Surveying stock market forecasting techniques–Part II: Soft computing methods
GS Atsalakis, KP Valavanis - Expert Systems with applications, 2009 - Elsevier
The key to successful stock market forecasting is achieving best results with minimum
required input data. Given stock market model uncertainty, soft computing techniques are …
required input data. Given stock market model uncertainty, soft computing techniques are …
[HTML][HTML] Application of artificial intelligence in stock market forecasting: a critique, review, and research agenda
The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal
and external environmental variables. Artificial intelligence (AI) techniques can detect such …
and external environmental variables. Artificial intelligence (AI) techniques can detect such …
Integrating metaheuristics and artificial neural networks for improved stock price prediction
Stock market price is one of the most important indicators of a country's economic growth.
That's why determining the exact movements of stock market price is considerably regarded …
That's why determining the exact movements of stock market price is considerably regarded …
Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange
Prediction of stock price index movement is regarded as a challenging task of financial time
series prediction. An accurate prediction of stock price movement may yield profits for …
series prediction. An accurate prediction of stock price movement may yield profits for …
An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange
MA Boyacioglu, D Avci - Expert Systems with Applications, 2010 - Elsevier
Stock market prediction is important and of great interest because successful prediction of
stock prices may promise attractive benefits. These tasks are highly complicated and very …
stock prices may promise attractive benefits. These tasks are highly complicated and very …
[HTML][HTML] Explainable stock prices prediction from financial news articles using sentiment analysis
The stock market is very complex and volatile. It is impacted by positive and negative
sentiments which are based on media releases. The scope of the stock price analysis relies …
sentiments which are based on media releases. The scope of the stock price analysis relies …
Stock values predictions using deep learning based hybrid models
Predicting the correct values of stock prices in fast fluctuating high‐frequency financial data
is always a challenging task. A deep learning‐based model for live predictions of stock …
is always a challenging task. A deep learning‐based model for live predictions of stock …
Predicting stock market trends using machine learning algorithms via public sentiment and political situation analysis
Stock market trends can be affected by external factors such as public sentiment and
political events. The goal of this research is to find whether or not public sentiment and …
political events. The goal of this research is to find whether or not public sentiment and …
Investigation of market efficiency and financial stability between S&P 500 and London stock exchange: monthly and yearly forecasting of time series stock returns …
MM Rounaghi, FN Zadeh - Physica A: Statistical Mechanics and its …, 2016 - Elsevier
We investigated the presence and changes in, long memory features in the returns and
volatility dynamics of S&P 500 and London Stock Exchange using ARMA model. Recently …
volatility dynamics of S&P 500 and London Stock Exchange using ARMA model. Recently …
The intraday high-frequency trading with different data ranges: A comparative study with artificial neural network and vector autoregressive models
With the High-Frequency Trading process, which is a subclass of algorithmic trading
transactions, intraday information has increasing importance. Traditional statistical methods …
transactions, intraday information has increasing importance. Traditional statistical methods …