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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 in-sample and…

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

Keywords

Article
Publication date: 21 December 2017

Marc Gürtler and Thomas Paulsen

Empirical publications on the time series modeling and forecasting of electricity prices vary widely regarding the conditions, and the findings make it difficult to generalize…

Abstract

Purpose

Empirical publications on the time series modeling and forecasting of electricity prices vary widely regarding the conditions, and the findings make it difficult to generalize results. Against this background, it is surprising that there is a lack of statistics-based literature reviews on the forecasting performance when comparing different models. The purpose of the present study is to fill this gap.

Design/methodology/approach

The authors conduct a comprehensive literature analysis from 2000 to 2015, covering 86 empirical studies on the time series modeling and forecasting of electricity spot prices. Various statistics are presented to characterize the empirical literature on electricity spot price modeling, and the forecasting performance of several model types and modifications is analyzed. The key issue of this study is to offer a comparison between different model types and modeling conditions regarding their forecasting performance, which is referred to as a quasi-meta-analysis, i.e. the analysis of analyses to achieve more general findings independent of the circumstances of single studies.

Findings

The authors find evidence that generalized autoregressive conditional heteroscedasticity models outperform their autoregressive–moving-average counterparts and that the consideration of explanatory variables improves forecasts.

Originality/value

To the best knowledge of the authors, this paper is the first to apply the methodology of meta-analyses in a literature review of the empirical forecasting literature on electricity spot markets.

Details

International Journal of Energy Sector Management, vol. 12 no. 1
Type: Research Article
ISSN: 1750-6220

Keywords

Article
Publication date: 20 November 2009

Sanjeev Kumar Aggarwal, L.M. Saini and Ashwani Kumar

Several research papers related to electricity price forecasting have been reported in the leading journals in last 20 years. The purpose of this paper is to present a…

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Abstract

Purpose

Several research papers related to electricity price forecasting have been reported in the leading journals in last 20 years. The purpose of this paper is to present a comprehensive survey and comparison of these techniques.

Design/methodology/approach

The present article provides an overview of the statistical short‐term price forecasting (STPF) models. The basic theory of these models, their further classification and their suitability to STPF has been discussed. Quantitative evaluation of the performance of these models in the framework of accuracy achieved and computation time taken has been performed. Some important observations of the literature survey and key issues regarding STPF methodologies are analyzed.

Findings

It has been observed that price forecasting accuracy of the reported models in day‐ahead markets is better as compared to that in real time markets. From a comparative analysis perspective, there is no hard evidence of out‐performance of one model over all other models on a consistent basis for a very long period. In some of the studies, linear models like dynamic regression and transfer function have shown superior performance as compared to non‐linear models like artificial neural networks (ANNs). On the other hand, recent variations in ANNs by employing wavelet transformation, fuzzy logic and genetic algorithm have shown considerable improvement in forecasting accuracy. However more complex models need further comparative analysis.

Originality/value

This paper is intended to supplement the recent survey papers, in which the researchers have restricted the scope to a bibliographical survey. Whereas, in this work, after providing detailed classification and chronological evolution of the STPF techniques, a comparative summary of various priceforecasting techniques, across different electricity markets, is presented.

Details

International Journal of Energy Sector Management, vol. 3 no. 4
Type: Research Article
ISSN: 1750-6220

Keywords

Article
Publication date: 21 November 2008

S.K. Aggarwal, L.M. Saini and Ashwani Kumar

Price forecasting is essential for risk management in deregulated electricity markets. The purpose of this paper is to propose a hybrid technique using wavelet transform (WT) and…

Abstract

Purpose

Price forecasting is essential for risk management in deregulated electricity markets. The purpose of this paper is to propose a hybrid technique using wavelet transform (WT) and multiple linear regression (MLR) to forecast price profile in electricity markets.

Design/methodology/approach

Price series is highly volatile and non‐stationary in nature. In this work, initially complete price series has been decomposed into separate 48 half‐hourly series and then these series have been categorized into different segments for price forecasting. For some segments, WT based MLR has been applied and for the other segments, simple MLR model has been applied. The model is general in nature and has been implemented for one day‐ahead price forecasting in National Electricity Market (NEM) of Australia. Participants can use the technique practically, since it predicts price well before submission of bids.

