Three essays on the interrelationships among financial restatements, corporate governance, market microstructure and the firm's rate of return

The increase in the number of financial restatements in recent years has resulted in a significant decrease in the amount of market capitalization for restated companies. Prior literature did not differentiate between single and multiple restatements announcements. This research investigated the inter-relationships among multiple financial restatements, corporate governance, market microstructure and the firm’s rate of return in the form of three essays by differentiating between single and multiple restatement announcement companies. First essay examined the stock performance of companies announcing the financial restatement multiple times. The postulation is that prior research overestimates the abnormal return by not separating single restatement companies from multiple restatement companies. This study investigated how market penalizes the companies that announce restatement more than once. Differentiating the restatement announcement data based on number of restatement announcements, the results supported the non persistence hypothesis that the market has no memory and negative abnormal returns obtained after each of the restatement announcements are completely random. Second essay examined the multiple restatement announcements and its perceived resultant information asymmetry around the announcement day. This study examined the pattern of information asymmetry for these announcements in terms of whether the bid-ask spread widens around the announcement day. The empirical analysis supported the hypotheses that the spread does widen not only around the first restatement announcement day but around every subsequent announcement days as well. The third essay empirically examined the financial and corporate governance characteristics of single and multiple restatement announcements companies. The analysis showed that corporate governance variables influence the occurrence of multiple restatement announcements and can distinguish multiple restatements announcement companies from single restatement announcement companies.

Small farmer market knowledge and specialty coffee commodity chains in Western Highlands Guatemala

For producers motivated by their new status as self-employed, landowning, capitalist coffee growers, specialty coffee presents an opportunity to proactively change the way they participate in the international market. Now responsible for determining their own path, many producers have jumped at the chance to enhance the value of their product and participate in the new "fair trade" market. But recent trends in the international coffee price have led many producers to wonder why their efforts to produce a certified Fair Trade and organic product are not generating the price advantage they had anticipated. My study incorporates data collected in eighteen months of fieldwork, including more than 45 interviews with coffee producers and fair trade roasters, 90 surveys of coffee growers, and ongoing participant observation to understand how fair trade certification, as both a market system and development program, meets the expectations of the coffee growers. By comparing three coffee cooperatives that have engaged the Fair Trade system to disparate ends, the results of this investigation are three case studies that demonstrate how global processes of certification, commodity trade, market interaction, and development aid effect social and cultural change within communities. This study frames several lessons learned in terms of (1) socioeconomic impacts of fair trade, (2) characteristics associated with positive development encounters, and (3) potential for commodity producers to capture value further along their global value chain. Commodity chain comparisons indicate the Fair Trade certified cooperative receives the highest per-pound price, though these findings are complicated by costs associate with certification and producers' perceptions of an "unjust" system. Fair trade-supported projects are demonstrated as more "successful" in the eyes of recipients, though their attention to detail can just as easily result in "failure". Finally, survey results reveal just how limited is the market knowledge of producers in each cooperative, though fair trade does, in fact, provide a rare opportunity for producers to learn about consumer demand for coffee quality. Though bittersweet, the fair trade experiences described here present a learning opportunity for a wide range of audiences, from the certified to the certifiers to the concerned public and conscientious consumer.

A comprehensive study of bankruptcy prediction: Accounting ratios, market variables, and microstructure

Bankruptcy prediction has been a fruitful area of research. Univariate analysis and discriminant analysis were the first methodologies used. While they perform relatively well at correctly classifying bankrupt and nonbankrupt firms, their predictive ability has come into question over time. Univariate analysis lacks the big picture that financial distress entails. Multivariate discriminant analysis requires stringent assumptions that are violated when dealing with accounting ratios and market variables. This has led to the use of more complex models such as neural networks. While the accuracy of the predictions has improved with the use of more technical models, there is still an important point missing. Accounting ratios are the usual discriminating variables used in bankruptcy prediction. However, accounting ratios are backward-looking variables. At best, they are a current snapshot of the firm. Market variables are forward-looking variables. They are determined by discounting future outcomes. Microstructure variables, such as the bid-ask spread, also contain important information. Insiders are privy to more information that the retail investor, so if any financial distress is looming, the insiders should know before the general public. Therefore, any model in bankruptcy prediction should include market and microstructure variables. That is the focus of this dissertation. The traditional models and the newer, more technical models were tested and compared to the previous literature by employing accounting ratios, market variables, and microstructure variables. Our findings suggest that the more technical models are preferable, and that a mix of accounting and market variables are best at correctly classifying and predicting bankrupt firms. Multi-layer perceptron appears to be the most accurate model following the results. The set of best discriminating variables includes price, standard deviation of price, the bid-ask spread, net income to sale, working capital to total assets, and current liabilities to total assets.

