Forecasting Daily Stock Movement Using a Hybrid Normalization Based Intersection Feature Selection and ANN
Study on forecasting stock movements using hybrid normalization, feature selection, and ANN, SVM, KNN classifiers.
What it examines
This study aims to improve stock market forecasting by using a hybrid normalization technique and combining features from various selection methods to create an optimal feature set. The approach involves calculating technical indicators, normalizing them, and using classifiers like SVM, KNN, and ANN for prediction.
What it concludes
The research demonstrates that hybrid normalization and intersection feature selection improve stock prediction accuracy. Potential applications include better investment strategies and risk management. Future research could explore combining more feature selection techniques for even better results.
Evidence objects
The research demonstrates that hybrid normalization and intersection feature selection improve stock prediction accuracy. Potential applications include better investment strategies and risk management. Future research could explore combining more feature selection techniques for even better results.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
The potential financial benefits of stock market forecasting have drawn a lot of interest. Due to the various interconnected aspects, predicting these markets is a difficult endeavour that necessitates a thorough as well as efficient feature selection procedure to discover the highest useful aspects. Stock price changes are also influenced by previous trading days' movements, which is a time series problem. In stock forecasting, feature selection techniques are commonly used, although most known systems use a single feature selection methodology that probably can neglect some key notions about the regression function that is at the root of the problem relating the variables for input and output. This study employs an artificial neural network (ANN) based generative model to forecast pricing changes in the future by combining features preferred by different feature picking strategies to build an ideal optimal feature group. We begin by calculating an expanded set of 83 technical indicators using day-to-day stock data of six stock indices, and then we normalize them using the Hybrid-Normalization (HN) technique. The important features are selected using various types of feature selection techniques and then considering the common features for the stock movement prediction. For stock trend predictions, we used a variety of classifiers such as Support Vector Machine, K Nearest Neighbour and Artificial Neural Network and. The system was given a performance review after simulations were done on 6 stock indices from various portions of the international market. The outcomes show that joining highlighted features got by various feature choice calculations and taking care of them into a profound generative model beats best in class techniques.
Source row: 894 · abstract type: unknown