The Debt-Equity Mix in Preferred Stock and Adverse Selection Costs: An Empirical Investigation. Janos K. Illessy* and Kuldeep Shastri** November 2000

Transcription

1 The Debt-Equity Mix in Preferred Stock and Adverse Selection Costs: An Empirical Investigation Janos K. Illessy* and Kuldeep Shastri** November 2000 *The International Management Center, Budapest, Hungary **Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA, USA We would like to thank Craig Dunbar, Esther Gal-or, Ken Lehn, Anil Makhija and Jean-Francois Richard for their comments and suggestions. Please address all correspondence to Kuldeep Shastri, Ahlbrandt Professor of Finance, Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260, USA (voice); (fax); ( ) 1

2 The Debt-Equity Mix in Preferred Stock and Adverse Selection Costs: An Empirical Investigation Abstract This paper investigates whether the hybrid nature of preferred stock is reflected in its market microstructure and, in particular, in its information dissemination process. Theory would suggest that information flows, informational asymmetry and transactions costs of trading associated with various kinds of preferred stocks would differ depending on whether debt-type or equity-type asset characteristics dominate in them. Specifically, debt dominated assets are expected to rely more heavily on non-firm-specific information, be exposed to adverse selection to a lesser extent and have lower information asymmetry induced transactions costs than securities dominated by equity features. This paper empirically tests this prediction. The main result in the paper is that asset characteristics affect microstructure parameters. In particular, the higher the equity dominance in straight preferred stock, the lower the public information induced volatility and the higher the information asymmetry induced transactions cost and the adverse selection component of the spread. Such relations are not observed in the case of convertible preferred stock. This is attributed to the existence of a lead-lag relationship between this security and its underlying common stock. Finally, the results also indicate that infrequent trading has a significant impact on the adverse selection component of spread. This paper can be downloaded from the Social Science Research Network Electronic Paper Collection: 2

3 The Debt-Equity Mix in Preferred Stock and Adverse Selection Costs: An Empirical Investigation 1. Introduction Issues related to market microstructure have received a great deal of attention in recent financial research. Although the number and sophistication of both theoretical and empirical studies are increasing, the extant literature almost exclusively focuses on common stocks and derivative securities with underlying equity claims such as stock options and index futures. The information dissemination process associated with common stock, however, may substantially differ from that for non-equity securities. Specifically, the price of equity primarily depends on expectations about firm-specific residual cash flows that are observed at different levels of precision by various market participants. The precision of the residual cash flow forecast critically depends on private information about idiosyncratic factors that is available to insiders only or can be observed by outsiders by incurring non-trivial transactions costs. On the other hand, firm-specific characteristics have a relatively smaller effect on the value of fixed-income instruments as compared to macro-economic factors such as interest rates and the monetary policy followed by the Federal Reserve. One implication of this argument is that due to differences in informational structure, the dissemination process for firm-specific information would vary across securities depending whether equity or debt features dominate them. 3

4 The contribution of this study is that it introduces asset characteristics as an additional determinant of market microstructure. By analyzing trading data for preferred stocks, it empirically investigates whether differences in asset characteristics are reflected in the way firm-specific information is revealed through trading. The availability of preferred stock microstructure data offers an excellent opportunity to address this question, because these financial instruments represent a combination of a fixed claim (debt) and a pure residual claim (equity). In addition, since they trade in the same marketplace and share the same market maker as the common stock, the analysis conducted here controls for differences in inter-market structure and trading characteristics. 1 Although this study is limited to preferred stocks only, the findings can be indicative of the microstructure characteristics of the market for other securities that have fixed-income characteristics. As stated earlier, certain types of preferred stocks have dominant equity features, while others are closer to debt. Preferred stock s hybrid nature is apparent in asset characteristics such as ownership, voting rights, dividend distributions and priority structure. 2 To a limited extent, it represents ownership in the issuer firm with an infinite investment horizon. For example, preferred stock carries voting rights under certain circumstances. It is a residual claim in the sense that distribution to preferred stockholders can only take place after all creditors claims are satisfied, although it is given priority over distributions to common stockholders. Also, 1 Preferred stocks share the same specialist as the corresponding common stocks in about 95 percent of the cases. The remaining 5 percent of preferred stock issues are lumped together and trade separately. Since the trading in this latter group of securities is extremely thin, they are excluded from the analysis in this dissertation. 2 For more details on asset characteristics of preferred stock, see Altman (ed.), Fabozzi and Zrab (eds.), Levine (ed.) and Logue (ed.). 4

