Probability Of Informed Trading Research Articles

정보거래자의 옵션 선택에 관한 연구

Purpose - The purpose of this study is to examine the option selection and optimal trading of informed traders in KOSPI 200 options market based on the PIN (probability of informed trading) model of Easley et al.(2002). Design/methodology/approach - This study uses TAQ (trade and quote) data provided by Korean Exchanges (KRX) which contains all the bids and trades recorded during the continuous auction trading hours for the KOSPI 200 options between May 2019 and September 2020. Findings - First, there was no difference in the PIN between call and put options in the 2019 data, but the PIN of put options was slightly higher in 2020. Second, regardless of the type of option, the PIN was higher for in-the-money (ITM) options, and the PIN of out-of-the-money (OTM) options was the same as or slightly higher than that of at-the-money (ATM) options. Third, we found that the PIN decreases as trading liquidity increases, and fourth, the PIN increased sharply as the expiration date approached, especially for OTM options, while ITM and ATM options showed relatively weak effects. Fifth, for foreign and institutional investors, the periodicity of orders was observed in milliseconds, especially for foreign investors, where the periodicity of orders was clear and frequent in OTM options. The results suggest that the purpose of option trading varies depending on the moneyness from the perspective of the informed trader.

PIN 추정을 위한 최적모형에 관한 연구

This study compares two models for estimating the probability of informed trading (PIN) and investigates which model is superior in the Korean index options market. The study uses the method of maximum likelihood estimation (MLE) and Bayesian methods to calculate the PIN. Using the bid and transaction data (TAQ) of KOSPI 200 index options from May 2019 to September 2020, we obtain the following results. First, there were no cases where there was no solution in either method. Second, when estimating delta using the Bayesian method, no boundary solutions occurred, but with the MLE method, multiple boundary solutions occurred. Specifically, 10% of the total solutions in 5-minute tick data and 47% in 20-minute tick data were boundary solutions. Third, in the case of 20-minute tick data, the PIN estimated by the MLE method for ITM options significantly decreased compared to that of the 5-minute data. This is estimated to be caused by the previous boundary solutions inducing a downward bias in the PIN. Fourth, in the MLE method, the standard error of the estimate is almost zero, while the Bayesian method shows an error rate of around 2% based on PIN from 20-minute data. In conclusion, the Bayesian method is superior when there is no boundary problem, given a sufficient size of closing data, but when not, the magnitude of the error rate is considered to be an important factor in the choice of method.

Probability of informed trading during the COVID-19 pandemic: the case of the Romanian stock market

Using data from the Bucharest Stock Exchange, we examine the factors influencing the probability of informed trading (PIN) during February—October 2020, a COVID-19 pandemic period. Based on an unconditional quantile regression approach, we show that PIN exhibit asymmetric dependency with liquidity and trading costs. Furthermore, building a customized database that contains all insider transactions on the Bucharest Stock Exchange, we reveal that these types of orders monotonically increase the information asymmetry from the 50th to the 90th quantile throughout the PIN distribution. Finally, we bring strong empirical evidence associating the level of information asymmetry to the level of fake news related to the COVID-19 pandemic. This novel result suggests that during episodes when the level of PIN is medium to high (between 15 and 50%), any COVID-19 related news classified as misinformation released during the lockdown period, is discouraging informed traders to place buy or sell orders conditioned by their private information.

Retail Investors and Stock Price Synchronicity

In this study, we use the number of retail investors in China’s stock market to investigate how retail investors affect stock price synchronicity. We find that a higher number of retail investors in a firm is associated with higher stock price synchronicity. Moreover, we trace this association to two sources. One is a negative effect of the number of retail investors on the probability of informed trading (PIN), suggesting that retail investors generate arbitrage risk which discourages informed trading. The other is a positive influence of the number of retail investors on price comovement (beta), resulting from correlated trading among retail investors.

Does information asymmetry predict audit fees?

