Volume-synchronized Probability Of Informed Trading Research Articles

Effects of investor sentiment on stock volatility: new evidences from multi-source data in China’s green stock markets

The effect of investor sentiment on stock volatility is a highly attractive research question in both the academic field and the real financial industry. With the proposal of China's "dual carbon" target, green stocks have gradually become an essential branch of Chinese stock markets. Focusing on 106 stocks from the new energy, environmental protection, and carbon–neutral sectors, we construct two investor sentiment proxies using Internet text and stock trading data, respectively. The Internet sentiment is based on posts from Eastmoney Guba, and the trading sentiment comes from a variety of trading indicators. In addition, we divide the realized volatility into continuous and jump parts, and then investigate the effects of investor sentiment on different types of volatilities. Our empirical findings show that both sentiment indices impose significant positive impacts on realized, continuous, and jump volatilities, where trading sentiment is the main factor. We further explore the mediating effect of information asymmetry, measured by the volume-synchronized probability of informed trading (VPIN), on the path of investor sentiment affecting stock volatility. It is evidenced that investor sentiments are positively correlated with the VPIN, and they can affect volatilities through the VPIN. We then divide the total sample around the coronavirus disease 2019 (COVID-19) pandemic. The empirical results reveal that the market volatility after the COVID-19 pandemic is more susceptible to investor sentiments, especially to Internet sentiment. Our study is of great significance for maintaining the stability of green stock markets and reducing market volatility.

Analysis of stock market volatility: Adjusted VPIN with high-frequency data

The volume-synchronized probability of informed trading (VPIN) is widely accepted as a proxy of volatility in the high-frequency market. We propose a novel VPIN model, called Adjusted VPIN, to improve the performance of VPIN so that it can directly analyze and better predict the information asymmetry of individual stocks. We extend the VPIN model by optimizing the classification algorithm with a neural network method and high-frequency data. Both trading volume and trends are considered to capture stock volatility. Empirical results on three different trading volume groups generate a 37.86% higher relevant result with logarithm stock yield than the VPIN model.

Information Contents of Order Flow Toxicity in the Options Market : The Case of KOSPI200 Index Options

This study estimates the VPIN (volume-synchronized probability of informed trading) of the KOSPI200 index options, the measure of order flow toxicity suggested by Easley et al. (2012), for the first time. To apply the VPIN approach, options are categorized by their real-time moneyness. I examine the predictive power of VPIN for the future stock market volatility using time-series regression analysis. The empirical result shows that the toxic order flow measure estimated by price changes has more information than that estimated by the actual order imbalance. In general, put options contain more information than call options, and the toxic order flow measure of OTM (out-of-the-money) put options contains the most significant information about the future stock market volatility. In addition, the predictive power of toxic order flow measure is much significant in the highly volatile than in the stable market. The volatility predictability of toxic order flow measure declined significantly after the option multiplier increase, whereas it has gradually recovered over time.

Flow toxicity of high‐frequency trading and its impact on price volatility: Evidence from the KOSPI 200 futures market

AbstrastWe examine the relation between high‐frequency trading, flow toxicity, and short‐term volatility during both normal and stressful periods. Using transaction data for the Korea Composite Stock Price Index 200 (KOSPI 200) futures, we find the Volume‐Synchronized Probability of Informed Trading (VPIN) useful in measuring flow toxicity as it predicts short‐term volatility effectively. We further show that high‐frequency trading is negatively related to VPIN and short‐term volatility during normal times but has a positive association during stressful periods. Finally, we advocate the use of bulk‐volume classification (BVC) by presenting evidence that the initiator identified by BVC trades at more favorable prices than the true trade initiator.

