Multi model-Based Hybrid Prediction Algorithm (MM-HPA) for Stock Market Prices Prediction Framework (SMPPF)

Polamuri, Subba Rao; Srinivas, Kudipudi; Mohan, A. Krishna

doi:10.1007/s13369-020-04782-2

Cited by 18 publications

(2 citation statements)

References 16 publications

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“…The Bayesian Ridge (BR), Light Gradient Boosting Machine (LGBM), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), Extra Random Tree (ETR), and Orthogonal Matching Pursuit (OMP) were tested to select the most suitable model using grid search strategies to increase efficiency [7]. Detailed descriptions of all these models [14][15][16] and their application in materials science can be found elsewhere [17][18].…”

Section: Model Selectionmentioning

confidence: 99%

Machine learning models for predicting density of sodium-ion battery materials

Monareng,

Maphanga,

Ntoahae

2023

MATEC Web Conf.

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With the unprecedented amounts of material data generated from high-throughput density functional theory, machine learning provides the ability to accelerate the discovery and design of new materials. In this work, machine learning regression techniques are applied to a large amount of data from Materials Project Database, to develop machine learning models capable of accurately predicting the densities of sodium-ion battery cathode materials. Different machine learning regression models are successfully developed and validated. Feature vectors derived from the properties of materials’ chemical compounds are evaluated. Extra trees regressor model is found to be the best model in predicting the density with an accuracy of 0.95 and 0.09 g/cm3 coefficient of determination and mean square error, respectively.

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Section: Model Selectionmentioning

confidence: 99%

Machine learning models for predicting density of sodium-ion battery materials

Monareng,

Maphanga,

Ntoahae

2023

MATEC Web Conf.

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“…Nasir, Shaukat [42] analyzed Dow Jones index prices based on user sentiment recorded on Twitter and showed that sentiment signals embedded in news are a reliable predictor of stock prices. Polamuri, Srinivas [43] used an RNN model with gated recurrent units to predict stock movements and fused numerical features of stock prices to examine the sentiment polarity of financial news on Twitter. Similarly, Priya, Revadi [26] used CNN and RNN to study the stock trend model that includes both news headlines and technical indicators, and showed that news headlines improve prediction accuracy more than news content.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Daradkeh

2022

Electronics

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Stock market analysis plays an indispensable role in gaining knowledge about the stock market, developing trading strategies, and determining the intrinsic value of stocks. Nevertheless, predicting stock trends remains extremely difficult due to a variety of influencing factors, volatile market news, and sentiments. In this study, we present a hybrid data analytics framework that integrates convolutional neural networks and bidirectional long short-term memory (CNN-BiLSTM) to evaluate the impact of convergence of news events and sentiment trends with quantitative financial data on predicting stock trends. We evaluated the proposed framework using two case studies from the real estate and communications sectors based on data collected from the Dubai Financial Market (DFM) between 1 January 2020 and 1 December 2021. The results show that combining news events and sentiment trends with quantitative financial data improves the accuracy of predicting stock trends. Compared to benchmarked machine learning models, CNN-BiLSTM offers an improvement of 11.6% in real estate and 25.6% in communications when news events and sentiment trends are combined. This study provides several theoretical and practical implications for further research on contextual factors that influence the prediction and analysis of stock trends.

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Stock Market Prediction Techniques Using Artificial Intelligence: A Systematic Review

Chaudhari¹,

Purswani²

2023

Third Congress on Intelligent Systems

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Multi model-Based Hybrid Prediction Algorithm (MM-HPA) for Stock Market Prices Prediction Framework (SMPPF)

Cited by 18 publications

References 16 publications

Machine learning models for predicting density of sodium-ion battery materials

Machine learning models for predicting density of sodium-ion battery materials

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Stock Market Prediction Techniques Using Artificial Intelligence: A Systematic Review

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