Three essays on information diffusion and market friction
How markets impound information into asset prices is one of the most important concerns of financial economics. Due to behavioural bias and transaction friction, information could be mispriced in the real world, thus driving market anomalies and return predictability of behavioural factors. My disse...
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2019
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| Online Access: | https://ink.library.smu.edu.sg/etd_coll/202 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1202&context=etd_coll |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | How markets impound information into asset prices is one of the most important concerns of financial economics. Due to behavioural bias and transaction friction, information could be mispriced in the real world, thus driving market anomalies and return predictability of behavioural factors. My dissertation contributes to the literature by investigating how information can be quantified, acquired, disseminated and priced in the financial market with the existence of market frictions.
In Chapter 2, we propose an efficient method based on machine learning and textual analysis to quantify cross industry news and shed light on how news travels across different industries. The results show that cross-industry news contains valuable information about firm fundamentals that is not fully captured by firms’ own news or within-industry peers’ news. Stock prices do not promptly incorporate cross-industry news, generating return predictability. Moreover, underreaction to cross-industry news is more pronounced among smaller stocks that are more illiquid, more volatile, and have fewer analysts following. A long–short strategy exploiting cross-industry news yields annual alphas of over 10%.
In Chapter 3, we construct a novel measure of market wide investor attention by applying a social network analysis to aggregate the attention spillover effects among stocks that are co-mentioned by media news. Empirically, we find that the News Network Triggered Attention index (NNTA), negatively predicts market returns with a monthly in-sample (out-of-sample) R-square of 5.97% (5.80%). In the cross-section, a long-short portfolio based on a news co-occurrence generates a significant monthly alpha of 68 basis points. We further validate the attention spillover effect by showing that news co-mentioning significantly increases Google and Bloomberg search volumes than that of unconditional news coverage. The results hence suggest that attention spillover in a news-based network can lead to significant stock market overvaluations, especially when arbitrage is limited.
Besides behavioural bias, security analysts seem to also contribute to the market friction by issuing biased recommendations. In Chapter 4, we find that the biased recommendations of analysts could be a source of market friction that impede the efficient correction of mispricing. In particular, analysts tend to make more favourable recommendations to overvalued stocks, which have particularly negative abnormal returns ex-post. While analysts whose recommendations are better aligned with anomaly signals are more skilled and elicit stronger recommendation announcement returns. |
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