Earnings Management and Investor Protection: An International Comparison

Algorithmic trading has sharply increased over the past decade Does it improve market quality, and should it be encouraged? We provide the first analysis of this question The NYSE automated quote dissemination in 2003, and we use this change in market structure that increases algorithmic trading as an exogenous instrument to measure the causal effect of algorithmic trading on liquidity For large stocks in particular, algorithmic trading narrows spreads, reduces adverse selection, and reduces trade-related price discovery The findings indicate that algorithmic trading improves liquidity and enhances the informativeness of quotes

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1668523_code49904.pdf?abstractid=1100635&rulid=5239579&mirid=2

Does Algorithmic Trading Improve Liquidity

This paper solves explicitly a simple equilibrium model with liquidity risk. In our liquidityadjusted capital asset pricing model, a security s required return depends on its expected liquidity as well as on the covariances of its own return and liquidity with the market return and liquidity. In addition, a persistent negative shock to a security s liquidity results in low contemporaneous returns and high predicted future returns. The model provides a unified framework for understanding the various channels through which liquidity risk may affect asset prices. Our empirical results shed light on the total and relative economic significance of these channels and provide evidence of flight to liquidity. r 2005 Elsevier B.V. All rights reserved.

Market Liquidity: Asset Pricing with Liquidity Risk

Algorithmic trading (AT) has increased sharply over the past decade. Does it improve market quality, and should it be encouraged? We provide the first analysis of this question. The New York Stock Exchange automated quote dissemination in 2003, and we use this change in market structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity. For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade-related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes. TECHNOLOGICAL CHANGE HAS REVOLUTIONIZED the way financial assets are traded. Every step of the trading process, from order entry to trading venue to back office, is now highly automated, dramatically reducing the costs incurred by intermediaries. By reducing the frictions and costs of trading, technology has the potential to enable more efficient risk sharing, facilitate hedging, improve liquidity, and make prices more efficient. This could ultimately reduce firms’ cost of capital. Algorithmic trading (AT) is a dramatic example of this far-reaching technological change. Many market participants now employ AT, commonly defined as the use of computer algorithms to automatically make certain trading decisions, submit orders, and manage those orders after submission. From a starting point near zero in the mid-1990s, AT is thought to be responsible for

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This paper analyzes how blockholders can exert governance even if they cannot intervene in a firm's operations. Blockholders have strong incentives to monitor the firm's fundamental value, since they can sell their stakes upon negative information. By trading on private information (following the "Wall Street Rule"), they cause prices to reflect fundamental value rather than current earnings. This in turn encourages managers to invest for long-run growth rather than short-term profits. Contrary to the view that the U.S.'s liquid markets and transient shareholders exacerbate myopia, I show that they can encourage investment by impounding its effects into prices.

https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=946669

Blockholder Trading, Market Efficiency, and Managerial Myopia

The percentage of U.S. equity held by institutional investors has quadrupled in the past four decades, and a prominent share of trading activity is due to institutions. Yet we know little about how institutions affect the informational efficiency of share prices, one important dimension of market quality. We study a broad cross-section of NYSE-listed stocks between 1983 and 2004 using measures of the relative informational efficiency of prices constructed from transaction data. We find that stocks with greater institutional ownership are priced more efficiently in the sense that their transaction prices more closely follow a random walk. This result cannot be attributed to liquidity effects and is not likely the result of reverse causality. We also show that institutional trading activity is one mechanism by which prices become more efficient, even when institutions trade passively or follow momentum strategies.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID838146_code38106.pdf?abstractid=687110&mirid=1&type=2

Institutional Investors and the Informational Efficiency of Prices

Using a broad panel of NYSE-listed stocks between 1983 and 2004, we study the relation between institutional shareholdings and the relative informational efficiency of prices, measured as deviations from a random walk. Stocks with greater institutional ownership are priced more efficiently, and we show that variation in liquidity does not drive this result. Onemechanismthroughwhichpricesbecomemoreefficientisinstitutionaltradingactivity, even when institutions trade passively. But efficiency is also directly related to institutional holdings, even after controlling for institutional trading, analyst coverage, short selling, variation in liquidity, and firm characteristics. (JEL G12, G14) Institutional shareholdings and trading have increased dramatically over the past several decades. In 1965, members of the Securities Industries Association held 16% of U.S. equities; in 2001, they held 61% according to the Securities Industry Association Fact Book (2002). Moreover, nonretail trading accounted for 96% of New York Stock Exchange (NYSE) trading volume in 2002 (Jones and Lipson 2004). How this broadened scope of institutional ownership and trading affects the quality of equity markets, however, is an open question. Using a broad cross-section of NYSE-listed stocks between 1983 and 2004, we link institutional ownership to the informational efficiency of transaction prices. Intraday prices of stocks with greater institutional ownership more closelytrackfundamentalvalues,inthattheymorepreciselyresemblearandom walk, than prices of stocks with less institutional ownership. Moreover, we use proprietary NYSE data to establish institutional trading activity as one source of the improved short-horizon information environment. These findings have

We analyze the role of retail investors in stock pricing using a database uniquely suited for this purpose. The data allow us to address selection bias concerns and to separately examine aggressive (market) and passive (limit) orders. Both aggressive and passive net buying positively predict firms’ monthly stock returns with no evidence of return reversal. Only aggressive orders correctly predict firm news, including earnings surprises, suggesting they convey novel cash flow information. Only passive net buying follows negative returns, consistent with traders providing liquidity and benefiting from the reversal of transitory price movements. These actions contribute to market efficiency.

/pdf/how-wise-are-crowds-insights-from-retail-orders-and-stock-4dnp24whsi.pdf

How Wise Are Crowds? Insights from Retail Orders and Stock Returns

We analyze the role of retail investors in stock pricing using a database uniquely suited for this purpose. The data allow us to address selection bias concerns and to separately examine aggressive (market) and passive (limit) orders. Both aggressive and passive net buying positively predict firms' monthly stock returns with no evidence of return reversal. Only aggressive orders correctly predict firm news, including earnings surprises, suggesting they convey novel cash flow information. Only passive net buying follows negative returns, consistent with traders providing liquidity and benefitting from the reversal of transitory price movements. These actions contribute to market efficiency.

Reversal is the current stylized fact of weekly returns. However, the brief reversal that follows extreme weekly returns is itself followed by an opposing and long-lasting stream of continuation in returns. These subsequent momentum profits are strong enough to offset the initial reversal and to produce a significant momentum effect over the full year following portfolio formation. Thus, ex post, extreme weekly returns are not too extreme. Our findings extend to weekly price movements with and without public news. In addition, there is no relation between news uncertainty and the momentum in one-week returns.

Eric K. Kelley

Papers

Institutional Investors and the Informational Efficiency of Prices

Institutional Investors and the Informational Efficiency of Prices

How Wise Are Crowds? Insights from Retail Orders and Stock Returns

How Wise Are Crowds? Insights from Retail Orders and Stock Returns

The Long-Lasting Momentum in Weekly Returns