Composition of a protein-like fluorophore of dissolved organic matter in coastal wetland and estuarine ecosystems

This study demonstrates the compositional heterogeneity of a protein-like fluorescence emission signal (T-peak; excitation/emission maximum at 280/325 nm) of dissolved organic matter (DOM) samples collected from subtropical river and estuarine environments. Natural water samples were collected from the Florida Coastal Everglades ecosystem. The samples were ultrafiltered and excitation–emission fluorescence matrices were obtained. The T-peak intensity correlated positively with N concentration of the ultrafiltered DOM solution (UDON), although, the low correlation coefficient (r2=0.140, p<0.05) suggested the coexistence of proteins with other classes of compounds in the T-peak. As such, the T-peak was unbundled on size exclusion chromatography. The elution curves showed that the T-peak was composed of two compounds with distinct molecular weights (MW) with nominal MWs of about >5×104 (T1) and ∼7.6×103 (T2) and with varying relative abundance among samples. The T1-peak intensity correlated strongly with [UDON] (r2=0.516, p<0.001), while T2-peak did not, which suggested that the T-peak is composed of a mixture of compounds with different chemical structures and ecological roles, namely proteinaceous materials and presumably phenolic moieties in humic-like substances. Natural source of the latter may include polyphenols leached from senescent plant materials, which are important precursors of humic substances. This idea is supported by the fact that polyphenols, such as gallic acid, an important constituent of hydrolysable tannins, and condensed tannins extracted from red mangrove (Rhizophora mangle) leaves exhibited the fluorescence peak in the close vicinity of the T-peak (260/346 and 275/313 nm, respectively). Based on this study the application of the T-peak as a proxy for [DON] in natural waters may have limitations in coastal zones with significant terrestrial DOM input.

Liquidity, Governance and Adverse Selection in Asset Pricing

A plethora of recent literature on asset pricing provides plenty of empirical evidence on the importance of liquidity, governance and adverse selection of equity on pricing of assets together with more traditional factors such as market beta and the Fama-French factors. However, literature has usually stressed that these factors are priced individually. In this dissertation we argue that these factors may be related to each other, hence not only individual but also joint tests of their significance is called for. In the three related essays, we examine the liquidity premium in the context of the finer three-digit SIC industry classification, joint importance of liquidity and governance factors as well as governance and adverse selection. Recent studies by Core, Guay and Rusticus (2006) and Ben-Rephael, Kadan and Wohl (2010) find that governance and liquidity premiums are dwindling in the last few years. One reason could be that liquidity is very unevenly distributed across industries. This could affect the interpretation of prior liquidity studies. Thus, in the first chapter we analyze the relation of industry clustering and liquidity risk following a finer industry classification suggested by Johnson, Moorman and Sorescu (2009). In the second chapter, we examine the dwindling influence of the governance factor if taken simultaneously with liquidity. We argue that this happens since governance characteristics are potentially a proxy for information asymmetry that may be better captured by market liquidity of a company’s shares. Hence, we jointly examine both the factors, namely, governance and liquidity – in a series of standard asset pricing tests. Our results reconfirm the importance of governance and liquidity in explaining stock returns thus independently corroborating the findings of Amihud (2002) and Gompers, Ishii and Metrick (2003). Moreover, governance is not subsumed by liquidity. Lastly, we analyze the relation of governance and adverse selection, and again corroborate previous findings of a priced governance factor. Furthermore, we ascertain the importance of microstructure measures in asset pricing by employing Huang and Stoll’s (1997) method to extract an adverse selection variable and finding evidence for its explanatory power in four-factor regressions.