Identifying Information Asymmetry in Securities Markets

Back, Kerry; Crotty, Kevin; Li, Tao

doi:10.1093/rfs/hhx133

Cited by 59 publications

(13 citation statements)

References 52 publications

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“…Disagreement among investor in the same group (dealer or customer) and/ or disagreement between dealer groups and customer groups create a different market position for each investor and make yield swing from one extreme position to other extremes (O'Hara, 2003) Each investor's peculiar investment horizon drives to a different information set. Different information creates a different trading position in the market (Back, Crotty, & Li, 2018). There are many research which investigates how price discovery process affects bond yield volatility.…”

Section: Buddi Wibowomentioning

confidence: 99%

Private information, yield volatility, and interaction between groups of investors: Case of Indonesia government bond market

Wibowo

2019

Jurnal Keuangan Dan Perbankan

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Relation of private information and yield volatility in the relatively low liquid market such as Indonesia government bond market is one of the recent important research topics in finance. Because private information accumulation mastery affects interaction pattern between dealers and their customers, it is urgent to identify this price mechanism in government bond market, especially in a low liquid market which small trading volume may create serious yield volatility. If there is a substantial disagreement on bond fair value because of distinct private information quality between each those groups of investors success to accumulate it, bond price will fluctuate wildly. This paper study the dealer and customer order flow interaction to identify which side that have a greater impact on yield volatility. The results show that from the two layers of order flow, disagreement between dealers and customers are slightly in a short-term and medium-term bond, so their volatility is relatively low. But for a long-term bond, disagreement is substantial and creates high yield volatility. Different trading platform and strategy between dealer and customer play an important role in creating high yield fluctuations. How to Cite: Wibowo, B. (2019). Private information, yield volatility, and interaction between groups of investors: Case of Indonesia government bond market.

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Section: Buddi Wibowomentioning

confidence: 99%

Private information, yield volatility, and interaction between groups of investors: Case of Indonesia government bond market

Wibowo

2019

Jurnal Keuangan Dan Perbankan

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“…To gauge the precision of our measures in this context, we repeat the simulation analysis while allowing the proportion of informed trade η to be random across observations. This version of our model can be interpreted as a hybrid of our Kyle (1985)-type model in Section 2 and the PIN model by Easley et al (1996), in which arrival of information is random, similar to Back et al (2017). Panel B of 2 reports simulation results for the case where the number of uninformed liquidity seekers is fixed at 1,000, while the number of informed liquidity seekers is in each of the T = 100 trading sessions randomly drawn from a Binomial distribution.…”

Section: Simulationsmentioning

confidence: 99%

Asymmetric Information and the Distribution of Trading Volume

Lof

Bommel

2017

SSRN Journal

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We propose the Volume Coefficient of Variation (VCV), the ratio of the standard deviation to the mean of trading volume, as a new and easily computable measure of information asymmetry in security markets. We use a simple microstructure model to demonstrate that VCV is strictly increasing in the proportion of informed trade. Empirically, we find that firm-year observations of VCV, computed from daily trading volumes, are correlated with extant firm-level measures of asymmetric information in the cross-section of US stocks. Moreover, VCV increases following exogenous reductions in analyst coverage induced by brokerage closures, and steeply decreases around earnings announcements.

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“…The second is related to problems such as numerical overflow and so-called time-horizon issues which arise in the estimation of PIN (e.g., see Easley, Engle, O'Hara, and Wu, 2008;Tay, Ting, Tse, and Warachka, 2009;Lin and Ke, 2011). The third is related to the bias in the theoretical underpinnings of PIN (e.g., see Collin-Dufresne and Fos, 2015; Duarte et al, 2015;Back, Crotty, and Li, 2016).…”

Section: Psos and Difference Of Opinionmentioning

confidence: 99%

PIN, Adjusted PIN, and PSOS: Difference of Opinion in the Korean Stock Market

Eom¹,

Kang

Kwon

2016

SSRN Journal

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Duarte and Young (2009) decompose PIN into adjusted PIN (AdjPIN) and probability of trading caused by symmetric order flow shocks (PSOS). We explore sources of PSOS in the Korean stock market and examine the relation between PSOS and stock returns. Using transaction data with trader types and initiator information, we find that AdjPIN is not priced, while PSOS is negatively priced, a finding that Lai, Ng, and Zhang (2014) labeled "puzzling." We find that the negative price of PSOS comes from differences of opinion among domestic individual investors on the significance of public news.

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Identifying Information Asymmetry in Securities Markets

Cited by 59 publications

References 52 publications

Private information, yield volatility, and interaction between groups of investors: Case of Indonesia government bond market

Private information, yield volatility, and interaction between groups of investors: Case of Indonesia government bond market

Asymmetric Information and the Distribution of Trading Volume

PIN, Adjusted PIN, and PSOS: Difference of Opinion in the Korean Stock Market

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