Are performance based arbitrage effects detectable? Evidence from merger arbitrage

This paper examines the predictions of the performance based arbitrage hypothesis for the merger arbitrage market. Performance based arbitrage [Shleifer, A., Vishny, R.W., 1997. The limits of arbitrage. Journal of Finance, 52 (1), 35–55] is the notion that funds under management are withdrawn from arbitrageurs following trading losses, resulting in inefficient prices for securities subject to arbitrage trades. I examine general comovement in merger arbitrage spreads and the response of spreads to large arbitrage losses and substantial changes in deal flow. I find little evidence that merger arbitrage spreads exhibit systematic comovement or are substantially affected by important liquidity events in this market.     

Market Quote and Spread Component Cost Behavior Around Trading Halts for Stocks Interlisted on the Montreal and Toronto Stock Exchanges

We use intraday quotes and transactions on halted securities that interlisted on the Toronto Stock Exchange and Montreal Exchange to decompose the spreads and examine quote depths. Our results show that order‐processing costs differ for trading halts at the open compared to halts during the rest of the trading day. We find that the adverse‐selection cost component of the spread is higher around trading halts and highest at the trading halt. We also find that print‐media articles that appear within the four‐day window centered on the halt have no impact on the time‐series behavior of the spread cost.     

Informed arbitrage with speculative noise trading

We consider speculative noise trading when some naïve speculators trade on noise as if it were information [Black, F., 1986. Noise. Journal of Finance 41, 529–543]. We examine the optimal trading strategy of an informed investor who faces such naïve speculators in the market. We find that the informed investor trades aggressively on her information and takes large, opposite positions against the naïve speculators. The trading volume is thereby drastically magnified. While such speculative noise trading enhances liquidity, it makes prices less efficient. The overall dynamic patterns that emerge from our model are most consistent with the evidence for interday variations in volume, volatility, and transaction costs.     

Strategic trading and learning about liquidity

Many practitioners point out that the speculative profits of institutional traders are eroded by the difficulty in gauging the price impact of their trades. In this paper, we develop a model of strategic trading where speculators face such a dilemma because of incomplete information about time-varying market liquidity. Unlike the competitive market makers that they trade against, informed traders do not know the distribution of liquidity (“noise”) trades. Instead, they have to learn about liquidity from past prices and trading volume. This learning implies that strategic trades and market statistics such as informational efficiency are path-dependent on past market outcomes. Our paper also has normative implications for practitioners.     

Price discovery around trading halts on the Montreal Exchange using trade-by-trade data

This paper investigates the price discovery process around exchange-initiated trading halts using 30 minute trade intervals on the Montreal Exchange. Trading halt price discovery, and regulatory and specialist effectiveness differ over the three time periods studied. Volatility and measures of trade activity increase significantly around trading halts, and return to lower levels in less than two days after the resumption of trading. The number of trades is a good measure of the information flow associated with informed trading pre-halt and the price discovery process post-halt.     

Trade classification algorithms for electronic communications network trades

Ellis et al. [Ellis, K., Michaely, R., O’Hara, M., 2000. The accuracy of trade classification rules: Evidence from Nasdaq. Journal of Financial and Quantitative Analysis 35 (4), 529–551] find that trade classification rules have limited success in classifying trades which execute inside the quotes. We reconfirm this result and propose an alternative algorithm to improve the classification accuracy for trades inside the quotes. This alternative algorithm improves the overall success rate for classifying trades, especially for trades that occur inside the quotes. Additionally, we show that the Lee and Ready [Lee, C., Ready, M., 1991. Inferring trade direction from intraday data. Journal of Finance 46, 733–747] and Ellis et al. (2000) trade classification algorithms provide biased estimates of the actual effective spreads and price impacts, while our algorithm provides statistically unbiased estimates of actual effective spreads and price impacts.     

Time-Varying Arrival Rates of Informed and Uninformed Trades

We propose a dynamic econometric microstructure model of trading,and we investigate how the dynamics of trades and trade compositioninteract with the evolution of market liquidity, market depth,and order flow. We estimate a bivariate generalized autoregressiveintensity process for the arrival rates of informed and uninformedtrades for 16 actively traded stocks over 15 years of transactiondata. Our results show that both informed and uninformed tradesare highly persistent, but that the uninformed arrival forecastsrespond negatively to past forecasts of the informed intensity.Our estimation generates daily conditional arrival rates ofinformed and uninformed trades, which we use to construct forecastsof the probability of information-based trade (PIN). These forecastsare used in turn to forecast market liquidity as measured bybid-ask spreads and the price impact of orders. We observe thatPINs vary across assets and over time, and most importantlythat they are correlated across assets. Our analysis shows thatone principal component explains much of the daily variationin PINs and that this systemic liquidity factor may be importantfor asset pricing. We also find that PINs tend to rise beforeearnings announcement days and decline afterwards.     