5 preferred stock is often convertible into common stock at a pre-determined conversion ratio. This feature enhances the correlation between preferred and common stocks, especially when the embedded option is in-the-money. 3 As with common stock, the payment of dividends to preferred stockholders is not contractually obligated and dividend omission is not a cause for bankruptcy. The tax treatment of preferred dividend is also closer to that of common. In particular, issuers cannot deduct preferred dividend payments as a cost, while corporations are allowed to exclude 80% of the dividends they receive from taxes. The same exclusion does not apply to individuals, therefore suggesting that preferred stock, as with common stock, may have clienteles. On the other hand, preferred stock has many debt-like features. Voting rights of preferred stockholders are largely limited except in situations where the firm omits preferred dividends for a certain period. 4 Like interest payments on debt, the preferred dividend is either fixed (as a percentage of par value or as a dollar amount) or varies in a pre-determined fashion (adjustable rate or reset preferred). The vast majority of preferred stocks are cumulative and non-participating, meaning that no distribution can be made to common stockholders until all preferred dividend in arrears are paid out, and preferred stockholders do not share extra earnings with common stockholders beyond their promised dividend. Still another feature of preferred stock that enhances its debt-like characteristics and shortens the 3 A convertible preferred stock can be thought of as a combination of a straight preferred plus a warrant. The security is said to be in the money if the conversion value (the value of the underlying common times the conversion ratio) is larger than the value of the unconverted preferred stock. For more details, see Ingersoll (1977) and Hingorani, Makhija and Shastri (1994). 4 In this situation, preferred stockholders get voting power or elect board members. 5

6 investment horizon is that issuers can recall or, in some cases, exchange it into subordinated debt. Finally as with debt contracts, the retirement of preferred issues is often done on a regular basis through sinking funds. 5 These characteristics suggest that the category of securities referred to as preferred stock is in fact an entire spectrum of securities ranging in equivalence from common stocks to long-term straight bonds. Applications of the option pricing model, especially those in Ingersoll (1977) and Smith (1979), make it possible to theoretically determine whether equity- or debt-type features are dominant in a particular preferred stock issue. These applications suggest that, in general, straight preferred stock is similar to debt-type financial claims, while the equity characteristics dominate in convertible preferred stock. Furthermore, within these two groups of preferred stocks, the dominance of equity versus debt characteristics displays cross-sectional variation as a function of relative firm value. Specifically, straight preferred issues by high quality firms would be valued as straight bonds with a fixed perpetual income, while the priority structure enhances the residual claim feature for issues by low quality firms. In the case of convertible preferred stocks, those that are deep in-the-money are closely related to the underlying common and are, therefore, dominated by equity features. This follows from the fact that the conversion feature dominates the value of these preferreds. On the other hand, for out-of-the-money convertible preferred stocks, value is primarily determined by the expected dividend payment, thus making them similar to fixed income securities. It should, however, be noted that even for this group of preferreds, the equity feature 5 Omission of a sinking fund payment, however, does not cause bankruptcy. 6

7 may dominate if the firm quality is low and the value of the preferred is being primarily determined by the value of the residual claim. Thus, these preferreds can be viewed as being more akin to common stock. This study examines the relationship between the market microstructure of preferred stocks and their characteristics in terms of the dominance of debt versus equity features. Specifically, it investigates whether debt-equity dominance affects (i) the relative importance of non-firm-specific (public) versus firm-specific (private) information in the price formation process, (ii) the market depth and the cost of transacting due to adverse selection, and (iii) the information asymmetry induced spread component. 6 Our main results suggest that asset characteristics have an impact on market microstructure. In particular, a cross-sectional analysis of straight preferred stocks reveals that the higher (lower) the dominance of the debt component of straight preferred, the higher the public (private) information induced volatility, the deeper (shallower) the market and the lower (higher) the information asymmetry reflected in bid-ask spreads. 7 The results from the analysis of convertible preferred stocks are not consistent with these findings. Equity dominated convertible preferred stock prices reflect higher levels of public information and display lower levels of information 6 Adverse selection is a term used to describe the situation that arises because of the existence of information based trading. The name derives from the fact that the market maker charges an average spread on each transaction to recover the losses incurred from trading with informed traders. See Bagehot (1971) for more details. In this context, Market depth is the sensitivity of the quoted price to the order flow with lower sensitivity indicating deeper markets. See Kyle (1985) for more details. 7 The bid and ask prices are those quoted prices at which the market maker is willing to buy and sell, respectively. The difference between the ask and bid is called the quoted spread. 7

