Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price will maximize investor’s gains. This paper proposes a machine learning model to predict stock market price. The proposed algorithm integrates Particle swarm optimization (PSO) and least squ...

In response to relatively little evidence on the determinants of financial distress in cooperative institutions (e.g., Credit Unions), this paper proposes a indicator Merton Distance default (Merton DD), which was constructed with z-score, possessed improved predictive capability, but reducing equity volatility. This model possesses advantages both hazard and modified DD model, could timely ref...

market crash is a phenomenon which occurs in stock markets occasionally and leads to loss of the investors’ wealth and assets in a relatively short period of time. therefore, attempts for prediction of this phenomenon are of much importance for the investors, financial institutions and government. to this date, numerous and varied studies have been carried out for predicting and modeling  stock...

The foreign currency exchange market is the highest and most liquid of the financial markets, with an estimated $1 trillion traded every day. Foreign exchange rates are the most important economic indices in the international financial markets. The prediction of them poses many theoretical and experimental challenges. This paper reports empirical proof that a neural network model is applicable ...

Company default prediction is a widely studied topic as it has a significant impact on banks and firms. Moreover, nowadays, due to the global financial crisis, there is a need to use even more advanced methods (such as soft computing techniques) which can pick up the signs of financial distress on time to evaluate firms (especially small firms). Thus, the author proposes a Genetic Algorithms (G...

This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition MEMD is employed here for multiband representation of multichannel financial time series together. Autoregressivemoving average ARMA model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the over...

Bankruptcy prediction is very important for all the organization since it affects the economy and rise many social problems with high costs. There are large number of techniques have been developed to predict the bankruptcy, which helps the decision makers such as investors and financial analysts. One of the bankruptcy prediction models is the hybrid model using Fuzzy C-means clustering and MAR...

One purpose of this paper is to propose the hybrid neural network models for bankruptcy prediction. The proposed hybrid neural network models are, respectively, a MDA model integrated with financial ratios, a MDA model integrated with financial ratios and intellectual capital ratios, a MDA-assisted neural network model integrated with financial ratios, and a MDA-assisted neural network model in...

Stock market prediction is a technique of predicting the future value of the stock markets on the basis of the current and the previous information available in the market.Stock market prediction is an important issue for investigating in academic and financial research. There are various techniques available for the prediction of the stock market value. Here in this paper a survey of all the t...