AbstractThis study investigates whether and how information asymmetry in the stock market affects the quantum of audit fees paid by auditees. It is based on a sample of 218 US publicly traded companies and adopts two well‐established proxies for information asymmetry, namely bid‐ask spread (BAS) and probability of informed trading (PIN). Empirical results provide evidence that, after controlling for all main audit fees determinants, information asymmetry is positively related to the quantum of audit fees paid. Overall, evidence supports the contention that less transparent companies convey higher audit risk, and therefore auditors require higher compensation.

Effect of futures trading on the liquidity of underlying stocks: Evidence from Vietnam

Our paper investigates the effect of the introduction of the VN30 futures contract on the liquidity of thirty blue-chip underlying stocks in Vietnam stock market. Using the difference-in-difference approach with carefully matched stocks, we find the quoted spread and the Amihud illiquidity of the VN30's component stocks increase after the introduction of index future trading. The decrease in liquidity is explained by a jump in the adverse selection costs proxied by the probability of informed trading (PIN) after such introduction. In addition, the prevalence of futures under-pricing indicates the lack of index arbitrage due to short-sale prohibition in the Vietnamese stock market.

Identifying information types in probability of informed trading (PIN) models: An improved algorithm

Identifying information types in probability of informed trading (PIN) models: An improved algorithm

Information asymmetry and the profitability of technical analysis

Do informed investors leave a trace in the market? This study shows that the portfolios composed of stocks with a high probability of informed trading (PIN) earn significantly higher returns under moving average strategies than a buy-and-hold strategy. The abnormal returns cannot be explained by a Fama-French five-factor model with an additional momentum factor or transaction costs and yet exists even after imposing delayed trades or controlling for firm size, volatility, and liquidity. Portfolios with alternative information asymmetry measures report similar albeit weaker results.

Stock price informativeness and managerial inefficiency

This paper investigates whether more informative stock prices lead to managers making more efficient corporate decisions. I find a negative relationship between probability of informed trading (PIN) and firm value shortfalls, which capture managerial inefficiency. This relationship is robust to controlling for endogeneity issues and using alternative measures. Moreover, I show that stock price informativeness reduces managerial inefficiency by revealing new information to guide managers’ decisions and by enhancing corporate governance mechanisms that incentivize managers to make value-maximization decisions. This paper adds firm-level evidence to studies on the connection between stock market (informationally) efficiency and real economy efficiency.

Data-driven tree structure for PIN models

Probability of informed trading (PIN) models characterize trading with certain types of information through a tree structure. Different tree structures with different numbers of groups for market participants have been proposed, with no clear, consistent tree used in the literature. One of the main causes of this inconsistency is that these trees are artificially proposed through a bottom-up approach rather than implied by actual market data. Therefore, in this paper, we propose a method that infers a tree structure directly from empirical data. More precisely, we use hierarchical clustering to construct a tree for each individual firm and then infer an aggregate tree through a voting mechanism. We test this method on US data from January 2002 for 7608 companies, which results in a tree with two layers and four groups. The characteristics of the resulting aggregate tree are between those of several proposed tree structures in the literature, demonstrating that these proposed trees all reflect only part of the market, and one should consider the proposed empirically driven method when seeking a tree representing the whole market.

Estimating the probability of informed trading: A Bayesian approach

The Probability of Informed Trading (PIN) is a widely used indicator of information asymmetry risk in the trading of securities. Its estimation using maximum likelihood algorithms has been shown to be problematic, resulting in biased or unavailable estimates, especially in the case of liquid and frequently traded assets. We provide an alternative approach to estimating PIN by means of a Bayesian method that addresses some of the shortcomings in the existing estimation strategies. The method leads to a natural quantification of the uncertainty of PIN estimates, which may prove helpful in their use and interpretation. We also provide an easy to use toolbox for estimating PIN.