The Microstructure of the ‘Flash Crash’: Flow Toxicity, Liquidity Crashes and the Probability of Informed Trading

The ‘flash crash’ of May 6th 2010 was the second largest point swing (1,010.14 points) and the biggest one-day point decline (998.5 points) in the history of the Dow Jones Industrial Average. For a few minutes, $1 trillion in market value vanished. In this paper, we argue that the ‘flash crash’ is the result of the new dynamics at play in the current market structure. We highlight the role played by order toxicity in affecting liquidity provision, and we show that a measure of this toxicity, the Volume-Synchronized Probability of Informed Trading (VPIN)*, captures the increasing toxicity of the order flow in the hours and days prior to collapse. Since the ‘flash crash’ might have been avoided had liquidity providers remained in the marketplace, a solution is proposed in the form of a ‘VPIN contract’ which would allow them to dynamically monitor and manage their risks.

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.

Can VPIN forecast geopolitical events? Evidence from the 2014 Crimean Crisis

We study the recent Crimean Crisis and the sequence of outcomes that led to the intervention by the Russian Army, which directly affected equity prices in Russia, to investigate how informed traders may have used their advantage to trade prior to the moment markets fell. We compute the Volume-synchronized Probability of Informed Trading (VPIN) for the Russian RTS equity index and for individual stocks, documenting that levels of informed trading increased considerably between one and three trading days before market prices reflected the invasion. We also investigate the predictive power of the cumulative distribution of VPIN on future stock prices, showing a statistically significant (negative) relation during the period of elevated tensions between Russia and Ukraine. Last, we investigate the levels of VPIN measured for global depositary receipts of Russian firms, documenting a similar increasing pattern prior to the invasion date but generally subsequent to the spikes obtained from the corresponding securities locally traded in Russia. Overall, our results provide additional support for the use of VPIN as a tool for monitoring the likelihood of undesirable geopolitical events.

An Improved Version of the Volume-Synchronized Probability of Informed Trading

This study provides a theoretical basis for the transformation of the probability of informed trading model to the volume-synchronized probability of informed trading (VPIN) setting based on volume buckets. Building on Easley et al. (2011, 2012b), who derive the VPIN metric and provide evidence of its usefulness, we expand the analytical basis of the model and clarify its derivation. We show mathematically that Easley et al.’s VPIN metric becomes unstable for small volume buckets and for infrequent informed trades. In contrast, we use a maximum likelihood estimation to capture the information in volume time, and as a result our improved VPIN mathematical model generates consistent estimates. We also show that the volume time measure helps improve the predictability of VPIN for the flow toxicity.

An Improved Version of the Volume-Synchronized Probability of Informed Trading: A Comment

Ke and Lin provide a valuable alternative approach to estimating the Volume-Synchronized Probability of Informed Trading (VPIN) measure. As we did with estimation of VPIN’s predecessor, PIN, they estimate the parameters underlying their modified VPIN measure. This allows Ke and Lin to extract more information from the available data than we extracted with our direct approximation to VPIN. For markets in which the parameters can be reliably estimated their approach seems both a valuable and viable modification.

Defeating the VPIN: Slipping into the Noise

We use the volume synchronized probability of informed trading (VPIN) to evaluate the performance of different trading strategies in an order-driven simulated market. The performance of this nonparametric metric is assessed in terms of informed traders exploiting its weaknesses when mimicking the trade behavior of uninformed traders and in its protection of market makers from the risk of trading with an informed trader. We show that an informed trader can accumulate a larger position while a pooling equilibrium holds by exploiting the VPIN structure, that the VPIN has a high rate of false-positives, and that it may detect informed trading under certain conditions.

