Search results

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.

The purpose of this paper is to propose the development of return forecasting model for mudharabah time deposit product in Islamic bank based on artificial neural networks (ANNs).

The analysis consists of two main elements. First element is the identification and selection of significant macroeconomic variables that determine return volatility of mudharabah time deposit in Indonesian Islamic bank industry. Second element is the implementation of appropriate ANNs model according to neural networks properties, and model evaluation based on simulated return predictions of mudharabah time deposit product in Bank Syariah Mandiri (RR‐BSM).

It is shown that monthly changes of return can be predicted quite well. The model provides a satisfactory result in forecasting RR‐BSM for 12 months ahead with 95.22 per cent accuracy. These results suggest that the ANNs can be applied as an adequate tool to help depositors in predicting future return of mudharabah time deposit product.

There is believed to be no other empirical study of Islamic banks that exclusively examines the utilization of ANNs to forecast time deposit return as well as return from other investment instruments.

Attitudes to risk play a central role in economics. Policy makers should know them in order to judge the certainty equivalent of the effects of policy on individuals. What might look like a policy improvement when judged by the average impact could easily entail a welfare loss for risk averse individuals if the variance of expected impacts is wide compared to the alternatives.

Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that facilitate the analysis of theoretically established models in terms of both explanation and prediction. This study aims to offer a comparative evaluation of GSCA and PLSPM in a predictive modeling framework.

A simulation study compares the predictive performance of GSCA and PLSPM under various simulation conditions and different prediction types of correctly specified and misspecified models.

The results suggest that GSCA with reflective composite indicators (GSCA_R) is the most versatile approach. For observed prediction, which uses the component scores to generate prediction for the indicators, GSCA_R performs slightly better than PLSPM with mode A. For operative prediction, which considers all parameter estimates to generate predictions, both methods perform equally well. GSCA with formative composite indicators and PLSPM with mode B generally lag behind the other methods.

Future research may further assess the methods’ prediction precision, considering more experimental factors with a wider range of levels, including more extreme ones.

When prediction is the primary study aim, researchers should generally revert to GSCA_R, considering its performance for observed and operative prediction together.

This research is the first to compare the relative efficacy of GSCA and PLSPM in terms of predictive power.

With the presence of pricing errors, the authors consider statistical inference on the variance risk premium (VRP) and the associated implied variance, constructed from the option prices and the historic returns.

The authors propose a nonparametric kernel smoothing approach that removes the adverse effects of pricing errors and leads to consistent estimation for both the implied variance and the VRP. The asymptotic distributions of the proposed VRP estimator are developed under three asymptotic regimes regarding the relative sample sizes between the option data and historic return data.

This study reveals that existing methods for estimating the implied variance are adversely affected by pricing errors in the option prices, which causes the estimators for VRP statistically inconsistent. By analyzing the S&P 500 option and return data, it demonstrates that, compared with other implied variance and VRP estimators, the proposed implied variance and VRP estimators are more significant variables in explaining variations in the excess S&P 500 returns, and the proposed VRP estimates have the smallest out-of-sample forecasting root mean squared error.

This study contributes to the estimation of the implied variance and the VRP and helps in the predictions of future realized variance and equity premium.

This study is the first to propose consistent estimations for the implied variance and the VRP with the presence of option pricing errors.

The purpose of this paper is to assess the behaviour of economic sentiment indicators at rent‐growth turning points and indicators' ability to forecast such turning points. More specifically, the paper looks at whether early signals are generated for forthcoming periods of negative and positive office rent growth. The analysis aims to complement structural model forecasting in the real estate market with short‐term forecasting techniques designed to predict turning points.

The objective of this study is achieved by deploying a probit model to examine the ability of economic sentiment indicator series to signal the direction of office rents and the strength of movement in this direction. The main advantage of this approach is that it is geared towards predicting turning points. Probit models are non‐linear in nature, and as such they can capture more effectively the likely asymmetric adjustments when turning points occur than linear methodologies would. The analysis is applied to three major office centres – La Défense, London City, and Frankfurt – to examine whether the results will differ by geography.

The findings reveal that the probit methodology utilising information from economic sentiment indicators generates advance signals for periods of contraction and expansion in office rents across all three markets: La Défense, London City, and Frankfurt. The lead times for La Défense and Frankfurt are longer than those for London City and range between three and nine months. The evidence in this paper clearly supports the appeal of sentiment indicators and probit analysis to inform forecasting and risk assessment processes.

