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.