Stealth trading: The case of the Tokyo Stock Exchange

The stealth trading hypothesis asserts that informed traders trade strategically by breaking up their orders so as to more easily hide among the liquidity traders. Using data for the Tokyo Stock Exchange (TSE), a pure order-driven market, we find evidence that price changes are driven by small- and medium-size trades, with small trades making the greatest contribution to price change relative to their contribution to trading volume. We also find that large trades explain a greater portion of the cumulative price change on high volatility days. Hence, our results support the stealth trading hypothesis for the TSE.     

Impact of MiFID II tick-size regime on equity markets—Evidence from the LSE

This study investigates the impact of MiFID II on the London Stock Exchange. We find that a tick-size reduction leads to lower bid–ask spreads, lower trade values, reduced cost of trading at and beyond the best bid-offer, an acceleration of quote updates, an increase in aggressive trades and a reduction in price impact. Increased tick size widens spreads and increases trading costs. Step functions reveal that liquidity adjusts opposite to the tick change. To determine if impacts are proportional, we identify potential functions that predict cost changes with tick updates, implying that traders adjust their trade sizes according to the new tick levels.     

Trading and Liquidity on the Tokyo Stock Exchange: A Bird's Eye View

The trading mechanism for equities on the Tokyo Stock Exchange (TSE) stands in sharp contrast to the primary mechanisms used to trade stocks in the United States. In the United States, exchange-designated specialists have affirmative obligations to provide continuous liquidity to the market. Specialists offer simultaneous and tight quotes to both buy and sell and supply sufficient liquidity to limit the magnitude of price changes between consecutive transactions. In contradistinction, the TSE has no exchange-designated liquidity suppliers. Instead, liquidity is provided through a public limit order book, and liquidity is organized through restrictions on maximum price changes between trades that serve to slow down trading. In this article, we examine the efficacy of the TSE's trading mechanisms at providing liquidity. Our analysis is based on a complete record of transactions and best-bid and best-offer quotes for most stocks in the First Section of the TSE over a period of 26 months. We study the size of the bid-ask spread and its cross-sectional and intertemporal stability; intertemporal patterns in returns, volatility, volume, trade size, and the frequency of trades; and market depth based on the response of quotes to trades and the frequency of trading halts and warning quotes.     

Estimating the Probability of Informed Trading

Using a new empirical model, I estimate the probability of trades being generated by privately informed traders. Inference is drawn on a trade‐by‐trade basis using data samples from the New York Stock Exchange (NYSE). The modeling setup facilitates in‐depth analysis of the estimated probability of informed trading at the intraday level and for stocks with different levels of trading activity. The most important empirical results are: (a) the intradaily pattern of the inferred probability of informed trading is highly correlated with the intradaily pattern of observed quoted spreads, (b) differences in the magnitude of quoted spreads across volume categories are not exclusively related to differences in the level of informed trading, and (c) private information is incorporated faster in the quotes for high‐volume stocks than in the quotes for low‐volume stocks.     

A microstructure examination of trading activity following stock splits

In a study of 1,131 stock splits spanning the period 1983–1989 we observe an increase in the number of trades as well as a reduction in the mean trade size following the split. Combined with earlier reported findings of an increase in the number of shareholders postsplit, we conclude that the number of liquidity traders increases after a split. We confirm the previously observed increase in the bid-ask spread following a split, and upon decomposition of the spread find an increase in its adverse selection component in the postsplit period. This is consistent with the finding by Brennan and Hughes (1991) of an increase in the number of analysts following a stock after a split. Further, observing a decrease in market depth following a split we determine that Kyle-type models incorporating diverse private information for informed traders most correctly describe the nature of security trading. Since this decrease in postsplit market depth is not related to the trading volume or the split factor, we reject price correction explanations for stock splits.     

Information-Based Trading and Price Improvement

Abstract:  In this study we test the information hypothesis of price improvement. Our results show that price improvement is negatively related to both the probability of information-based trading and the price impact of trades. We interpret these results as evidence that liquidity providers selectively offer price improvements according to the information content of trades. We also show that liquidity providers offer greater (and more frequent) price improvements when they are at the NBBO, and for stocks with wider spreads, fewer trades, or smaller trade sizes relative to the quoted depth. Buyer-initiated trades receive smaller (larger) price improvements than seller-initiated trades on the NYSE (NASDAQ).     

