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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.

To achieve a full understanding of the role of marketing from plan to profit requires a knowledge of the basic building blocks. This textbook introduces the key concepts in the art or science of marketing to practising managers. Understanding your customers and consumers, the 4 Ps (Product, Place, Price and Promotion) provides the basic tools for effective marketing. Deploying your resources and informing your managerial decision making is dealt with in Unit VII introducing marketing intelligence, competition, budgeting and organisational issues. The logical conclusion of this effort is achieving sales and the particular techniques involved are explored in the final section.

An updated version of the original (1985) text, the book covers all aspects of marketing and selling bank services: the role of marketing; behaviour of customers; intelligence, planning and organisation; product decisions; promotion decisions; place decisions; price decisions; achieving sales. Application questions help to focus the readers' minds on key issues affecting practice.

The expected rate of return for individual firms is determined by multiple firm‐specific factors. There is no evidence on how firm life cycle contributes to the determination of the expected rate of return. This study explores how life cycle stage affects the expected rate of return.

Regression analysis is applied to observe the effect of life cycle. Expected rate of return is dependent variable. Life cycle measures are interacted with commonly identified risk factors. Empirical data was collected for publicly traded firms from COMPUSTAT.

The major finding of this study is the significant impact of life cycle stage. Results indicate that the value relevance of risk factors is conditional on firm life cycle stage. Findings suggest that capital markets do realize and incorporate information conveyed in firm life cycle stage when interpreting risk factors.

Future research can explore effects of life cycle stage on share return volatility as investors trade off between return and risk.

This study targets a major aspect (i.e. what determine the expected rate of return in the finance literature) to shed light on the limited understanding of what contribute to individual firms’ risk premium. This study has implications for investor risk assessment and corporate risk management.

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.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

The purpose of this study is to examine the performances of liquidity factors in the stock market cycle. It aims to investigate whether the contribution of liquidity factors changes with stock market trends.

Six liquidity proxies and two-factor construction methods are compared in this study. The spanning regression method was applied to examine the contribution of liquidity factors to the asset pricing model, while the Fama and MacBeth regression method was used for examining the pricing power of liquidity factors.

The result shows that liquidity factors are accretive to models explaining returns in bull markets but not accretive to models in bear markets. The most appropriate method of constructing liquidity factors in the Japanese stock market has also been clarified.

In the Japanese stock market, there has never been a comprehensive test of the role of the liquidity risk factor in different market trends using the long-run data. This study helps with identifying the importance of liquidity pricing risk in different market trends. It also fills the gaps by comparing liquidity factors that are constructed through different methods and proxies and provides evidence for further confirming the correct asset pricing model in the future.

The purpose of this paper is to investigate the pricing of European real estate equities. The study examines the main drivers of real estate equity returns and determines whether loadings on systematic risk factors – the excess market return, small minus big (SMB), HIGH minus low (HML) – can explain cross‐sectional return differences in unconditional as well as in conditional asset pricing tests.

The paper draws upon time‐series regressions to investigate determinants of real estate equity returns. Rolling Fama‐French regressions are applied to estimate time‐varying loadings on systematic risk factors. Unconditional as well as conditional monthly Fama‐MacBeth regressions are employed to explain cross‐sectional return variations.

Systematic risk factors are important drivers of European real estate equity returns. Returns are positively related to the excess market return and to a value factor. A size factor impacts predominantly negatively on real estate returns. The results indicate increasing market integration after the introduction of the Euro. Loadings on systematic risk factors have weak explanatory power in unconditional cross‐section regressions but can explain returns in a conditional framework. Beta – and to a lesser extent the loading on HML – is positively related to returns in up‐markets and negatively in down markets. Equities which load positively on SMB outperform in down markets.

The implementation of a liquidity or a momentum factor could provide further evidence on the pricing of European real estate equities.

The findings could help investors to manage the risk exposure more effectively. Investors should furthermore be able to estimate their cost of equity more precisely and might better be able to pick stocks for time varying investment strategies.

This is the first paper to examine the pricing of real estate equity returns in a pan‐European setting.

The purpose of this paper is to examine the relationships between the higher moments of returns (realized skewness and kurtosis) and subsequent returns at the industry level, with a focus on both empirical predictability and practical application via trading strategies.

Daily returns for 48 US industries over the period 1970–2019 from Kenneth French’s data library are used to calculate the higher moments and to construct short- and medium-term single-sort trading strategies. The analysis adjusts returns for common risk factors (market, size, value, investment, profitability and illiquidity) to confirm whether conventional asset pricing models can capture these relationships.

Past skewness positively relates to subsequent industry returns and this relationship is unexplained by common risk factors. There is also a time-varying effect in which the predictive role of skewness is much stronger over business cycle expansions than recessions, a result consistent with varying investor optimism. However, there is no significant relationship between kurtosis and subsequent industry returns. The analysis confirms robustness using both value- and equal-weighted returns.

The calculation of realized moments conventionally uses high-frequency intra-day data, regrettably unavailable for industries. In addition, the chosen portfolio-sorting method may omit some information, as it compares only average group returns. Nonetheless, the close relationship between skewness and future returns at the industry level suggests variations in returns unexplained by common risk factors. This enriches knowledge of market anomalies and questions yet again weak-form market efficiency and the validity of conventional asset pricing models. One suggestion is that it is possible to significantly improve the existing multi-factor asset pricing models by including industry skewness as a risk factor.

Given the relationship between skewness and future returns at the industry level, investors may predict subsequent industry returns to select better-performing funds. They may even construct trading strategies based on return distributions that would generate abnormal returns. Further, as the evaluation of individual stocks also contains industry information, and stocks in industries with better performance earn higher returns, risks related to industry return distributions can also shed light on individual stock picking.

While there is abundant evidence of the relationships between higher moments and future returns at the firm level, there is little at the industry level. Further, by testing whether there is time variation in the relationship between industry higher moments and future returns, the paper yields novel evidence concerning the asymmetric effect of stock return predictability over business cycles. Finally, the analysis supplements firm-level results focusing only on the decomposed components of higher moments.

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.