Costly Information Acquisition, Social Networks, and Asset Prices: Experimental Evidence

Halim, Edward; Riyanto, Yohanes E.; Roy, Nilanjan

doi:10.1111/jofi.12768

Cited by 36 publications

(28 citation statements)

References 101 publications

(139 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…What might be the potential drivers for the observed negative relationship between Twitter sentiment and liquidity? First, Halim et al (2019) find that information exchange via social communication does not improve the quality of price informativeness. The authors show that the ability of prices to correctly predict the true price is not improved with information sharing in a social network.…”

Section: Resultsmentioning

confidence: 94%

Measuring the effect of investor sentiment on liquidity

Dunham¹,

García

2020

View full text Add to dashboard Cite

PurposeThe purpose of this paper is to examine the effect of firm-level investor sentiment on a firm's share liquidity.Design/methodology/approachThe authors use Bloomberg's firm-level, daily investor sentiment scores derived from firm-level news and Twitter content in a regression model to explain the variability in a firm's share liquidity.FindingsThe results indicate that improvements (deterioration) in investor sentiment derived solely from Twitter content lead to a decrease (increase) in the average firm's share liquidity. Results, although not as strong, are opposite for investor sentiment derived solely from news articles: improvements (deterioration) in news sentiment leads to an increase (decrease) in the average firm's share liquidity.Research limitations/implicationsThe proxy for share liquidity is the bid-ask spread, which may be an imperfect measure of liquidity. The Amihud illiquidity measure was used as an alternative proxy and yield similar results. The results have important implications for investors in assessing the determinants of share liquidity.Practical implicationsThe sample period covers four years (2015–2018), which is determined by the availability of the Bloomberg sentiment data.Social implicationsInvestors increasing use of social media to express views on particular stocks and seek information that might be used in the investment decision-making process. The study links investor sentiment derived from social media (Twitter) to share liquidity.Originality/valueBy examining the relationship between a firm's sentiment and the firm's share liquidity, this paper advances the authors' understanding of the factors that drive a firm's share liquidity. To the authors' knowledge, this is the first study to link investor sentiment derived from firm-level news and Twitter content to a firm's share liquidity.

show abstract

Section: Resultsmentioning

confidence: 94%

Measuring the effect of investor sentiment on liquidity

Dunham¹,

García

2020

View full text Add to dashboard Cite

show abstract

“…However, the Capital Asset Pricing Model (CAPM) as in Eq. (1) is very often employed as shown in some studies ( Bartholdy and Peare, 2005 ; Da et al., 2012 ; Fama and French, 1996b ; Jacobs and Shivdasani, 2012 ; Zhang, 2017 ) even some recent studies (e.g., Binsbergen and Opp, 2019 ; Clementi and Palazzo, 2019 ; Doshi et al., 2019 ; Halim et al., 2019 ; Martin and Wagner, 2019 ; Zhang, 2019 ) showed its weaknesses. One of the possible reasons the CAPM is widely used in practice is that this model shows a very simple linear relationship between a stock's Beta and expected returns.…”

Section: Introductionmentioning

confidence: 99%

Is estimating the Capital Asset Pricing Model using monthly and short-horizon data a good choice?

Pham

Phuoc

2020

Heliyon

View full text Add to dashboard Cite

This research argued for estimating the Capital Asset Pricing Model (CAPM) using daily and medium-horizon data over monthly and short horizon-data. Using a Gibbs sample, the Bayesian framework via both parametric and non-parametric Bayes estimators, confidence interval approach, and six data sets (two daily, two weekly, and two monthly data) from a sample of 150 randomly selected S&P 500 stocks from 2007 – 2019, the empirical results showed that the CAPM using daily data yielded a statistically significant higher model fit and smaller Beta standard deviation, model error, and Alpha compared with monthly data. The CAPM using medium-horizon data yielded a statistically significant higher model fit, smaller Beta standard deviation and Alpha, and much less zeroed Betas compared with short-horizon data. These findings show 1) daily data is more reliable and efficient, has higher forecasting power, and fits better with the assumption of market efficiency compared with monthly data. 2) Medium-horizon data is more reliable and efficient, has more explanatory power, and fits better with the assumption of market efficiency compared with monthly data. Therefore, these findings challenge the common practices of using monthly (quarterly/annually) and short-horizon data among the practitioners and researchers in asset pricing work.

show abstract

“…The closest paper to ours is the recent experimental work of Halim, Riyanto and Roy (2018), which assesses the effect of social networks on traders' incentives to acquire information. This acquisition, in turn, impacts the aggregation of information in markets.…”

Section: On Trader Communication and Informational Efficiencymentioning

confidence: 99%

“…We are thus interested in the strategic transmission of private information among traders in the absence of social networks and costly acquisition of information. In doing so, we take up where Halim, Riyanto and Roy (2018) left off:…”

Section: On Trader Communication and Informational Efficiencymentioning

confidence: 99%

Let’s Chat… When Communication Promotes Efficiency in Experimental Asset Markets

2020

View full text Add to dashboard Cite

The growing prevalence of stock market chat rooms and social media suggests communication between traders may affect market outcomes. Using data from a series of laboratory experiments, we study the causal effect of trader communication on the price efficiency of markets. We show that communication allows markets to convey private information more effectively. This effect is most pronounced when the communication platform publicizes a reputation score that might identify a person as not being truthful. This illustrates the need for market designers to consider social interactions when designing market institutions to leverage the social motives that foster information aggregation.

show abstract

Costly Information Acquisition, Social Networks, and Asset Prices: Experimental Evidence

Cited by 36 publications

References 101 publications

Measuring the effect of investor sentiment on liquidity

Measuring the effect of investor sentiment on liquidity

Is estimating the Capital Asset Pricing Model using monthly and short-horizon data a good choice?

Let’s Chat… When Communication Promotes Efficiency in Experimental Asset Markets

Contact Info

Product

Resources

About