Measure Of Informed Trading Research Articles

Informed Trading Intensity

ABSTRACTWe train a machine learning method on a class of informed trades to develop a new measure of informed trading, informed trading intensity (ITI). ITI increases before earnings, mergers and acquisitions, and news announcements, and has implications for return reversal and asset pricing. ITI is effective because it captures nonlinearities and interactions between informed trading, volume, and volatility. This data‐driven approach can shed light on the economics of informed trading, including impatient informed trading, commonality in informed trading, and models of informed trading. Overall, learning from informed trading data can generate an effective informed trading measure.

Informed trading by hedge funds

AbstractUsing daily equity transactions, we create a hedge fund informed trading measure (ITM) that separates concentrated information‐related trades from liquidity‐driven basket trades. We find that stocks with higher ITM are associated with higher future stock performance. The long–short portfolio delivers 4% annual alpha after controlling for size, value, momentum, and illiquidity factors. We attribute informed trading to hedge funds' ability to identify and correct stock underpricing. The results are robust to several ways of constructing and sorting the measure, and we do not find a return reversal in four quarters, indicating that the measure is information related.

Do Proxies for Informed Trading Measure Informed Trading? Evidence from Illegal Insider Trades

AbstractThis paper exploits hand-collected data on illegal insider trades to provide new evidence on the ability of a host of standard measures of illiquidity to detect informed trading. Controlling for unobserved cross-sectional and time-series variation, sampling bias, and strategic timing of insider trades, I find that when information is short-lived, only absolute order imbalance and effective spread are statistically and economically robust predictors of illegal insider trading. However, when information is long-lasting, insiders strategically time their trades to avoid illiquidity, and none of the standard measures considered are reliable predictors, including bid-ask spreads, order imbalance, Kyle’s λ, and Amihud illiquidity. (JEL D53D82G12G14K42)Received: March 14, 2019; Editorial decision: February 18, 2020 by Editor Thierry Foucault. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Do Proxies for Informed Trading Measure Informed Trading? Evidence from Illegal Insider Trades

This paper exploits hand-collected data on illegal insider trades to test whether standard illiquidity measures can detect informed trading. Controlling for unobserved cross-sectional and time-series variation, sampling bias, and strategic timing of insider trades, I find that only absolute order imbalance and the negative autocorrelation of order flows are statistically and economically robust predictors of insider trading. However, this result only holds for short-lived information. When information is long-lived, none of the measures of illiquidity I consider detect informed trading, including bid-ask spreads, Kyle's lambda, and Amihud illiquidity. These results suggest that standard measures of illiquidity have limited applications.

Informed Trading by Hedge Funds

Using daily equity transactions, we create a hedge fund informed trading measure (ITM) that separates information related trades from liquidity driven trades. We find that stocks with higher hedge fund informed trading are associated with higher future stock performance. The long-short portfolio delivers 4% annual alpha after controlling for size, value, momentum, and illiquidity factors. The results are mainly driven by the long side of informed trading. We attribute the informed trading to funds' ability to identify and correct stock underpricing. The results are robust to various ways of constructing and sorting the measure, and we do not find a return reversal in 4 quarters, indicating that the measure is information related.

What do stock price levels tell us about the firms?

We hypothesize that high stock price levels impede informed trading on the stocks and reduce price informativeness. This is because uninformed trading is needed to facilitate informed trading, and high stock prices may impose budget constraints on uninformed investors. Indeed, we find, for high-price firms, (i) options to stock trading volume (O/S), an informed trading measure in options market, is higher, (ii) price informativeness about future earnings is lower, and (iii) investment sensitivity to price is lower. We also find these patterns reverse after stock splits, suggesting that firms can use splits to improve informed trading and enhance price informativeness.

Multilayer Probability of Informed Trading

This paper demonstrates the multilayer structure of information in financial markets. While only 3.59% of 8,190 stock/quarter pairs have single information layer, 75% have two to five layers and 18% have six to eight layers. We develop a clustering algorithm which determines the number of information layers with the accuracy rates between 95% and 97% in simulated datasets. We propose a multilayer informed trading measure, MPIN by extending the original model of Easley et al. (1996). Respective solutions for computational problems regarding MPIN estimation are also provided. PIN model's estimates on informed trading and five intermediate parameters are heavily biased for datasets with multiple information layers, while MPIN model consistently provides accurate estimates. When compared to PIN, MPIN yields substantially higher probabilities of informed trading and information event occurrence; much lower rate of informed traders for real stock/quarter pairs. We obtain daily posterior multilayer probabilities conditional on the quarterly estimates of MPIN model. Posterior probabilities derived from MPIN model are approximately two fold when compared to the ones from PIN model surrounding earnings announcements.

An Empirical Analysis of the Dynamic Probability of Institutional Informed Trading: Evidence from the Taiwan Futures Exchange

This paper analyzes the informational role of institutional investors’ trading using the dynamic intraday measure of the probability of informed trading (hereafter DPIN). Using a unique account-level dataset of institutional investors from the Taiwan index futures market, we show that the DPINs of foreign institutional buy trades are significantly positively related to future market returns. Moreover, compared to using trading imbalance as the informed trading measure, we find that the DPIN provides consistent predictive power for the market volatility, particularly during intense trading periods. Overall, our results also provide support for the notion that foreign institutional traders are better informed than domestic institutional traders in the emerging markets.