Trade Disclosure,Information Learning and Securities Market Performance

In this paper we examine the effect of information disclosure on securities market performance when liquidity traders are able to acquire information about inside trading. We show that the bid-ask spread increases with the liquidity trader's learning efficiency, which is greater when trade information is disclosed. The bid-ask spread is always higher when trade information is not disclosed. However, the discrepancy between the bid-ask spreads with and without information disclosure narrows when the learning efficiency increases. We also show that the gains of the informed traders in a market without trade information disclosure are reduced in the presence of the liquidity trader's learning. Nevertheless, liquidity traders do not necessarily benefit from increased transparency. In particular, liquidity traders may face higher trading costs.     

The securitization of gold and its potential impact on gold stocks

We study the market impact of a very successful financial innovation – the SPDR Gold Trust exchange-traded fund (GLD). GLD holds physical gold, and provides traders with a convenient and cost-effective way to gain exposure to gold. We find that after the introduction of GLD, the liquidity of gold company stocks declined, and their adverse-selection risk increased. Over the two-month period after GLD’s introduction, the stocks’ relative effective bid-ask spreads increased by over 15%, while their adverse-selection cost, as measured by the price impact of trades, went up by more than 30%. Gold stocks also experienced significant negative abnormal returns (−12% on average) in the month after GLD started trading. Our findings suggest that GLD attracted traders, especially uninformed traders, away from gold company stocks, and that the resulting negative demand shocks and decrease in the stocks’ liquidity caused their prices to decline. Our results show that existing securities can be seriously adversely affected when a new security enters the market.     

Whose trades convey information? Evidence from a cross-section of traders

This paper contributes empirically to our understanding of informed traders. It analyzes traders’ characteristics in a foreign exchange electronic limit order market via anonymous trader identities. We use six indicators of informed trading in a cross-sectional multivariate approach to identify traders with high price impact. More information is conveyed by those traders’ trades which—simultaneously—use medium-sized orders (practice stealth trading), have large trading volume, are located in a financial center, trade early in the trading session, at times of wide spreads and when the order book is thin.     

The competitive effects of US decimalization: Evidence from the US-listed Canadian stocks

This paper analyzes the impact of US decimalization on the Canadian stocks listed on the Toronto Stock Exchange (TSE) and either the New York Stock Exchange (NYSE) or National Association of Securities Dealers Automated Quotation System (Nasdaq) in the US. Using a sample of 126 firms, we find that the US trading of these stocks increases after decimalization, but this increase is not at the expense of TSE volume. Indeed, the TSE volume increases substantially for those securities that are traded on Nasdaq and increases marginally for those securities that are traded on the NYSE. Most of the increase in volume is in retail-sized trades. The bid–ask spreads and the quote depths decline on all exchanges, but by a greater amount in the US than in Canada. The depths on the NYSE decline from being above the TSE depths to well below the TSE depths. We also find that the decline in the TSE spread is directly related to the size of the firm and to the decline in the US spread, and is inversely related to the pre-decimalization ratio of spreads on the US exchange and the TSE. Overall, our results indicate that the US decimalization had the desired positive impact on trading in both the US and Canada, with a decrease in spreads and an increase in retail-sized trading.     

VPIN,liquidity, and return volatility in the U.S. equity markets

We examine the volume-synchronized probability of informed trading metric (the VPIN flow toxicity metric, developed by Easley, Lopez de Prado, & O'Hara, 2012) as a real-time risk management tool for liquidity deteriorations in the U.S. equity markets. We find that VPIN provides information about market liquidity and stock return volatility on ex-ante basis. These results indicate that VPIN can be a useful risk-management tool for market makers, regulators and traders in the U.S. equity markets. We also document that VPIN is negatively associated with volume and number of trades, but positively associated with trade size and volume fragmentation. These findings suggest that VPIN indicates the adverse selection problem of liquidity providers by capturing the information in volume.     

Order Flow and Liquidity around NYSE Trading Halts

We study order flow and liquidity around NYSE trading halts. We find that market and limit order submissions and cancellations increase significantly during trading halts, that a large proportion of the limit order book at the reopen is composed of orders submitted during the halt, and that the market-clearing price at the reopen is a good predictor of future prices. Depth near the quotes is unusually low around trading halts, though specialists and/or floor traders appear to provide additional liquidity at these times. Finally, specialists appear to 'spread the quote' prior to imbalance halts to convey information to market participants.     

An empirical investigation of trading on asymmetric information and heterogeneous prior beliefs

The purpose of this study is to identify and analyze inter-temporal trading patterns attributable to informed trading. Recent theoretical models posit that heterogeneous prior beliefs provide a source of trading volume in addition to the commonly accepted trading motives of liquidity and asymmetric information. After separating informed from uninformed trading using the estimation procedure of Easley et al. [Journal of Finance 51 (1996) 1405], we test for the presence of trading on heterogeneous beliefs as opposed to asymmetric information. The empirical findings confirm the existence of trading on heterogeneous prior beliefs and generally support the inter-temporal patterns proposed by Wang [Journal of Financial Markets 1 (1998) 321].