Can VPIN Forecast Geopolitical Events? Evidence from the 2014 Crimean Crisis

We take the recent Crimean Crisis and the sequence of outcomes that led to the intervention by the Russian Army which directly affected equity prices in Russia, investigating how informed traders may have used their advantage to trade prior to the moment markets fell. We compute the Volume-synchronized Probability of Informed Trading (VPIN) for the Russian RTS equity index and for individual stocks, documenting that both in the index level and for most individual equities VPIN levels soared considerably between one and three trading days before market prices reflect the invasion. We also investigate the predictive power of the cumulative distribution of VPIN on future stock prices, showing a statistically significant (negative) relation during the period tensions between Russia and Ukraine were elevated. Last, we investigate the behavior patterns of VPIN to global depository receipts (GDRs) of Russian firms, documenting a similar increasing pattern prior to the invasion date but subsequent to the spikes obtained from the corresponding securities locally traded in Russia. Overall, our results provide additional support for the use of VPIN as a measure of monitoring the likelihood of undesirable geopolitical events.

VPIN, Jump Dynamics and Inventory Announcements in Energy Futures Markets

The Volume‐Synchronized Probability of Informed Trading (VPIN) metric is proposed by Easley et al. (2011, 2012) (Journal of Portfolio Management, 37:118–128; Review of Financial Studies, 25:1457–1493) as a real‐time measure of order flow toxicity in an electronic trading market. This study examines the performance of VPIN around inventory announcements and price jumps in crude oil and natural gas futures markets with a sample period from January 2009 to May 2015. We obtain several interesting results: (i) VPIN increases significantly around inventory announcements with price jumps as well as at jumps not associated with any scheduled announcements. (ii) VPIN does not peak prior to the events but shortly after. (iii) A minor variation of VPIN based on exponential smoothing significantly improves the early warning signal property of VPIN, and this estimate of toxicity returns faster to the pre‐event level. © 2017 Wiley Periodicals, Inc. Jrl Fut Mark 37:542–577, 2017

Predicting a flash crash in the yen/dollar foreign exchange market

ABSTRACTI examine whether the volume-synchronized probability of informed trading (VPIN) can predict a flash crash in the yen/dollar foreign exchange market. The results show that VPIN using bulk volume classification predicted a recent event. However, VPIN using order flows, which are the amount of the ask-side transaction minus those of bid-side, does not.

The role of HFTs in order flow toxicity and stock price variance, and predicting changes in HFTs’ liquidity provisions

This study examines relations between high frequency trading, order flow toxicity, stock price volatility during normal and high order flow toxicity periods, and the predictability of changes in high frequency traders’ liquidity supply and demand. By employing volume-synchronized probability of informed trading (VPIN), a flow toxicity metric, we find a negative relation between high frequency trading and order flow toxicity. Our results also show that VPIN can be a good predictor of high frequency traders’ liquidity supply and demand changes. Finally, we find that high frequency traders’ impact on stock price variance is not uniform and change with order flow toxicity levels and stock volume.

The Role of HFTs in Order Flow Toxicity and Stock Price Variance, and Predicting Changes in HFTss Liquidity Provisions

This study examines relations between high frequency trading, order flow toxicity, stock price volatility during normal and high order flow toxicity periods, and predictability of changes in high frequency traders’ liquidity supply and demand. By employing Volume-synchronized probability of informed trading (VPIN) flow toxicity metric, we find a negative relation between high frequency trading and order flow toxicity. Our results also show that VPIN can be a good predictor of high frequency traders’ liquidity supply and demand changes. Finally, we find that high frequency traders’ impacts on stock price variance are not uniform and change with order flow toxicity levels of markets and stock volume.

On high frequency dynamics between information asymmetry and volatility for securities

This paper investigates the relationship between the volatility of Volume Synchronized Probability of Informed Trading (VPIN) and future short-term volatility of stock returns. We construct a transaction-signed version of VPIN (TR-VPIN) based on tick by tick data on securities traded in the Athens Stock Exchange (ASE) during the Greek sovereign debt crisis. The results show a positive and statistically significant correlation between the volatility of TR-VPIN and future short-term volatility for securities that are exposed to asymmetric information during the period under examination. This evidence expands the existent literature which shows that the absolute order imbalance forecasts absolute returns, suggesting that TR-VPIN is a real-time informative indicator of the Probability of Informed Trading (PIN) in the high frequency domain. Further, the long-range dependence between the conditional volatilities of TR-VPIN and stock returns becomes more significant as we move towards securities which display stronger long memory. This is perfectly in line with the recent empirical evidence in microstructure literature that large past shocks of flow toxicity can lead to volatility through liquidity shortages.