Acknowledging the limitations of structural models and related methodologies and the lack of adequate research on turning‐point prediction in the real estate market, this study forecasts episodes of negative and positive office rent growth applying appropriate techniques and data that lead economic activity, are of monthly frequency, and are not revised historically. The paper raises awareness of a forecasting approach that should complement structural models and judgmental forecasting, given its suitability for short‐term forecasting and for signalling turning points in advance.

In Canada, grain handling is an important agri-business that has traditionally been cooperative in nature (for example, Saskatchewan Wheat Pool). At the same time the industry is heavily regulated. There has been a dramatic change in the structure of the industry over the past 20 years and there are currently no major cooperatives present in the market. If the “yardstick effect” hypothesis of the role of cooperatives in an imperfectly competitive market is true, the disappearance of cooperatives could result in the ability of remaining firms to exercise market power over producers. To investigate the impact of changes in ownership structure in the market, we estimated two types of pricing games that might have been played between a cooperative, Saskatchewan Wheat Pool (SWP) and an investor-owned firm (IOF), Pioneer Grain (PG) in the Saskatchewan wheat-handling market over the period 1980–2004, with different assumptions about their pricing behavior imposed. We find that SWP and PG have likely been playing a Bertrand pricing game in the market over the period. We thus conclude that SWP, as the largest cooperative in the market, likely played a “yardstick effect” role in the market.

Structural equation modeling (SEM) has been widely used to examine complex research models in international business and marketing research. While the covariance-based SEM (CB-SEM) approach is dominant, the authors argue that the field’s dynamic nature and the sometimes early stage of theory development more often require a partial least squares SEM (PLS-SEM) approach. The purpose of this paper is to critically review the application of SEM techniques in the field.

The authors searched six journals with an international business (and marketing) focus (Management International Review, Journal of International Business Studies, Journal of International Management, International Marketing Review, Journal of World Business, International Business Review) from 1990 to 2013. The authors reviewed all articles that apply SEM, analyzed their research objectives and methodology choices, and assessed whether the PLS-SEM papers followed the best practices outlined in the past.

Of the articles, 379 utilized CB-SEM and 45 PLS-SEM. The reasons for using PLS-SEM referred largely to sampling and data measurement issues and did not sufficiently build on the procedure’s benefits that stem from its design for predictive and exploratory purposes. Thus, the procedure’s key benefits, which might be fruitful for the theorizing process, are not being fully exploited. Furthermore, authors need to better follow best practices to truly advance theory building.

The authors examined a subset of journals in the field and did not include general management journals that publish international business and marketing-related studies. Fur-thermore, the authors found only limited use of PLS-SEM in the journals the authors considered relevant to the study.

The study contributes to the literature by providing researchers seeking to adopt SEM as an analytical method with practical guidelines for making better choices concerning an appropriate SEM approach. Furthermore, based on a systematic review of current practices in the international business and marketing literature, the authors identify critical challenges in the selection and use of SEM procedures and offer concrete recommendations for better practice.

This paper aims to examine and compare the dividend ratios’ statistical and economic ability to predict the equity premium in the UK and US markets and two US sub-indices (S&P 500 Growth and S&P 500 Value).

In this paper, the authors use the linear regression models to examine the dividend ratios’ statistical ability to predict the equity premium. The in-sample and out-of-sample approaches, including Diebold and Mariano (1995) statistics, and Goyal and Welch’s (2003) graphical approach, are used. Also, the mean-variance analysis is used to test the economic significance.

The paper findings indicate that the dividend ratios have in-sample and out-of-sample predictive abilities in both UK and US markets and both US sub-indices. However, the results show that the dividend ratios have a less impressive predictive ability in the US market compared to the UK market and less in the US value index than the US growth index. This could indicate that there is no relation between the number of companies that distribute dividends in each index and the informativeness of dividends ratios. Furthermore, the tests show the dividend ratios’ predictive ability departure during particular periods and in some indices.

Results and implications of this research are exclusively applied to the US and UK markets. These results can also be applied with caution to other markets, taking into consideration the distinctive characteristics of these markets.

Results revealed in this paper imply that the investors in any of the indices may experience economic gain by adopting a dynamic trading strategy using the information content of the dividend ratios prediction models instead of the benchmark model, which is the prevailing simple moving average model.

This paper adds value through testing the prediction models’ economic significance in two well-developed markets, in addition to exploring the relationship between the number of companies distributing cash dividends and the dividends ratio prediction ability. Unlike most of the previous studies in which dividend ratios’ prediction ability is attributed to the number of companies that distribute dividends in the market, this paper denied this interpretation by studying two S&P 500 sub-indices. To the best of the authors’ knowledge, this is the first study to test the prediction models’ ability for these sub-indices.