Investigation Into Machine Learning Models for Predicting Stock Price and Spread Movements From News Articles

2020 ◽  
Author(s):  
Pontus Wistbacka ◽  
Samuel Rönnqvist ◽  
Katia Vozian ◽  
Satchit Sagade
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
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.

Value Analysis and Prediction Through Machine Learning Techniques for Popular Basketball Brands

2022 ◽  
pp. 323-345
Author(s):  
Jason Michaud
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Stock Price ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Value Analysis ◽  
Learning Techniques ◽  
The World ◽  
Machine Learning Models

For popular sports brands such as Nike, Adidas, and Puma, value often depends upon the performance of star athletes and the success of professional leagues. These leagues and players are watched closely by many around the world, and exposure to a brand may ultimately cause someone to buy a product. This can be explored statistically, and the interconnectedness of brands, athletes, and the sport of basketball are covered in this chapter. Specifically, data about the NBA and Google Ngrams data are explored in relation to the stock price of these various sports brands. This is done through both statistical analysis and machine learning models. Ultimately, it was concluded that these factors do influence the stock price of Nike, Adidas, and Puma. This conclusion is supported by the machine learning models where this diverse dataset was utilized to accurately predict the stock price of sports brands.

scholarly journals S&P 500 Stock Price Prediction Using Technical, Fundamental and Text Data

2021 ◽  
Vol 9 (4) ◽  
pp. 769-788
Author(s):  
Shan Zhong ◽  
David Hitchcock
Keyword(s):  
Machine Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Language Models ◽  
News Item ◽  
Learning Models ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Using Data ◽  
Machine Learning Models

We summarized both common and novel predictive models used for stock price prediction and combined them with technical indices, fundamental characteristics and text-based sentiment data to predict S&P stock prices. A 66.18% accuracy in S&P 500 index directional prediction and 62.09% accuracy in individual stock directional prediction was achieved by combining different machine learning models such as Random Forest and LSTM together into state-of-the-art ensemble models. The data we use contains weekly historical prices, finance reports, and text information from news items associated with 518 different common stocks issued by current and former S&P 500 large-cap companies, from January 1, 2000 to December 31, 2019. Our study's innovation includes utilizing deep language models to categorize and infer financial news item sentiment; fusing different models containing different combinations of variables and stocks to jointly make predictions; and overcoming the insufficient data problem for machine learning models in time series by using data across different stocks.

scholarly journals Analysis of L2 Regularization Hyper Parameter for Stock Price Prediction

2021 ◽  
Vol 26 (1) ◽  
pp. 83-88
Author(s):  
Arjun Singh Saud ◽  
Subarna Shakya
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Stock Exchange ◽  
Research Work ◽  
Learning Models ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Machine Learning Model ◽  
Regularization Techniques ◽  
Machine Learning Models

Nowadays stock price prediction is an active area of research among machine learning researchers. One of the main problems with machine learning models is overfitting. Regularization techniques are widely used approaches to avoid over-fitted models. L2 regularization is one of the most popular and widely used regularization techniques. Regularization hyperparameter (ʎ) is one key parameter to be optimized for a well-generalized machine learning model. Hyperparameters can’t be learned by machine learning models during the learning process. We need to find their optimal value through experiments. This research work analyzed the L2 regularization hyperparameter used with a gated recurrent unit (GRU) network for stock price prediction. We experimented with five stocks from the Nepal Stock Exchange (NEPSE) and observed that stock price can be predicted with lower mean squared errors (MSEs) when the value of ʎ was around 0.0005. Therefore, this research paper recommended using ʎ=0.0005 with L2 regularization for stock price prediction.

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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