Thy Neighbor's Portfolio: Word‐of‐Mouth Effects in the Holdings and Trades of Money Managers

Hong, Harrison; Kubik, Jeffrey D.; Stein, Jeremy C.

doi:10.1111/j.1540-6261.2005.00817.x

Cited by 843 publications

(366 citation statements)

References 41 publications

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“…Our work contributes most directly to the empirical literature on peer effects in investment decisions, some observational (e.g., Hong, Kubik, andStein (2004, 2005), Ivkovic and Weisbenner (2007), Brown, Ivkovic, Smith, and Weisbenner (2008), Li (2009), andBanerjee, Chandrasekhar, Duflo, andJackson (2011)), some experimental (e.g., Duflo and Saez (2003), Beshears, Choi, Laibson, Madrian, and Milkman (2011)). Our paper goes beyond the existing literature by using experimental variation to separately identify the causal roles of different channels of peer effects.…”

Section: Introductionmentioning

confidence: 99%

Understanding Mechanisms Underlying Peer Effects: Evidence From a Field Experiment on Financial Decisions

et al. 2014

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Using a high-stakes field experiment conducted with a financial brokerage, we implement a novel design to separately identify two channels of social influence in financial decisions, both widely studied theoretically. When someone purchases an asset, his peers may also want to purchase it, both because they learn from his choice ("social learning") and because his possession of the asset directly affects others' utility of owning the same asset ("social utility"). We randomize whether one member of a peer pair who chose to purchase an asset has that choice implemented, thus randomizing his ability to possess the asset. Then, we randomize whether the second member of the pair: (i) receives no information about the first member, or (ii) is informed of the first member's desire to purchase the asset and the result of the randomization that determined possession. This allows us to estimate the effects of learning plus possession, and learning alone, relative to a (no information) control group. We find that both social learning and social utility channels have statistically and economically significant effects on investment decisions. Evidence from a follow-up survey reveals that social learning effects are greatest when the first (second) investor is financially sophisticated (financially unsophisticated); investors report updating their beliefs about asset quality after learning about their peer's revealed preference; and, they report motivations consistent with "keeping up with the Joneses" when learning about their peer's possession of the asset. These results can help shed light on the mechanisms underlying herding behavior in financial markets and peer effects in consumption and investment decisions.

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Section: Introductionmentioning

confidence: 99%

Understanding Mechanisms Underlying Peer Effects: Evidence From a Field Experiment on Financial Decisions

et al. 2014

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“…Their seminal empirical herding measure has thereafter been applied in many studies providing similar evidence (Note 2). Regarding the communication among institutional investors, positive evidence of influential effects has been provided by Shiller and Pound (1989); Arnswald (2001); Hong et al (2005); Pareek (2011). In the domain of retail investors, analogous behaviour has been revealed by Hong et al (2004); Ivkovic and Weisbenner (2007); Simonov (2005a, 2005b).…”

Section: Introductionmentioning

confidence: 78%

Does Social Interaction destabilise Financial Markets?

König¹

2014

RBM

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for valuable comments and suggestions. AbstractWith this paper, I propose a simple asset pricing model that accounts for the influence from social interaction. Investors are assumed to make up their mind about an asset's price based on a forecasting strategy and its past profitability as well as on the contemporaneous expectations of other market participants. Empirically analysing stocks in the DAX30 index, I provide evidence that social interaction rather destabilises financial markets. At least, it does not have a stabilising effect.

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“…For informed agents with limited investment capacity, they are motivated to spread informative but imprecise stock tips so that followers trade on the advice and move prices as they wanted [64]. In particular, investors are often influenced by word of mouth [65, 66, 67], reflecting their bounded rationalities in trading. Consequently, they subject to a persuasion bias, thus generating a positive relationship between the stock return and Bullishness indicator which measures the share of surplus positive signals and gives more weight to a greater number of positive posts [68].…”

Section: Resultsmentioning

confidence: 99%

Weibo sentiments and stock return: A time-frequency view

Liu

Zhao

et al. 2017

PLoS ONE

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This study provides new insights into the relationships between social media sentiments and the stock market in China. Based on machine learning, we classify microblogs posted on Sina Weibo, a Twitter’s variant in China into five detailed sentiments of anger, disgust, fear, joy, and sadness. Using wavelet analysis, we find close positive linkages between sentiments and the stock return, which have both frequency and time-varying features. Five detailed sentiments are positively related to the stock return for certain periods, particularly since October 2014 at medium to high frequencies of less than ten trading days, when the stock return is undergoing significant fluctuations. Sadness appears to have a closer relationship with the stock return than the other four sentiments. As to the lead-lag relationships, the stock return causes Weibo sentiments rather than reverse for most of the periods with significant linkages. Compared with polarity sentiments (negative vs. positive), detailed sentiments provide more information regarding relationships between Weibo sentiments and the stock market. The stock market exerts positive effects on bullishness and agreement of microblogs. Meanwhile, agreement leads the stock return in-phase at the frequency of approximately 40 trading days, indicating that less disagreement improves certainty about the stock market.

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Thy Neighbor's Portfolio: Word‐of‐Mouth Effects in the Holdings and Trades of Money Managers

Cited by 843 publications

References 41 publications

Understanding Mechanisms Underlying Peer Effects: Evidence From a Field Experiment on Financial Decisions

Understanding Mechanisms Underlying Peer Effects: Evidence From a Field Experiment on Financial Decisions

Does Social Interaction destabilise Financial Markets?

Weibo sentiments and stock return: A time-frequency view

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