8 asymmetry and adverse selection induced transactions cost. These results are attributed to the fact that private information that affects the value of both common and convertible preferred stocks is typically reflected in the common stock price first. Consequently, there exist a lead-lag relationship between the two securities. 8 Due to that lead-lag effect, the private information already reflected in common s price can be regarded as publicly available information from the perspective of convertible preferred. Further results from both cross-sectional analyses of the two preferred stock classes as well as a comparison between the groups of common and preferred stocks are consistent with the argument in Easley, Kiefer, O Hara and Paperman (1996) that infrequent trading has an impact on adverse selection. 9 Specifically, in the absence of large trading volume, both straight and convertible preferred stocks suffer from more severe adverse selection problem than their corresponding common stock, indicating relatively concentrated informed and little liquidity trading. The remainder of this paper is organized as follows. Section 2 presents the framework used to identify the debt-equity characteristics of preferred stocks, analyzes some major factors affecting the trading of preferred stocks and develops testable hypotheses that can be derived from the existing theories. The next section discusses the specific empirical tests that are performed here, while Section 4 describes the data. Section 5 presents and interprets the results. The last section concludes the dissertation. 8 For empirical evidence regarding the lead-lag relation between common stock and convertible preferred, see Linn and Rozeff (1995). 8

9 2. The Hypotheses 2.1. Theoretical and Empirical Valuation Models for Preferred Stocks As argued earlier, preferred stocks are hybrid securities in the sense that they share the features of both pure equity type (common stock) and income type (bond) securities. To develop testable hypotheses about the effect of asset characteristics on market microstructure, we need to be able to classify preferred stocks in terms of their mix of debt and equity characteristics. Toward this purpose, we employ an option pricing framework. Ingersoll (1977) and Smith (1979) suggests that a corporate bond can be valued as a written put plus a cash position, while common stock can be viewed as a purchased call (see Figure 1). The strike price of both options is equal to the par value of debt. As long as the market value of the firm significantly exceeds the par value of debt, the put option (bond) is deep out-ofthe-money and the call option (common stock) is deep in-the-money. In this situation, the firm is solvent in terms of its contractual cash distributions. Conversely, if the market value of the firm falls below the par value of debt, the put and call options become in-the-money and out-of-the-money respectively. In this scenario, the firm is not solvent and is subject to bankruptcy or liquidation procedures. Figure 2 shows the payoff structure in the case when non-convertible (straight) preferred stock is part of a firm s capital structure in addition to debt and common stock. Straight preferred stocks do not participate in any capital 9 See Easley, Kiefer, O Hara and Paperman (1996) for a discussion of the impact of infrequent trading on adverse selection. 9

10 appreciation beyond the promised dividend yield, but are lower in priority as compared to debt. Hence, they behave in a fashion similar to subordinated debt. Specifically, straight preferred stocks of high quality firms are valued as pure debt, while those issued by low quality firms are valued more like equity due to their enhanced residual claim feature and low priority. As shown in Figure 3, the payoff structure of convertible preferred stocks is fundamentally different from that of straight preferred. Although at low firm values, convertible preferred stocks are similar to straight preferreds, the convertibility feature ensures that holders participate in capital appreciation at high firm values where the conversion value exceeds the value of an unconverted preferred stock. 10 The deeper in-the-money the convertible preferred stock is, the closer the relation between the value of the preferred and its underlying common stock. The above argument suggests that the classification of preferred stocks according to their debt-equity mix requires us to be able measure relative firm quality. For convertible preferred stocks, moneyness is used as a proxy for firm quality. Based on the arguments presented above, deep in-the-money and deep out-of-the-money convertible preferred stocks are dominated by equity features, while those falling in the middle of the moneyness region have dominant fixed income securities. Following previous literature, moneyness is measured as the ratio of the conversion value and the unconverted preferred stock price. The higher 10 The conversion value is the product of the common stock s value and the conversion ratio. The conversion ratio is the number of common stocks the convertible preferred stock can be converted into. 10

11 the moneyness variable the closer the convertible is to being in-the-money. Values above (below) unity imply that the security is in-the-money (out-of-the-money). For straight preferred stocks, the moneyness measure is not applicable. In this situation, the preferred dividend yield is used as proxy for firm quality. To see this, consider the following valuation formula for straight preferred: P sp divsp = (1) yld sp where P sp, div sp, and yld sp are the straight preferred stock s price, dividend and market-determined yield, respectively. The yield that the market applies in the valuation process reflects the time value of money as well as the firm-specific risk premium associated with the uncertainty of the perpetual cash flow stream. The higher the firm quality the lower the discount factor. This suggests that the discount factor can be used as a proxy for firm quality. Since according to equation (1) the discount factor is equal to dividend yield, straight preferred stocks can be classified as debt or equity dominated based on their expected dividends yields. Thus, low (high) dividend yield preferred is classified as a security with a dominant fixed claim (residual claim). It should, however, be noted that both dividend yield and moneyness are not precise measures of firm quality. For example, straight preferred stock issued by a growth company may have high level of yield due to the firm s high asset beta, and not necessarily because the firm is perceived to be of inferior quality. Similarly, the level of moneyness of a convertible preferred is not necessarily indicative about the quality of the issuer, since the convertible can be deep out-of-the-money due to the 11

