Using essentially all Moody's bond ratings changes between 1970 and 1997, we find no reliable abnormal returns following upgrades. However, we find negative abnormal returns on the magnitude of 10 to 14 percent in the first year following downgrades. Additional results reveal that this underperformance is especially pronounced for small, low‐credit‐quality firms. Also, downgrades underperform in nearly all years in the sample, and a large part of the abnormal returns occur at subsequent earnings announcements. Thus, the evidence suggests that the poor returns result from an underreaction to the announcement of downgrades, rather than from lower systematic risk.
This paper contributes to the ongoing debate over the relative merits of floor versus automated systems. Although this is one of the most contentious issues in market microstructure (Madhavan (2000) provides a survey), relatively little empirical analysis has been performed. This paper fills this void; it compares and contrasts execution costs in Paris and New York in an effort to provide empirical evidence for the relative merits of the two systems. The two markets studied are both auction markets-involving public-public trades for the most part-but differ in the level of automation. Specifically, Paris operates as a continuous automated auction while the NYSE uses a floor-based system. I enjoyed the paper, especially the careful and thorough discussion of the different dimensions in which the two systems differ. For this reason alone, I would recommend the paper to anyone interested in learning more about this issue. On the empirical side, the paper compares execution costs (using now-standard metrics) in the two systems, controlling for firm effects by using matched samples. Stocks are carefully matched by characteristics including price, size, volume, and industry. The paper finds that the NYSE offers lower trading costs, concluding: “These results suggest that the present form of the automated trading system may not be able to fully replicate the benefits of human intermediation on a trading floor.” While the study is well-motivated and carefully executed, I have some concerns about the interpretation of the empirical results presented here. No study can control for all the factors affecting market outcomes, but there are many institutional details unrelated to automated trading that affect costs in Paris and NYSE. These factors operate not necessarily just at the level of the market, but also at the overall “environmental” level. Some of the more important ones include: (1) The NYSE uses a specialist system, with a designated market maker or specialist, while the Paris Bourse has no such functionary. Numerous studies (e.g., Madhavan and Sofianos (1997)) show that specialists can and do affect the speed of price discovery, the efficiency of prices, spreads, and so forth. Are the results here a comment on the floor system or the specialist system? (2) The overall market structure in the United States is very different from the market structure in Paris. NYSE-listed stocks can be traded by alternative trading systems (POSIT, Arizona Stock Exchange), Regional exchanges (Boston, Philadelphia), and ECNs, among others. By contrast, Paris has a much more centralized and less fragmented market. Could greater competition for the NYSE explain its lower costs? (3) Other differences between France and the United States, such as insider trading rules, macro risk factors and overall level of market activity, might affect even a matched sample of stocks. Again, are these factors secondary to automation or not? Intermarket comparisons are extremely tricky. Can we ever assure a fair “apples to apples” comparison? Perhaps, but only in very controlled situations with natural experiments (Amihud, Mendelson, and Lauterbach (1997)) or with the use of experimental economics. While it is common to make inferences about market structure based on effective spreads, this can be misleading in an auction market. Consider the following example of a buy order that executes at the ask price of $102 when the midquote is $100, for an effective spread of two percent. In this case, the liquidity demander (who submitted the market order) pays two percent while the liquidity provider (not necessarily a specialist or market maker) receives two percent. Who would be disadvantaged if the spread were to fall? Narrower spreads benefit liquidity demanders but make liquidity providers worse off. In a dealer market where competitive dealers provide liquidity, higher spreads mean higher costs for investors but not necessarily higher rents for dealers. In this case, lower effective spreads are unambiguously better for the economy. But it is unclear to me how in an auction market to interpret effective spreads or a mixed hybrid market like the NYSE where the dealer has a small, but significant participation rate (Madhavan and Sofianos (1997)). So-called upstairs intermediaries can cushion the impact of large-block trades; often upstairs-intermediated trades occur within the quoted bid-offer spread. Consequently, the measured cost of trading for blocks might be very low if these markets exist and are efficient (Madhavan and Cheng (1997)), as they are in both Paris and New York. Differences in the costs of trading across systems may have less to do with automation than with the state of development of the upstairs market. The key point to note here is that the existence of upstairs markets is not necessarily linked to the existence of a physical floor. Nor does automation preclude upstairs trading. Upstairs trading could be fully electronic, anonymous (e.g., POSIT), and automated, or could rely on brokers making telephone calls. Without information on how upstairs activity varies across the two systems, it is impossible to assess the cost of trading for large blocks. The results are most clearly interpreted for smaller trades. Previous studies document a wide range in trading costs across countries. Domowitz, Glen, and Madhavan (2000) report that Paris and New York are among the lowest cost markets in a comparison of 45 countries. Costs in Paris are just 30 basis points (one way) versus New York with 35 basis points. This is consistent with the result here, but it means that we are comparing two very low-cost systems to learn more about one dimension in which they differ, namely automation. The second problem is that these differences are economically small, and measuring trading costs (especially implicit trading costs) is very difficult. Indeed, the standard error with these estimates could easily swamp the differences reported here of 14 basis points. Essentially, we have no economically compelling evidence that costs differ between the two systems. Most major non-U.S. markets use automated auctions. Examples in equities include ECNs, Toronto Stock Exchange, Euronext (Paris, Amsterdam, Brussels), and the Deutsche Börse; in fixed income: eSpeed and BondNet; in foreign exchange: Reuters 2002 and EBS; and in derivatives: Eurex, Globex, Matif, and LIFFE. This “revealed preference” suggests that the value of the floor, if any, is dominated by other advantages automated systems have including speed, anonymity, transparency, reliability, and lower costs of operation. Given this clear preference for the automated auction, it is instructive to ask why we do not see the same pressures in the United States. One factor is undoubtedly the nature of governance structures, a factor closely related to automation. The institution of exchange seats, and the mutualized governance structure, are associated with floor markets where physical space is at a premium. An automated, electronic market has no such capacity constraints, obviating the need to limit entry. Demutualization allows automation, because a for-profit governance structure leads to more rational decision making. In the United States, the pressures to demutualize, and hence to automate, are not yet as apparent as in the rest of the world.
