Abstract—In industries, how to improve forecasting accuracy such as sales, shipping is an important issue. Correct sales forecasting is inevitable in industries. There are many researches made on this. In this paper, a hybrid method is introduced and plural methods are compared. Focusing that the equation of exponential smoothing method(ESM) is equivalent to (1,1) order ARMA model equation, a n...

We study the forecasting of future realized volatility in the foreign exchange, stock, and bond markets from variables in the information set, including implied volatility backed out from option prices. Realized volatility is separated into its continuous and jump components, and the heterogeneous autoregressive (HAR) model is applied with implied volatility as an additional forecasting variabl...

Prediction of the movement of stock market is a long-time attractive topic to researchers from different fields. The Hang Seng Index (abbreviated: HSI) is a free float-adjusted market capitalization-weighted stock market index in Hong Kong. We believe the HSI is influenced by other major financial indexes across the world and try different machine learning method to get a prediction based on th...


 The availability of goods in a store is very important. Forecasting tool that used to help predict data needed by an organization or company. purpose this study the sale product has high risk damage and fast expiration time using existing techniques forecasting. can also be make stock safety at XYZ Supermarket. results are form forecasting methods adjusted sales one product. method ARIMA...

We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. Exploiting different Neural Networks architectures, we provide numerical analysis of concrete financial time series. In particular, after a brief résumé of the existing literature on the subject, we consider the Multi-layer Percep...

Classification is the attribution of labels to records according to a criterion automatically learned from a training set of labeled records. This task is needed in a huge number of practical applications, and consequently it has been studied intensively and several classification algorithms are available today. In finance, a stock market index is a measurement of value of a section of the stoc...

The primary purpose of this paper was to provide an in-depth analysis of the ability of modern analytical platforms (using IBM Watson Analytics as an example) to generate predictive models for stock prices forecasting in comparison with traditional analytical econometric platforms and models. Series of stock predictive models based on the suggestions of IBM Watson Analytics have demonstrated re...

In this paper I conduct tests of an intertemporal asset pricing model using variables that forecast stock returns as the risk factors. I document that the forecasting variables are priced so that expected excess returns are related to their conditional covariances with the forecasting variables. The variability in the covariance risk fails to explain the cross-sectional and time-series variatio...

We propose a new computational method of input selection for stock market forecasting with neural networks. The method results from synthetically considering the special feature of input variables of neural networks and the special feature of stock market time series. We conduct the experiments to compare the prediction performance of the neural networks based on the different input variables b...

We use game theory and Santa Fe Artificial Stock Market, an agent-based model of an evolving stock market, to study the optimal frequency for traders to revise their market forecasting rules. We discover two things: There is a unique strategic Nash equilibrium in the game of choosing forecast revision rates, and this equilibrium is sub-optimal in the sense that traders’ earnings are not maximiz...