scholarly journals Multiclass Classifier for Stock Price Prediction

2021 ◽  
Vol 12 (3) ◽  
pp. 4157-4169
Author(s):  
Rebwar M. Nabi
Keyword(s):  
Stock Price ◽  
Binary Classification ◽  
Multiclass Classification ◽  
Support Vector ◽  
Risk Modeling ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Movement Prediction ◽  
Comparison Results ◽  
Multiclass Classifier

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.

scholarly journals Hierarchical Multi-Scale Gaussian Transformer for Stock Movement Prediction

2020 ◽  
Author(s):  
Qianggang Ding ◽  
Sifan Wu ◽  
Hao Sun ◽  
Jiadong Guo ◽  
Jian Guo
Keyword(s):  
Financial Markets ◽  
Stock Price ◽  
Financial Time Series ◽  
Multi Scale ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Movement Prediction ◽  
Novel Approach ◽  
Hierarchical Features

Predicting the price movement of finance securities like stocks is an important but challenging task, due to the uncertainty of financial markets. In this paper, we propose a novel approach based on the Transformer to tackle the stock movement prediction task. Furthermore, we present several enhancements for the proposed basic Transformer. Firstly, we propose a Multi-Scale Gaussian Prior to enhance the locality of Transformer. Secondly, we develop an Orthogonal Regularization to avoid learning redundant heads in the multi-head self-attention mechanism. Thirdly, we design a Trading Gap Splitter for Transformer to learn hierarchical features of high-frequency finance data. Compared with other popular recurrent neural networks such as LSTM, the proposed method has the advantage to mine extremely long-term dependencies from financial time series. Experimental results show our proposed models outperform several competitive methods in stock price prediction tasks for the NASDAQ exchange market and the China A-shares market.

scholarly journals Comparison of Stock Price Prediction Models using Pre-trained Neural Networks

2021 ◽  
Vol 3 (2) ◽  
pp. 122-134
Author(s):  
C Anand
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stock Price ◽  
Linear Models ◽  
Moving Average ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Data Prediction ◽  
Comparison Results ◽  
Auto Regressive

Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto-Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. The deep learning architectures inclusive of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) are used in this paper for stock price prediction of an organization by using the previously available stock prices. The National Stock Exchange (NSE) of India dataset is used for training the model with day-wise closing price. Data prediction is performed for a few sample companies selected on a random basis. Based on the comparison results, it is evident that the existing models are outperformed by CNN. The network can also perform stock predictions for other stock markets despite being trained with single market data as a common inner dynamics that has been shared between certain stock markets. When compared to the existing linear models, the neural network model outperforms them in a significant manner, which can be observed from the comparison results.

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