Search results

1 – 10 of over 7000
To view the access options for this content please click here
Article
Publication date: 17 August 2010

Chenyi Hu

The purpose of this paper is to associate a probabilistic confidence with the stock market interval forecasts obtained with the interval least squares (ILS) algorithm. The…

Abstract

Purpose

The purpose of this paper is to associate a probabilistic confidence with the stock market interval forecasts obtained with the interval least squares (ILS) algorithm. The term probabilistic confidence in this paper means the probability of a point observation that will fall in the interval forecast.

Design/methodology/approach

Using confidence interval as input, annual ILS forecasts of the stock market were made. Then the probability of point observation that fall in the annual forecasts was examined empirically.

Findings

When using confidence interval as ILS input, the stock market annual interval forecasts may have the same level of confidence as that of the input intervals.

Research limitations/implications

At the same confidence level, the ILS can produce much better quality forecasts than the traditional ordinary least squares method for the stock market. Although the algorithmic approach can be applied to analyze other datasets, one should examine implications of computational results as always.

Practical implications

Results of this specific paper may be interesting to executive officers, other financial decision makers and to investors.

Originality/value

Although the ILS algorithm has been recently developed in forecasting the variability of the stock market, this paper presents the first successful attempt in associating a probabilistic confidence with ILS interval forecasts.

Details

The Journal of Risk Finance, vol. 11 no. 4
Type: Research Article
ISSN: 1526-5943

Keywords

To view the access options for this content please click here
Article
Publication date: 20 October 2011

Huayou Chen, Lei Jin, Xiang Li and Mengjie Yao

The purpose of this paper is to propose the optimal combination forecasting model based on closeness degree and induced ordered weighted harmonic averaging (IOWHA…

Abstract

Purpose

The purpose of this paper is to propose the optimal combination forecasting model based on closeness degree and induced ordered weighted harmonic averaging (IOWHA) operator under the uncertain environment in which the raw data are provided by interval numbers.

Design/methodology/approach

Starting from maximizing the closeness degree of combination forecasting, which is different from minimizing absolute errors, weighted coefficient vectors of combination forecasting methods are obtained. The new concepts of closeness degree for the center and radius of interval numbers sequences are put forward and the optimal interval combination forecasting model is constructed by maximizing the sum of convex combination with closeness degree of interval center and closeness degree of interval radius. The solution to the model is discussed.

Findings

The results show that this model can improve the combination forecasting accuracy efficiently compared with that of each single forecasting method.

Practical implications

The method proposed in the paper can be used to forecast future tendency in a wide ranges of fields, such as engineering, economics and management. In particular, the raw data are provided in the form of interval numbers under the uncertain environment.

Originality/value

The combination forecasting model proposed in this paper is based on closeness degree and IOWHA operator, which is a new kind of combination forecasting model with variant weights.

Details

Grey Systems: Theory and Application, vol. 1 no. 3
Type: Research Article
ISSN: 2043-9377

Keywords

To view the access options for this content please click here
Article
Publication date: 13 November 2007

Ling T. He and Chenyi Hu

The purpose of this study is to investigate the impacts of interval measured data, rather than traditional point data, on economic variability studies.

Abstract

Purpose

The purpose of this study is to investigate the impacts of interval measured data, rather than traditional point data, on economic variability studies.

Design/methodology/approach

The study uses interval measured data to forecast the variability of future stock market changes. The variability (interval) forecasts are then compared with point data‐based confidence interval forecasts.

Findings

Using interval measured data in stock market variability forecasting can significantly increase forecasting accuracy, compared with using traditional point data.

Originality/value

An interval forecast for stock prices essentially consists of predicted levels and a predicted variability which can reduce perceived uncertainty or risk embedded in future investments, and therefore, may influence required returns and capital asset prices.

Details

The Journal of Risk Finance, vol. 8 no. 5
Type: Research Article
ISSN: 1526-5943

Keywords

To view the access options for this content please click here
Article
Publication date: 27 February 2009

Ling T. He, Chenyi Hu and K. Michael Casey

The purpose of this paper is to forecast variability in mortgage rates by using interval measured data and interval computing method.

Abstract

Purpose

The purpose of this paper is to forecast variability in mortgage rates by using interval measured data and interval computing method.

Design/methodology/approach

Variability (interval) forecasts generated by the interval computing are compared with lower‐ and upper‐bound forecasts based on the ordinary least squares (OLS) rolling regressions.

Findings

On average, 56 per cent of annual changes in mortgage rates may be predicted by OLS lower‐ and upper‐bound forecasts while the interval method improves forecasting accuracy to 72 per cent.

