Soft Computing Techniques for Stock Market Prediction: A Literature Survey

Stock market trading is an unending investment exercise globally. It has potentials to generate high returns on investors’ investment. However, it is characterized by high risk of investment hence, having knowledge and ability to predict stock price or market movement is invaluable to investors in the stock market. Over the years, several soft computing techniques have been used to analyze various stock markets to retrieve knowledge to guide investors on when to buy or sell. This paper surveys over 100 published articles that focus on the application of soft computing techniques to forecast stock markets. The aim of this paper is to present a coherent of information on various soft computing techniques employed for stock market prediction. This research work will enable researchers in this field to know the current trend as well as help to inform their future research efforts. From the surveyed articles, it is evident that researchers have firmly focused on the development of hybrid prediction models and substantial work has also been done on the use of social media data for stock market prediction. It is also revealing that most studies have focused on the prediction of stock prices in emerging market

Prédiction de la tendance des actions basée sur les réseaux convolutifs graphiques et les LSTM

Abstract: As stocks have been developing over decades, the trend and the price of a stock are more often used for predictions in stock market analysis. In the field of finance, an accurate stock future trending can not only help decision-makers estimate the possibility of profit, but also help them avoid risks. In this research, we present a quantitative approach to predicting the trend of stocks in which a clustering model is employed to mine the stock trends patterns from historical stock price data. Stock series clustering is a special kind of time series clustering. We aim to find out the trend types, e.g. rising, falling and others, of a stock at time intervals, and then make use of the past trends to predict its future trend. The proposed prediction method is based on Graph Convolutional Neural Network for clustering and Long Short-Term Memory model for prediction. This method is suitable for the data clustering of unbalanced classes too. The experiments on real-world stock data demonstrate that our method can yield accurate forecasts. In the long run, the proposed method can be used to explore new possibilities in the research field of time series clustering, such as using other graph neural networks to predict stock trends.Comme les prix des actions évoluent au fil des décennies, la tendance et le prix d’une action sont souvent utilisés pour effectuer des prévisions en bourse. Bien anticiper la tendance future des actions peut non seulement aider les décideurs à mieux estimer les possibilités de profit, mais aussi les risques. Dans cette thèse, une approche quantitative est présentée pour prédire les fluctuations d’actions. L’approche se base sur une méthode de clustering pour identifier la tendance des actions à partir de leurs données historiques. C’est un type particulier de clustering appliqué sur des séries chronologiques. Il consiste à découvrir les tendances des actions sur des intervalles de temps, tel que des tendances haussières, des tendances baissières, et ensuite d’utiliser ces tendances pour prédire leurs états futurs. La méthode de prédiction proposée se base sur les réseaux de neurones convolutionnels graphiques et des réseaux récurrents mémoire pour la prédiction. Cette méthode fonctionne également sur des ensembles de données où la proportion des classes est déséquilibrée. Les données historiques des actions démontrent que la méthode proposée effectue des prévisions précises. La méthode proposée peut ouvrir une nouvelle perspective de recherche pour le clustering de séries chronologiques, notamment l’utilisation d‘autres réseaux de neurones graphiques pour prédire les tendances des actions

Review on Financial Forecasting using Neural Network and Data Mining Technique

The rise of economic globalization and evolution of information technology, financial data are being generated and accumulated at an extraordinary speed. As a result, there has been a critical need for automated approaches to effective and efficient utilization of massive amount of financial data to support companies and individuals in strategic planning and investment decision-making. The competitive advantages achieved by data mining include increased revenue, reduced cost, and much improved marketplace responsiveness and awareness. There has been a large body of research and practice focusing on exploring data mining techniques to solve financial problems. This paper describes data mining in the context of financial application from both technical and application perspective by comparing different data mining techniques

Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks

Finding suitable forecasting methods for an effective management of energy resources is of paramount importance for improving the efficiency in energy consumption and decreasing its impact on the environment. Natural gas is one of the main sources of electrical energy in Algeria and worldwide. To address this demand, this paper introduces a novel hybrid forecasting approach that resolves the two-stage method's deficiency, by designing a Multi Layered Perceptron (MLP) neural network as a nonlinear forecasting monitor. This model estimates the next day gas consumption profile and selects one of several local models to perform the forecast. The study focuses firstly on an analysis and clustering of natural gas daily consumption profiles, and secondly on building a comprehensive Long Short Term Memory (LSTM) recurrent models according to load behavior. The results are compared with four benchmark approaches: the MLP neural network approach, LSTM, seasonal time series with exogenous variables models and multiple linear regression models. Compared with these alternative approaches and their high dependence on historical loads, the proposed approach presents a new efficient functionality. It estimates the next day consumption profile, which leads to a significant improvement of the forecasting accuracy, especially for days with exceptional customers consumption behavior change

Classification of potential electric vehicle purchasers: A machine learning approach

13 p.Among the many approaches towards fuel economy, the adoption of electric vehicles (EV) may have the greatest impact. However, existing studies on EV adoption predict very different market evolutions, which causes a lack of solid ground for strategic decision making. New methodological tools, based on Artificial Intelligence, might offer a different perspective. This paper proposes supervised Machine Learning (ML) techniques to identify key elements in EV adoption, comparing different ML methods for the classification of potential EV purchasers. Namely, Support Vector Machines, Artificial Neural Networks, Deep Neural Networks, Gradient Boosting Models, Distributed Random Forests, and Extremely Randomized Forests are modeled utilizing data gathered on users’ inclinations towards EV. Although a Support Vector Machine with polynomial kernel slightly outperforms the other algorithms, all of them exhibit comparable predictability, implying robust findings. Further analysis provides evidence that having only partial information (e.g. only socioeconomic variables) has a significant negative impact on model performance, and that the synergy across several types of variables leads to higher accuracy. Finally, the examination of misclassified observations reveals two well-differentiated groups, unveiling the importance that the profiling of potential purchaser may have for marketing campaigns as well as for public agencies that seek to promote EV adoption

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues