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The purpose of this paper is to compare the performance of various multifactor asset pricing models across ten emerging and developed markets.

The general methodology to test asset pricing models involves regressing test asset returns (left-hand side assets) on pricing factors (right-hand side assets). Then the performance of different models is evaluated based on how well they price multiple test assets together. The parameters used to compare relative performance of different models are their pricing errors (GRS statistic and average absolute intercepts) and explained variation (average adjusted R²).

The Fama-French five-factor model improves the pricing performance for stocks in Australia, Canada, China and the USA. The pricing in these countries appears to be more integrated. However, the superior performance in these four countries is not consistent across a variety of test assets and the magnitude of reduction in pricing errors vis-à-vis three- or four-factor models is often economically insignificant. For other markets, the parsimonious three-factor model or its four-factor variants appear to be more suitable.

Unlike most asset pricing studies that use test assets based on variables that are already used to construct RHS factors, this study uses test assets that are generally different from RHS sorts. This makes the tests more robust and less biased to be in favour of any multifactor model. Also, most international studies of asset pricing tests use data for different markets and combine them into regions. This study provides the evidence from ten countries separately because prior research has shown that locally constructed factors are more suitable to explain asset prices. Further, this study also tests for the usefulness of adding a quality factor in the existing asset pricing models.

This paper expands upon previous work by the authors in which an unique, non‐subjective, quantitative model was developed that could be used to indicate the presence and evaluate the effectiveness of a total quality management programme in manufacturing organisations. That model was proven to be not only an indicator of TQ performance but also a predictor of general business performance. By incorporating an automation component using information technology and DBMS, the enhanced model developed in this paper integrates technology into the prior model. This increases the ease and speed with which the companies can conduct a self‐assessment of their TQ programmes. Accordingly, the finished product provides an improved tool for evaluating TQ, resulting in a more effective strategic decision‐making.

This paper aims to investigate whether the use of conditional and unconditional Fama and French (1993) three-factor and Carhart (1997) four-factor asset pricing models (APMs) captures the role of asset pricing anomalies in the context of emerging stock market like India.

The first step time series regression approach has been used to drive the risk-adjusted returns of individual securities. For examining the predictability of firm characteristics or asset pricing anomalies on the risk-adjusted returns of individual securities, the panel data estimation technique has been used.

Fama and French (1993) three-factor and Carhart (1997) four-factor model in their unconditional specifications capture the impact of book-to-market price and liquidity effects completely. When alternative APMs in their conditional specifications are tested, the importance of medium- and long-term momentum effects has been captured to a greater extent. The size, market leverage and short-term momentum effects still persist even in the case of alternative unconditional and conditional APMs.

The empirical analysis does not extend for different market scenarios like high and low volatile market or good and bad macroeconomic environment. Because of the constraint of data availability, the authors could not include certain important anomalies like net operating assets, change in gross profit margin, external equity and debt financing and idiosyncratic risk.

Although the active investment approach in stock market shares a common ground of semi-strong form of market efficiency hypothesis which also supports the presence of asset pricing anomalies, less empirical evidence has been explored in this regard to support or repute such belief of practitioners. Our empirical findings make an attempt in this regard to suggest certain anomaly-based trading strategy that can be followed for active portfolio management.

From an emerging market perspective, this paper provides out-of-sample empirical evidence toward the use of conditional Fama and French three-factor and Carhart four-factor APMs for the complete explanation of market anomalies. This approach retains its importance with respect to the comprehensiveness of analysis considering alternative APMs for capturing unique effects of market anomalies.

The main aspect of security analysis is its valuation through a relationship between the security return and the associated risk. The purpose of this paper is to review the traditional capital asset pricing model (CAPM) and its variants adopted in empirical investigations of asset pricing.

Pricing models are discussed under five categories: the single‐factor model, multifactor models, CAPM with higher order systematic co‐moments, CAPM conditional on market movements and time‐varying volatility models.

The paper finds that the last half‐century has witnessed the proliferation of empirical studies testing on the validity of the CAPM. A growing number of studies find that the cross‐asset variation in expected returns cannot be explained by the systematic risk alone. Therefore a variety of models have been developed to predict asset returns.

There is no consensus in the literature as to what a suitable measure of risk is, and consequently, as to what is a suitable measure for evaluating risk‐adjusted performance. So the quest for robust asset pricing models continues.

From its beginning to its possible demise the paper reviews the history of the CAPM assuring that we are all up to speed with what has been done.

Daniel and Titman (1997) contend that the Fama‐French three‐factor model’s ability to explain cross‐sectional variation in expected returns is a result of characteristics that firms have in common rather than any risk‐based explanation. The primary aim of the current paper is to provide out‐of‐sample tests of the characteristics versus risk factor argument. The main focus of our tests is to examine the intercept terms in Fama‐French regressions, wherein test portfolios are formed by a three‐way sorting procedure on book‐to‐market, size and factor loadings. Our main test focuses on ‘characteristic‐balanced’ portfolio returns of high minus low factor loading portfolios, for different size and book‐to‐market groups. The Fama‐French model predicts that these regression intercepts should be zero while the characteristics model predicts that they should be negative. Generally, despite the short sample period employed, our findings support a risk‐factor interpretation as opposed to a characteristics interpretation. This is particularly so for the HML loading‐based test portfolios. More specifically, we find that: the majority of test portfolios tend to reveal higher returns for higher loadings (while controlling for book‐to‐market and size characteristics); the majority of the Fama‐French regression intercepts are statistically insignificant; for the characteristic‐balanced portfolios, very few of the Fama‐French regression intercepts are significant.

