Support vector regression with chaos-based firefly algorithm for stock market price forecasting

Multiple - output support vector regression with a firefly algorithm for 1 interval - valued stock price index forecasting

6 Highly accurate interval forecasting of a stock price index is fundamental to 7 successfully making a profit when making investment decisions, by providing a range 8 of values rather than a point estimate. In this study, we investigate the possibility of 9 forecasting an interval-valued stock price index series over short and long horizons 10 using multi-output support vector regression (MSVR...

Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting

Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short and long horizons using multi-output support vector regression (MSVR). Furtherm...

Tehran Stock Price Modeling and Forecasting Using Support Vector Regression (SVR) and Its Comparison with the Classic Model ARIMA

Stock Price Prediction using Machine Learning and Swarm Intelligence

Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...

Forecasting the Tehran Stock market by Machine ‎Learning Methods using a New Loss Function

Stock market forecasting has attracted so many researchers and investors that ‎many studies have been done in this field. These studies have led to the ‎development of many predictive methods, the most widely used of which are ‎machine learning-based methods. In machine learning-based methods, loss ‎function has a key role in determining the model weights. In this study a new loss ‎function is ...