12 initially low conversion value and not because of inferior firm quality. These considerations suggest that cross-sectional analyses require the use of both levels and time series changes in these proxies for firm quality. In this framework, a decrease in the yield of straight preferred or a change in the moneyness of convertible preferred toward being closer to in-the-money indicate an increase in the quality perception of the underlying assets of the firm. Analyzing changes can also be justified by the unobservability of threshold values of the levels. Specifically, one cannot directly observe whether a convertible preferred stock is substantially out-of-the-money or a straight preferred stock has substantially high yield to be regarded as securities dominated by equity features. The above lines of argument suggest that straight and convertible preferred stocks display both between-group and within-group variations in terms of asset characteristics. These differences can be summarized as follows: a) Difference between straight and convertible preferreds: Debt features dominate straight preferred stocks due to the fixed nature of their future claims on firm cash flows. Equity features due to the option that is embedded in the contract to convert the preferred into common stock dominate convertible preferred stocks. b) Differences within the group of straight preferred stocks: Among straight preferred stocks, those issued by low quality firms (that have high dividend yields) are equity dominated. Straight preferred stocks issued by high quality firms (that have low dividends yields) are debt dominated. 12

13 c) Differences within the group of straight preferred stocks: Among convertible preferred stocks, those in the money are closely related to their underlying common stock and are, therefore, dominated by equity features. Debt features are dominant in out-of-the-money convertible preferred stocks as long as the market value does not fall below a threshold value. Below this threshold value, the value of the preferred stock is based mainly on its residual claim and is equity dominated Additional Factors Affecting the Microstructure of Preferred Stocks This study focuses on asset characteristics of preferred stocks that potentially affect their market microstructure and, in particular, their information dissemination process. Besides asset characteristics two additional factors must be considered in this framework. First, one must consider the impact of a potential lead-lag relation between preferred and the underlying common stocks. This relation is due to the fact that both securities derive their values from the assets of the firm and can, therefore, be viewed to some extent as substituted for each other. Second, the impact of the lower liquidity in preferred stock markets must be taken into account. This low liquidity would impact the information dissemination process through its effect on the concentration of information based trading Substitutability and the Lead-Lag Effect When two assets that are close substitutes for each other trade simultaneously, informed traders can choose to trade in either one of the them to take advantage of their superior information. For example, in the case of derivative 13

14 securities, theory suggests that informed traders primarily reveal their information in the derivatives market because of its greater depth and higher leverage. Consequently, derivative prices are predicted to lead underlying stock prices. This prediction is empirically supported by Manaster and Rendleman (1982), Bhattacharya (1987), Anthony (1988) and Chan, Chung and Johnson (1993). The argument that a lead-lag relationship should exist between derivatives and their underlying assets can be applied to preferred and common stocks only to the extent the two securities derive their value from the assets of the firm. A lead-lag effect would exist only if the information affecting the value of the underlying common stock is relevant for the preferred stock. On the other hand, no such relationship would be expected if the market of preferred stock reacts to different types of information than that revealed in the common stock market. As a result, the common stock price series is expected to lead equity dominated preferred stocks, and no such relationship is expected with debt dominated preferred stocks. 11 The possibility that price changes of common stocks lead those of equitydominated preferred stocks has implications for the informational structure of quotes and prices. In particular, price changes of equity-dominated preferred stock should be mainly affected by the arrival of non-firm specific, rather than firm specific, information. This follows from the fact that since common stock price changes lead that for preferred, firm specific private information should already be reflected in the common s price and can, therefore, be regarded as publicly available information from the point of view of the preferred stock 14

15 2.2.2 The Relative Amount of Noise Trading and the Liquidity Argument As discussed earlier, if certain traders are better informed about the terminal value of a security than others, there exists a nontrivial transactions cost due to the adverse selection situation faced by the market maker. The higher the uncertainty about the asset s liquidation value (measured, for example, by its standard deviation), the higher the potential information asymmetry between informed and uninformed traders, including the market maker. However, uncertainty (i.e. the potential magnitude of information asymmetry) is not the only determinant of the adverse selection induced transactions cost. The latter also depends on the relative arrival rates of informed and liquidity traders, i.e. the concentration of informed traders in the marketplace. The extent of the adverse selection problem is therefore a function of both the potential magnitude of information asymmetry and the relative probabilities of information and liquidity motivated trades. 12 The implication of this argument is that in the cross-sectional analysis of adverse selection across various securities, one needs to account for the impact of the level of liquidity trading on information asymmetry The Hypotheses This study focuses on the relation between market microstructure parameters and asset characteristics. In particular, it has been argued earlier that 11 This prediction is empirically supported by Lin and Rozeff (1995) for in-the-money convertible preferred stocks. 12 See, among others, Copeland and Galai (1983), Glosten and Milgrom (1985), Easley and O Hara (1987) and Kyle (1985). Easley, Kiefer, O Hara and Paperman (1996) explicitly model this phenomena and empirically find a negative relationship between adverse selection and trading frequency. 15

