Three Essays in Asset Pricing and Investment

The first essay documents that when Treasury securities go “off the run,” search frictions heighten and matching trades becomes more difficult. This event allows us to isolate the effects of search frictions on market quality and asset pricing. Price impact and the sensitivity of bid-ask spreads to trade size are significantly higher for off-the-runs, which are difficult to explain using models of asymmetric information. High search frictions result in sluggish price adjustment to new information and low informativeness of trades, leading to poor price discovery. Despite these adverse effects on market quality, we find no evidence that search frictions cause segmentation in the Treasury market. The second essay investigates the role of style-investing in bond price formation and trading processes. Using a unique dataset from Moody’s rating re-calibration, we find that changes in rating labels have power impacts not only on comovement in bond prices but also volatility, liquidity, and trading. Comovements are magnified by interdealer trading to balance inventories and institutional trading to reach for yields. Importantly, rating-style investing induces return predictability in municipal bonds and cross-market comovement with corporate bonds. There is strong evidence that rating-style investing drives these results through correlated trading, and that the effects increase with behavioral biases and trading frictions. The third essay employs a regression-based approach for combining analyst forecasts to obtain optimal forecasts. We find that this approach produces substantially better earnings forecasts than consensus forecast and other conventional methods. Forecasting gain increases with dispersion and biasedness of analyst forecasts and under/overreactions to earnings news. Regression-based combination forecasts consistently outperform consensus forecasts by significant margins over time and across different types of firms and sectors. Using the regressed combination forecast generates larger earnings response coefficients and reduces the anomaly of post-earnings-announcement drift. Results strongly suggest that the regressed combination of analyst forecasts provides a better proxy for expected earnings.

Grantor dc:publisher

State University of New York at Buffalo

Year dc:date.issued

2018

Author dc:creator

• Tao, Xinyuan; 0000-0003-1888-3211

Contributors dc:contributor

• Wu, Chunchi
• Finance

• finance

Statement dc:rights

• Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.
• Copyright retained by author.

Language dc:language

eng

Handle dc:identifier

http://hdl.handle.net/10477/78006

OAI record oai:identifier

https://ubir.buffalo.edu/oai/request?verb=GetRecord&metadataPrefix=qdc&identifier=oai%3Aubir.buffalo.edu%3A10477%2F78006