Name: Bei Pei Tutor: P.F.A. Tuijp. ANR: Program: International Business Administration. Pages: 21 Date:

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1 Asset pricing and liquidity Name: Bei Pei Tutor: P.F.A. Tuijp ANR: Program: International Business Administration Pages: 21 Date:

2 Abstract With the popularity of market microstructure theory and behavioral finance theory, a large number of studies have shown that market liquidity and investor behavior are closely related to each other, and investor sentiment can affect market liquidity. In this paper, I introduce a theoretical model based on behavioral finance, Baker - Stine Model, to make a new interpretation of the causes of market liquidity. 1

3 I. Introduction Liquidity is an important risk of asset, as many empirical evidences 1 suggest that liquidity affect asset returns. From the theoretical point of view, the traditional asset pricing theory states that the return of asset is only related to the uncertainty risk, the higher the uncertainty of future return the greater the return of asset. Amihud and Mendelson (1986) use the Bid - Ask Spread as a measure of liquidity to investigate the relationship between liquidity and asset return. They find that those stocks with relatively less liquidity tend to have higher returns. Then Amihud and Mendelson (1989) take into account volatility and find that the liquidity risk cannot be diversified. The better the liquidity of an asset, the easier it could be transacted at a lower cost on market and at the same time the asset prices will not fluctuate on a wide extent. The most directly affected by liquidity is the transaction cost of asset, thereby affecting investors' investment decisions. In general, the worse the liquidity the higher the expected return required by investors. Thus liquidity could be used as a systematic risk and be taken as a state variable in asset pricing. Why the market liquidity can affect stock returns, the traditional explanation is that: in the stock market, in order to compensate investors who holding illiquidity asset the liquidity risk, those illiquid asset sold for a relative low price, and the expected rate of return is higher than those asset with a high liquidity, thus explained by illiquidity premium. With the popularity of market microstructure theory and behavioral finance theory, a large number of studies have shown that market liquidity and investor behavior are closely related to each other, and investor sentiment can affect market liquidity. On the basis of summarizes of the relevant literature this paper first make an analysis for the liquidity - related phenomena. Then introduce a theoretical model based on 1 Amihud and Mendelson (1986), Brennan and Subrahmanyam (1996), Brennan et al. (1998), Pastor and Stambaugh (2003) etc. 2

4 behavioral finance, Baker - Stine Model 2, to make a new interpretation of the causes of market liquidity. This model mainly studies the relationship between liquidity and investor sentiment on the constraint of short sales. And under the conditions of existence of the short selling restrictions, market liquidity can be used as an indicator to measure investor sentiment. The uniqueness of this model lies on the introduction of a group of irrational investors who underreact to the information contained in order flow. When their investment sentiment is optimistic, they will actively participate in the market what significantly increase market liquidity. Therefore, under the short sales constraints, high market liquidity is often a symbol that market is mainly dominated by irrational investors, the market value of stocks is generally overestimated. By introducing the Baker - Stein mode, this paper will explain the relations between liquidity and returns from a perspective of behavior finance, and further explain why market liquidity can predict the subsequent returns. In addition, by using the data get from CRSP to make an empirical analysis. The first purpose of this paper is on the basis of previous studies to get a better understanding of the role of liquidity on investment decision. Second, deepen the understanding of the Baker - Stein model. A lot of financial theory assumption about market liquidity is that investors are rational investors. However, a large number of researches on behavioral finance show that investors are not always rational, and the stock market is not that effective as effective market hypothesis states. According to the logic of this model, I use the stock market data make an empirical analysis, and find that the logic of this model are consistent with two conclusions: 1). The growth rate of new share issuance is closely related to the growth rate of market turnover rate, which shows that companies use market timing to make financing decisions to some extent but not solely based on the market value and risk of investment projects; 2). The change of market liquidity (turnover rate) and corporate 2 The Baker - Stein model is established by Baker and Stein (2003). It is used to explain the relationship between market liquidity and investors' sentiment. 3

