News-Driven Stock Prediction With Attention-Based Noisy Recurrent State Transition

We consider direct modeling of underlying stock value movement sequences over time in the news-driven stock movement prediction. A recurrent state transition model is constructed, which better captures a gradual process of stock movement continuously by modeling the correlation between past and future price movements. By separating the effects of news and noise, a noisy random factor is also explicitly fitted based on the recurrent states. Results show that the proposed model outperforms strong baselines. Thanks to the use of attention over news events, our model is also more explainable. To our knowledge, we are the first to explicitly model both events and noise over a fundamental stock value state for news-driven stock movement prediction.Comment: 12 page

Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review

Recent crises, recessions and bubbles have stressed the non-stationary nature and the presence of drastic structural changes in the financial domain. The most recent literature suggests the use of conventional machine learning and statistical approaches in this context. Unfortunately, several of these techniques are unable or slow to adapt to changes in the price-generation process. This study aims to survey the relevant literature on Machine Learning for financial prediction under regime change employing a systematic approach. It reviews key papers with a special emphasis on technical analysis. The study discusses the growing number of contributions that are bridging the gap between two separate communities, one focused on data stream learning and the other on economic research. However, it also makes apparent that we are still in an early stage. The range of machine learning algorithms that have been tested in this domain is very wide, but the results of the study do not suggest that currently there is a specific technique that is clearly dominant

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

A co-evolutionary approach to data-driven agent-based modelling: simulating the virtual interaction application experiments.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.The dynamics of social interactions are barely captured by the traditional methods of research in social psychology, vis-à-vis, interviews, surveyed data and experiments. To capture the dynamics of social interactions, researchers adopt computer-mediated experiments and agent-based simulations (ABSs). These methods have been efficiently applied to game theories. While strategic games such as the prisoner’s dilemma and GO have optimal outcomes, interactive social exchanges can have obscure and multiple conflicting objectives (fairness, selfishness, group bias) whose relative importance evolves in interaction. Discovering and understanding the mechanisms underlying these objectives become even more difficult when there is little or no information about the interacting individual(s). This study describes this as an information-scarce interactive social exchange context. This study, therefore, forms part of a larger initiative on developing efficient simulations of social interaction in an information-scarce interactive social exchange context. First, this dissertation develops a context for and justifies the importance of simulation in an information-scarce interactive social exchange context (Chapter 2). It then performs a literature review of the studies that have developed a computational model and simulation in this context (Chapter 3). Next, the dissertation develops a co-evolutionary data-driven model and simulates exchange behaviour in an information-scarce context (Chapter 4). To benchmark the data-driven model, this dissertation develops a rule-based model. Furthermore, it creates agents that use the rule-based model, integrates them into Virtual Interaction APPLication (VIAPPL) and tests their usefulness in predicting and influencing exchange decisions. Precisely, it measures the agent’s ability in reducing in-group bias during interaction in an information-scarce context (Chapter 5). Likewise, it creates machine learning (adaptive) agents that use the data-drivel model, and tests them in a similar experimental context. These chapters were written independently; thus, their objectives, methods and results are discussed in each chapter. Finally, the study presents a general conclusion (Chapter 6)