The Empirical Risk-Return Relation: a factor analysis approach

Financial economists have long been interested in the empirical relation between the conditional mean and conditional volatility of excess stock market returns, often referred to as the risk-return relation. Unfortunately, the body of empirical evidence on the risk-return relation is mixed and inconclusive. A key criticism of the existing empirical literature relates to the relatively small amount of conditioning information used to model the conditional mean and conditional volatility of excess stock market returns. To the extent that financial market participants have information not reflected in the chosen conditioning variables, measures of conditional mean and conditional volatility--and ultimately the risk-return relation itself--will be misspecified and possibly highly misleading. We consider one remedy to these problems using the methodology of dynamic factor analysis for large datasets, whereby a large amount of economic information can be summarized by a few estimated factors. We find that several estimated factors contain important information about one-quarter ahead excess returns and volatility that is not contained in commonly used predictor variables. Moreover, the factor-augmented specifications we examine predict an unusual 16-20 percent of the one-quarter ahead variation in excess stock market returns, and exhibit remarkably stable and strongly statistically significant out-of-sample forecasting power. Finally, in contrast to several pre-existing studies that rely on a small number of conditioning variables, we find a positive conditional correlation between risk and return that is strongly statistically significant, whereas the unconditional correlation is weakly negative and statistically snginficantpredictability, conditioning information, large dimension factor models

Stock Ownership Patterns, Stock Market Fluctuations, and Consumption

macroeconomics, Stock Ownership Patterns, Stock Market Fluctuations, Consumption

Big Data Analytics and Information Science for Business and Biomedical Applications II

The analysis of big data in biomedical, business and financial research has drawn much attention from researchers worldwide. This collection of articles aims to provide a platform for an in-depth discussion of novel statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions to these areas are showcased

Learning understandable classifier models.

The topic of this dissertation is the automation of the process of extracting understandable patterns and rules from data. An unprecedented amount of data is available to anyone with a computer connected to the Internet. The disciplines of Data Mining and Machine Learning have emerged over the last two decades to face this challenge. This has led to the development of many tools and methods. These tools often produce models that make very accurate predictions about previously unseen data. However, models built by the most accurate methods are usually hard to understand or interpret by humans. In consequence, they deliver only decisions, and are short of any explanations. Hence they do not directly lead to the acquisition of new knowledge. This dissertation contributes to bridging the gap between the accurate opaque models and those less accurate but more transparent for humans. This dissertation first defines the problem of learning from data. It surveys the state-of-the-art methods for supervised learning of both understandable and opaque models from data, as well as unsupervised methods that detect features present in the data. It describes popular methods of rule extraction from unintelligible models which rewrite them into an understandable form. Limitations of rule extraction are described. A novel definition of understandability which ties computational complexity and learning is provided to show that rule extraction is an NP-hard problem. Next, a discussion whether one can expect that even an accurate classifier has learned new knowledge. The survey ends with a presentation of two approaches to building of understandable classifiers. On the one hand, understandable models must be able to accurately describe relations in the data. On the other hand, often a description of the output of a system in terms of its input requires the introduction of intermediate concepts, called features. Therefore it is crucial to develop methods that describe the data with understandable features and are able to use those features to present the relation that describes the data. Novel contributions of this thesis follow the survey. Two families of rule extraction algorithms are considered. First, a method that can work with any opaque classifier is introduced. Artificial training patterns are generated in a mathematically sound way and used to train more accurate understandable models. Subsequently, two novel algorithms that require that the opaque model is a Neural Network are presented. They rely on access to the network\u27s weights and biases to induce rules encoded as Decision Diagrams. Finally, the topic of feature extraction is considered. The impact on imposing non-negativity constraints on the weights of a neural network is considered. It is proved that a three layer network with non-negative weights can shatter any given set of points and experiments are conducted to assess the accuracy and interpretability of such networks. Then, a novel path-following algorithm that finds robust sparse encodings of data is presented. In summary, this dissertation contributes to improved understandability of classifiers in several tangible and original ways. It introduces three distinct aspects of achieving this goal: infusion of additional patterns from the underlying pattern distribution into rule learners, the derivation of decision diagrams from neural networks, and achieving sparse coding with neural networks with non-negative weights

Ownership structure as a mechanism of corporate governance

The presence of agency conflicts between shareholders and managers who control corporate resources in modern companies has led to the emergence of governance mechanisms assuring that financiers' funds are not expropriated or wasted on unattractive projects. In a vast majority of European countries, ownership concentration is one of the most important internal mechanisms of corporate governance. While the theoretical literature stipulates that the presence of a large shareholder procures benefits, it also acknowledges the costs it involves. This dissertation investigates the role of shareholder control structures in different corporate governance regimes and tries to assess the resulting costs and benefits. Chapter 1 motivates the thesis. Chapter 2 analyzes the effects of substantial changes in the ownership structures of the Polish listed companies. Chapter 3 investigates the link between shareholder control structures and the governance efficiency of managerial labor market mechanisms in the UK. Chapter 4 examines the patterns in payout policy of UK firms in the 1990s and assesses empirically the validity of clientele theories of payout. Chapter 5 relates payout ratios to control structures for the UK firms

The Effect of Covid-19 on the Probability of Default of South African Firms Listed on the Johannesburg Stock Exchange (JSE)

The aim of this study is to quantify and investigate the effect of the Covid-19 pandemic on non-financial South African firms listed on the Johannesburg Stock Exchange. The study implemented the Merton (1974) model on the 59 largest non-financial firms and calculated the probability of default for each firm before the pandemic and during the pandemic as at each firm's financial year-end. The default probabilities are calculated predominantly from the value and volatility of firm equity. The results emphasize that the Covid-19 pandemic, on average, had a dramatic impact on the probability of default of publicly traded South African firms. The observed increase in default probability was found to be statistically significant at the 5% significance level

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio