Multiclass Classifier for Stock Price Prediction

Rebwar M Nabi

doi:10.17762/turcomat.v12i3.1707

Abstract

The stock market has been a crucial factor of investments in the financial domain. Risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task. Stock Price forecasting is complex that could have a significant influence on the financial market. The Machine Learning (ML) type of artificial intelligence (AI) provides a more accurate forecast for binary and multiclass classification. Different effective methods have been recommended to resolve the problem in the binary classification case but the multiclass classification case is a more delicate one. This paper discusses the application of multiclass classifier mappings such as One v/s All (OvA) and One v/s One (OvO) for stock movement prediction. The proposed approach comprises four main steps: data collection, assign a multi-label (up, down, or same), discover the best classifier methods, and comparison of classifiers on evaluation metrics of 10k cross-validation for stock price movement. In this study, a stock NASDAQ dataset for about ten years of ten companies from yahoo finance on daily basis is used. The resultant Stock Price prediction uncovers Neural Network classifier has good performance in some case whereas Multiclass (One V/s One) and (One V/s All) have overall better performance among all other classifiers as AdaBoost, Support Vector Machine, OneR, Bagging, Simple Logistic, Hoeffding trees, PART, Decision Tree and Random Forest. The Precision, Recall, F-Measure, and ROC area comparison results show that Multiclass (One V/s All) is better than Multiclass (One V/s one). The proposed method Multiclass classification (One v/s All) yields an accuracy of 97.63% for average prediction performance on all ten stock companies, also the highest accuracy achieved as 98.7% for QCOM. The individual stock-wise evaluation of the Multiclass (One V/s All) classifier is found to achieve the highest accuracy among all other classifiers which is outperforming all the recent proposals.

Highlights

Despite the rapid development in the world's economy and technology, the stock market's passion and enthusiasm have never diminished
The results showed that using the multiclass classification mapping method partially improved the overall accuracy especially on One v/s One they got 87.78% and One v/s All 70%
The research [12] had been done on the Indian stock market on daily basis to predict the future moment of the stock trends using the Artificial Neural Networks (ANN) and Support Vector Machine (SVM) formula with Multiclass classification: One v/s All SVM (OVA-SVM) algorithms

Summary

Introduction

Despite the rapid development in the world's economy and technology, the stock market's passion and enthusiasm have never diminished. The stock market [1] is one of the most effective financial systems available, it is unpredictable. The Stock market price forecast has continuously been an area for research and development. It is mainly since the market is non-linear, volatile, and dynamic, and unpredictable. The stock market's groups and movements are affected by several economic factors such as political events, general economic conditions, commodity price index, investors' expectations, movements of other stock markets, the psychology of investors, etc. To minimize the high risk, the investor needs information as a reference for decision making of which stock they should buy, sell, and maintain for the future. In recent years to analyze the market trends, the trending technology proposed is machine learning with artificial intelligence [2]

Methods

Findings

Discussion

Conclusion