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Forecasting product returns plays an important role in the operations of reverse logistics (RL). However, their contribution to sustainability performance is yet to be explored. The purpose of this paper is to explore the product returns in Indian electronics industry and examine the relationship of forecasting product returns with triple bottom line performance of RL.

In this study, based on past literature review, four hypotheses, relating to forecasting of product returns and its association with performance, were developed. A questionnaire was sent to 700 respondents from the Indian electronics industry. Overall, 208 received responses were found suitable for the research. The necessary statistical analysis was carried out to ensure the reliability and validity of the questionnaire. In order to test different hypotheses, partial least square path modelling (PLSPM) technique of structural equation modeling was utilized.

Measurement model had shown sufficient data fit for the modeling. PLSPM results reveal that the accuracy in forecasting product returns is positively associated with operational performance of RL. It also plays an important role in the sustainability efforts of an organization.

Managers can utilize results of study for exploring and emphasizing issues of product returns for improving RL performance. One of the limitations is that data are collected only from Indian electronics industry. Another limitation is that only product returns are considered for the operational and TBL performance of RL. In future, study may be carried out considering different factors in other sectors and countries.

The intent of forecasting product returns is considered to be operational efficiency. It can make significant contributions to the sustainability efforts of an organization. Review of the past literature indicates that research in the field of RL is in developing stage, and issues related to forecasting product returns are under-represented. The paper adds value to the few available articles on product returns.

The purpose of this paper is to create an endurance index of housing investor sentiment and use it to forecast housing stock returns. This study performs not only in-sample and out-of-sample forecasting, like many previous studies did, but also a true forecasting by using all lag terms of independent variables. In addition, an evaluation procedure is applied to quantify the quality of forecasts.

Using a binomial probability distribution model, this paper creates an endurance index of housing investor sentiment. The index reflects the probability of the high or low stock price being the close price for the Philadelphia Stock Exchange Housing Sector Index. This housing investor sentiment endurance index directly uses housing stock price differentials to measure housing investor reactions to all relevant news. Empirical results in this study suggest that the index can not only play a significant role in explaining variations in housing stock returns but also have decent forecasting ability.

Results of this study reveal the considerable forecasting ability of the index. Monthly forecasts of housing stock returns have an overall accuracy of 51 per cent, while the overall accuracy of 8-quarter rolling forecasts even reaches 84 per cent. In addition, the index has decent forecasting ability on changes in housing prices as suggested by the strong evidence of one-direction causal relations running from the endurance index to housing prices. However, extreme volatility of housing stock returns may impair the forecasting quality.

The endurance index of housing investor sentiment is easy to construct and use for forecasting housing stock returns. The demonstrated predictability of the index on housing stock returns in this study can have broad implications on housing-related business practices through providing an effective forecasting tool to investors and analysts of housing stocks, as well as housing policy-makers.

Despite different investor sentiment proxies suggested in the previous studies, few of them can effectively predict stock returns, due to some embedded limitations. Many increases and decreases inn prices cancel out each other during the trading day, as many unreliable sentiments cancel out each other. This dynamic process reveals not only investor sentiment but also resilience or endurance of sentiment. It is only long-lasting resilient sentiment that can be built in the closing price. It means that the only feasible way to use investor sentiment contained in stock prices to forecast future stock prices is to detach resilient investor sentiment from stock prices and construct an index of endurance of investor sentiment.

This paper aims to investigate the performance of various return forecasting variables and methods in Saudi Arabia and Malaysia. The authors document that market excess returns in Saudi Arabia are predicted by changes in oil prices, the dividend yield and inflation, whereas the equity premium in Malaysia is predicted only by the US market excess returns. In both countries, the authors find that the diffusion index is the best forecasting method and stock return predictability is stronger in expansions than in recessions. To interpret the findings, the authors perform two tests. The empirical results suggest irrational pricing in Malaysia and rationally time-varying expected returns in Saudi Arabia.

The authors apply the state-of-the-art in-sample and out-of-sample forecasting techniques to predict stock returns in Saudi Arabia and Malaysia.

The Saudi equity premium is predicted by oil prices, dividend yield and inflation. The Malaysian equity premium is predicted by the US market excess returns. In both countries, the authors find that the diffusion index is the best forecasting method. In both countries, predictability is stronger in expansions than in recessions. The tests suggest irrational pricing in Malaysia and rationality in Saudi Arabia.

