The probability of informed trading and mergers and acquisitions

This paper investigates the role of the probability of informed trading (PIN) in mergers and acquisitions (M&A). We show that acquirers with higher PINs use more cash to finance their deals due to their higher cost of equity, and acquirers use more equity financing when acquiring targets with higher PINs to share the information risk with the target shareholders. We also find that acquirers and targets with higher PINs both experience higher announcement returns when cash financing is used, indicating that PINs are priced in the M&A market.     

A computing bias in estimating the probability of informed trading

This study identifies a factor that leads to a bias in estimating the probability of informed trading (PIN), a widely-used microstructure measure. It is shown that, along with the numerical maximization of the likelihood function for PIN, the floating-point exception (i.e., overflow or underflow) may eliminate feasible solutions to the actual parameters in the optimization problem. Approximately 44% of PIN estimates for recent stock market data may have been subject to a downward bias that is more pronounced for active stocks than for inactive stocks. This study develops a remedy to mitigate the resulting bias.     

Investor protection, adverse selection, and the probability of informed trading

The purpose of this study is to investigate the relation between investor protection, adverse selection, and the probability of informed trading. Previous research has established a direct relation between investor protection and firm liquidity, measured by bid-ask spreads and depths. In this study, we test the hypothesis that adverse selection is the mechanism through which poor investor protection leads to higher costs of liquidity. The Hong Kong equity market provides a unique opportunity to compare adverse selection differences across distinct investor protection environments, holding constant the trading platform and currency. Using various bid-ask spread decomposition models and probability of informed trading estimates, we confirm the hypothesized relation between investor protection quality and adverse selection costs. These findings contribute to the literature by establishing one of the links in the chain connecting investor protection to firm valuation.

Estimating the probability of informed trading—does trade misclassification matter?

Easley et al. [1996. Journal of Finance 51, 1405–1436] have proposed an empirical methodology to estimate the probability of informed trading (PIN). This approach has been employed in a wide range of applications in market microstructure, corporate finance, and asset pricing. To estimate the model, a researcher only needs the number of buyer- and seller-initiated trades. This information, however, is generally unobservable and has to be inferred from trade-classification algorithms, which are known to be inaccurate. In this paper, we show analytically that inaccurate trade classification leads to downward-biased PIN estimates and that the magnitude of the bias is related to a security's trading intensity. Simulation results and empirical evidence based on order and transaction data from the New York Stock Exchange are consistent with this argument. We propose a data-based adjustment procedure that substantially reduces the misclassification bias.     

An improved estimation method and empirical properties of the probability of informed trading

We report evidence that boundary solutions can cause a bias in the estimate of the probability of informed trading (PIN). We develop an algorithm to overcome this bias and use it to estimate PIN for nearly 80,000 stock-quarters between 1993 and 2004. We obtain two sets of PIN estimates by using the factorized likelihood functions in both [Easley et al., 2010] and [Lin and Ke, 2011], respectively. We find that the estimate based on the EHO factorization is systematically smaller than the estimate based on the LK factorization, meaning that there is a downward bias associated with the EHO factorization. In addition, we find that boundary solutions appear with a very high frequency when the LK factorization is used. Thus it is necessary to use the LK factorization together with the algorithm in this paper. At last, we document several interesting empirical properties of PIN.     

A faster estimation method for the probability of informed trading using hierarchical agglomerative clustering

The probability of informed trading (PIN) is a commonly used market microstructure measure for detecting the level of information asymmetry. Estimating PIN can be problematic due to corner solutions, local maxima and floating point exceptions (FPE). Yan and Zhang [J. Bank. Finance, 2012, 36, 454–467] show that whilst factorization can solve FPE, boundary solutions appear frequently in maximum likelihood estimation for PIN. A grid search initial value algorithm is suggested to overcome this problem. We present a faster method for reducing the likelihood of boundary solutions and local maxima based on hierarchical agglomerative clustering (HAC). We show that HAC can be used to determine an accurate and fast starting value approximation for PIN. This assists the maximum likelihood estimation process in both speed and accuracy.     

The impact of informed trading on corporate liquidity

The purpose of this paper is to analyze the impact of informed trading on corporate liquidity. Although theory posits an inverse relation between informed trading and firm liquidity, there is relatively little evidence on precisely how this relation is established or maintained. The trading model of Easley et al. (J. Finance 51 (1996) 1405) is employed to estimate the probability of informed trading and to identify specific days of informed trading using posterior probabilities. The results show that corporate liquidity, both in terms of spreads and depths, is a decreasing function of the probability of informed trading. The main finding is that spreads narrow and depths increase on actual information days even after controlling for variations in price, volume, and volatility.     