Improvements in estimating the probability of informed trading models

Two advances have been made in the estimation of probability of informed trading (PIN) models. First, an initial-value-setting scheme has been proposed, that sets up a grid for initial values of mixture probabilities and uses the probabilities to divide the sample so as to derive the initial values of Poisson parameters. Second, the mixture bivariate normal distribution can help approximate the compound Poisson distribution in estimating PIN models. This study implements two approaches to simulated and real data for the PIN and Adjusted PIN models and compares their performance with the literature. The new initial-value-setting scheme performs better than those of Yan and Zhang [An improved estimation method and empirical properties of the probability of informed trading. J. Banking Finance, 2012, 36(2), 454–467] and Ersan and Alıcı [An unbiased computation methodology for estimating the probability of informed trading (PIN). J. Int. Financ. Markets, Inst. Money, 2016, 43, 74–94], and using the normal distribution outperforms the Poisson distribution under certain variance specifications.

Measuring and Explaining the Probability of Informed Trading and its Relationship with the Cost of Capital with an Emphasis on Family Ownership

One of the most important topics studied in the field of market microstructures is to measure information asymmetry in the capital market. In recent years, the Probability of Informed Trading (PIN) has been introduced to measure information asymmetry. The use of private information in stock exchanges reduces stock liquidity, thereby increasing the cost of equity capital. The main purpose of this study is to investigate the relationship between the probability of informed trading and cost of capital as well as to examine the moderating role of family ownership in the relationship between the probability of informed trading and cost of capital in 113 companies listed on the Tehran Stock Exchange during 2012-2016. The independent variable of information asymmetry is measured by the probability of informed trading criterion and the dependent variable of cost of capital by the criterion of cost of equity and cost of debt, and the weighted average cost of capital, and the moderating variable is family ownership. The research method is correlational and the multivariate regression using combined data with fixed effect regression model approach is used. The research findings show that there is a positive and significant direct relationship between the probability of informed trading with the cost of equity and the weighted average cost of capital, as well as the variable of family ownership has a positive and significant direct effect on the relationship between the probability of informed trading and the weighted average cost of capital. When the probability of trading by private information holders increases, due to the increase in information asymmetry between informed and uninformed investors, uninformed investors demand higher returns to cover the investment risk, thereby increasing the company's cost of capital. This will increase the cost of financing through bonds.

The probability of informed trading and mergers and acquisitions

AbstractThis 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.

How to survive and compete: the impact of information asymmetry on productivity

This paper studies the impact of information asymmetry on productivity through the foreign direct investment (FDI) channel. Higher information asymmetry (lower transparency) could increase the information cost for investors to distinguish between good and bad investments and thus impede investment, particularly, FDI in which investors are “information disadvantageous of alien”. FDI is an important determinant of productivity growth. Naturally, we connect the two key economic variables and study the influence of information asymmetry on productivity through the channel of FDI. We use data from China’s publicly listed manufacturing firms. Based on a market micro-structure model, we estimate the unobserved information asymmetry using the probability of informed trading (PIN). We estimate the unobserved firm-level productivity using the proxy variable method, assuming that the evolution of productivity is not exogenous but can be shifted by FDI. We find that information asymmetry affects the productivity of domestic firms both positively and negatively, depending on firms’ technology-intensity. When domestic firms are technology-intensive, they can benefit from the knowledge spillovers of FDI. In that case, we find lower information asymmetry, associated with more FDI, results in higher productivity. When domestic firms are non-technology-intensive, they can be hurt by the foreign competition. In this case, we find that lower information asymmetry results in lower productivity.

Informed trading, order flow shocks and the cross section of expected returns in Borsa Istanbul

ABSTRACTThis paper examines the relationship between information asymmetry and stock returns in Borsa Istanbul. For all stocks that are traded in Borsa Istanbul between March 2005 and April 2017, we estimate the probability of informed trading (PIN) to proxy for information asymmetry. Firm-level cross-sectional regressions indicate a statistically insignificant relationship between PIN estimates and future returns. Moreover, univariate and multivariate portfolio analyses assert that investors that hold stocks that have high information asymmetry do not obtain significant future returns. Consequently, our results suggest that information asymmetry proxied by PIN is a firm-specific risk and can be eliminated with portfolio diversification. Findings are robust to different factorizations in estimating PIN and free of any bias due to trade classification algorithms, boundary solutions, floating-point exceptions and symmetric order flow shocks.

