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Asset pricing across asset classes: the impacts of fines and flows

Rajagopalan, R. D. (2017) Asset pricing across asset classes: the impacts of fines and flows. PhD thesis, University of Reading

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Abstract/Summary

This thesis consists of four empirical studies that examine two types of information, using a unique set of fines and fund flow data, on a multi–asset setting. The first study finds underperformance of between 29 and 57 basis points per month measured as Carhart model alphas on long-term stock returns of firms after announcements of monetary fines. Additionally, environmental fines are perceived by investors to be more of a concern while social, governance and also long-term aspects matter somewhat less. In the second study, I extend the research on fines by examining the inter-link between equities and bonds using short selling ratios and bond returns. Analysis using a fixed-income model shows that high short selling in the context of fines induces negative underperformances in bond returns. In addition, the underperformances are more profound for portfolios with longer remaining years to maturity and in crisis periods. The third study examines short-term reaction of Credit Default Swaps (CDS) spread changes and stock returns to fines. I find the CDS market is able to anticipate illegality news. Both markets react very differently to fines in different legal stages, industries and also by type of fine. Environmental issues are also a key concern in both CDS and stock markets and they also react more to higher fines per market cap. These empirical studies show that information about fines are valuable for investors as on average companies with illegalities underperform relevant benchmarks in the short and long-term. The fourth study involves fund flows on a global scale in Exchange Traded Funds (ETFs). I use panel data models and find that the explanatory power of ETF fund flows are similar to macro-economic variables in explaining indices returns. I also find investors could use ETF fund flows as information to understand market movements especially globally.

Item Type:Thesis (PhD)
Thesis Supervisor:Hoepner, A.
Thesis/Report Department:Henley Business School
Identification Number/DOI:
Refereed:No
Divisions:Henley Business School > ICMA Centre
ID Code:73735

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