Predicting Stock Price Direction using Support Vector Machines

By S. Madge, Swati Bhatt. 2015

65 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Manan Shah
2. Pavan Doshi
3. Akshit Kurani
4. Aarya Vakharia

Citations in the corpus

• Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets (2018, neutral): “Most of the related articles use the traditional data partitioning method; that is, the entire dataset is directly split into training set and testing set [12, 22, 40, 42].”
• A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting (2021, background): “Hereby SVM classifies the plane and divides so that most of the points of a particular category fall on one side of the boundary while most of the different categories fall on the other side of the boundary [20].”