I propose a neurally motivated network model of advertising where advertisements create complementarities among the directly observable attributes of a product, allowing a brand to obtain social value. The formalization of this process allows us to examine the indirect effects of advertising in...

The purpose of this paper is to examine if there are calendar anomalies in the Greek Stock market and to confirm the findings of other researches. Specifically two models are presented, one for the day of the week effect test and other for the month of the year effect. We provide GARCH...

It is conjectured that the size of a hedge fund has some impact on its return. In this paper, we investigate the relationship between them and apply the results to construct an investment model. We first implement a learning algorithm to construct a piecewise linear regression model which shows...

This paper investigates the nonlinear predictability of technical trading rules based on a recurrent neural network as well as a neurofuzzy model. The efficiency of the trading strategies was considered upon the prediction of the direction of the market in case of NASDAQ and NIKKEI returns. The...

This paper presents an application of neural network models to predictive classification for data quality control. Our aim is to identify data affected by measurement error in the Bank of Italy's business surveys. We build an architecture consisting of three feed-forward networks for variables...

In this paper we design the neural network consumer credit scoring models for financial institutions where data usually used in previous research are not available. We use extensive primarily accounting data set on transactions and account balances of clients available in each financial...

The present study, investigates the predictability of Samp;P CNX NIFTY Index returns using Support vector machines (SVM). The performance of the SVM model in forecasting Nifty index returns is rigorously evaluated in terms of widely used statistical metrics like mean absolute error, root mean...

Recent episodes of financial crises have revived the interest in developing models that are able to timely signal their occurrence. The literature has developed both parametric and non parametric models to predict these crises, the so called Early Warning Systems. Using data related to sovereign...

The neural network models have proven to be a very effective tool in prediction and classification problems in a variety of business applications. In this study, we apply the neural network approach to predict the firm's decision to list shares on a foreign stock exchange. Using a sample of 95...

We use a machine learning algorithm called Adaboost to find direction-of-change patterns for the Samp;P 500 index using daily prices from 1962 to 2004. The patterns are able to identify periods to take long and short positions in the index. This result, however, can largely be explained by...