The information content of CDS implied volatility and associated trading strategies

Using the theoretical link between put options and credit default swaps (CDS) in a very general setting, we develop a robust measure of CDS implied volatility (CIV) that captures the information content of CDS markets. Specifically, we use the unit recovery claim to bridge CDS and deep out-of-the-money put options of the same firm and then back out CIV via the binomial tree. Our CIV measure strongly co-moves with the option implied volatility (OIV), with a correlation coefficient of 0.8. Based on the standardized difference between CIV and OIV, we construct CDS and option trading strategies. Without taking transaction costs into account, the long–short CDS trading strategy achieves an annualized return of 58.29% and a Sharpe ratio of 2.97, which cannot be explained by non-parametric skewness and volatility risk.     

The intra-day performance of market timing strategies and trading systems based on Japanese candlesticks

Abstract

We develop market timing strategies and trading systems to test the intra-day predictive power of Japanese candlesticks at the 5-minute interval on the 30 constituents of the DJIA index. Around a third of the candlestick rules outperform the buy-and-hold strategy at the conservative Bonferroni level. After adjusting for trading costs, however, just a few rules remain profitable. When we correct for data snooping by applying the SSPA test on double-or-out market timing strategies, no single candlestick rule beats the buy-and-hold strategy after transaction costs. We also design fully automated trading systems by combining the best-performing candlestick rules. No evidence of out-performance is found after transaction costs. Although Japanese candlesticks can somewhat predict intra-day returns on large US caps, we show that such predictive power is too limited for active portfolio management to outperform the buy-and-hold strategy when luck, risk, and trading costs are correctly measured.     

The characteristics of informed trading: Implications for asset pricing

This paper investigates the linkage of microstructure, accounting, and asset pricing. We determine the relationship between firm characteristics as captured by accounting and market data and a firm's probability of private information-based trade (PIN) as estimated from trade data. This allows us to determine what types of firms have high information risk. We then use these data to create an instrument for PIN, the PPIN, which we can estimate from firm-specific data. We show that PPINs have explanatory power for the cross-section of asset returns in long sample tests. We also investigate whether information risk vitiates the influence of other variables on asset returns. We develop a PPIN factor and show that it dominates the Amihud factor in asset returns. Our results provide strong support for information risk affecting asset returns in long sample tests.     

Endogenous informed trading in the presence of trading costs: Theory and evidence

The basic premise of the model we propose is that market frictions (trading costs) force traders with market-wide information to strategically choose which securities to trade in. We study the effect of recognizing trading costs on the choices of informed traders and the resulting statistical properties of security prices. Specifically, we show that (1) stocks with intermediate β's have the least informative prices, even though they are traded by the greatest number of informed traders; (2) for high β securities, the contemporaneous correlation of prices is close to the correlation in fundamental values; (3) a security with a higher β, higher volume of liquidity trading and lower idiosyncratic variance is more likely to lead another security. With market capitalization as a proxy for the level of liquidity trading, these specific predictions of the model on the lead–lag relationship are also shown to be strongly supported by the data.     

Adverse selection and the presence of informed trading

We combine two concepts of informed trading – contrarian trades and stealth trading – to develop proxies for the probability of informed trading. These proxies are used to test the link between informed trading and adverse selection as measured by bid–ask spreads and stock illiquidity. The estimation results show that these proxies, which are based on the probability of contrarian trading (PC) and progressively refined thereon, are all highly significantly positive in various empirical specifications of the cross-sectional determinants of spreads and illiquidity across stocks, and after controlling for important firm characteristics and trading factors. The robustness of our PC-based proxies for informed trading in these analyses, especially for the further refined measures, suggests that they successfully capture the adverse selection component of bid–ask spreads and illiquidity due to information asymmetry.     

Optimal informed trading in the foreign exchange market

We formulate a market microstructure model of exchange determination that we employ to investigate the impact of informed trading on exchange rates and on foreign exchange (FX) market conditions. With our formulation, we show how strategic informed agents influence exchange rates via both the portfolio-balance and information effects. We outline the connection which exists between the private value of information, market efficiency, liquidity and exchange rate volatility. Our model is also consistent with recent empirical research on the micro-structure of FX markets.     

