Corporate directors are influenced by overconfidence, which is one of the personality traits of individuals; it may take irrational decisions that will have a significant impact on the company's performance in the long run. The purpose of this paper is to validate and compare the Naive Bayesian Classification algorithm and probit regression in the prediction of Management's overconfident at pre...

A state-of-the-art review of the literature related to economic and financial prediction using rough sets model is presented, with special emphasis on the business failure prediction, database marketing and financial investment. These three applications require the accurate prediction of the future states based on the identification of patterns in the historical data. In addition, the historica...

This chapter examines a new class of markets at the intersection of trad· itional betting and traditional financial markets. We call these 'prediction markets'. Like both financial and betting markets, prediction markets focus on uncertain outcomes and involve trading in risks. Prices from these markets establish forecasts about the probabilities, mean and median outcomes, and correlations amon...

the current study aims to investigate the relationship between stock liquidity risk and financial information quality criteria (i.e. the timely dividends announcement, accruals quality and the percentage of profitability prediction error) of companies listed on the tehran stock exchange. for this purpose, 148 cases of data from listed companies, collected from 2007 to 2012, were employed in ord...

We explore the use of deep learning hierarchical models for problems in financial prediction and classification. Financial prediction problems – such as those presented in designing and pricing securities, constructing portfolios, and risk management – often involve large data sets with complex data interactions that currently are difficult or impossible to specify in a full economic model. App...

Prediction of financial distress of companies is analyzed with several machine learning approaches. We used DIANE, a large database containing financial records of small and medium size French companies from the year of 2002 up to 2007. It is shown that inclusion of historical data, up to 3 years priori to the analysis, increase the prediction accuracy. In particular, fluctuations of some finan...