Profitable Ranking of Stock Market Organizations Using Different Machine Learning Models

2021 ◽  
Author(s):  
Muhaddid Alavi ◽  
Selina Sharmin ◽  
Ashraf Uddin ◽  
Tanvir Ahammad ◽  
Fatema Siddika
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Learning Models ◽  
Machine Learning Models

Stock market tweets annotated with emotions

Corpora ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. 343-354
Author(s):  
Fernando J. Vieira da Silva ◽  
Norton T. Roman ◽  
Ariadne M.B.R. Carvalho
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Brazilian Portuguese ◽  
Learning Models ◽  
Stock Trading ◽  
Emotion Identification ◽  
Price Movements ◽  
Machine Learning Models ◽  
Stock Price Movements

As stock trading became a popular topic on Twitter, many researchers have proposed different approaches to make predictions on it, relying on the emotions found in messages. However, detailed studies require a reasonably sized corpus with emotions properly annotated. In this work, we introduce a corpus of tweets in Brazilian Portuguese annotated with emotions. Comprising 4,277 tweets, this is, to the best of our knowledge, the largest annotated corpus available in the stock market domain for this language. Amongst its possible uses, the corpus lends itself to the application of machine learning models for automatic emotion identification, as well as to the study of correlations between emotions and stock price movements.

scholarly journals Modeling Volatility in the Stock Market for Accuracy in Forecasting

2020 ◽  
Vol 8 (5) ◽  
pp. 41-49
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Garch Model ◽  
Type Model ◽  
Model Parameters ◽  
Finite Sample ◽  
Learning Models ◽  
Monthly Return ◽  
Modeling And Forecasting ◽  
Machine Learning Models

In this paper, the best GARCH type model was selected and compared with the machine learning models, such as Extreme Learning Machine (ELM) and Multilayer Perceptron Neural Network (MLP-NN) models in modeling and forecasting monthly return of the financial market data. The objective of the study was to compare the best model in forecasting New York and Shanghai Stock Composite indices, for the period 01.01.1996 to 01.09.2019. The GJR-GARCH model outperformed other GARCH type models based on the Schwarz Bayesian Information Criterion (SSBIC). The Monte Carlo simulation carried at 1000, 2000, 3000, 4000 and 5000 finite sample (window) sizes to test the consistency of the GJR-GARCH model parameters has shown perfect results between true and the simulated coefficients. Finally, the GJR-GARCH model was compared with the MLPNN and ELM machine learning models. The monthly return forecasting of two years (24 months) was done starting from period 01.09.2019 to 01.09.2021. The study found the MLP-NN model as the best in the modeling and forecasting monthly returns of the two composite stock indices for the two years by considering the Root Mean Square Error (RMSE).The study recommends that further research should focus on the formulation of the hybrid model that combines machine learning and the GJR-GARCH models in forecasting stock market volatility.

scholarly journals Stock market decision support modeling with tree-based AdaBoost ensemble machine learning models

Informatica ◽  
2021 ◽  
Vol 44 (4) ◽  
Author(s):  
Ernest Kwame Ampomah ◽  
Zhiguang Qin ◽  
Gabriel Nyame ◽  
Francis Effirm Botchey
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Stock Market ◽  
Learning Models ◽  
Ensemble Machine Learning ◽  
Machine Learning Models

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

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