The empirical results have some practical implications. The fact that stock returns are predictable in Saudi Arabia makes it possible for policymakers to better evaluate future business conditions, and thus to take appropriate decisions regarding economic and monetary policy. In Malaysia, the results of this study have interesting implications for portfolio management. The fact that the Malaysian market seems to be inefficient suggests the presence of strong opportunities for sophisticated investors, such as hedge and mutual funds.

First, there are no papers that have studied the return predictability in Saudi Arabia in spite of its importance as an emerging market. Second, the methods that combine all predictive variables such as the diffusion index or the kitchen sink methods have not been implemented in emerging markets. Third, this paper is the first study to deal with time-varying short-horizon predictability in emerging countries.

The purpose of this paper is to provide a generic forecasting approach for predicting product returns in closed‐loop supply chains.

The approach is based on Bayesian estimation techniques. It permits to forecast product returns on the basis of fewer restrictions than existing approaches in CLSC literature. A numerical example demonstrates the application of the proposed approach using return times drawn from a Poisson distribution.

The Bayesian estimation approach provides at least 50 percent higher accuracy in terms of error measures compared to traditional methods in all scenarios examined in the empirical part. Hence, more precise results can be obtained when predicting product returns.

The flexibility of the proposed approach allows for numerous applications in the field of CLSC research. Areas that depend on the results from a forecasting system, such as inventory management, can embed our estimation procedure in order to reduce safety stocks. Further research should address the incorporation of the quality of returned products and its impact on the actual utilizable amount of product returns.

The generic character of the proposed forecasting approach leaves degrees of freedom to the user when adapting it to a specific problem. This adaptability is enabled by the following features: first, an arbitrary function is allowed for capturing the customers' demand. Second, the stochastic timeframe between sale and product return may follow an arbitrary distribution. Third, by adjusting two parameters finite as well as infinite planning horizons can be incorporated. Fourth, no assumptions regarding the joint distribution of product returns are necessary.

The purpose of this paper is to develop a model for forecasting product returns to the company for recycling in terms of quantity and time.

Graphical Evaluation and Review Technique (GERT) has been applied for developing the forecasting model for product returns. A case of Indian mobile manufacturing company is discussed for the validation of this model. Survey conducted by the company and findings from previous research were used for data collection on probabilities and product life cycle.

Product returns for their recycling are stochastic, random and uncertain. Therefore, to address the uncertainty, randomness and stochastic nature of product returns, GERT is very useful tool for forecasting product returns.

GERT provides the visual picture of the reverse supply chain system and helps in determining the expected time of product returns in a much easier way but it requires probabilities of different flows and product life cycle. Both factors vary over a period, so require data update time to time before implementation.

This model is developed by considering all possible flows of sold products from customer to their reuse, store or recycle or landfill. First time this type of real life flows have been considered and GERT has been applied for forecasting product returns. This model can be utilized by managers for better forecasting that will help them for effective reverse supply chain design.

The purpose of this paper is to compare different models’ performance in modelling and forecasting the Finnish house price returns and volatility.

The competing models are the autoregressive moving average (ARMA) model and autoregressive fractional integrated moving average (ARFIMA) model for house price returns. For house price volatility, the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model is competing with the fractional integrated GARCH (FIGARCH) and component GARCH (CGARCH) models.

Results reveal that, for modelling Finnish house price returns, the data set under study drives the performance of ARMA or ARFIMA model. The EGARCH model stands as the leading model for Finnish house price volatility modelling. The long memory models (ARFIMA, CGARCH and FIGARCH) provide superior out-of-sample forecasts for house price returns and volatility; they outperform their short memory counterparts in most regions. Additionally, the models’ in-sample fit performances vary from region to region, while in some areas, the models manifest a geographical pattern in their out-of-sample forecasting performances.

The research results have vital implications, namely, portfolio allocation, investment risk assessment and decision-making.

To the best of the author’s knowledge, for Finland, there has yet to be empirical forecasting of either house price returns or/and volatility. Therefore, this study aims to bridge that gap by comparing different models’ performance in modelling, as well as forecasting the house price returns and volatility of the studied market.

The purpose of this study is to develop a fuzzy expert system to design robust forecast of return quantity in order to handle uncertainties from the return process in reverse logistic network.

