This paper presents a literature review on methods that have been used in the last two decades to predict Stock Market Indexes. Methods studied range from those enabling to grab the linear characteristics present in the stock market indexes, going through those that focus on non-linear features and finally hybrid methods that are more robust, since they capture linear and non-linear features. In addition, this research includes methods that use macroeconomic variables to predict indexes from different stock exchanges around the world
Abstract Conventional statistical techniques for forecasting are constrained by the underlying seaso...
This paper surveys recent literature in the area of Neural Network, Data Mining, Hidden Markov Model...
The stock market has been one of the primary revenue streams for many for years. The stock market is...
This paper presents a literature review on methods that have been used in the last two decades to pr...
This paper presents a literature review on methods that have been used in the last two decades to pr...
Stock market prices are intrinsically dynamic, volatile, highly sensitive, nonparametric, nonlinear ...
Stock market prediction has always caught the attention of many analysts and researchers. Popular th...
Investment in the stock market is one of the much-admired investment actions. However, prediction of...
This study examines the value of technical, financial and macroeconomic variables in stock index pre...
My research intends to derive an intelligent multistep scalable hybrid model Integrating technical, ...
Abstract — With an easy access to share information and data nowadays, many investors worldwide are ...
Prediction of stock markets is a complex and challenging task due to price data generated is huge in...
With the advent of technological marvels like global digitization, the prediction of the stock marke...
AbstractMany studies in finance literature aims to find which macro-economic factors influence stock...
Financial time series prediction is a very important economical problem but the data available is ve...
Abstract Conventional statistical techniques for forecasting are constrained by the underlying seaso...
This paper surveys recent literature in the area of Neural Network, Data Mining, Hidden Markov Model...
The stock market has been one of the primary revenue streams for many for years. The stock market is...
This paper presents a literature review on methods that have been used in the last two decades to pr...
This paper presents a literature review on methods that have been used in the last two decades to pr...
Stock market prices are intrinsically dynamic, volatile, highly sensitive, nonparametric, nonlinear ...
Stock market prediction has always caught the attention of many analysts and researchers. Popular th...
Investment in the stock market is one of the much-admired investment actions. However, prediction of...
This study examines the value of technical, financial and macroeconomic variables in stock index pre...
My research intends to derive an intelligent multistep scalable hybrid model Integrating technical, ...
Abstract — With an easy access to share information and data nowadays, many investors worldwide are ...
Prediction of stock markets is a complex and challenging task due to price data generated is huge in...
With the advent of technological marvels like global digitization, the prediction of the stock marke...
AbstractMany studies in finance literature aims to find which macro-economic factors influence stock...
Financial time series prediction is a very important economical problem but the data available is ve...
Abstract Conventional statistical techniques for forecasting are constrained by the underlying seaso...
This paper surveys recent literature in the area of Neural Network, Data Mining, Hidden Markov Model...
The stock market has been one of the primary revenue streams for many for years. The stock market is...