluctuations vary with liquidity. Since new listings tend to increase with market peaks (Suchard and Woo: 2003 andLoughran et al. :1994) andtrading volume inclines to grow during the bull market due to wellknown disposition effect (Ritter: 2001), liquidity can be the core element of IPO processes. Determinants of cyclical behaviour is noted in the literature.Lowry (2003) presents evidence on substantial fluctuations in the number of initial public offerings and determines that firm’s demand on capital and investor sentiment are strong causal factors of new issuesin both statistical and economic conditions. Fama and French (2004) suggest that changing characteristic of new listings is affected by cost of capital. Ghosh (2004), examining Indian market, demonstrates insignificant relationship between IPO’s and initial returns and detects that firm’s listing decisions are dependent to the number of previously listed companies. Ghosh et al. (1999) state that secondary market volume rises around public offerings because of selling activities arise from the diversions to the discounted primary market. Levine and Zervos (1998) show that liquidity persists correlated with capital accumulation, growth and productivity growth consistent with the view that the ease of trading ownership maintains efficient capital allocation. 245 © Center for Promoting Ideas, USA www.ijbssnet.com Johannes et al. (2011) state that liquidity affects real economy through facilitating investment channels andbears valuable information for predicting current and future state of the economy. Naceur etal. (2007) exhibit that liquidity is important determinant of stock market development in Middle Eastern and North African countries. This study tries to find whether new listingsare responsive to shocks of liquidity and new listings information is potential predictive variable of market liquidity movements. The Vector Autoregressive (VAR) methodology is implemented. Examining this relationship is new to the current literature. The importance of the study is that we analyse a theoretically possible impact which is not concerned by empirical studies. The results of the study reveal the listing decisions of the market agents in terms of liquidity. It helps to understand the market’s focuses. Analysis results give useful implications for financial management. The study also discovers a determinant of liquidity. Liquidity is essential function of financial and economic development. Listings can be influenced by policy planners of country as a way of maintain liquid financial markets. Materials and Methods VAR framework (Sims: 1980) is chosen in order to analyse the monthly data. Trading volume is important feature of liquidity as bid-ask spreads is a function of trade size (Glosten and Harris: 1988). It became usual measure of liquidity in the literature (Aitken and Forde: 2003). The market trading volume is employed as a proxy for market liquidity in this study. New listings include number of the initial public offerings and newly quoted funds. The data applied in this study are obtained from Istanbul Stock Exchange at a monthly frequency and cover 20002012 period. Stationary of components is crucial when one needs to employ a hypothesis test in VAR approach.The summary statistics presented in table 1 reveal that both series satisfy stationary condition for level (I=0) according to The Augmented Dickey-Fuller unit root tests. Table 1: Descriptive Statistics Variable Liquidity Listings Mean 28310917 1.365385 StandardDeviation 18665964 2.076245 Median 24721304 1.000000 ADF Test Prob. Value 0.0001 0.0015 Median of listings is 1 due to non-existence of new listing in most of the months of sample period, as seen in Figure 2. Figure 3 shows that liquidity measure, unlike listings, has increased gradually. 16 14 12 10 8 6 4 2 0 00 01 02 03 04 05 06 07 08 09 10 11 12 Figure 2: Listings (2000-2012) 90,000,000 80,000,000 70,000,000 60,000,000 50,000,000 40,000,000 30,000,000 20,000,000 10,000,000 0 00 01 02 03 04 05 06 07 08 09 10 11 12 Figure 3: Liquidity (2000-2012) In contrast to single-equation regression, VAR model explains causes, allows to see lag effects and overcomes endogeneity bias. Causes can give benefits to individuals who want to know the future values of the variables. 246 International Journal of Business and Social Science Vol. 5 No. 2; February 2014 Lag effects are also important for the examination of reaction time to the news. According to the literature, the variables can interact bidirectionally, therefore, there is need to eliminate endogeneity bias. VAR model is chosen, sinceit helps to clear these issues. VAR model used in the study is displayed in Equation (1) and Equation (2) ( 1) = ( 2) = + + + + + + + + + + In equations, j is number of lags, LIQ is the liquidity, LIST is the listings, et is the error term, bj and βj are the coefficients of lagged independent variable and α j and aj show the coefficients of lagged regressors. The lag length is selected using Akaike information criteria, determining the maximum lag at 14 and the minimum at 1. VAR model is able to explain granger causes, impulse-responses and variance decomposition. Next section presents the results of the model. ResultsandDiscussion It is shown that listings have part in liquidity of the market. Liquidity does not affect the listings. Impulse response and granger causality analyses are implemented. The results are obtained with aid of the model which is explained in previous section. Figure 3 shows the impulse response functions. Response of LIQUIDITY to LIQUIDITY Response of LIQUIDITY to LISTINGS 8,000,000 8,000,000 6,000,000 6,000,000 4,000,000 4,000,000 2,000,000 2,000,000 0 0 -2,000,000 -2,000,000 -4,000,000 -4,000,000 2 4 6 8 10 12 2 14 Response of LISTINGS to LIQUIDITY 4 6 8 10 12 14 Response of LISTINGS to LISTINGS 2.0 2. 