Findings

Forecasting performance of the proposed WT and MLR based mixed model has been compared with the three other models, an analytical model, a MLR model and an artificial neural network (ANN) based model. The proposed model was found to be better. Performance evaluation for different wavelets was performed, and it has been observed that for improving forecasting accuracy using WT, Daubechies wavelet of order two gives the best performance.

Originality/value

Forecasting accuracy improvement of an established technique by incorporating time domain and wavelet domain variables of the same time series into one set has been implemented in this work. The paper also attempts to explain how non‐stationarity can be removed from a non‐stationary time series by applying WT after appropriate statistical investigation. Moreover, real time electricity markets are highly unpredictable and yet under investigated. The model has been applied to NEM for the same reason.

Details

International Journal of Energy Sector Management, vol. 2 no. 4
Type: Research Article
ISSN: 1750-6220

Keywords

Book part
Publication date: 5 October 2018

Olalekan Shamsideen Oshodi and Ka Chi Lam

Fluctuations in the tender price index have an adverse effect on the construction sector and the economy at large. This is largely due to the positive relationship that exists…

Abstract

Fluctuations in the tender price index have an adverse effect on the construction sector and the economy at large. This is largely due to the positive relationship that exists between the construction industry and economic growth. The consequences of these variations include cost overruns and schedule delays, among others. An accurate forecast of the tender price index is good for controlling the uncertainty associated with its variation. In the present study, the efficacy of using an adaptive neuro-fuzzy inference system (ANFIS) for tender price forecasting is investigated. In addition, the Box–Jenkins model, which is considered a benchmark technique, was used to evaluate the performance of the ANFIS model. The results demonstrate that the ANFIS model is superior to the Box–Jenkins model in terms of the accuracy and reliability of the forecast. The ANFIS could provide an accurate and reliable forecast of the tender price index in the medium term (i.e. over a three-year period). This chapter provides evidence of the advantages of applying nonlinear modelling techniques (such as the ANFIS) to tender price index forecasting. Although the proposed ANFIS model is applied to the tender price index in this study, it can also be applied to a wider range of problems in the field of construction engineering and management.

Details

Fuzzy Hybrid Computing in Construction Engineering and Management
Type: Book
ISBN: 978-1-78743-868-2

Keywords

Article
Publication date: 27 June 2023

Fatemeh Binesh, Amanda Mapel Belarmino, Jean-Pierre van der Rest, Ashok K. Singh and Carola Raab

This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Abstract

Purpose

This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Design/methodology/approach

Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.

Findings

The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.

Research limitations/implications

This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.

Practical implications

This study produced a reliable, accurate forecasting model considering risk and competitor behavior.

Theoretical implications

This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.

Originality/value

This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.

Details

International Journal of Contemporary Hospitality Management, vol. 36 no. 4
Type: Research Article
ISSN: 0959-6119

Keywords

Article
Publication date: 21 October 2019

Xiaoquan Chu, Yue Li, Dong Tian, Jianying Feng and Weisong Mu

The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the price

Abstract

Purpose

The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the price prediction issue of China’s table grape.

Design/methodology/approach

The approaches follows the framework of “decomposition and ensemble,” using ensemble empirical mode decomposition (EEMD) to optimize the conventional price forecasting methods, and, integrating the multiple linear regression and support vector machine to build a hybrid model which could be applied in solving price series predicting problems.

Findings

The proposed EEMD-ADD optimized hybrid model is validated to be considered satisfactory in a case of China’ grape price forecasting in terms of its statistical measures and prediction performance.

Practical implications

This study would resolve the difficulties in grape price forecasting and provides an adaptive strategy for other agricultural economic predicting problems as well.

Originality/value

The paper fills the vacancy of concerning researches, proposes an optimized hybrid model integrating both classical econometric and artificial intelligence models to forecast price using time series method.