Information asymmetry, market state, and implementation risk

This study demonstrates that the information risk premium is strongly dependent on the state of the market. Stocks with high information risk, as measured by the probability of informed trading (PIN) or adjusted PIN (ADJPIN), exhibit higher returns than stocks with low information risk when the market is in a good state, but this is not the case when the market is bad. Further analysis reveals that the information risk premium is high when the implementation risk is low, suggesting that uninformed traders require high compensation from informed traders if the informed can easily arbitrage by exploiting the information advantage.

A New Way to Compute the Probability of Informed Trading

Volume-Synchronized Probability of Informed Trading (VPIN) is a tool designed to predict extreme events like flash crashes in high-frequency trading. Its aim is to estimate the Probability of Informed Trading (PIN), which was built from a probabilistic framework. Some concerns have been raised about its theoretical foundations and its reliability. More precisely, it has been shown that theoretically the VPIN does not approximate the PIN as the PIN has been built with a time-clock framework and the VPIN with a volume clock one. On a practical point of view, the VPIN has been found to be sensitive to the starting point of computation of a data set and to different parameters, such as the classification rule. In this paper, in order to improve the PIN theoretical framework, we firstly analyze the theoretical foundations of the PIN and the VPIN models to have a better view of all its different assumption subtleties. It secondly makes it possible to point out some approximation flaws in the formula used to approximate the PIN and to propose another exact way to compute the PIN. All different results are illustrated with simulations.

A note of techniques that mitigate floating-point errors in PIN estimation

Summary This study aims at the estimation of the probability of informed trading (PIN), which may fail for stocks with high levels of trading activities due to a computer's floating-point exception (FPE). In this paper, we discuss two solutions of adopting scaled trade counts and reformulating the likelihood to estimate PIN for actively traded stocks. This study shows that, although scaled data mitigates the impact of the FPE, the effectiveness of scaled data, however, appears to underperform when users adopt the unsuitable expression of the likelihood function. In contrast, the remedy of reformulating the likelihood is more stable.

Informed trading around biotech M&As

PurposeThis paper aims to test the extent to which downward bias due to a floating-point exception in probability of informed trading (PIN) estimates obtained using the Easley, Hvidkjaer and O’Hara (EHO; 2002) method is remedied using the Yan and Zhang (YZ; 2012) method. The paper also aims to test the sample-size sensitivity of EHO PIN and identify PIN determinants for acquirers and targets in the biotech sector.Design/methodology/approachEHO and YZ PIN performances are compared for US biotech acquirers and targets around their mergers and acquisition (M&A) announcements. The sampling method of Kryzanowski and Lazrak (2007) is used to assess sample-size sensitivity of announcement window EHO PIN estimates. Cross-sectional regressions are estimated to identify PIN determinants.FindingsEHO and YZ PIN are not significantly different. EHO PIN exhibits significant sample-size sensitivity. Information leakage prior to M&A announcements is strongly affected by some firm characteristics. Significant determinants of PIN behavior around M&A announcements include insider and institutional holdings and research and development (R&D) expense.Research limitations/implicationsFindings imply that PIN partially reflects the activities of insiders and other informed investors about takeover intentions. Subsequent research can examine PIN behavior around pre-announcement rumors for M&As in the same or other industries and for potential targets that are peers of the M&A targets.Originality/valueThis paper contributes to the ongoing debate in the empirical finance literature on whether PIN measures informed trading by examining its behavior and the importance of some methodological issues associated with its use in examining market behavior around M&A announcements.