Identifying and Pricing Adverse Selection Risk with VPIN

We perform the first large-sample estimation of the Volume Synchronized Probability of Informed Trading (VPIN) measure on the NYSE TAQ universe, enabling us to test the validity of VPIN with high statistical power and to do traditional asset pricing tests of informed trading. Informed trading measured by VPIN is priced, and is not explained by firm characteristics such as volume, volatility, or liquidity, supporting the validity of the measure. Additionally, we create a novel signed version of VPIN to identify the direction of informed trades. A portfolio long low-VPIN stocks and short high-VPIN ones delivers a monthly five-factor alpha of .18%, which rises to .29% when using signed VPIN. A trading strategy following this signed VPIN factor delivers an annualized five-factor BHAR of 11.45%. We further document a reversal in stock performance in portfolio sorts on signed VPIN, the incorporation of which into a trading strategy improves performance to an annualized BHAR of 17.34%.

Exchange‐Traded Barrier Option and VPIN: Evidence from Hong Kong

We study the validity of the volume‐synchronized probability of informed trading (VPIN) metric in measuring the order flow toxicity around the mandatory call events of callable bull/bear contracts. High VPIN around mandatory call events indicates the existence of large volume imbalances, suggesting high market risk surrounding those call events. In this study, we provide the first direct evidence of the validity of VPIN outside the U.S. market. © 2015 Wiley Periodicals, Inc. Jrl Fut Mark 35:561–581, 2015

Parameter Analysis of the VPIN (Volume Synchronized Probability of Informed Trading) Metric

VPIN (Volume synchronized Probability of Informed trading) is a leading indicator of liquidity-induced volatility. It is best known for having produced a signal more than hours before the Flash Crash of 2010. On that day, the market saw the biggest one-day point decline in the Dow Jones Industrial Average, which culminated to the market value of $1 trillion disappearing, but only to recover those losses twenty minutes later (Lauricella 2010).The computation of VPIN requires the user to set up a handful of free parameters. The values of these parameters significantly affect the effectiveness of VPIN as measured by the false positive rate (FPR). An earlier publication reported that a brute-force search of simple parameter combinations yielded a number of parameter combinations with FPR of 7%. This work is a systematic attempt to find an optimal parameter set using an optimization package, NOMAD (Nonlinear Optimization by Mesh Adaptive Direct Search) by Audet, le digabel, and tribes (2009) and le digabel (2011). We have implemented a number of techniques to reduce the computation time with NOMAD. Tests show that we can reduce the FPR to only 2%.To better understand the parameter choices, we have conducted a series of sensitivity analysis via uncertainty quantification on the parameter spaces using UQTK (Uncertainty Quantification Toolkit). Results have shown dominance of 2 parameters in the computation of FPR. Using the outputs from NOMAD optimization and sensitivity analysis, We recommend A range of values for each of the free parameters that perform well on a large set of futures trading records.

Measuring the Toxicity of Order Flow using High Frequency Data: The Case of KOSPI200 Index Futures

This study presents a new informed trading probability measure VPIN (Volume-Synchronized Probability of Informed Trading) to estimate toxic order flow of KOSPI200 index futures in a high frequency world. This measure does not require to estimate non-observable parameters as PIN. Also, it is estimated based on volume time, so it can estimate toxicity of order flow in a high frequency world. We show a relation between KOSPI200 index futures VPIN and futures market volatility using correlation and conditional probability distribution. A main empirical result is that persistently high VPIN signifies a high risk of subsequent large futures market volatility. It means that VPIN is a useful measure to estimate a toxicity induced volatility.