Information asymmetry and investment–cash flow sensitivity
ABSTRACT Models of capital market imperfections predict that information asymmetry decreases firm investment and increases the sensitivity of investment expenditures to fluctuations in internal funds. Previous empirical tests of the link between investment and financing decisions have relied on indirect measures of financial constraint due to market frictions. In contrast, we use more direct measures derived from the market microstructure literature. Consistent with the theoretical predictions, our analysis shows that scaled investment expenditures are on average lower and the investment–cash flow sensitivity is greater when the probability of informed trading is high. Our results are robust to alternative measures of informed trading and liquidity, but they are not pervasive in our sample.
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ABSTRACT: We develop an information risk measure (ECIN) based on the price discovery of large trades. As the price series of large trades and small trades are cointegrated, the price discovery of trades can be easily estimated via the vector error-correction model (VECM). Intuitively, we use the VECM to study how a temporary gap between the large trade price and the small trade price for the same stock is closed. If most of the gap is closed through adjustment in the small trade price with little movement in the large trade price, this indicates large trade price has been closer to the long-run equilibrium price and hence that the large trade price has a greater price discovery function for the stock in question. Since informed traders prefer to trade in large size, firms whose large trades have a larger price discovery are deemed to have larger information risk. An important feature of ECIN that is inherent to its construction is that higher ECIN also means lower illiquidity. This feature helps to disentangle the pricing impact of information risk from that of illiquidity - a major advantage over other information risk measures in the asset pricing tests of information risk. We show that ECIN is priced and its predictive power of stock returns is far more significant than those of book-to-market and momentum.10/2010;
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ABSTRACT: We use the fact that limit orders on the New York Stock Exchange are not state-dependent to examine the nature of information flows during trading hours. If public information events were an important factor in equity return dynamics then either limit orders would not be a material source of liquidity, or the picking-off of stale limit orders would be a prominent feature. We examine the efficacy of six measures of asymmetric information and explore the extent to which stale limit orders confound their ability to measure the importance of private information in return dynamics. We confirm French and Roll's (1986) conclusion that public information releases during trading hours do not have a significant effect on these dynamics.01/2011;
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ABSTRACT: This study provides novel evidence of the impact of corporate social responsibility (CSR) on investment sensitivity to cash flows. We posit that CSR affects investment–cash flow sensitivity (ICFS) through information asymmetry and agency costs, commonly viewed as the two channels through which investment responds to the availability of internal cash flows. We find that CSR performance leads to a decrease in ICFS. We further find that ICFS decreases (increases) when CSR strengths (concerns) increase. Finally, we find that the effect of CSR on ICFS is driven by the areas Community, Diversity, and Human Rights. In sum, the findings of this study stress the relevance of CSR — in particular, of CSR activities that extend beyond compliance behavior and reflect what is desired by society — in reducing market frictions and improving firms’ access to financial capital.Journal of Business Ethics 02/2013; · 0.96 Impact Factor
Information Asymmetry and Investment-Cash
Department of Finance
Shantaram P. Hegde
University of Connecticut
Department of Finance
John B. McDermott*
Department of Finance
Models of capital market imperfections predict that information asymmetry
increases the sensitivity of a firm’s investment expenditures to fluctuations in
internal funds by making external capital more costly. Previous empirical tests
of the link between investment and financing decisions have relied on indirect
measures of the degree to which a firm becomes financially constrained due to
market frictions. In contrast, we use more direct measures of informational
frictions derived from the market microstructure literature. Consistent with the
theoretical prediction, our analysis shows that the scaled investment
expenditures of firms with greater informed trade have greater investment- cash
flows sensitivity. We also find the relationship between investment-cash flow
sensitivity as a function of informed trade is nonmonotonic. Our results are
robust to multiple alternative measures of informed trade and liquidity.
DO NOT QUOTE WITHOUT PERMISSION
Investment – Cash Flow Sensitivity
In the pioneering work of Modigliani and Miller (1958) the financing and investment decisions of the firm
can be considered independent in the absence of market frictions. Many studies of information asymmetry
and capital market imperfections show that market frictions make external financing more costly than
internal financing because the former contains a ‘lemons’ premium (e.g., Myers and Majluf (1984))1. In
this environment, Fazzari, Hubbard, and Petersen (1988) argue that the investment decision of firms that
nearly exhaust all their low-cost internal funds (i.e., have low dividend payout ratios) would be more
sensitive to fluctuations in their cash flows as compared with firms that pay high dividends. Holding
constant the investment opportunities of a firm, a reduction in internal funds would reduce capital
expenditures by firms facing information costs. They observe “If information problems in capital markets
lead to financing constraints on investment, they should be most evident for the classes of firms that retain
most of their income. If internal and external finance are nearly perfect substitutes, however, then retention
practices should reveal little about investment by the firm. Firms would simply use external finance to
smooth investment when internal finance fluctuates,” (p. 164).
To test the predicted link between investment and financing, Fazzari et. al. classify firms with low-dividend
payouts as ‘most financially constrained’ while those with high dividend payouts as ‘least constrained’
firms. They argue that the ‘most constrained’ firms should have investment expenditures that are more
sensitive to internal cash flows and stock of liquidity then the ‘least constrained’ firms. Their empirical
tests show substantially higher sensitivity of investment to cash flow and liquidity for firms that retain
nearly all of their income.
