Academic journal article Academy of Accounting and Financial Studies Journal

Analysis of Size Portfolios and Risk Factor Loadings of Asset Pricing Models-Threshold Regression Approach (Evidence from Psx)

Academic journal article Academy of Accounting and Financial Studies Journal

Analysis of Size Portfolios and Risk Factor Loadings of Asset Pricing Models-Threshold Regression Approach (Evidence from Psx)

Article excerpt

INTRODUCTION

Background of the Study

Empirical asset pricing is one of the emerging areas in the field of capital market investment. The quest for the identification of market factors which can influence the risk return characteristics of a security resulted in the augmentation of capital asset pricing model. CAPM provides a framework for driving the intrinsic value of the securities based on systematic risk. In 1980, Roll and Ross worked on Arbitrage Pricing Theory (APT), which incorporates multiple risk factors in asset pricing. In 1993, Fama and French Multifactor Model based market anomalies, was a breakthrough in the area of asset pricing. They identified two additional factors i.e. firm size and firm value as market anomalies in capital asset pricing. Afterwards, Fama and French added two more factors i.e. profitability and investment as proxies for asset pricing. Fama and French (1993) applied their model in US stock markets; they used panel data having time dimension from 1962 to 1989 and cross section of 25 portfolios of three stock markets sorted by size and by value.

Research Problem

Considerable research has been performed to test the validity of these multifactor asset pricing models globally as well as domestically. Results are somewhat mix regarding the validity. One of the reasons for such mixed evidences might be the varying level of influence (regime shifts) of exogenous factors i.e. macroeconomic variables on capital market. But, there is hardly any study which investigates the possibility of multiple equilibriums in asset pricing, especially in the context of PSX. Threshold regression can be a good tool to identify multiple factor loadings in asset pricing model. Hansen (2000) states that threshold regression is applied to multiple equilibriums and split samples, where sampling is done on continuous variables like size of firm.

Objectives of the Study

The focus of this study is to analysis size portfolios and risk factor loadings of asset pricing models in the presence of certain macroeconomic variables with the help of Threshold regression approach. Incorporating macroeconomic variables in asset pricing will evaluate the impact of regime shifts and macroeconomic shocks on risk factor loadings. In addition to that this study is also aiming to investigate the variability found in previous studies regarding validity of asset pricing models, which is also a motivation of this study.

Limitations

1. The study is limited to four economic indicators as threshold variables.

2. The study is conducted up to second moment of return.

3. The sample frame of the study limited to 11 years (relatively short period for regime shifts).

4. Results are valid for PMX only, and cannot be generalized.

Organization of Study

This study is started with the background of the research problem, which is then linked with objective of the study. In the literature review, firstly we cited those research papers in which threshold regression is used in asset pricing, portfolio management, and finance in general. Selection of macroeconomic variables as threshold variables and empirical testing of asset pricing has also been covered, and finally, the theory development of asset pricing model has been discussed in the literature review. The frameworks of capital asset pricing model and Fama French Multifactor Model have also been presented in the study. Methodology section of the study encompasses research design, conceptual framework, data description, variable definitions and hypotheses. With 4 microeconomic variables, 3 asset pricing models and 5 types of portfolio, we have developed 60 hypotheses. Statistical analysis with their assumptions follows results with their interpretations and discussion, which leads to conclusion of the study.

LITERATURE REVIEW

Li and Chen (2016) have applied panel threshold regression to reexamining the relation between analysts' forecast dispersion and stock returns. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.