5 finance behavior have some predict power about the future changes of market rate of return. In fact, the existence of market transaction costs and short sales constraints prevents the realization of arbitrage, so the investor sentiment can affect the stock price (Black, 1986; Sheleifer and Vishny, 1997). Shleifer (2000) pointed out that the stock market speculation is prevalent worldwide, and it is always highly correlated with investor sentiment. II. Literature review 1. The relationship between market liquidity and returns The traditional asset pricing models holds that high stock yield only compensate for risk, ignoring other important factors like illiquidity risk and transaction costs. The market microstructure theory takes into account the issue of illiquidity, and come up with the illiquidity premium. Theoretically, in stock market, in order to compensate for the liquidity risk faced by the investors holding illiquidity asset, the selling price of these asset is relatively lower and expected rate of returns is higher than liquidity asset. In recent years, Many scholars and literatures on the relationship between market liquidity and stock returns focus on whether liquidity is a factor in determining asset price, namely whether liquidity have a major impact on asset pricing. Despite the various selected indicators 3, most of them reached a conclusion that liquidity should be set as an important common factor in asset pricing. Amihud and Mendeison (1986) have studied the relationship between liquidity and asset pricing. They use the bid-ask spread as a measure of liquidity indicator to test 3 bid - ask spread (Amihud & Mendelson, 1986); turnover rate (Datar, Naik & Radcliffe, 1998); volume (Brennan, Chordia & Subrahmanyam, 1998), Illiquidity (Amihud, 2000) etc. 4

6 the predicted spread- return relation using data for the period 1961 to 1980, they find that average portfolio risk-adjusted returns increase with their bid- ask spread, and the slope of the return spread relationship decrease with the spread. Moreover, the stock price and liquidity is negatively related which supports the liquidity premium theory, namely less liquid assets have relatively higher expected return than better liquid assets, as a compensation of liquidity risk for those investors who holding relatively illiquid asset assets. In others words, the higher the spread the higher the returns to the holders and investor expecting a long holding period can gain by holding high spread assets. Brennan and Subrahmanyam (1998) also use the spread as a measure of liquidity, they use a new way to test the relationship between liquidity and stock returns, and get a similar conclusion as Amihud and Mendelson. Chordia, Subrahmanyam & Anshuman (2001) analyze the relation between expected equity returns and the volatility of trading activity. They document the result that there is a negative and surprisingly strong cross-sectional relationship between stock returns and the variability of trading volume and share turnover after controlling for size book to market, momentum, and the level of dollar volume or share turnover. Amihud (2000) use the stock price and trading volume establish a new proxy of illiquidity, ILLIQ, and use the ILLIQ to test the relationship between illiquidity and stock returns. The results show that expected stock returns are an increasing function of expected illiquidity across sections. But on the time series, Amihud creatively break ILLIQ into expected ILLIQ and unexpected ILLIQ. The result shows that the expected ILLIQ and stock returns are significantly positively correlated, but the unexpected ILLIQ and stock returns actually is significantly negatively correlated. Pastor & Stambaugh (2003) study the sensitivity of individual stocks and portfolios on market liquidity and the relationship between the individual stocks and portfolios 5

7 to investigate whether market-wide liquidity is a state variable important for asset pricing. What they find is that expected stock returns are related cross-sectional to the sensitivities of returns to fluctuations in aggregate liquid. The study results shows over a 34-year period, the average return on stocks with high sensitivities to liquidity exceeds that for stocks with low sensitivities by 7.5% annually, adjusted for exposures to the market returns as well as size, value and momentum factors. That means that the greater the sensitivity of the asset returns relative to market, the higher the yield. Besides, the study shows that the liquidity risk is one of the nondiversified market risks. Acharya & Pedersen (2005) integrate previous research results, directly revise the traditional capital asset pricing model, and clearly set the liquidity risk as an impact factor of asset pricing. On this basis, they establish the liquidity adjusted capital asset pricing model. (LA - CAMP). In summary, a large number of empirical studies have shown that the illiquidity premium is widely present in the stock market. Liquidity is a very important factor to affect stock returns. 2. Behavioral Finance Theory The idea behind behavioral finance theory is that although the classic financial theory system is perfect and can make a logical explanation for a large number of financial phenomena, the assumption that people behave with extreme rationality is very harsh and restrict. At the same time, the existence of a lot of financial market anomalies and psychology empirical studies show that classical finance demonstrate there is a fundamental defect. Behavioral finance relaxes the traditional assumptions of financial economics by incorporating human psychology. One of the explanations about why investors 6