Kaplan and Zingales (1997) criticize Fazzari et al.’s classification procedure by pointing out that a firm’s
dividend policy is a choice variable, hence firms that choose to pay low dividends even though they could
pay out more are not necessarily financially constrained. For example, firms may raise dividends in
response to a reduction in personal dividend income tax rates. Using qualitative and quantitative
information from financial statements and reports, they identify firms as ‘never constrained’ if they have
more funds than needed to finance their investment and as ‘likely constrained’ if they are without access to
more funds than needed to finance their capital expenditures. In contrast to Fazzari et al., their findings
indicate that the investments of ‘likely constrained’ firms are less sensitive to cash flows than the
investments of ‘never constrained’ firms. Kaplan and Zingales (2000) also point out that we would not
expect investment cash-flow sensitivities to be a good measure of financing constraints. As Moyen (2004)
demonstrates with simulated data, it is hard to identify firms with financing constraints, and the investment-
cash flow sensitivity critically hinges on the classification procedure used. While some methods of
financial constraint identification show low sensitivity between investments and cash flows, others show
just the opposite.
Since a very important root cause of firms’ financial constraints and higher external capital costs is
information asymmetry between firms and uninformed investors, we use measures of asymmetric
information derived from the market microstructure literature to classify firms as more or less financially
constrained. Following previous theoretical work, we assume firms have private information about their
investment opportunities. Informed investors invest in gathering info about firms’ prospects and trade on
that information, but the uninformed investors do not. Studies by Demsetz (1968), Copeland and Galai
(1983), Glosten and Milgrom (1985), Kyle (1985), Glosten and Harris (1988), Fialkowski and Petersen
(1994), Bessembinder and Kaufman (1997), Easley, Hvidkjaer, and O’Hara (2002), and Easley and O’Hara
(2004) indicate that measures of market liquidity (e.g., effective spread and price impact of trade measures)
and probability of informed trading (e.g., PIN) serve to capture information asymmetry between informed
and uninformed investors. That is the higher the liquidity costs, the more expensive is external financing as
compared to internal financing. Therefore, we argue that firms with higher effective spreads, greater price
impact of trades, and higher probability of informed trade are likely to rely more on internal cash flows and
internally generated capital for investment spending than firms with lower effective spreads and PIN.
Since we use a more direct measure of capital market frictions and of financing constraint, our
classification procedure better addresses the Kaplan and Zingales (1997) criticism of Fazzari et al.
Our research also seeks to bridge the gap between the existing but largely distinct literature on investment
in the corporate finance literature and liquidity in the market microstructure literature. A recent paper by
Easley and O’Hara (2004) makes clear the link between information and the cost of capital. Recently,
notable scholars in the microstructure literature (e.g., Madhavan (2004) and O’Hara (2003)) have suggested
that the microstructure literature must show economic meaning to become more relevant. Our study is a
modest step in that direction – specifically, linking liquidity in general and adverse selection in particular to
the investment decision of the firm.
Data and Methodology
2.1. Sample Selection
Our original data set is the 1,224 firms of Standard and Poor’s 1500 (S&P 1500) with revenues greater than
$10,000,000 in 2000. The S&P 1500 is a well-known representative market index. Of the 1,224 firms in
the original sample, 509 firms satisfy the following selection criteria:
(a) Accounting data available in Standard and Poor’s COMPUSTAT to form the variables
necessary for our study.
(b) Data available in Center for Research in Securities Prices (CRSP) daily master file
(c) Transactions data for January through June 2000 is available in the NYSE Transactions and
Quote (TAQ) database.
(d) Firm’s equity was traded on New York Stock Exchange (NYSE) or American Stock
Exchange (AMEX) for the entire period January to June 2000 inclusive.
(e) Fiscal year-end in June or later.
(f) Firm is not in the financial services industry.
The exclusion of NASDAQ firms is to ensure that our results are not driven by and to minimize the noise
due to the very different market structures. Further, the transaction-based models that we employ to
measure adverse selection are developed theoretically in a specialist (not dealer) market. The exclusion of
financial services industry firs in study of investment cash-flow sensitivities s standard practice in this
literature (e.g. Fazzari et. al (1988), Kaplan and Zingales (1997, 2000).
2.2. Data Sources
The Center for Research in Security Prices (CRSP) daily master file is used to calculate average daily
volume, price, and return volatility. The NYSE TAQ database is used to obtain empirical estimates of our
transaction data based liquidity measures.
We use the first 6 months of year 2000 quotes and trades from TAQ data. In using the TAQ data we apply
the following filters which are standard in the study of transactions data:
a) Only BBO eligible primary market quotes are retained. (NYSE quotes—if it is a NYSE listed
stock, AMEX quotes if it is a AMEX listed stock)
b) Quotes and trades that have time stamp between 9:30 am to 4:00 p.m. are included.
c) Use quotations at least 15 seconds before the trade when we calculate trade execution costs
d) Use contemporaneous quotations for trade indicator identification. (See Bessembinder (2002))
e) Trade price must be > 0
f) Ask price must be > bid price must be > 0
g) Eliminate trades and quotes when trade price, ask quote, and bid quote that are lower (higher) than
7.5 standard deviations of the daily variation.
h) Keep trades with value of correction code is zero or one.
Exclude re-opening quotes.
2.3. Descriptive Statistics
Descriptive data on the final sample of 509 firms are presented in Table 1. As expected, the firms are large
but do vary greatly in size, volume, and spreads. The mean (median) market value is $8.89 billion ($1.87
billion). The mean (median) daily trading volume is 888,744 (362,062) shares. The mean (median) of
daily average trade weighted quoted half-spread is 7.49 cents (6.88 cents). The average (median) share
price is $34.17 ($28.00).