ARMA model

A firm in the early stages of financial distress exhibits characteristics different from those of healthy firms. As the economic condition of a firm worsens, its financial characteristics shift toward those of failed firms. Practitioners in the financial sector have long been interested in the early detection of a firm's slide toward insolvency. Several models have been developed with this purpose in mind, but these older models are static in nature. Therefore, a need exists for the development of business failure prediction models that assess the financial condition of firms sequentially over time. This article addresses this need by presenting a sequential business failure prediction model.

This study aims to develop a stochastic framework of model to forecast future sales for pharmaceutical industry. In this regard, the study focuses on Merck Pharmaceutical monthly sales data. This study examines the Sale forecasting models. The study includes monthly data published in the annual reports of the company from Jan. 2008 to Dec. 2012.The time series diagram shows unequal means over the time period that suggests the data is stationary. Having transformed the data, ARMA (1, 1) model is applied which shows that there will be increase
in sales by $6.784m given that in the last month sales were $1bn. On the contrary, last month’s residual has an
adverse effect on current month sales up to the extent of $432.942m. In this study AR (1) and MA (1) both the processes are significant at 1%.

Luna august a anului 2015 marchează un deceniu de țintire directă a inflației (engl. inflation targeting – IT) în România, motiv pentru care dorim să dedicăm acest buletin problematicii acestui regim de politică monetară. Provocarea principală a acestuia o reprezintă dezvoltarea unui model de prognoză în baza căruia pot fi fundamentate deciziile privind acțiunile Băncii Naționale a României (BNR).

Stock price forecasting has attracted tremendous attention of researchers over the past several decades. Many techniques thus have been proposed so far to deal with the problem. This paper presents an application of a computational intelligence technique - a fuzzy inference system, namely Standard Additive Model (SAM), for predicting stock price time series data. The modelling and learning power of the SAM have been benefited to build the model that is capable of prediction functionalities. Experimental results have demonstrated that the proposed approach outperforms the traditional Auto Regressive Moving Average (ARMA) model in terms of the forecasting performance.

In recent years the ecological conditions in areas of important wetlands have markedly changed. One of the areas is also Kláštorské Lúky the national natural reservation wetland, which is situated in the Strážovské mountains in the northwest of the Slovak Republic. This study deals with the modeling and forecasting of discharge and rainfall time series in the area of the

This article applied GARCH model instead AR or ARMA model to compare with the standard BP and SVM in forecasting of the four international including two Asian stock markets indices.These models were evaluated on five performance metrics or criteria. Our experimental results showed the superiority of SVM and GARCH models, compared to the standard BP in forecasting of the four international stock markets indices.

Most high-frequency asset returns exhibit seasonal volatility patterns. This article proposes a new class of models featuring periodicity in conditional heteroscedasticity explicitly designed to capture the repetitive seasonal time variation in the second-order moments. ...