This study aims to provide a comprehensive analysis of various approaches to the residential property market evolution modelling and to examine the macroeconomic fundamentals that have shaped this market development in Ukraine in recent years.

The study uses a comprehensive data set encompassing relevant macroeconomic indicators and historical apartment prices. Multifactor linear regression (MLR) and ridge regression (RR) models are constructed to identify the impact of multiple predictors on apartment prices. Additionally, the ARIMAX model integrates time series analysis and external factors to enhance modelling and forecasting accuracy.

The investigation reveals that MLR and RR yield accurate predictions by considering a range of influential variables. The hybrid ARIMAX model further enhances predictive performance by fusing external indicators with time series analysis. These findings underscore the effectiveness of a multidimensional approach in capturing the complexity of housing price dynamics.

This research contributes to the real estate modelling and forecasting literature by providing an analysis of multiple linear regression, RR and ARIMAX models within the specific context of property price prediction in the turbulent Ukrainian real estate market. This comprehensive analysis not only offers insights into the performance of these methodologies but also explores their adaptability and robustness in a market characterized by evolving dynamics, including the significant influence of external geopolitical factors.

This study aims to comparatively analyze the systematic, idiosyncratic and downside risk exposure of both Islamic and conventional funds in Pakistan to see which of the funds has higher risk exposure.

The study analyzes different types of risks involved in both Islamic and conventional funds for the period from 2009 to 2016 by using different risk measures. For systematic and idiosyncratic risk single factor CAPM and multifactor models such as Fama French three factors model and Carhart four factors model are used. For downside risk analysis different measures such as downside beta, relative beta, value at risk and expected short fall are used.

The study finds that Islamic funds have lower risk exposure (including total, systematic, idiosyncratic and downside risk) compared with their conventional counterparts in most of the sample years, and hence, making them appear more attractive for investment especially for Sharīʿah-compliant investors preferring low risk preferences.

As this study shows, Islamic mutual funds exhibit lower risk exposure than their conventional counterparts so investors with lower risk preferences can invest in these kinds of funds. In this way, this research provides the input to the individual investors (especially Sharīʿah-compliant investors who want to avoid interest based investment) to help them with their investment decisions as they can make a more diversified portfolio by considering Islamic funds as a mean for reducing the risk exposure.

To the best of the author’s knowledge, this study is the first attempt at world level in looking at the comparative risk analysis of various types of the risks as follows: systematic, idiosyncratic and downside risk, for both Islamic and conventional funds, and thus, provides significant contribution in the literature of mutual funds.

There is a growing interest in fuzzy time series (FTS) forecasting, and several improvements are presented in the last few decades. Among these improvements, the development of causal models (i.e., multiple factor FTS) has sparked a particular literature dealing with the causal inference and its integration in the FTS framework. However, causality among variables is usually introduced as a subjective assumption rather than empirical evidence. As a result of arbitrary causal modeling, the existing multiple factor FTS models are developed with implicit forecasting failure. Since post-sample control (unknown future, as in the business practice) is usually ignored, the spurious accuracy gain through increasing factors is not identified by scholars. This paper discloses the use of causality in the FTS method, and investigates the spurious causal inference problem in the literature with a justification approach. It invalidates the contribution of dozens of previously published papers while justifying its claim with illustrative examples and a comprehensive set of experiments with random data, as well as real business data from maritime transportation (Baltic Dry Index).

In recent years, several methodologies for measuring portfolio credit risk have been introduced that demonstrate the benefits of using internal models to measure credit risk in the loan book. These models measure economic credit capital and are specifically designed to capture portfolio effects and account for obligor default correlations. An example of an integrated market and credit risk model that overcomes this limitation is given in Iscoe et al. [1999], which is equally applicable to commercial and retail credit portfolios. However, the measurement of portfolio credit risk in retail loan portfolios has received much less attention than the commercial credit markets. This article proposes a methodology for measuring the credit risk of a retail portfolio, based on the general portfolio credit risk framework of Iscoe et al. The authors discuss the practical estimation and implementation of the model. They demonstrate its applicability with a case study based on the credit card portfolio of a North American financial institution. They also analyze the sensitivity of the results to various assumptions.

This paper investigates the global financial integration of the Gulf Cooperation Council markets, which is important for financial economists, global investors and policymakers.

The first step is to estimate a benchmark one-factor model and multifactor models over the entire sample period to obtain the time-invariant global integration estimates for the Gulf Cooperation Council markets. Because the global integration of the Gulf Cooperation Council markets may be time varying, the second step is to use 24-month rolling regressions to estimate the time-varying integration estimates. To explicitly test for structural breaks in global integration, this study applies a supremum Wald test to endogenously search for structural breaks.

Empirically, consistent evidence suggests that the Gulf Cooperation Council markets are increasingly integrated with international equity markets at different levels of financial development and from different regions. However, compared to other emerging and frontier markets, the global integration of the Gulf Cooperation Council markets is still relatively low, suggesting that these markets still offer significant diversification benefits for global investors.

This study contributes to the literature by systematically investigating the global integration of the Gulf Cooperation Council markets with monthly data (to account for the gradual information diffusion in international equity markets) and a longer sample period (to more robustly identify the trend in the global integration).