Sentiment Analysis in the Norwegian Stock Market: Predicting Stock Price Movements Using Media Sentiment

A historical belief in financial economics states that stock prices react immediately to available information, making it impossible to predict changes in stock prices. However, research in the last few decades contradicts this theory to a degree. Some research has been done on whether the sentiment or “tone” of financial media can be used to predict stock price movements, although it has often been focused on U.S. newspapers and markets. This thesis will analyse Norwegian news articles from the online newspaper Dagens Næringsliv and stock prices from companies listed on the Oslo Stock Exchange, or Oslo Børs, to explore whether a relationship can be found between the sentiment of news articles and stock price changes. Sentiment analysis will be used to identify the level of positivity or negativity in the news articles and four statistical methods will be performed to attempt to predict stock prices using media sentiment. The methods are logistic regression, K-nearest neighbors, gradient boosted trees and support vector classifier. The accuracy of using media sentiment to predict stock price changes varies from 53.69% to 57.38% using the four methods. These results are in line with the accuracy that previous research on U.S. newspapers and companies has found, suggesting that there is a weak but significant relationship between the sentiment of Norwegian news articles and the stock price movements of Oslo Børs-listed companies.nhhma

Scalable Privacy-Compliant Virality Prediction on Twitter

The digital town hall of Twitter becomes a preferred medium of communication for individuals and organizations across the globe. Some of them reach audiences of millions, while others struggle to get noticed. Given the impact of social media, the question remains more relevant than ever: how to model the dynamics of attention in Twitter. Researchers around the world turn to machine learning to predict the most influential tweets and authors, navigating the volume, velocity, and variety of social big data, with many compromises. In this paper, we revisit content popularity prediction on Twitter. We argue that strict alignment of data acquisition, storage and analysis algorithms is necessary to avoid the common trade-offs between scalability, accuracy and privacy compliance. We propose a new framework for the rapid acquisition of large-scale datasets, high accuracy supervisory signal and multilanguage sentiment prediction while respecting every privacy request applicable. We then apply a novel gradient boosting framework to achieve state-of-the-art results in virality ranking, already before including tweet's visual or propagation features. Our Gradient Boosted Regression Tree is the first to offer explainable, strong ranking performance on benchmark datasets. Since the analysis focused on features available early, the model is immediately applicable to incoming tweets in 18 languages.Comment: AffCon@AAAI-19 Best Paper Award; Presented at AAAI-19 W1: Affective Content Analysi

Machine Learning in Stock Price Prediction Using Long Short-Term Memory Networks and Gradient Boosted Decision Trees

Quantitative analysis has been a staple of the financial world and investing for many years. Recently, machine learning has been applied to this field with varying levels of success. In this paper, two different methods of machine learning (ML) are applied to predicting stock prices. The first utilizes deep learning and Long Short-Term Memory networks (LSTMs), and the second uses ensemble learning in the form of gradient tree boosting. Using closing price as the training data and Root Mean Squared Error (RMSE) as the error metric, experimental results suggest the gradient boosting approach is more viable. Honors Symposium: ML is an unbelievably powerful tool, and the application of ML must be subject to our biblical calling as stewards. As technology progresses to make us increasingly productive, we must direct what we produce towards ends that glorify God. Just as importantly, we must be vigilant to the great temptation to become lost in decadence. ML has wildly successful applications in the financial world that far surpass the scope of this paper, but we cannot lose sight of He who provides. A firm grounding in scripture and a healthy understanding of Providence should be enough to keep those of us who pursue the blessing of technology from becoming lost in our own grandeur

Algorithmic trading with cryptocurrencies

Since its inception in 2009, Bitcoin has gained popularity and importance in financial markets. The Bitcoin price is highly volatile entailing high risk and chances of high returns for traders. We define a holistic approach to build an intraday Bitcoin trading algorithm based on predictive analysis of ML models. The results show that our trading algorithm generates positive returns and to outperform its benchmark strategies after considerations for feasibility and profitability

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

Short-term bitcoin market prediction via machine learning

We analyze the predictability of the bitcoin market across prediction horizons ranging from 1 to 60 min. In doing so, we test various machine learning models and find that, while all models outperform a random classifier, recurrent neural networks and gradient boosting classifiers are especially well-suited for the examined prediction tasks. We use a comprehensive feature set, including technical, blockchain-based, sentiment-/interest-based, and asset-based features. Our results show that technical features remain most relevant for most methods, followed by selected blockchain-based and sentiment-/interest-based features. Additionally, we find that predictability increases for longer prediction horizons. Although a quantile-based long-short trading strategy generates monthly returns of up to 39% before transaction costs, it leads to negative returns after taking transaction costs into account due to the particularly short holding periods

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

This literature review identifies and analyzes research topic trends, types of data sets, learning algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81 studies were investigated, which were published regarding stock predictions in the period January 2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results. Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization