An Equilibrium Model of Asset Trading with Sequential Information Arrival

Jennings, Robert H.; Starks, Laura T.; Fellingham, John C.

doi:10.2307/2327469

Cited by 122 publications

(110 citation statements)

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“…21 We control for liquidity, specifically the volume traded, since there is likely to be a causal relationship between volume traded and stock returns. Evidence of this is given in a number of studies including Jennings, Starks and Fellingham (1981), Gallant, Rossi and Tauchen (1992), Heimstra and Jones (1994), Blume, Easley and O'Hara (1994) and Brennan, Chordia and Subramanyam (1998).…”

Section: Liquidity Effectsmentioning

confidence: 95%

Lead-lag relationships in an embryonic stock market: Exploring the role of institutional ownership and liquidity

Arjoon

Bougheas

Milner

2016

Research in International Business and Finance

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This paper investigates the influence of institutional ownership and liquidity on stock return relationships for an embryonic and relatively illiquid stock market. Using daily, individual stock data for Trinidad and Tobago from 2001 to 2012 and a VAR modelling approach, we find for firms of all sizes and levels of analyst coverage that the returns of more institutionally favoured stocks lead those with less institutional ownership. Distinctively, greater institutional coverage is shown not to be associated with greater liquidity, though liquidity levels do condition the influence of institutional ownership. This indicates that institutional owners have information advantages relative to other stock owners.JEL Classification: G14, G23

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Section: Liquidity Effectsmentioning

confidence: 95%

Lead-lag relationships in an embryonic stock market: Exploring the role of institutional ownership and liquidity

Arjoon

Bougheas

Milner

2016

Research in International Business and Finance

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“…In contrast, some of the latter studies (Chen et al, 2001;Aroga and Nieto, 2004) suggest no reduction in the persistence of volatility. Sequential information arrival hypothesis is another framework to explain volume and volatility correlation developed and extended by the studies of Copeland (1976), Jennings et al, (1981), Jennings and Barry (1983) and Samlock and Starks (1985). In this model, new information is disseminated sequentially to investors.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Volatility Estimation and Stock Price Prediction in the Nigerian Stock Market

Ajao

Ugochukwu

2012

International Journal of Financial Research

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This study aimed at understanding the Nigerian Stock Market with regards to volatility and prediction, to this effect the month end stock prices of four major companies from the period January 2005 to December, 2009 was used as proxy. The study made use of the Autoregressive Conditional heteroskedasticity (ARCH) to estimate and find out the presence of volatility. The study found the presence of volatility in all the four stock prices used, while stock price volatility was then regressed against stock prices to determine their predictability. The results however, revealed that out of the four companies, only two companies' stock prices were predicted by volatility in their stock prices, while past stock prices predicted current stock prices implying that the market does not follow a random walk. As a result of these, it is recommended that activities of corporate insiders should be properly checked, to reduce the predictability of stock prices, information should be known and made public to all investors. Also policy makers are advised to review their economic policies and should be careful in their use of the Nigerian bourse as a barometer to reflect performance in the general economy as our findings suggests that this could be misleading.

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“…Various theories have been developed to explain the volume-volatility relationship including the mixture of distribution hypothesis Clark (1973), Epps and Epps (1976), Tauchen and Pitts (1983), Lamoureux and Lastrapes (1990), Andersen (1996), Bollerslev and Jubinski (1999), the sequential information arrival hypothesis (Copeland, 1976;Morse, 1980;Jennings, Starks, & Fellingham, 1981;Jennings & Barry, 1983;Darrat, Rahman, & Zhong, 2003), the differences of opinion hypothesis (Harris & Raviv, 1993;Shale, 1993), and the asymmetric information hypothesis (Kyle, 1985;Admati & Pfleiderer, 1988;Holthausen & Verrecchia, 1990;Foster & Viswanathan, 1996;Chordia & Subrahmanyam, 2004). All these theories predict a positive relationship between www.ccsenet.org/ibr International Business Research Vol.…”

Section: Introductionmentioning

confidence: 99%

Number of Transactions, Trade Size and the Volume-Volatility Relationship: An Interday and Intraday Analysis on the Tunisian Stock Market

Belhaj¹,

Abaoub²,

Mahjoubi³

2015

IBR

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The purpose of this paper is to study the relationship between trading volume and unconditional price volatility on the Tunisian Stock Market in order to provide an empirical support to either the hypothesis of the strategic asymmetric information models or the hypothesis of competitive asymmetric information Models. More specifically, it aims to test the volume-volatility relationship and identify the component of trading volume (number of transactions or trade size) that explains more price volatility and drives this relationship. Our empirical tests are based on daily and intraday data related to the 43 most active and dynamic listed stocks on the Tunisian Stock Market from 02 January 2008 to 29 June 2012 for the daily analysis and from 03 October 2011 to 28 September 2012 for the intraday data. Our empirical analysis reveals several results. First, we confirm the strong positive contemporaneous relationship between trading volume and unconditional price volatility similarly to the Mixture of Distribution Hypothesis (MDH). Second, we show that whatever the frequency of used data, daily or intraday, the number of transactions is much more significant than the trade size in the explanation of the volatility and seems to be the dominant factor that drives the positive volume-volatility relationship. Third, we find that trade size has no explanatory power of the volatility beyond that the number of transactions and the positivity of the volume-volatility relationship reflects simply the positive relationship between number of transactions and volatility. Overall, our empirical results support the hypothesis of the strategic models and challenge the hypothesis of competitive models.Keywords: trading volume, unconditional volatility, number of transactions, trade size, volme-volatility relationship, information flow, mixture of distribution hypothesis, competitive asymmetric information models, strategic asymmetric information models

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An Equilibrium Model of Asset Trading with Sequential Information Arrival

Cited by 122 publications

References 0 publications

Lead-lag relationships in an embryonic stock market: Exploring the role of institutional ownership and liquidity

Lead-lag relationships in an embryonic stock market: Exploring the role of institutional ownership and liquidity

Volatility Estimation and Stock Price Prediction in the Nigerian Stock Market

Number of Transactions, Trade Size and the Volume-Volatility Relationship: An Interday and Intraday Analysis on the Tunisian Stock Market

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