16 the process of information dissemination differs for fixed claims (for example, straight preferred stocks of high quality firms) relative to residual claims the implications of this argument can be formalized as follows: Hypothesis 1 (Asset Characteristics): Other factors being equal, assets that are dominated by fixed-income (debt) features display - more reliance on non-firm specific information - less exposure to adverse selection, and - a lower information asymmetry induced transactions cost than those dominated by residual claim (equity) features. The above hypothesis ignores the impact of any lead-lag relation between common and preferred stocks, and assumes homogeneity in terms of informed trader concentration and liquidity. If a lead-lag relation exists between common and preferred stocks and the effect of this relation dominates the asset characteristics effect, we would expect more reliance on non-firm-specific information for equity dominated assets. This follows from the argument that the lead-lag relation allows traders to learn about the value of the asset being led (equity dominated preferred stock) by observing the value of the asset that leads (common stock). Similarly, the liquidity argument predicts a more severe adverse selection problem and a higher information asymmetry component of spread for assets traded infrequently (preferred stocks) even if they are dominated by fixed income characteristics. This prediction is based on the argument that a low level liquidity trading enhances the impact of informed trades on prices. Therefore, the competing hypotheses are: Hypothesis 2 (Lead-lag): Assets that are equity dominated display - more reliance on publicly available information 16

17 - less exposure to adverse selection, and - a lower information asymmetry induced transactions cost (spread) than those that are debt dominated. Hypothesis 3 (Liquidity): Assets that trade relatively infrequently display - more exposure to adverse selection, and - a higher information asymmetry induced transactions cost (spread) than those traded relatively frequently. The implications and predictions of the hypotheses in the context of various between- and within-group analyses are summarized in Table 2. Panel A displays the predictions for the between-group analyses. When the group of common stocks is compared to that of preferreds (upper half of Panel A), the asset characteristics hypothesis predicts more public information content, lower adverse selection costs and lower information asymmetry spread for preferred stocks. The liquidity hypothesis, on the other hand, has the opposite predictions. This is due to the fact that common stocks are substantially more liquid than that preferred. This, in turn, implies a deeper market for common stocks. In the case of the between-group analysis of straight versus convertible preferred (lower half of Panel A), the asset characteristics hypothesis predicts more public information content and lower adverse selection for straight preferred, since this security is in general more debt dominated than its convertible counterpart. The existence of a lead-lag relationship between convertible and common reverses these predictions, because information about convertible prices can be inferred from the common stock. The lead-lag hypothesis therefore implies more public information content and lower adverse selection for the group of convertibles. 17

18 Panel B displays the implications of the hypotheses for the within-group cross-sectional analyses. In the case of straight preferred (upper half of Panel B), the asset characteristics hypothesis suggests a negative relationship between yield and public information content, and a positive relationship between yield and adverse selection and the information asymmetry component of spread. These predictions are based on the assumption that the higher the yield of straight preferred, the higher the dominance of equity features. It should also be noted, that there is no competing hypothesis in this case due to the assumed absence of a lead-lag relationship between common and straight preferred as well as the homogeneity in liquidity across straight preferreds. For convertible preferred (lower half of Panel B), the asset characteristics hypothesis suggests a positive relationship between moneyness and public information content, adverse selection and the information asymmetry component of spread. The line of argument is similar to that used for straight preferred with the distinction that instead of yield, moneyness proxies for equity dominance in this case. In this case, there also exists a competing hypothesis based on the existence of possible lead-lag relations between convertible preferreds and common stocks. If common stock prices lead that of equity dominated convertible preferreds, the value of the latter can effectively be observed from the common stock. The lead-lag hypothesis therefore suggests that the private information content of prices, the adverse selection problem and the information asymmetry spread are negative functions of moneyness for convertible preferreds. 18

19 Since the competing hypotheses have opposing predictions as compared to the asset characteristics hypothesis, the combined effect of the interaction between asset characteristics (debt versus equity), the potential lead-lag relationships and concentration of informed trading is an empirical question. 3. Methodology Given the market microstructure parameters that the study focuses on (the nature of information reflected in prices, the adverse selection induced transactions cost and the various components of the spread), three models are of particular interest for the purposes of our analysis. They are (a) the technique proposed by Glosten and Harris (1988) to estimate the adverse selection transactions cost through the market depth parameter λ, (b) the empirical model in Jones, Kaul and Lipson (1994) to assess the relative importance of public information in the price formation process and (c) the spread decomposition technique in George, Kaul and Nimalendran (1991) to measure the fraction of spread due to information asymmetry. 3.1 Measuring the Inverted Market Depth Parameter The price formation model applied in the present study is similar in spirit to Kyle (1985) and Glosten and Harris (1988), while the empirical details follow Brennan and Subramanyam (1995). In this model, the terminal value of an asset in time period t, m t, is defined as: m t = m t-1 + λq t + y t (2) 19