8 behavior irrational is overconfidence hypothesis which states a psychological phenomenon when people make a judgment often overconfidence and overestimate the chances of success. They overconfident either over their ability (Frank, 1935) or over the accuracy of their possessed knowledge(fischoff, Slovic and Lichtenstein, 1997). Other reason for the flourish of behavioral economics is prospect theory proposed by Kahneman and Tverskey (1979) which is used to explain various divergences of economic decision making from neoclassical theory when people face uncertain situations. As said by Sewell (2005) that behavioral finance is the study of the influence of psychology on the behavior of financial practitioners and the subsequent effect on markets. Put it more straightforward, behavioral finance theory focus on the study of individual behavior, it first concerns about what is actually happened, namely it first pay attention to the study of reality, then go further trying to figure out the explanations behind the phenomena and beyond economy. Compared with the traditional classic financial theory, behavioral finance theory holds that the economic behavior on the financial markets are the results of social interactions and express objectively on a economic stimulus. Behavioral finance absorbs psychological research and financial research, providing a new perspective of finance. Behavioral finance used in the field of corporate finance yield Behavioral Corporate Finance Theory which assume that people are loss aversion, susceptible to framing or packaging that leads them to select inferior option and overconfidence and prone to confirmation bias. The impact of irrational behavior on corporate financing decisions manifest as follows. Stein (1996) point out in its Market Timing Model that company's management will use equity financing when they think the stock of the company is overvalued, and they will repurchase back shares when they think the stocks are undervalued. Besides, executives do not invest ineffective investments with the funds raised when the stocks are overestimate, but use the funds to increase the company's cash flows. Simply speaking, irrational investors affect the time of equity issuance but not affect the company's investment plans. The impact of irrational behavior on stock price reflects on the overreaction or under reaction of a major event. 7

9 III. Baker - Stein Model Baker and Stein (2003) establish the Baker - Stein Model based on the model by DeLong, Shleifer, Summers, and Waldmann (1990a), henceforth DSSW model and behavioral corporate finance. DSSW model explains the effect of noise traders on the pricing of financial assets and why noised traders can earn higher expected returns. Noise is flase or wrong judgement information. Noise traders are those investors who cannot get inside information and irrationally treat noise as information. The Baker - Stein model is used to explain the relationship between market liquidity and investors' sentiment. By appropriate extension, the model still can be used to explain market liquidity and be used to predict the expected rate of return. In other words, the Baker - Stein model can explain why on the time series the change on the overall market liquidity can predict the changes of expected rate of return in market. Baker - Stein model has two main assumptions. The first is that there is a group of irrational investors on the market who underreact to the information contained in the trading volume. The existence of such investors can reduce the price impact of transactions, thereby increase market liquidity. The second assumption is the short sales constraint. The purpose of this short sales constraint assumption is to ensure that when the sentiment of irrational investors are optimistic on the market, namely compared with rational investors irrational investors overestimate the market value of stock, the irrational investors will be very active and actively participate in the market buying stocks. On the contrary, when the irrational investors have a very pessimistic sentiment on the market, namely the irrational investors underestimate the market value of stock as opposed to the estimation of the rational investors, due to the existence of short sales constraint, the irrational investors cannot profit by short selling so that irrational investors will get out of market and refuse to participate in the market transactions. Therefore, the presence of irrational investors can increase the market liquidity. The Baker - Stein model describes the short term fluctuations of 8

10 stock market stem from the changes in the investors' market sentiment. The measeure of liquidity indicators can be used to measure the sentiment of irrational investors, and the level of stock price. One of the contribution of the Baker - Stein model is that it can be used to explain why there exits "new issues puzzle". Empirical researches and many literatures 4 show that the new shares will underperformance or have a lower rate of returns relative to the average market returns after its initial public offerings (IPOs), this is called new issues puzzle". However Fama (1998) believes that researchers maybe wrongly estimate the risk factors when comparing the new shares returns with its benchmarks' returns, and without the correct adjustment of risk factors the yield spread can not reflect the real situation. Most important, consistent with the market efficiency prediction that apparent anomalies are fragile, it could be due to methodology or statistics, and most long term return anomalies tend to disappear with reasonable changes in the way they are measured. So the current popular explanation of the new issues puzzle phenomenon is the existence of market timing, which means that when the investors sentiment are positive, the stock is overvalued and the market is highly liquid the company will tend to issue new shares. Stein (1996) explains that managers are rations, they have a better estimation of the long term value of the company stocks, so they will carefully choose the equity issuance timing and make good use of the information advantage they have. Graham & Harvey (2001) prove the above interpretation is reasonable, they find that most of managers regard market timing as an important financing consideration. However, in Baker - Stein model it does not need to assume that managers' investment decisions are rational, it does not require managers have a good estimation on the company's fundamental value. Managers' financing decisions only need to follow a simple rule of thumb that is when the market liquidity is high go to issuing equity. 4 Stigler (1964), Ritter (1991), Loughran and Ritter (1995), Speiss and Afflect - Graves (1995), Brav and Gompers (1997) etc. 9

11 This financing behavior will convey the information of future stock returns. Why assume that managers tend to issue equity when market liquidity is high? On one hand, because there is a high liquidity on the market, the price impact can be reduced when issuing equity and improve the probability of successful financing; on the other hand, it is in line with the actual investigation of managers' financing behavior. If the managers' financing decision follows this rule, then issuing equity is the perfect reflection of market liquidity. And based on the above reasons, equity market timing can predict the future investment returns. IV. Definition of liquidity and its dimensions According to Tsay (2005)'s definition of market liquidity, market liquidity is the ability to buy or sell significant quantities of security quickly at a lower cost in a short time, and with litter impact on price. So, any impact on prices through the trading activity indicators can be used as a measure of liquidity. However, liquidity is an abstract concept which contains multiple dimensions. The current literature tends to divide market liquidity into four dimensions: Breadth, Depth, Immediacy and Resiliency. Breadth means that orders are both numerous and large in volume with minimal impact on prices, and it is measured by the indicator of Bid - Ask Spread. Depth refers to the existence of abundant orders, either actual or easily uncovered of potential buyers and sellers, both above and below the price at which a security now trades, and it is measured by Volume. Immediacy represents the speed with which orders can be executed and, in this context also, settled, and thus reflects, among other things, the efficiency of the trading, clearing, and settlement systems. Immediacy is measured by Turnover Rate. Resiliency is a characteristic of markets in which new orders flow quickly to correct order imbalance, which tend to move prices away from what is warranted by fundamentals. And resiliency is measured by price impact. Whereas, these indicators only reflect one side of liquidity and there exist limitations of those indicators respectively. 10

12 The indicator of Breadth: according to Grossman & Miller (1998) the Bid - Ask Spread measures exactly the market maker's return for providing immediacy only in the special case in which the market maker simultaneously "crosses" the trade, one at the bid and the other at the ask, and thus may underestimate or overestimate the actual bid - ask spread. In addition, it does not consider the price of the stick, the impact of different prices on the bid- ask spread on investors is obviously not the same. Also, the bid - ask spread also get constraint by the minimum price change unit which is called tick. A large tick is artificially expand the bid - ask spread and a small tick will reduce the depth of the market. For example, NYSE currently uniformly adopt the 0.01$ the minimum tick. To some extend artificially increase the bid - ask spread of low- priced stocks which directly reducing the bid - ask spread of high - priced stocks. The inadequacies of the depth indicator is mainly due to that the liquidity providers, market makers, are often reluctant to disclose the full amount of the transaction at a price. Therefore, the number of trading on the best bid and ask cannot be a true representative of the depth of the market. In addition, Madhavan (1992) prove that in a continuous auction order - driven trading mechanism, if there is a limited number of participants involved, then the effective bid - ask spread is an increasing function of order size. Besides, the two indicators of breadth which is indicated by bid - ask spread and depth indicated by volume cannot exist subcutaneously at any moments and consider one the both will result in the lack of information. Due to NYSE is primarily a continuous auction order - driven system, investors should analysis the market and information by the combination of the bid - ask spread and the trading volume in order to get a more comprehensive measure of liquidity. Turnover as a proxy of immediacy must satisfy two conditions. First, there is a Correspondence between turnover and trading frequency; second, the trading frequency can really pass the information about the degree of active trading, and both are indispensable. Transaction costs usually decrease the trading frequency, thereby 11