0 1.5 1. 5 1.0 1. 0 0.5 0. 5 0.0 0. 0 -0.5 -0.5 -1.0 -1.0 2 4 6 8 10 12 14 2 4 6 8 10 12 14 Figure 3: One Standard Deviation Impulse Responses of Liquidity and Listings in A ±2 Standard Error Band Impulse-response functions is called a primary tool for VAR analysis (Bhargava and Malhotra: 2007) and used to explain the effect of shock in one variable on other variables in the system.Figures show that liquidity and listings increase in response to their own shocks. Instant reaction of liquidity to listings is insignificant. However, liquidity subsequently responds positively and significantly to listings and finally the effect disappears. Positive relationship is expected owing to the fact that market gains size with new listings. The samecan be true for listings when we consider benefits of liquid markets. Nevertheless, listings present insignificant reaction to liquidity. Table 2: Granger Causality Test of Liquidity and Listings Dependent Variable Liquidity Listings Chi-sq 29.12731 19.06129 Df 14 14 Prob. 0.0100 0.1626 Causalities of the variables are presented in Table 2. Granger’s test of causality allows us to decide whether we can predict the future values of a variable by using another variable. 247 © Center for Promoting Ideas, USA www.ijbssnet.com When listings are considered as a dependent variable, liquidity becomes insignificant in order to forecast listings. This result is in aligning with the impulse response analysis. The impulse response functions demonstrate significant positive impact of listings on liquidity. This pattern is also same according to the granger causality test. The test provides evidence of causality at one percent level of significance from listings to liquidity. Corwin et al. (2005) state that transaction costs, underpricing and volatility problems can be less destructive in liquid financial markets. There are potential benefits for new issuers of financial products. Thus, positive impact of the market liquidity on listings is anticipated. However,our finding is different than what was expected. The benefits of liquidity are notinterested by new issuers. A possible reason for that could be different concerns of agents in Turkey. According to Lowry (2003) and Fama and French (2004), agents can pursuit cost of capital and may become highly affected by investor sentiment. The demand on new capital could be another major factor for new issues in Turkey. O’Hara (2001) explains that liquidity can respond to new listings on account of raising market size. Our results are consistent with this view. This finding can also support the idea that investors diverge between markets as stated in Ghosh et al. (1999). The basic form of VAR is applied in the study. Complex analyses, considering other potential factors that can take part in the system, may exhibit different results. The variables of the study are proxied by trading volume and numbers of listings. Different proxies are plentiful in the literature. These matters can be considered as the limitations of the study. Conclusion and Policy Implications This study employed VAR techniques to enquire the interactions between liquidity and listings. The Causality test results showthat listings granger cause liquidity.New listings have information content to predict future market liquidity in Turkey.The opposite relationship is found insignificant. Liquid markets do not manifest the hot issue periods. These findings are supported by impulse-response analysis. Listings do not respond to the liquidity and liquidity begins to react to innovations in new listings after eight month. The effect disappears in one year. The study is crucial for evidencing the link between liquid and hot markets. Liquidity does not seem to be an element for hot issue markets and practical for new listings. The other factors cited in the literature, like cost of capital, demand on capital and investor sentiment, can be the main concerns of individuals in Turkey. The finding that liquidity is significantly affected by liquidity can manifest the deviation between primary and secondary markets. As liquidity is one of the core attributes of financial markets, determining its relations presents important information. The paper presents some policy implications for Turkey.Liquidity affects growth of the real economy and financial market soundness. The costs and legal requirements of new issues create problems for individuals. Policy planners in Turkey should ease new issue restrictions when the market liquidity wanted to be increased. Comparing interactions of liquidity and listing conditions between developed and other developing countriescould be the intention of future research investigations. Calculating performance of new listings in the conditions of liquidityin Turkey can show useful results.The determinants of liquidity and listings vary in the literature. These factorscould be examined by future studies.Surveying practitioners’ beliefs can support the relations between liquidity and listings. 248 International Journal of Business and Social Science Vol. 5 No. 2; February 2014 References Aitken, F. and C. C. Forde (2003). How ShouldLiquidity Be Measured?. Pacific- Basin Finance Journal 11, 4559. Bhargava, V.and D.K. Malhotra (2007). TheRelationshipBetweenFuturesTrading Activity And Exchange Rate Volatility, Revisited. Journal of Multinational Financial Management 17, 95-111. Borsa Istanbul (2013). 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