20 where λ, q t, and y t are the inverted market depth parameter, the signed trade size and the innovation term due to the arrival of public information, respectively. 13 The trade size q t is determined exogenously and is not affected by the price. The next assumption is that the market maker charges a fixed cost component, ψ, on every trade for his services. The observable price process, p t, is then defined as: p t = m t + ψd t (3) where D t denotes the direction of the trade, which is not directly observable in the data set. The estimation technique used to determine D t is that suggested by Lee and Ready (1991), whereby a trade is assumed to be seller (buyer) initiated if the transaction price is lower (higher) then the prevailing quote midpoint. If the transaction takes place at the quote midpoint, the tick-test is applied which infers a purchase on zero up-tick and a sale on zero down-tick. 14 Substituting equation (2) for m t in equation (3) and taking first differences yields: p t = λq t + ψ(d t - D t-1 ) + y t. (4) The innovation process y t in (4) is assumed to be i.i.d. normal and uncorrelated with both the order flow q t and trade direction D t. Parameters λ and ψ are estimated by the ordinary least squares (OLS) regression technique. To account for potential misspecification, an intercept term is allowed in the regression. 13 The signed trade size is the product of the transaction size and the direction of the trade. The direction of the trade is +1 if the order is buyer initiated and -1 otherwise. 14 A transaction is said to be on a zero up-tick (zero down-tick) if the transaction price is higher (lower) than the last different transaction price. 20

21 In our analysis, the inverted market depth parameter λ is obtained for each security in our sample by estimating equation 4. The individual estimates are then analyzed cross-sectionally to examine their relation to asset characteristics. 3.2 Measuring the Relative Importance of Public Information This study employs a measure of the relative importance of public versus private information developed by Jones, Kaul and Lipson (1994) (JKL). JKL estimate the volatility of the closing price separately on trading days, when the daily volume in the stock is non-zero, and non-trading days, when no transaction occurs during the entire day. The advantage of this approach is that the choice to participate in the trading process is endogenized, i.e. the trading/non-trading periods are not determined exogenously by the open hours of the marketplace. The two-step procedure used to measure volatility is as follows. JKL first estimate the residuals from the following regression equation to control for intraweek closing price patterns: 5 5 R = α D + θ R ( ) + β R ( ) + ε it ik kt il i t l ih m t h it k = 1 l= 1 h= 0 5 (5) where R it is the quote midpoint based return of security i on day t and D kt are the day-of-the-week dummies The lagged firm and market returns, R i(t-l) and R m(t-h) control for potential delays in the adjustment of price to firm specific and marketwide factors. JKL s proposed unconditional daily volatility measure is $ε 2 it, the square of the estimated residual from the estimation of equation (5). They also recognize that because of the different period length and a potentially different information arrival 21

22 process, the weekend volatility (that of the returns between Friday close and Monday close) is different from weekday volatilities. In the next step, JKL condition the volaitilities on trading and non-trading days as well as on weekdays and weekends by regressing the unconditional daily volatility estimates on four dummy variables: $ε = σ D + σ D + σ D + σ D + ν (6) it intm NTM itm TM inttf NTF ittf TTF it where the indices NTM, TM, NTTF and TTF denote non-trading Monday, trading Monday, non-trading Tuesday-Friday and trading Tuesday-Friday, respectively. 15 The σ 2 ij coefficients are the estimated conditional volatilities for firm i in the particular period j. The weekend and weekday volatility ratios can then be written as: V1 i = σ σ 2 2 intm itm (7) V2 i = σ σ 2 2 inttf ittf (8) JKL report the median estimates of variables V 1 and V 2, because the sampling distribution of the variance ratios are highly asymmetric and the medians are more representative of a typical firm than the sample mean. In order to capture cross-sectional variation in the importance of public information, our sample of straight and convertible stocks are divided into subgroups based on their yield and moneyness value. The volatility ratios are then estimated for each subgroup according to equations (7) and (8). Significance tests 15 JKL include two additional dummies for 3-day holidays. Since the holidays are discarded in our sample, those two additional dummies are excluded from the equation. 22

23 on the differences between these groups are conducted using the Wilcoxon Rank Sum Kruskal Wallis tests. 3.3 Spread Decomposition George, Kaul and Nimalendran (GKN) (1991) argue that covariance-based spread estimators that ignore the fact that expected returns exhibit positive autocorrelation (for example, the Roll (1984) estimator) are severely biased downwards. They propose another estimator that uses the time series properties of the difference between the bid price and the transaction price. Formally, the GKN measure of the order processing component of the quoted spread is (,, ) sgkni = 2 Cov RDi t, RDi t 1 (9) where RD it = ln[(p t -P bt )/(P t-1 -P bt-1 )] and P t and P bt are the transaction price and the bid quote at time t, respectively. Furthermore, they show that if 1-π denotes the information asymmetry fraction of the quoted spread s q, then the slope coefficient β of the following regression is an unbiased estimator of π: s ( s ) = α+ β + η (10) GKNi qi i The error term η i in regression (10) is assumed to follow an MA(1) process. Equation (9) is used to estimate the individual GKN spreads in our analysis. Then, the sample securities are arranged into portfolios by their yield (straight preferred stocks) and moneyness (convertible preferred stocks) and the information asymmetry spread component for each portfolio is estimated using equation (10). 23