Continuous-time trading and the emergence of probability

This paper establishes a non-stochastic analog of the celebrated result by Dubins and Schwarz about reduction of continuous martingales to Brownian motion via time change. We consider an idealized financial security with continuous price paths, without making any stochastic assumptions. It is shown that typical price paths possess quadratic variation, where “typical” is understood in the following game-theoretic sense: there exists a trading strategy that earns infinite capital without risking more than one monetary unit if the process of quadratic variation does not exist. Replacing time by the quadratic variation process, we show that the price path becomes Brownian motion. This is essentially the same conclusion as in the Dubins–Schwarz result, except that the probabilities (constituting the Wiener measure) emerge instead of being postulated. We also give an elegant statement, inspired by Peter McCullagh’s unpublished work, of this result in terms of game-theoretic probability theory.     

Complete markets,informed trading and equity option introductions

This paper proposes and tests a new hypothesis concerning the price impact of option introductions on the underlying asset. We argue that the leverage properties of options induce a higher level of informed trading in the aggregate market (underlying plus derivative), resulting in excess listing-day price movements in the newly optioned equity. Using an alternative dataset, our results suggest that this may be an explanation for the observed positive than negative excess listing-day returns of US optioned stocks over the past thirty years.     

Cross-market informed trading in the CDS and option markets

This paper analyzes how informed traders balance leverage and liquidity in making cross-market trading decisions. Our findings are three-fold. Using daily data on the actively traded North American CDX Investment Grade and High Yield indexes from 2010 to 2017, first, we find robust evidence that, when the credit default swap (CDS) market is illiquid, informed traders do not hesitate to exploit their information advantage in the option market, and this manifests mainly for negative credit news. Second, we find that informed trading occurs predominantly in the option market for low-rated firms due to high adverse selection risk in the CDS market. Third, there is strong evidence of informed trading taking place in the CDS market for financial firms and we argue that this is due to the interdealer network through which CDS dealers obtain private information about other financial firms and their privileged position as market makers to be able to disguise their informed trades as ordinary market-making activities. Finally, we propose re-thinking market efficiency and how to advance it in a direction which does not privilege a small circle of financiers or create monopoly while keeping the markets liquid and tranquil at all times.     

The efficacy of regulatory intervention: Evidence from the distribution of informed option trading

A substantive body of equity-market academic research documents an extensive range of costs arising from the SEC’s October 2000 adoption of strictures on selective disclosure and insider trading; suggesting an unusual outcome, specifically, an increase in informed trading. We investigate the efficacy of the SEC’s regulations by examining informed trading in an attractive setting for exploiting private information; the options market. Using data on the S&P 1500 industrial firms, our analysis indicates that about 38% of firms exhibited symptoms of informed option trading prior to regulatory intervention. After regulatory intervention, we observe that only 19% of firms show symptoms of informed trading. In additional testing of ADR firms – explicitly exempt from complying with Reg FD, we find no evidence of a change in informed option trading from pre- to post-regulation; suggesting that the SEC’s strictures on US firms led a to a significant reduction in informed option trading. Notably, our proxies for large shareholder and financial analyst access are associated with the largest decreases in informed option trading. In developing a unique measure of informed trading based on option market data, we provide evidence on the efficacy of security regulation in limiting informed trading.     

The impact of informed trading on dividend signaling: a theoretical and empirical examination

This paper examines how the trading behavior of various investors impacts the market reaction to a dividend signal. The dividend signaling model incorporates asymmetric information among traders, firm insiders, and the market. This interaction among market participants explains why not all dividend increases are viewed by the market as good news. The model predicts that the announcement day returns for a dividend increase are inversely related to measures of informed trading and decreasing in the level of buy demand relative to sell demand. Further, the model hypothesizes that more informed trading results in larger dividend increases. Empirical tests confirm these predictions.     

Security price formation and informed trading with constrained short selling

Short sale orders account for a substantial portion of trading volume in recent years. This paper develops a sequential trade model with constrained short selling to derive the effect on prices when the market maker can observe short selling in the order flow. The model predicts that market quotes will adjust differently to short sales and regular sales. Furthermore, the model shows that the probability of informed trading is impacted both by the level of short sale constraints and the intensity of actual short sale trades. Simulation evidence confirms that estimates of the probability of informed trade are improved when accounting for past short selling activity. The results demonstrate the information benefits of short selling transparency.