We compare equilibrium trading outcomes with and without participation by an informed insider, assuming inflexible ex ante aggregate investment choices by agents. Noise trading arises from aggregate uncertainty regarding other agents' intertemporal consumption preferences. The welfare levels of outsiders can thus be ascertained. The allocations without insider trading are not ex ante Pareto efficient, because our model differs from standard ones with negative exponential utility functions and normal returns. We characterize the circumstances under which the revelation of payoff‐relevant information via prices—arising from insider trading—benefits outsiders with stochastic liquidity needs, by improving risk‐sharing among them.
We analyze the market for corporate assets. There is an active market for corporate assets, with close to seven percent of plants changing ownership annually through mergers, acquisitions, and asset sales in peak expansion years. The probability of asset sales and whole‐firm transactions is related to firm organization and ex ante efficiency of buyers and sellers. The timing of sales and the pattern of efficiency gains suggests that the transactions that occur, especially through asset sales of plants and divisions, tend to improve the allocation of resources and are consistent with a simple neoclassical model of profit maximizing by firms.
A unique data set allows us to monitor the buys, sells, and holds of individuals and institutions in the Finnish stock market on a daily basis. With this data set, we employ Logit regressions to identify the determinants of buying and selling activity over a two‐year period. We find evidence that investors are reluctant to realize losses, that they engage in tax‐loss selling activity, and that past returns and historical price patterns, such as being at a monthly high or low, affect trading. There also is modest evidence that life‐cycle trading plays a role in the pattern of buys and sells.
This paper offers a model in which asset prices reflect both covariance risk and misperceptions of firms' prospects, and in which arbitrageurs trade against mispricing. In equilibrium, expected returns are linearly related to both risk and mispricing measures (e.g., fundamental/price ratios). With many securities, mispricing of idiosyncratic value components diminishes but systematic mispricing does not. The theory offers untested empirical implications about volume, volatility, fundamental/price ratios, and mean returns, and is consistent with several empirical findings. These include the ability of fundamental/price ratios and market value to forecast returns, and the domination of beta by these variables in some studies.
By examining how executive compensation structure determines corporate acquisition decisions, we document a strong positive relation between acquiring managers' equity‐based compensation (EBC) and stock price performance around and following acquisition announcements. This relation is highly robust when we control for acquisition mode (mergers), means of payment, managerial ownership, and previous option grants. Compared to low EBC managers, high EBC managers pay lower acquisition premiums, acquire targets with higher growth opportunities, and make acquisitions engendering larger increases in firm risk. EBC significantly explains postacquisition stock price performance even after controlling for acquisition mode, means of payment, and “glamour” versus “value” acquirers.
This paper examines expected option returns in the context of mainstream asset‐pricing theory. Under mild assumptions, expected call returns exceed those of the underlying security and increase with the strike price. Likewise, expected put returns are below the risk‐free rate and increase with the strike price. S&P index option returns consistently exhibit these characteristics. Under stronger assumptions, expected option returns vary linearly with option betas. However, zero‐beta, at‐the‐money straddle positions produce average losses of approximately three percent per week. This suggests that some additional factor, such as systematic stochastic volatility, is priced in option returns.
Diversified conglomerates are valued less than matched portfolios of pure‐play firms. Recent studies find that this diversification discount results from conglomerates' inefficient allocation of capital expenditures across divisions. Much of this work uses Tobin's q as a proxy for investment opportunities, therefore hypothesizing that q is a good proxy. This paper treats measurement error in q . Using a measurement‐error consistent estimator on the sorts regressions in the literature, I find no evidence of inefficient allocation of investment. The results in the literature appear to be artifacts of measurement error and of the correlation between investment opportunities and liquidity.