Semiparametric Characteristics-based Models of Asset Returns

This thesis, which includes three chapters, studies asset-specific characteristics such as capitalization, book-to-market ratio etc., and their implications on assets prices and portfolio management. This thesis selects characteristics that have prediction powers on assets excess returns and specifies a flexible regression model, including linear, non-linear and pairwise interactive parts. This thesis further analyses whether characteristics are relevant as mispricing components and factor loadings in an asset pricing factor model. Finally, this thesis develops an optimal portfolio selection method based on the constructed characteristics-based asset pricing model. Methodologies in this thesis are mainly proposed for two popular questions in financial econometrics, namely, high dimensional analysis and the approximation of uni-variate and multi-variate unknown functions. The tools extended by this thesis are B-splines and orthogonal series, and multi-variate unknown functions are approximated by tensor products. In terms of high dimensional problems, which are caused by both abundant financial data and diverging B-splines bases used to approximate unknown functions, they are solved by LASSO-style selection model and power enhanced hypothesis tests. The details of the three chapters are summarized below: Specification LASSO and an Application in Financial Markets This chapter proposes the method of Specification-LASSO in a flexible semi-parametric regression model that allows for the interactive effects between different covariates. Specification-LASSO extends LASSO and Adaptive Group LASSO to achieve both relevant variable selection and model specification. Specification-LASSO also gives preliminary estimates that facilitate the estimation of the regression model. Monte Carlo simulations show that the Specification-LASSO can accurately specify partially linear additive models with interactive effects. Finally, the proposed methods are applied in an empirical study, which examines the topic proposed by \cite{freyberger2020dissecting}, arguing that firms’ sizes may have interactive effects with other security-specific characteristics, which can explain the stocks excess returns together. Dynamic Peer Groups of Arbitrage Characteristics This chapter proposes an asset pricing factor model constructed with semi-parametric characteristics-based mispricing and factor loading functions. We approximate the unknown functions by B-splines sieve where the number of B-splines coefficients is diverging. We estimate this model and test the existence of the mispricing function by a power-enhanced hypothesis test. The enhanced test solves the low power problem caused by diverging B-splines coefficients, with the strengthened power approaches one asymptotically. We also investigate the structure of mispricing components through Hierarchical K-means Clusterings. We apply our methodology to CRSP (Center for Research in Security Prices) and Compustat data for the US stock market with one-year rolling windows during 1967-2017. This empirical study shows the presence of mispricing functions in certain time blocks. We also find that distinct clusters of the same characteristics lead to similar arbitrage returns, forming a “peer group” of arbitrage characteristics. A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection This paper develops a two-step semiparametric methodology for portfolio weight selection for characteristics-based factor-tilt and factor-timing investment strategies. We build upon the expected utility maximization framework of \cite{brandt1999estimating} and \cite{ait2001variable}. We assume that assets returns obey a characteristics-based factor model with time-varying factor risk premia as in \cite{li2020dynamic}. We prove under our return-generating assumptions that an approximately optimal portfolio can be established using a two-step procedure in a market with a large number of assets. The first step finds optimal factor-mimicking sub-portfolios using a quadratic objective function over linear combinations of characteristics-based factor loadings. The second step dynamically combines these factor-mimicking sub-portfolios based on a time-varying signal, using the investor’s expected utility as the objective function. We develop and implement a two-stage semiparametric estimator. We apply it to CRSP (Center for Research in Security Prices) and FRED (Federal Reserve Economic Data) data and find excellent in-sample and out-sample performance consistent with investors’ risk aversion levels

Asset Pricing Theories, Models, and Tests

An important but still partially unanswered question in the investment field is why different assets earn substantially different returns on average. Financial economists have typically addressed this question in the context of theoretically or empirically motivated asset pricing models. Since many of the proposed “risk” theories are plausible, a common practice in the literature is to take the models to the data and perform “horse races” among competing asset pricing specifications. A “good” asset pricing model should produce small pricing (expected return) errors on a set of test assets and should deliver reasonable estimates of the underlying market and economic risk premia. This chapter provides an up-to-date review of the statistical methods that are typically used to estimate, evaluate, and compare competing asset pricing models. The analysis also highlights several pitfalls in the current econometric practice and offers suggestions for improving empirical tests

Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression

Ordinary linear and generalized linear regression models relate the mean of a response variable to a linear combination of covariate effects and, as a consequence, focus on average properties of the response. Analyzing childhood malnutrition in developing or transition countries based on such a regression model implies that the estimated effects describe the average nutritional status. However, it is of even larger interest to analyze quantiles of the response distribution such as the 5% or 10% quantile that relate to the risk of children for extreme malnutrition. In this paper, we analyze data on childhood malnutrition collected in the 2005/2006 India Demographic and Health Survey based on a semiparametric extension of quantile regression models where nonlinear effects are included in the model equation, leading to additive quantile regression. The variable selection and model choice problems associated with estimating an additive quantile regression model are addressed by a novel boosting approach. Based on this rather general class of statistical learning procedures for empirical risk minimization, we develop, evaluate and apply a boosting algorithm for quantile regression. Our proposal allows for data-driven determination of the amount of smoothness required for the nonlinear effects and combines model selection with an automatic variable selection property. The results of our empirical evaluation suggest that boosting is an appropriate tool for estimation in linear and additive quantile regression models and helps to identify yet unknown risk factors for childhood malnutrition

Corporate responsibility reporting in the UK and Japan

There is an increasing concern for the environment and society in today’s world. Stakeholders call for corporations to take responsibility for the impact that their organisational activities have on the environment and society by publicly disclosing such impacts and how they are being managed. Thus, the practice of corporate responsibility reporting (hereafter CRR) has been established. Unlike the provision of financial information in an annual report, CRR tends to be a voluntary reporting practice. As firms have the choice to provide CRR, logical economic thinking says that they will only do so if they derive some benefit from it. Therefore, the objective of this study is to investigate whether CRR is associated with firms’ market values in order to assess whether CRR provides incremental value relevant information to investors

Do Job, Age, and Place of Residence Matter for Gaming Activity? A Study of the Mid-Colorado River Communities

A household survey in the mid-Colorado River communities of Laughlin, Nevada and Bullhead City, Arizona examined local residents\u27 gaming activities. A censored regression analysis distinguished between factors affecting gaming participation versus expenditures. Results suggest that gaming behavior can often be predicted with knowledge of individuals\u27 residence, workplace, and other household demographic characteristics. Both local government agencies and casino managers can use the results to make better-informed decisions

Are the dimensions of private information more multiple than expected? Information asymmetries in the market of supplementary private health insurance in England

Our study reexamines standard econometric approaches for the detection of information asymmetries on insurance markets. We claim that evidence based on a standard framework with 2 equations, which uses potential sources of information asymmetries, should stress the importance of heterogeneity in the parameters. We argue that conclusions derived from this methodology can be misleading if the estimated coefficients in such an `unused characteristics' framework are driven by different parts of the population. We show formally that an individual's expected risk from the perspective of insurance, conditioned on certain characteristics (which are not used for calculating the risk premium), can equal the population's expectation in risk { although such characteristics are both related to risk and insurance probability, which is usually interpreted as an indicator of information asymmetries. We provide empirical evidence on the existence of information asymmetries in the market for supplementary private health insurance in the UK. Overall, we found evidence for advantageous selection into the private risk pool; ie people with lower health risk tend to insure more. The main drivers of this phenomenon seem to be characteristics such as income and wealth. Nevertheless, we also found parameter heterogeneity to be relevant, leading to possible misinterpretation if the standard `unused characteristics' approach is applied