13 interfere the delivery of the trading activity degree. For example, in the stock market, trading frequency is high, but is caused more by the low spread level. Thus this does not truly reflect the level of activity of the market trading and take turnover as indicators to measure the liquidity of the securities market has significant limitations. The study of Resiliency of current literature is by measure the tension between liquidity and efficiency a market in which prices move significantly and immediately in response to all new information as it flows in the marketplace. However there are also some limitations of this proxy measurement. Bernstein (1987) notes that factors such as inaccurate price determination, the effect of now information on price, market maker invention and market volatility etc. make price inaccurately change. V. Data The Baker - Stein model assume that new equity issuance decision of the manager is not based on the long term fundamental analysis but based on the understanding of market timing, market liquidity and market ability to absorb new shares. If managers make equity financing decision is based on market timing, then the future stock returns can be predicted. Next, I will use the actual data to make an empirical analysis on the Baker - Stein model, mainly test the relationship between liquidity, equity share and returns. There are two purposes of this empirical test. First, test whether new equity financing as the stock rate of return predictors is significant; second, whether there is a strong positive correlation between new equity issue and liquidity. 12

14 1. Liquidity and the equity share in new issues I will use the data of aggregate liquidity which is available on Pastor & Stambaugh's Home. Base on previous literatures, in this paper, I assume turnover perfectly measures the liquidity. Dividend yield is calculated on the CRSP value - weighted return basis. Rate of return does not consider the impact of dividends. R is the CRSP value - weighted return. The equity financing ratio is the proportion of equity financing to the total financing. e is the amount of equity financing, d is the amount of debt financing, S stands for the proportion of equity financing, then S= e / (e+d). Panel A Descriptive statistics for variables. Turnover is indicated by market liquidity with the data available from Pastor & Stambaugh's Home page. S t, equity financing ratio, is equal to the amount of equity financing to the total financing amount. The relevant data get from Baker and Wurgle's "The equity share in new issued and aggregate stock returns" (journal of Finance, October 2000). Rt is the rethrn on the CRSP value Weithted including dividends Min. Max. Mean Std. Dev. Skewness Kurtosis St Turnover 3.26E Dividend yield D/Pt Return R t

15 Panel B correlations between statistics St Turnover Dividend yield D/Pt Return Rt St Turnover Dividend yield D/P t Return R t The table above shows the simple statistical characteristics of the data of liquidity and the proportion of equity financing and dividend yield. Through this table, I find that there is a slightly weak correlation between liquidity and equity financing ratio, with the correlation coefficient close to This indicates that the when the liquidity is high in the market, the proportion of equity financing will increase, the company's financing behavior will increase as well. The dividend yield is negatively correlated with liquidity. To make the relationship between liquidity and the proportion of equity financing more clearly, I will use a multivariate regression model to test the relationship between them. In the multiple regression equation, I set the lag of last three years' rate of returns as control variables. The reason is that many studies have find there exists a significant relationship between turnover and past returns. [Shefrin and Statman,1985; Lakonishokand Smidt, 1986; Odean, 1998b]. Specific regression equations are as follows: St = a + b Turnovert + c D/Pt + d1 Rt-1 t-1 + d2 Rt-2 + d3 Rt-3 + ut In the above formula, S is the equity financing ratio, turnover indicates liquidity, D/P is the CRSP value - weighted dividend, R is the return on the CRSP value weighted exclude dividend. By OLS the results get is as follows: 14

