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Article
Publication date: 25 October 2019

Saurabh Agrawal and Rajesh Kumar Singh

Forecasting product returns plays an important role in the operations of reverse logistics (RL). However, their contribution to sustainability performance is yet to be…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

Management of Environmental Quality: An International Journal, vol. 31 no. 5
Type: Research Article
ISSN: 1477-7835

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Article
Publication date: 5 May 2015

Ling T. He

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Details

Journal of Financial Economic Policy, vol. 7 no. 2
Type: Research Article
ISSN: 1757-6385

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Book part
Publication date: 29 February 2008

David E. Rapach, Jack K. Strauss and Mark E. Wohar

We examine the role of structural breaks in forecasting stock return volatility. We begin by testing for structural breaks in the unconditional variance of daily returns

Abstract

We examine the role of structural breaks in forecasting stock return volatility. We begin by testing for structural breaks in the unconditional variance of daily returns for the S&P 500 market index and ten sectoral stock indices for 9/12/1989–1/19/2006 using an iterative cumulative sum of squares procedure. We find evidence of multiple variance breaks in almost all of the return series, indicating that structural breaks are an empirically relevant feature of return volatility. We then undertake an out-of-sample forecasting exercise to analyze how instabilities in unconditional variance affect the forecasting performance of asymmetric volatility models, focusing on procedures that employ a variety of estimation window sizes designed to accommodate potential structural breaks. The exercise demonstrates that structural breaks present important challenges to forecasting stock return volatility. We find that averaging across volatility forecasts generated by individual forecasting models estimated using different window sizes performs well in many cases and appears to offer a useful approach to forecasting stock return volatility in the presence of structural breaks.

Details

Forecasting in the Presence of Structural Breaks and Model Uncertainty
Type: Book
ISBN: 978-1-84950-540-6

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Article
Publication date: 14 May 2018

Abdelmonem Oueslati and Yacine Hammami

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Details

Review of Accounting and Finance, vol. 17 no. 2
Type: Research Article
ISSN: 1475-7702

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Article
Publication date: 30 August 2013

Michael Krapp, Johannes Nebel and Ramin Sahamie

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

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Abstract

Purpose

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

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

International Journal of Physical Distribution & Logistics Management, vol. 43 no. 8
Type: Research Article
ISSN: 0960-0035

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Article
Publication date: 29 April 2014

Saurabh Agrawal, Rajesh K. Singh and Qasim Murtaza

– 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.

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Details

Journal of Advances in Management Research, vol. 11 no. 1
Type: Research Article
ISSN: 0972-7981

Keywords

Content available
Article
Publication date: 11 April 2021

Josephine Dufitinema

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

Abstract

Purpose

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

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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

Originality/value

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.

Details

International Journal of Housing Markets and Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-8270

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Book part
Publication date: 30 November 2011

Massimo Guidolin

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of…

Abstract

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

Details

Missing Data Methods: Time-Series Methods and Applications
Type: Book
ISBN: 978-1-78052-526-6

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Article
Publication date: 8 April 2014

Gül Tekin Temur, Muhammet Balcilar and Bersam Bolat

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…

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1267

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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

Originality/value

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.

Details

Journal of Enterprise Information Management, vol. 27 no. 3
Type: Research Article
ISSN: 1741-0398

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Book part
Publication date: 29 February 2008

Massimo Guidolin and Carrie Fangzhou Na

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the…

Abstract

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.

Details

Forecasting in the Presence of Structural Breaks and Model Uncertainty
Type: Book
ISBN: 978-1-84950-540-6

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