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The purpose of this paper is to test a discrete time asset pricing model where a non‐marketable asset (human capital), along with other factors predicting stock returns, explain risk return relationship. The paper will add to the literature on risk return relationship with human capital by investigating the hypothesis that human capital is a significant factor affecting stock prices.

The dynamic inter‐linkages of factors representing financial and human components of wealth in predicting stock returns is tested in the Indian market for the period of 1996:04 to 2005:06. The procedures employed include Granger causality tests, impulse response functions and seemingly unrelated regression estimates.

Empirical findings validate the model that including human capital as a proxy for aggregate wealth in the economy can better predict stock prices than the standard empirical capital asset pricing model. There is a Granger cause relationship between security prices and labor income and it is further concluded that labor and dividend are significant factors affecting security prices.

This is one of the first papers to study the human capital aspect in predicting stock returns in the Indian market. In addition, the paper provides important insights into the causal relationship of human capital and market return in explaining the risk return relationship.

This paper shows that for a large class of single and multi‐factor term structure models, including the affine class, the market price of risk is directly related to the parameters of the stochastic processes of the underlying factors of the economy. It is shown that the market price of risk is proportional to the limit of the volatility of zero coupon bond returns. This means that the market price of risk is not entirely arbitrary. Not only it must be consistent with no arbitrage conditions, also it must be consistent with the parameters of stochastic processes of the factors that describe the economy. If the market price of risk is not correctly specified, then it could lead to profit opportunities of the type discussed in Backus et al (1996). Another consequence of our result is that in empirical tests of interest rate processes, the market price of risk should not be specified exogenously since its value is a function of the parameters of the model. We extend our result to forward processes. The market price of risk is shown to be a function of the volatility of the forward rate processes.

It is an indispensable part of airworthiness certification to evaluate the fuel tank flammability exposure time for transport aircraft. There are many factors and complex coupling relationships affecting the fuel tank flammability exposure time. The current work not only lacks a comprehensive analysis of these factors but also lacks the significance of each factor, the interaction relationship and the prediction method of flammability exposure time. The lack of research in these aspects seriously restricts the smooth development of the airworthiness forensics work of domestic large aircraft. This paper aims to clarify the internal relationship between user input parameters and predict the flammability exposure time of fuel tanks for transport aircraft.

Based on the requirements of airworthiness certification for large aircraft, an in-depth analysis of the Monte Carlo flammability evaluation source procedures specified in China Civil Aviation Regulation/FAR25 airworthiness regulations was made, the internal relationship between factors affecting the fuel tank flammability exposure time was clarified and the significant effects and interactions of input parameters in the Monte Carlo evaluation model were studied using the response surface method. And the BP artificial neural network training samples with high significance factors were used to establish the prediction model of flammability exposure time.

The input parameters in the Monte Carlo program directly or indirectly affect the fuel tank flammability exposure time by means of the influence on the flammability limit or fuel temperature. Among the factors affecting flammability exposure time, the cruising Mach number, balance temperature difference and maximum range are the most significant, and they are all positively correlated with flammability exposure time. Although there are interactions among all factors, the degree of influence on flammability exposure time is not the same. The interaction between maximum range and equilibrium temperature difference is more significant than other factors. The prediction model of flammability exposure time based on multifactor interaction and BP neural network has good accuracy and can be applied to the prediction of fuel tank flammability exposure time.

The flammability exposure time prediction model was established based on multifactor interaction and BP neural network. The limited test results were combined with intelligent algorithm to achieve rapid prediction, which saved the test cost and time.

This study aims to provide a comprehensive overview of asset pricing research and identifies the general research trends in the area. The study also aims to provide future direction to the researchers in the area of asset pricing.

The study uses bibliometric analysis techniques to achieve the stated purpose. The study covers 3,007 articles published in the top 50 finance and economics journals, accessed from the Scopus database for a period of 47 years (1973–2020). After initial searching for “asset pricing” as the main keyword in “title, abstract, keywords”, the database yields 6,583 articles. This number further reduces to 3,007 articles when the search is restricted to research and review articles published in the top 50 peer-reviewed journals.

The tabular and pictorial representation obtained from the analysis exhibit that asset pricing is an extensively researched area; however, a sudden rise in the number of publications (242) observed for 2019 demonstrates a growing interest amongst researchers. Further, affiliation statistics indicate that the volume of research is mainly concentrated in the USA and other developed nations; hence it opens vistas for the exploration of risk-return dynamics in the context of emerging markets.