The most important factors which have impact on return of products are defined. Then the factors which have collinearity with others are eliminated by using dimension redundancy analysis. By training data of selected factors with fuzzy expert system, the return amounts of alternative cities are forecasted.

The performance metrics of the proposed model are found as satisfactory. That means the result of this study indicates that fuzzy expert systems can be used as a supportive tool for forecasting return quantity of alternative areas.

In the future, the proposed model can be used for forecasting other uncertain parameters such as return quality and return time. Other fuzzy systems such as type-2 fuzzy sets can be used, or other expert systems such as artificial neural networks can be integrated into fuzzy systems.

An application at an e-recycling facility is conducted for clarifying how the method is used in a real decision process.

It is the first study which aims to model an alternative forecasting by utilizing fuzzy expert system. Furthermore, a comprehensive factor list which includes predictors of the system is defined. Then, a dimension redundancy analysis is developed to reveal factors having significant impact on the return process and eliminate the rest.

The purpose of this paper is to examine the risk-adjusted performance of US-based global real estate mutual funds (GREMFs) with emphasis on their ability to manage their domestic and foreign portfolios exposures.

The paper applies common econometric measures of portfolio performance and implements a non-traditional methodology called attribution returns to measure forecasting ability. In this setting the paper compares the actual monthly fund return to what would have been earned by the set of indices that best reflects the fund's investment strategy during the previous month. Performance and forecasting ability is examined during two different time periods: 2001-2005 and 2006-2010.

It is found that global real estate fund managers outperform the market and show good forecasting ability during the 2001-2005 time period. Good forecasting ability translates to positive risk-adjusted performance, as attribution returns are positively correlated with α.

Despite the significant growth in the number of US-based GREMFs and the ample coverage these funds receive in the popular press, few studies are solely devoted to the examination of these funds. In this study the paper empirically examines the ability of fund managers to successfully forecast country/regional political and economic conditions as well as fluctuations in currency exchanges rates brought about by the changes they made to their portfolios’ domestic and foreign exposures.

This study aims to implement a novel approach of using the Realized generalized autoregressive conditional heteroskedasticity (GARCH) model within the conditional extreme value theory (EVT) framework to generate quantile forecasts. The Realized GARCH-EVT models are estimated with different realized volatility measures. The forecasting ability of the Realized GARCH-EVT models is compared with that of the standard GARCH-EVT models.

One-step-ahead forecasts of Value-at-Risk (VaR) and expected shortfall (ES) for five European stock indices, using different two-stage GARCH-EVT models, are generated. The forecasting ability of the standard GARCH-EVT model and the asymmetric exponential GARCH (EGARCH)-EVT model is compared with that of the Realized GARCH-EVT model. Additionally, five realized volatility measures are used to test whether the choice of realized volatility measure affects the forecasting performance of the Realized GARCH-EVT model.

In terms of the out-of-sample comparisons, the Realized GARCH-EVT models generally outperform the standard GARCH-EVT and EGARCH-EVT models. However, the choice of the realized estimator does not affect the forecasting ability of the Realized GARCH-EVT model.

It is one of the earliest implementations of the two-stage Realized GARCH-EVT model for generating quantile forecasts. To the best of the authors’ knowledge, this is the first study that compares the performance of different realized estimators within Realized GARCH-EVT framework. In the context of high-frequency data-based forecasting studies, a sample period of around 11 years is reasonably large. More importantly, the data set has a cross-sectional dimension with multiple European stock indices, whereas most of the earlier studies are based on the US market.

The aim of this paper is to forecast the office property returns in Helsinki CBD using both short‐run and long‐run econometric specifications. Real economy, monetary and financial market indicators are included in these specifications to explain the variation in office property returns and forecast them. The paper illustrates the steps that analysts can follow to select models based on common diagnostics criteria and ex post forecasting evaluation tests. The findings of this research are in accordance with the results of previous comparative research in Europe and suggest that the growth of the gross domestic product in Finland is a key variable for modelling and forecasting office property returns in Helsinki. Moreover, the analysis indicated that information from a long‐run relationship of the gross domestic product and the real office return index should be monitored in the future as a way of improving the forecasts through an error correction model. It is predicted that Helsinki office returns will show a growth of about 7.1 per cent on average in the period 1999‐2001.