Research limitations/implications

This paper uses the interval computing method to forecast variability in mortgage rates. Future studies may expand variability forecasting into more risk‐managing areas.

Practical implications

Results of this study may be interesting to executive officers of banks, mortgage companies, and insurance companies, builders, investors, and other financial decision makers with an interest in mortgage rates.

Originality/value

Although it is well‐known that changes in mortgage rates can significantly affect the housing market and economy, there is not much serious research that attempts to forecast variability in mortgage rates in the literature. This study is the first endeavor in variability forecasting for mortgage rates.

Details

The Journal of Risk Finance, vol. 10 no. 2
Type: Research Article
ISSN: 1526-5943

Keywords

To view the access options for this content please click here
Article
Publication date: 29 July 2014

Yinao Wang

The purpose of this paper is to discuss the interval forecasting, prediction interval and its reliability. When the predicted interval and its reliability are…

Abstract

Purpose

The purpose of this paper is to discuss the interval forecasting, prediction interval and its reliability. When the predicted interval and its reliability are construction, the general rule which must satisfy is studied, grey wrapping band forecasting method is perfect.

Design/methodology/approach

A forecasting method puts forward a process of prediction interval. It also elaborates on the meaning of interval (the probability of the prediction interval including the real value of predicted variable). The general rule is abstracted and summarized by many forecasting cases. The general rule is discussed by axiomatic method.

Findings

The prediction interval is categorized into three types. Three axioms that construction predicted interval must satisfy are put forward. Grey wrapping band forecasting method is improved based on the proposed axioms.

Practical implications

Take the Shanghai composite index as the example, according to the K-line diagram from 4 January 2013 to 9 May 2013, the reliability of predicted rebound height of subsequent two or three trading day does not exceed the upper wrapping curve is 80 per cent. It is significant to understand the forecasting range correctly, build a reasonable range forecasting method and to apply grey wrapping band forecasting method correctly.

Originality/value

Grey wrapping band forecasting method is improved based on the proposed axioms.

Details

Grey Systems: Theory and Application, vol. 4 no. 2
Type: Research Article
ISSN: 2043-9377

Keywords

To view the access options for this content please click here
Article
Publication date: 11 April 2016

Sumit Sakhuja, Vipul Jain, Sameer Kumar , Charu Chandra and Sarit K Ghildayal

Many studies have proposed variant fuzzy time series models for uncertain and vague data. The purpose of this paper is to adapt a fuzzy time series combined with genetic…

Abstract

Purpose

Many studies have proposed variant fuzzy time series models for uncertain and vague data. The purpose of this paper is to adapt a fuzzy time series combined with genetic algorithm (GA) to forecast tourist arrivals in Taiwan.

Design/methodology/approach

Different cases are studied to understand the effect of variation of fuzzy time series order, number of intervals and population size on the fitness function which decreases with increase in fuzzy time series order and number of fuzzy intervals, but do not have marginal effect due to change in population size.

Findings

Results based on an example of forecasting Taiwan’s tourism demand was used to verify the efficacy of proposed model and confirmed its superiority to existing models providing solutions for different orders of fuzzy time series, number of intervals and population size with a smaller forecasting error as measured by root mean square error.

Originality/value

This study provides a viable forecasting methodology, adapting a fuzzy time series combined with an evolutionary GA. The proposed hybridized framework of fuzzy time series and GA, where GA is used to calibrate fuzzy interval length, is flexible and replicable to many industrial situations.

Details

Industrial Management & Data Systems, vol. 116 no. 3
Type: Research Article
ISSN: 0263-5577

Keywords

To view the access options for this content please click here
Article
Publication date: 1 August 1998

Jay Nathan and Ray Venkataraman

This paper examines the impact of forecast window intervals on replanning frequencies for a rolling horizon master production schedule (MPS). The problem environment for…

Abstract

This paper examines the impact of forecast window intervals on replanning frequencies for a rolling horizon master production schedule (MPS). The problem environment for this study is an actual MPS operation of a paint company and includes features such as multiple production lines, multiple products, capacity constraints, minimum inventory requirements. A mixed integer goal programming model formulated for the MPS problem is used to analyze the impact of forecast window interval length on replanning frequencies and MPS performance in a rolling horizon setting. Given demand certainty, results indicate that the length of the forecast window interval influences the choice of replanning frequency for this company environment. A three‐month forecast window interval with a two‐month replanning frequency provided the best MPS performance in terms of total cost.