Article
Publication date: 27 March 2020

Luyao Wang, Jianying Feng, Xiaojie Sui, Xiaoquan Chu and Weisong Mu

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

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Abstract

Purpose

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

Design/methodology/approach

This paper reviews the main research methods and their application of forecasting of agricultural product prices, summarizes the application examples of common forecasting methods, and prospects the future research directions.

Findings

1) It is the trend to use hybrid models to predict agricultural products prices in the future research; 2) the application of the prediction model based on price influencing factors should be further expanded in the future research; 3) the performance of the model should be evaluated based on DS rather than just error-based metrics in the future research; 4) seasonal adjustment models can be applied to the difficult seasonal forecasting tasks in the agriculture product prices in the future research; 5) hybrid optimization algorithm can be used to improve the prediction performance of the model in the future research.

Originality/value

The methods from this paper can provide reference for researchers, and the research trends proposed at the end of this paper can provide solutions or new research directions for relevant researchers.

Article
Publication date: 1 October 2018

Marc Gürtler and Thomas Paulsen

Study conditions of empirical publications on time series modeling and forecasting of electricity prices vary widely, making it difficult to generalize results. The key purpose of…

Abstract

Purpose

Study conditions of empirical publications on time series modeling and forecasting of electricity prices vary widely, making it difficult to generalize results. The key purpose of the present study is to offer a comparison of different model types and modeling conditions regarding their forecasting performance.

Design/methodology/approach

The authors analyze the forecasting performance of AR (autoregressive), MA (moving average), ARMA (autoregressive moving average) and GARCH (generalized autoregressive moving average) models with and without the explanatory variables, that is, power consumption and power generation from wind and solar. Additionally, the authors vary the detailed model specifications (choice of lag-terms) and transformations (using differenced time series or log-prices) of data and, thereby, obtain individual results from various perspectives. All analyses are conducted on rolling calibrating and testing time horizons between 2010 and 2014 on the German/Austrian electricity spot market.

Findings

The main result is that the best forecasts are generated by ARMAX models after spike preprocessing and differencing the data.

Originality/value

The present study extends the existing literature on electricity price forecasting by conducting a comprehensive analysis of the forecasting performance of different time series models under varying market conditions. The results of this study, in general, support the decision-making of electricity spot price modelers or forecasting tools regarding the choice of data transformation, segmentation and the specific model selection.

Details

International Journal of Energy Sector Management, vol. 12 no. 4
Type: Research Article
ISSN: 1750-6220

Keywords

Article
Publication date: 14 February 2024

Huiyu Cui, Honggang Guo, Jianzhou Wang and Yong Wang

With the rise in wine consumption, accurate wine price forecasts have significantly impacted restaurant and hotel purchasing decisions and inventory management. This study aims to…

Abstract

Purpose

With the rise in wine consumption, accurate wine price forecasts have significantly impacted restaurant and hotel purchasing decisions and inventory management. This study aims to develop a precise and effective wine price point and interval forecasting model.

Design/methodology/approach

The proposed forecast model uses an improved hybrid kernel extreme learning machine with an attention mechanism and a multi-objective swarm intelligent optimization algorithm to produce more accurate price estimates. To the best of the authors’ knowledge, this is the first attempt at applying artificial intelligence techniques to improve wine price prediction. Additionally, an effective method for predicting price intervals was constructed by leveraging the characteristics of the error distribution. This approach facilitates quantifying the uncertainty of wine price fluctuations, thus rendering decision-making by relevant practitioners more reliable and controllable.

Findings

The empirical findings indicated that the proposed forecast model provides accurate wine price predictions and reliable uncertainty analysis results. Compared with the benchmark models, the proposed model exhibited superiority in both one-step- and multi-step-ahead forecasts. Meanwhile, the model provides new evidence from artificial intelligence to explain wine prices and understand their driving factors.

Originality/value

This study is a pioneering attempt to evaluate the applicability and effectiveness of advanced artificial intelligence techniques in wine price forecasts. The proposed forecast model not only provides useful options for wine price forecasting but also introduces an innovative addition to existing forecasting research methods and literature.

Details

International Journal of Contemporary Hospitality Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0959-6119

Keywords

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