The work presents an exhaustive and comprehensive review along with potential research implications. The present study reconciles various contradictory views of the prior studies under asset pricing such as risk-return trade-off, low-risk anomaly and provides the researchers with potential research gaps.

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.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

The purpose of this paper is to investigate the firm-specific anomaly effect and to identify market anomalies that account for the cross-sectional regularity in the Indian stock market. The paper also examines the cross-sectional return predictability of market anomalies after making the firm-specific raw return risk adjusted with respect to the systematic risk factors in the unconditional and conditional multifactor specifications.

The paper employs first step time series regression approach to drive the risk-adjusted return of individual firms. For examining the predictability of firm characteristics on the risk-adjusted return, the panel data estimation technique has been used.

There is a weak anomaly effect in the Indian stock market. The choice of a five-factor model (FFM) in its unconditional and conditional specifications is able to capture the book-to-market equity, liquidity and medium-term momentum effect. The size, market leverage and short-run momentum effect are found to be persistent in the Indian stock market even with the alternative conditional specifications of the FFM. The results also suggest that it is naï argue for disappearing size effect in the cross-sectional regularity.

Constrained upon the data availability, certain market anomalies and conditioning variables cannot be included in the analysis.

Considering the practitioners' prospective, the results indicate that the profitable investment strategy with respect to the small size effect is still persistent and warrants close-ended mutual fund investment portfolio strategy for enhancing the long-term profitability. The short-run momentum effect can generate potential profits given a short-term investment horizon.

This paper provides the first-ever empirical evidence from an emerging stock market towards the use of alternative conditional multifactor models for the complete explanation of market anomalies. In an attempt to analyze the anomaly effect in the Indian stock market, this paper provides further evidence towards the long-short hedge portfolio return variations in terms of a wide set of market anomalies that have been documented in prior literature.

The purpose of this paper is to investigate the strategy that a turnaround in the USA will portend a turnaround in the UK's economy and property market. For this strategy to operate, it is assumed that the capital and property markets in and between the two nations are highly integrated with endogenous pricing functions.

Given the endogenous assumptions of the conjectured research statement, tests of integration (or segmentation) between two capital and property markets are conducted. Correlation, tracking error analysis, and a multiple systematic risk factor model are used to test the pricing relationships. The methodological form employs variant macroeconomic variable pricing models (MVM) of alternative combinations of systematic affects operating across and between the national markets.

Pricing integration is noted between the UK and US capital markets, while the property markets are economically and statistically segmented. Opportunities for arbitrage based on different prices/returns for equivalent risk exposures are statistically observed between the UK and USA. The effect is that systematic pricing between the two markets cannot be addressed solely by diversification options. This infers a potential for arbitrage (statistically, strategically or in practice) is possible, given that systematic risk exposures between the two markets are not equivalently priced across cyclical phases. In this context it is inferred that the probable measure of pricing differences across the two markets is more than a cyclical lag effect.

The paper delineates the degrees of integration/segmentation in the UK and US property and capital markets as a function of systematic risks in changing economic conditions. These differences support the existence of statistical arbitrage and the specification of investment behaviour as a function of differencing pricing expectations. These findings can assist in the formulation of investment and hedging strategies to assist in managing international portfolios subject to cyclical market exposures. This paper contributes to an understanding of and foundation for testing the nature and impact of cycles on property investment performance as a function of pricing changes.

This paper aims to propose an alternative method to measure idiosyncratic volatility and test whether the idiosyncratic volatility puzzle holds in commodity futures markets.

This paper proposes a partially new measure of idiosyncratic volatility in commodity futures markets based on the Schwartz and Smith (2000) short-term/long-term model. This model enables us to capture systematic risks of commodity futures markets in a parsimonious way.

Using a sample of futures contracts for 20 commodities from 1973 to 2022, this paper demonstrates that idiosyncratic volatility is more significant than systematic volatility in commodity futures markets, and that the idiosyncratic volatility puzzle does not hold in these markets. This paper also performs robustness tests to investigate whether the puzzle holds during subsample periods when commodity markets are more volatile and find consistent results. This study highlights the differences between commodity futures markets and equity markets and emphasizes the importance of investigating idiosyncratic volatility in commodity futures markets.

The contributions of this paper are threefold. First, this paper contributes to the literature by focusing on the idiosyncratic volatility of commodity futures returns. Second, this paper constructs a partially new measure of idiosyncratic volatility in commodity futures markets. Finally, this paper also contributes to the literature on the idiosyncratic volatility puzzle and demonstrates that the puzzle may not exist in commodity futures markets.

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.