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163 results for “Forecasting”
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Research ArticleOpen accessEvery word in the titleForecasting the Spanish Stock Market Returns with Fractional and Non-Fractional ModelsGuglielmo Maria Caporale, Juncal Cunado, Luis A. Gil-AlanaProblem statement: The content of this note was to assess the forecasting accuracy of various models of the Spanish stock market returns. Approach: We use daily data on the IBEX 35 for the time period January 4th, 2001-March 28th, 2006 and employ both fractional and non-fractional models. Results: The results on the prediction errors for the out-of-sample forecasts indicate that … …American Journal of Economics and Business Administration, Vol. 3, Issue 4 · 8 Dec 2011 · 7,316 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleDesign and Implementation of Novel Artificial Neural Network Based Stock Market Forecasting System on Field-Programmable Gate ArraysMd. Syedul Amin, Md. Mamun, Fazida Hanim Hashim et al.Problem statement: Multiagent system is very proficient and has rules well-suited for financial forecast with its neural network. In financial forecasting, the approach for rules extractions is less pertinent and involves algorithms which are complex. The unsupervised network method lacks in comprehensibility and leads to ambiguity. Approach: The application of neural network technology to real-time processing of financial market analysis … …American Journal of Applied Sciences, Vol. 8, Issue 10 · 12 Sep 2011 · 7,627 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Hybrid Approach based on Winter's Model and Weighted Fuzzy Time Series for Forecasting Trend and Seasonal DataSuhartono, Muhammad Hisyam LeeProblem statement: In the literature, the most studied of fuzzy time series for the purpose of forecasting is the first order fuzzy time series model. In this model, only the first lagged variable is used when constructing the first order fuzzy time series model. Therefore, such approaches fail to analyze accurately trend and seasonal time series which is an important … …Journal of Mathematics and Statistics, Vol. 7, Issue 3 · 18 Jul 2011 · 7,942 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleTime Series Forecasting by using Seasonal Autoregressive Integrated Moving Average: Subset, Multiplicative or Additive ModelSuhartonoProblem statement: Most of Seasonal Autoregressive Integrated Moving Average (SARIMA) models that used for forecasting seasonal time series are multiplicative SARIMA models. These models assume that there is a significant parameter as a result of multiplication between nonseasonal and seasonal parameters without testing by certain statistical test. Moreover, most popular statistical software such as MINITAB and SPSS only has facility … …Journal of Mathematics and Statistics, Vol. 7, Issue 1 · 29 Jan 2011 · 18,295 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSuitability of Volatility Models for Forecasting Stock Market Returns: A Study on the Indian National Stock ExchangeTrilochan Tripathy, Luis A. Gil-AlanaProblem statement: Measuring volatility is an important issue for stock market traders. Also, volatility has been used as a proxy for riskiness associated with the asset. This study aims to compare the different volatility models based on how well they model the volatility of the India NSE. Approach: The study has made use of five models which are Historical/Rolling Window … …American Journal of Applied Sciences, Vol. 7, Issue 11 · 8 Nov 2010 · 7,851 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSeasonal Time Series Data Forecasting by Using Neural Networks Multiscale Autoregressive ModelSuhartono, B. S.S. Ulama, A. J. EndhartaProblem statement: The aim of this research was to study further some latest progress of wavelet transform for time series forecasting, particularly about Neural Networks Multiscale Autoregressive (NN-MAR). Approach: There were three main issues that be considered further in this research. The first was some properties of scale and wavelet coefficients from Maximal Overlap Discrete Wavelet Transform (MODWT) decomposition, particularly … …American Journal of Applied Sciences, Vol. 7, Issue 10 · 31 Oct 2010 · 7,751 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleForecasting Financial Market Annual Performance Measures: Further Evidence+Edward J. Lusk, Michael Halperin, Atanas Tetikov, Niya StefanovaProblem statement: Forecasting is simple; producing accurate forecasts is the essential task. Experience suggests that financial managers often assume that because models used in forecasting are appropriate that they are effective. This study addresses this assumption. Effective is taken to mean forecasts where the Absolute Percentage Error (APE) is equal to or less than 10%. It has been reported that … …American Journal of Economics and Business Administration, Vol. 2, Issue 3 · 30 Sep 2010 · 7,526 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleModeling and Forecasting Volatility of the Malaysian Stock MarketsAhmed Shamiri, Zaidi IsaProblem statement: One of the main purposes of modeling variance is forecasting, which is crucial in many areas of finance. Despite the burgeoning interest in and evaluation of volatility forecasts, a clear consensus on witch volatility model/or distribution specification to use has not yet been reached. Therefore, the out of-sample forecasting ability should be a natural model selection criterion for … …Journal of Mathematics and Statistics, Vol. 5, Issue 3 · 30 Sep 2009 · 7,900 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleData-Driven Forecasting Schemes: Evaluation and ApplicationsJosip Vrbanek Jr, Wilson WangA reliable multi-step predictor is very useful to a wide array of applications to forecast the behavior of dynamic systems. The objective of this paper is to develop a more robust data-driven predictor for time series forecasting. Based on simulation analysis, it is found that multi-step-ahead forecasting schemes based on step inputs perform better than those based on sequential inputs. … …Journal of Computer Science, Vol. 3, Issue 9 · 30 Sep 2007 · 6,565 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleForecasting Corporate Financial Distress Using Explainable Machine Learning: Implications for Risk Management TechnologyKanjana Hinthaw, Warawut NarkbunnumFinancial distress prediction is a critical task in financial risk management, yet it remains challenging due to class imbalance, complex financial structures, and limited model interpretability. This study develops an integrated machine learning framework that combines feature selection, class imbalance handling, threshold optimization, and Explainable Artificial Intelligence (XAI) to improve both predictive performance and decision-making reliability. Using a dataset of … …Journal of Computer Science, Vol. 22, Issue 7 · 22 Jul 2026 · 1,130 accessesView PDFCite