Frameworks for Systemic and Structural Analysis of Financial Innovations in Infrastructure

Financial innovations have emerged globally to close the gap between the rising global demand for infrastructures and the availability of financing sources offered by traditional financing mechanisms such as fuel taxation, tax-exempt bonds, and federal and state funds. The key to sustainable innovative financing mechanisms is effective policymaking. This paper discusses the theoretical framework of a research study whose objective is to structurally and systemically assess financial innovations in global infrastructures. The research aims to create analysis frameworks, taxonomies and constructs, and simulation models pertaining to the dynamics of the innovation process to be used in policy analysis. Structural assessment of innovative financing focuses on the typologies and loci of innovations and evaluates the performance of different types of innovative financing mechanisms. Systemic analysis of innovative financing explores the determinants of the innovation process using the System of Innovation approach. The final deliverables of the research include propositions pertaining to the constituents of System of Innovation for infrastructure finance which include the players, institutions, activities, and networks. These static constructs are used to develop a hybrid Agent-Based/System Dynamics simulation model to derive propositions regarding the emergent dynamics of the system. The initial outcomes of the research study are presented in this paper and include: (a) an archetype for mapping innovative financing mechanisms, (b) a System of Systems-based analysis framework to identify the dimensions of Systems of Innovation analyses, and (c) initial observations regarding the players, institutions, activities, and networks of the System of Innovation in the context of the U.S. transportation infrastructure financing.

Development, validation and uncertainty analysis of quantitative structure and activity relationship models for Log P of disinfection by -products

Hydrophobicity as measured by Log P is an important molecular property related to toxicity and carcinogenicity. With increasing public health concerns for the effects of Disinfection By-Products (DBPs), there are considerable benefits in developing Quantitative Structure and Activity Relationship (QSAR) models capable of accurately predicting Log P. In this research, Log P values of 173 DBP compounds in 6 functional classes were used to develop QSAR models, by applying 3 molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) by Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated based on the Organization for Economic Co-operation and Development (OECD) principles. The model Applicability Domain (AD) and mechanistic interpretation were explored. Considering the very complex nature of DBPs, the established QSAR models performed very well with respect to goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models were found to be significant with a correlation coefficient R2 from 81% to 98%. The Leverage Approach by Williams Plot was applied to detect and remove outliers, consequently increasing R 2 by approximately 2% to 13% for different DBP classes. The developed QSAR models were statistically validated for their predictive power by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods. Finally, Monte Carlo simulation was used to assess the variations and inherent uncertainties in the QSAR models of Log P and determine the most influential parameters in connection with Log P prediction. The developed QSAR models in this dissertation will have a broad applicability domain because the research data set covered six out of eight common DBP classes, including halogenated alkane, halogenated alkene, halogenated aromatic, halogenated aldehyde, halogenated ketone, and halogenated carboxylic acid, which have been brought to the attention of regulatory agencies in recent years. Furthermore, the QSAR models are suitable to be used for prediction of similar DBP compounds within the same applicability domain. The selection and integration of various methodologies developed in this research may also benefit future research in similar fields.

Three Essays on the Interrelationships Among Financial Restatements, Corporate Governance, Market Microstructure and the Firm's Rate of Return

The increase in the number of financial restatements in recent years has resulted in a significant decrease in the amount of market capitalization for restated companies. Prior literature does not differentiate between single and multiple restatements announcements. This research investigates the inter-relationships among multiple financial restatements, corporate governance, market microstructure and the firm's rate of return in the form of three essays by differentiating between single and multiple restatement announcement companies. First essay examines the stock performance of companies announcing the financial restatement multiple times. The postulation is that prior research overestimates the abnormal return by not separating single restatement companies from multiple restatement companies. This study investigates how market penalizes the companies that announce restatement more than once. Differentiating the restatement announcement data based on number of restatement announcements, the results support for non persistence hypothesis that the market has no memory and negative abnormal returns obtained after each of the restatement announcements are completely random. Second essay examines the multiple restatement announcements and its perceived resultant information asymmetry around the announcement day. This study examines the pattern of information asymmetry for these announcements in terms of whether the bid-ask spread widens around the announcement day. The empirical analysis supports the hypotheses that the spread does widen not only around the first restatement announcement day but around every subsequent announcement days as well. The third essay empirically examines the financial and corporate governance characteristics of single and multiple restatement announcements companies. The analysis shows that corporate governance variables influence the occurrence of multiple restatement announcements and can distinguish multiple restatements announcement companies from single restatement announcement companies.