16 Coef (t-stat) Coef (t-stat) Turnover Dividendyield D/Pt R t-1 Rt-2 R t R With the significance level of 5%, this model is insignificant. What I find from the above table is that when using turnover (Baker & Stein, 2004) there is an effect on equity financing timing but when using Paster & Stambaugh market liquidity measure there is no effect on equity financing timing with the coefficient not significant. Paster & Stambaugh market liquidity measure focus on temporary price fluctuations induced by order flow which is the resiliency dimension of liquidity. This indicates that the effect of liquidity is driven by Depth and Immediacy but not by Resiliency which is as expected from Baker & Stein (2004) theory. 2. Liquidity and the equity share as predictors of future returns In this section, I will take advantage of liquid and equity financing ratio to forecast the one year ahead returns. The specific regression equation is as follows: Rt = a + b St-1 + c Turnovert-1 + d D/Pt + ut Where Rt is the current rate is return, St-1 is the last period equity share, Turnovert-1 is the last period market liquidity. By OLS, I get the following results: 15

17 Coef (t-stat) Coef (t-stat) S Turnover D/P R The following table shows the statistic of the difference between expected value and actual values of return. As can be seen the both the mean and standard deviation are relatively small, indicating that the predictive value and the actual value is closer together and the equation has a good predictive power. Y = E(R) - R where E(R) is the expected value and R is the real value. Y = E(R) - R Min Max Mean 9.38E-18 Std. Dev Skewness Kurtosis

18 VI. Conclusion The core of this paper is the description of Baker - Stein model and the empirical analysis of this model. The main assumptions of Baker - Stein model are that when there is short sale constraint, market liquidity is an indicator of market sentiment. High market liquidity indicates that among the market participants, most of the investors are underreact to the information contained in the market orders of new equity issuance; these investors have a considerable impact on the asset pricing. When the market liquidity is high, they will generally overestimate the stock price, reducing the entire market expected rate of return, causing a serious bubble of stock price. When the market bubble burst, the rate of return of the stock market is exceptionally low. So high liquidity in the market can be seen as an indicator of investors' optimistic sentiment, it indicates that the later stock market rate of return will be low. One of the big advantages of the Baker - Stein model is that it provides a unified explanations for market liquidity related phenomena. The Baker - Stein model shows that liquidity related phenomena are closely related and can be reasonably explained by using a same basic model. In addition, Baker - Stein model has a good extension capability through appropriate adjustments. After extension, the model can be applied to other markets such as real estate market. Although there is a lower liquidity relative to stock market, seen from the worldwide actual situation the real estate prices and the volatility of liquidity is sill great. The real estate market is also prone to bubbles which also reflect that the real estate market liquidity and investor sentiment is closely related. Take the current Chinese real estate market as an actual example. Recently, local governments have promulgated various regulations to control the speculation behavior in the real estate market, hoping to reduce the speed of the rise of the house prices. Shanghai, for instance, strictly prohibit transferring forward delivery house, increase the down payment. As can be seen from these measures, the basic starting point of local 17

19 governments is to improve the transaction costs of real estate and reduce the liquidity of real estate market. The problem is whether the real estate market is overheating according to the liquidity status and whether decrease market liquidity can reduce the house price. According to the analysis of Baker - Stein model, the answer is undoubtedly yes. The unusually high real estate market liquidity indicates that there are too many traders in this market, prices are overvalued and to some certain extent there are market bubbles. By increasing the transaction costs, some traders are forced to leave out of the real estate market, thus slow down the speed of rising of house prices. These are the theoretical explanations based analysis, the actual trend of property prices are affected by many factors. The contribution of Baker - Stein model lies in the theoretical explanation that through the intervention of real estate market liquidity the house prices indeed have a certain impact. Although Baker - Stein model has strong explanation ability, it is less than perfect. The main problem lies in the assumptions of the model. Baker - Stein model has a critical assumption that the market has a short sales constraint. However, this assumption is not practical. In most countries there is no such restriction of short sales mechanism. Therefore, there is a significant application limitation. The other limitation of this model exists in the other assumption of this model that there is a group of irrational investors. Their sentiments affect market liquidity through market transaction. However some problems still waiting to be answered such as how market liquidity affect investors' sentiment, how investors' sentiment impacts each other etc. Baker - Stein model does not address these problems. 18

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