Details

International Journal of Operations & Production Management, vol. 18 no. 8
Type: Research Article
ISSN: 0144-3577

Keywords

To view the access options for this content please click here
Article
Publication date: 1 March 2006

Neil Hartnett

This paper aims to extend the research into company financial forecasts by modelling naïve earnings forecasts derived from normalised historic accounting data disclosed…

Abstract

Purpose

This paper aims to extend the research into company financial forecasts by modelling naïve earnings forecasts derived from normalised historic accounting data disclosed during Australian initial public offerings (IPOs). It seeks to investigate naïve forecast errors and compare them against their management forecast counterparts. It also seeks to investigate determinants of differential error behaviour.

Design/methodology/approach

IPOs were sampled and their prospectus forecasts, historic financial data and subsequent actual financial performance were analysed. Directional and absolute forecast error behaviour was analysed using univariate and multivariate techniques.

Findings

Systematic factors associated with error behaviour were observed across the management forecasts and the naïve forecasts, the most notable being audit quality. In certain circumstances, the naïve forecasts performed at least as well as management forecasts. In particular, forecast interval was an important discriminator for accuracy, with the superiority of management forecasts only observed for shorter forecast intervals.

Originality/value

The results imply a level of “disclosure management” regarding company IPO forecasts and normalised historic accounting data, with forecast overestimation and error size more extreme in the absence of higher quality third‐party monitoring services via the audit process. The results also raise questions regarding the serviceability of normalised historic financial information disclosed in prospectuses, in that many of those data do not appear to enhance the forecasting process, particularly when accompanied by published management forecasts and shorter forecast intervals.

Details

Asian Review of Accounting, vol. 14 no. 1/2
Type: Research Article
ISSN: 1321-7348

Keywords

To view the access options for this content please click here
Article
Publication date: 4 May 2021

Sandang Guo, Yaqian Jing and Bingjun Li

The purpose of this paper is to make multivariable gray model to be available for the application on interval gray number sequences directly, the matrix form of interval

Abstract

Purpose

The purpose of this paper is to make multivariable gray model to be available for the application on interval gray number sequences directly, the matrix form of interval multivariable gray model (IMGM(1,m,k) model) is constructed to simulate and forecast original interval gray number sequences in this paper.

Design/methodology/approach

Firstly, the interval gray number is regarded as a three-dimensional column vector, and the parameters of multivariable gray model are expressed in matrix form. Based on the dynamic gray action and optimized background value, the interval multivariable gray model is constructed. Finally, two examples and comparisons are carried out to verify the effectiveness of IMGM(1,m,k) model.

Findings

The model is applied to simulate and predict expert value, foreign direct investment, automobile sales and steel output, respectively. The results show that the proposed model has better simulation and prediction performance than another two models.

Practical implications

Due to the uncertainty information and continuous changing of reality, the interval gray numbers are used to characterize full information of original data. And the IMGM(1,m,k) model not only considers the characteristics of parameters changing with time but also takes into account information on lower, middle and upper bounds of interval gray numbers simultaneously to make better suitable for practical application.

Originality/value

The main contribution of this paper is to propose a new interval multivariable gray model, which considers the interaction between the lower, middle and upper bounds of interval numbers and need not to transform interval gray number sequences into real sequences. According to combining different characteristics of each bound of interval gray numbers, the matrix form of interval multivariable gray model is established to simulate and forecast interval gray numbers. In addition, the model introduces dynamic gray action to reflect the changes of parameters over time. Instead of white equation of classic MGM(1,m), the difference equation is directly used to solve the simulated and predicted values.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

Keywords

To view the access options for this content please click here
Book part
Publication date: 30 August 2019

Joshua C. C. Chan, Liana Jacobi and Dan Zhu

Vector autoregressions (VAR) combined with Minnesota-type priors are widely used for macroeconomic forecasting. The fact that strong but sensible priors can substantially…

Abstract

Vector autoregressions (VAR) combined with Minnesota-type priors are widely used for macroeconomic forecasting. The fact that strong but sensible priors can substantially improve forecast performance implies VAR forecasts are sensitive to prior hyperparameters. But the nature of this sensitivity is seldom investigated. We develop a general method based on Automatic Differentiation to systematically compute the sensitivities of forecasts – both points and intervals – with respect to any prior hyperparameters. In a forecasting exercise using US data, we find that forecasts are relatively sensitive to the strength of shrinkage for the VAR coefficients, but they are not much affected by the prior mean of the error covariance matrix or the strength of shrinkage for the intercepts.

Details

Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A
Type: Book
ISBN: 978-1-78973-241-2

Keywords

1 – 10 of over 7000