24 4. The Data The analysis is based on a 253 trading day period starting October 1, 1993, and ending September 30, Because of various known day-of-the-week effects, only regular trading days are considered, and 17 trading days around exchange holidays are excluded from the sample. The primary source of data used in the analysis is the Trades and Quotes (TAQ) database published by the New York Stock Exchange (NYSE). The database is a collection of all trades and quotes on the American Stock Exchange (AMEX), the National Association of Securities Dealers Automated Quotation (Nasdaq) markets and the NYSE. In this database, quotes as well as transactions records are time-stamped in seconds. The information of interest in the trade records include the issue identifier (ticker symbol), date, time, price, and size of the transaction. The quote records include the issue identifier, the time stamp, and the posted bid and ask prices along with their size (quoted depth). The database includes distribution records (for example, cash and stock dividends and stock splits) and daily summary statistics (open, close, high and low prices and daily volume) as well. In order to ensure the integrity of the data, the price and return records are filtered according to some standard procedures suggested in the literature. All transaction and quote records reported out of sequence are ignored. Transaction prices below $1.00 and above $ are deleted. Any bid-ask quote midpoint or transaction price that deviate more than 50 percent from the previous quote 24

25 midpoint or transaction price are also discarded. Finally, all bid-ask pairs that display a spread exceeding 20 percent of the quote midpoint are deleted. Another source of data used in this analysis is Standard and Poor s Stock and Bond Guide, 1994 Edition. This source provides information about the status of the preferred stock issue (convertibility, exchangeability, voting rights, fixed or variable dividend rate), the call price, common and preferred stock ratings for the firm under consideration, price ranges, the closing prices as of December 1993, earnings, nominal and recently paid dividends, the dividend yield, and the P/E ratio. 16 For convertible preferred stock issues, the Stock and Bond Guide also lists the applicable conversion ratio. The Center for Research in Security Prices (CRSP) tapes are used to extract the value weighted market returns during the sample period as well as the market value of equity for the NYSE population as of December 31, Earnings announcement dates are obtained from the 1993 and 1994 volumes of the Wall Street Journal Index. Accounting information (number of common and preferred shares outstanding, book value of preferred stocks and debt) are collected from the Compact Disclosure database for the fiscal years 1993 and The sample is constructed from the above data sources using the following search criteria: 16 The S&P Stock and Bond Guide lists the common and preferred stock ratings similar to that of debt issues. The ratings range from AAA+ (highest) to C- (lowest) with a rating of D indicating reorganization. It must, however, be noted that ratings are not available for all issues. The dividend yield is defined as the ratio of total indicated dividend to the price of stock. 25

26 1. The preferred and corresponding common issues are quoted and traded on the NYSE for all or part of the of the period starting October 1, 1993, and ending September 30, The corresponding common stock is identified unambiguously The common and preferred stock issues are listed in 1994 edition of the Standard and Poor s Stock and Bond Guide. 4. The issue is not an American Depository Receipt or American Depository Share 5. The issue does not experience any stock split over the sample period 6. The issue does not have mandatory conversion provisions. The above criteria yield a sample of 310 preferred stock series issued by 185 companies. Although the majority of firms have one or two classes of preferred stocks, some have as many as twelve. The time series of all preferred stocks contain approximately 720,000 quote revisions and 460,000 trades in the sample period. The corresponding numbers for the common stock series of the firms under consideration are 4.3 and 3.8 million records, respectively. Table 3 shows the distribution of the 185 sample firms according to industry classes. 18 Preferred stock seems to be a more popular financing vehicle with utilities and firms in the finance and real estate industries, since they account for approximately 50 percent of the sample of 185 firms. The apparent overrepresentation of these two industry classes with regard to the NYSE population 17 In a few cases, the same ticker identifier apparently belongs to different issues. In other instances, different data sources display different tickers for the same issue. These issues are deleted from the sample. 26