A Better Measure of Institutional Informed Trading

AbstractAlthough many studies show that the presence of institutional investors facilitates the incorporation of accounting information into financial markets, the evidence of informed trading by institutions is rather limited in the extant literature. We address these inconsistent findings by proposing PC_NII, percentage changes in the number of a stock's institutional investors, as a novel informed trading measure. PC_NII is better able to detect informed trading than are changes in institutional ownership (ΔIO)—the measure commonly used in previous studies—because (i) entries and exits are usually triggered by substantive private information and (ii) only a small fraction of institutions have superior information. As conjectured, PC_NII subsumes the information content of ΔIO and other institutional trading and herding measures in the forecast of stock returns, and its strong predictive power for stock returns reflects mainly its close correlation with future earnings surprises. We also show that PC_NII helps address empirical issues that require a reliable measure of institutional informed trading.

Does PIN measure information? Informed trading effects on returns and liquidity in six emerging markets

Market microstructure models imply that informed trading reduces liquidity and moves prices in the direction of the information. We test this implication using the dynamic PIN model (Easley, Engle, O'Hara and Wu 2008) as a time-varying measure of informed trading in the six largest Latin America stock markets. Under alternative specifications and robustness tests, the results suggest that signed dynamic PIN is related to returns, as a proxy for information asymmetry rather than just liquidity effects. These results contribute to the ongoing discussion on whether PIN is a valid informed trading measure, and to a better understanding of price formation in emerging markets.

Does the Probability of Informed Trading Measure Informed Trading?

Recent research has raised concerns over whether the probability of informed trading model (PIN) is an appropriate proxy of information asymmetry. We investigate PIN and test whether the model can detect illegal insider trading prior to M&A announcements. We then compare the performance of PIN to an alternative proxy, the PIN asymmetric autoregressive conditional duration model (PIN-AACD), to determine if this model offers a better proxy for informed trading. We find that PIN does not measure informed trading prior to M&A announcements, and that PIN-AACD offers a better measure of information asymmetry.

Was There Informed Trading in LIBOR Referenced Interest Rate Futures?

During 2008 a series of allegations emerged is respect of systematic attempts to manipulate the London Interbank Oered Rate (LIBOR). In this paper we implement a version of the probability of informed trading measure rst introduced in Easley et al. (1996), for the Eurodollar futures market over the 1996 to 2013 period. The Eurodollar future is one of the most actively traded US Dollar LIBOR benchmarked derivatives in the world with total traded volume in excess of half a quadrillion US dollars in 2011 (Source: CFTC) and is possibly the world’s largest nancial market. The objective of this paper is an ex-post review of the eectiveness of the PIN in determining changes in the information structure of the market around documented episodes of recorded manipulation of the benchmark rate, from the various publicly available regulatory reports. In keeping with previous studies on interest rate derivatives, we nd that the average PIN is far higher than for the equity market at or around 2/3 to 3/4. When implementing a rolling measure of the PIN to detect time variation we nd a clear pattern of increases and decreases in informed trading relative to the recorded activity in the current regulatory reports. Furthermore, we also nd a strong maturity eect that appears to arise from the strong time variation in trading in these contracts over their life-cycle, from inception to maturity. We construct a series of experiments to determine the signicance of the trading time for dierent epochs from 1996 to 2014 using all inside quotes and trades in these contracts. Our results indicate that the PIN could have been used as an early warning of unusual activity in the LIBOR reference rate and anecdotally we demonstrate that on specic dates identied

A Better Measure of Institutional Informed Trading

Although many studies show that the presence of institutional investors facilitates the incorporation of accounting information into financial markets, the evidence of informed trading by institutions is rather limited in the extant literature. We address these inconsistent findings by proposing PC_NII, percentage changes in the number of a stock’s institutional investors, as a novel informed trading measure. PC_NII is better able to detect informed trading than are changes in institutional ownership (∆IO) — the measure commonly used in previous studies — because (1) entries and exits are usually triggered by substantive private information and (2) only a small fraction of institutions have superior information. As conjectured, PC_NII subsumes the information content of ∆IO and other institutional trading and herding measures in the forecast of stock returns, and its strong predictive power for stock returns reflects mainly its close correlation with future earnings surprises. We also show that PC_NII helps address empirical issues that require a reliable measure of institutional informed trading.

Decomposing the Probability of Informed Trading Measure

This paper aims to analyze the dynamics of information asymmetry in market microstructure through the Easley et al. (2002)'s PIN framework in two segments. Firstly, we test to see if factors such as size, value and illiquidity can be used to explain PIN. Secondly, we extend beyond the traditional literature by examining individual components of PIN, especially the informed and uninformed trade intensities. We contribute to the literature by documenting non-linear relationships between trade intensities, and their autocorrelation functions. Our study show that uninformed intensity is more persistent than informed trading and that there exists statistically significant spillover effects from informed trading into liquidity trades, suggesting that liquidity trades lag behind that of informed trades.