27 calls for some caution when comparing results of this analysis to those reported previously. These industries (especially utilities and banking) are typically subject to more regulation than others, which, in turn, imply different information structures. Thus, the level of information asymmetry is, in all probability, lower in the sample analyzed as compared to that reported in previous studies. Summary statistics on size and leverage related variables are presented in Table 4. The figures in the table indicate that issuers of preferred stocks tend to be relatively large firms. The market capitalization of equity of an average firm in our sample is approximately double that of an average firm from the CRSP population ($4,195 million versus $2,181 million), while the median is approximately five-fold ($2,095 million versus $445 million). The average portion of preferred stock in long term financing is 6.46 percent (median of 4.86 percent). Table 5 presents the trading characteristics of the straight preferreds, the convertible preferreds and the common stocks in our sample. Straight and convertible preferred stocks trade at about the same frequency (means of 6.15 and 6.50 trades per day with corresponding medians of 3.81 and 3.61, respectively), while the average number of trades is about 14 times higher for the underlying common issues (a mean of trades per day and a median of 62.25). Further investigation of the trading characteristics show that average daily volume (in round lots) is smallest for straight preferred (a mean of and a median of 17.95), followed by convertible preferred (a mean of and a median of 35.92) and 18 The industry classification shown in the table is that defined by the New York Stock Exchange, which differs from the Standard Industry Classification (SIC) code. 27

28 common (a mean of 2, and a median of 1,535.02).The ranking is the same in terms of average daily dollar volume (in thousands), since straight preferred display the lowest values (a mean of and a median of 56.59), followed by convertible preferred (a mean of and a median of ) and common (a mean of 8, and a median of 3,967.33). The active trading in common stocks corresponds to the fact that our sample typically contains large, widely held companies. The percentiles indicate that the distribution of all variables are skewed to the right. 5. Results 5.1 Public versus Private Information In this section, we present results on the relative importance of public versus private information in the price formation process of preferred stocks. As stated previously, the test procedure follows that in Jones, Kaul and Lipson (1994). To increase the number of observations, the dataset is partitioned into two 6-month periods and the volatility ratios are estimated for each stock issue that satisfies the inclusion criteria of having at least one non-trading day on both Mondays and Tuesdays through Fridays during the half year straight and 98 convertible preferred stock series satisfy this criterion. The appropriate ratios are calculated according to equations (4.6) and (4.7) for weekend (V 1 ) and weekday (V 2 ) volatility ratios, respectively. 28

29 Table 6 shows the median values for V 1 and V 2 for the various subgroups of straight and convertible preferred stocks. Panel A of this table presents the volatility ratios for straight and convertible preferred stocks. The results indicate that 65.9 and 28.8 percent of the trading day volatility of a representative convertible preferred occurs without any trade during weekends (from Friday close to Monday close) and weekdays, respectively. The corresponding figures for straight preferred stocks are 35.8 percent and 22.2 percent, respectively. The group medians are statistically different at the 1 percent level according to the Wilcoxon Rank Sum Test. These results are consistent with the lead-lag hypothesis that predicts a relatively more important role of publicly available information for assets closely related to common stock. Panels B and C present volatility ratios for the two classes of preferred stocks (straight and convertible) sorted by their corresponding proxy for firm quality. The results are reported for three sub-groups of equal size that are formed based on high, medium and low values of the proxy. As stated earlier, the proxies used are the dividend yield for straight preferred, and the moneyness measure (ITM) for convertible preferred. As can be seen from Panel B, trading activity accounts for 55.5 percent of the total volatility during weekends for the group of low yield (debt dominated) straight preferred stocks. During weekdays the corresponding figure is 72.9 percent. These figures suggest a relatively high influence of public information on volatility for this group of straight preferreds. The corresponding values for the 19 Meaningful analysis could not be conducted for the corresponding common stocks, because they are liquid securities of large firms with few non-trading days. Only 10 of the 370 firm half-years in our sample had sufficient number of 29

30 high yield (equity dominated) group are higher with values of 69.1 percent and 78.8 percent for weekends and weekday volatilities, respectively. In addition, the results indicate that the values for the medium yield group fall in between those for the low and high yield groups. Although this suggests that firm-specific private information is more important the higher the yield, the Kruskal-Wallis test scores do not indicate statistically significant differences across the three groups. The results in Panel B therefore provide only weak support for the asset characteristics hypothesis. Panel C presents the results of a similar analysis for convertible preferred stocks. The results reject the asset characteristics hypothesis in favor of the lead-lag hypothesis. Specifically, convertible preferred stocks with high ITM values have higher price volatility in the absence of trading (84.5 percent and 50.2 percent for weekends and weekdays, respectively) than the medium ITM group (55.3 and 22.3 percent). This implies that the price formation process is increasingly influenced by information that is already public in nature as the equity features dominate. A comparison of the low and the medium ITM groups suggests that the volatility ratios for the former group are smaller than those for the latter group. The Kruskal-Wallis test indicates that the group medians are statistically different at the 10 percent and 1 percent level for weekend and weekday volatilities, respectively. In summary, the evidence presented in Table 6 is weakly consistent with the proposition that prices of straight preferred stocks with different asset characteristics reflect information of a different nature. In the case of convertible non-trading days for inclusion. 30