Decision support system for stock trading using multiple indicators decision tree

Nugroho, Febryanto; Adji, Teguh Bharata; Fauziati, Silmi

doi:10.1109/icitacee.2014.7065759

Cited by 10 publications

(2 citation statements)

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“…Traditional machine learning methods such as artificial neural networks (ANNs) [3], tree-based models [4,5] and support vector machines (SVM) [6] provide the necessary flexibly in modelling the existing nonlinear relationships. However, the performance of traditional machine learning methods largely depends on the selection of features, which affects the prediction capabilities of the model to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Models for Predicting Stock Market Performance

Valova,

Gueorguieva,

Aayushi

et al. 2023

JMIDS

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Research on deep learning time series regression for stock market predictions and forecasting has grown exponentially in recent years. Considering the complexity of financial time series, combining deep learning with financial market prediction is regarded as a very important topic of research. The experiments in this study are divided into three settings which offer different topologies. For our first experimental setting we create hybrid sequential nonlinear models. For our second experimental setting we propose a new deep learning topology based on Attention CNN_BiLSTM for pretraining and light gradient boosting machine (LGBM) as a regressor (Attn_CNN_BiLSTM_LGBM). With our third experimental setting we propose a new model based on weighted average ensemble and the following four topologies: Attn_CNN_BiLSTM_LGBM, and attention_ CNN_ BiLSTM with various regressors. The ensemble model (EM) allows the contribution of each ensemble member to the prediction to be weighted proportionally to the performance of the EM. The latter achieves significantly better performance in predicting stock market.

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Section: Introductionmentioning

confidence: 99%

Hybrid Models for Predicting Stock Market Performance

Valova,

Gueorguieva,

Aayushi

et al. 2023

JMIDS

View full text Add to dashboard Cite

show abstract

“…Traditional machine learning methods such as Artificial Neural Networks (ANNs) [3], tree-based models [4,5] and Support Vector Machines (SVM) [6] provided the necessary flexibly in modelling the existing nonlinear relationships. However, the performance of traditional machine learning methods largely depends on the selection of features, which affects the prediction capabilities of the model to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Architectures for Stock Market Prediction

Valova,

Gueorguieva,

Aayushi

et al. 2023

World Congress on Electrical Engineering and Computer Systems and Science

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Accurate stock market prediction is a challenging task due to the volatile and nonlinear nature of it which depends on numerous factors as local and global economic conditions, company specific performance etc. It is not possible to account all existing relevant factors which influence the stock market in order to make respective trading decisions without having appropriate algorithms and techniques. A recent development of deep learning for making trading decisions has been growing rapidly with numerous research papers addressing stock market forecasting as time series regression problem. In recent years, Long Short-Term Memory (LSTM) neural networks have become the state-of-the-art models for a variety of machine learning problems which differ significantly in scale and nature. The central idea behind the LSTM architecture is a memory cell which can maintain its state over time, and non-linear gating units which regulate the information flow into and out of the cell. Most modern studies incorporate many improvements that have been made to the LSTM architecture since its original formulation. Considering the complexity of financial time series, combining deep learning with financial market prediction is regarded as a very important topic of research. The experiments in this study are divided into two sets which use different topologies. For our first experimental set we created the following hybrid sequential nonlinear models Convolutional Neural Networks (CNN), stacked LSTM, BiDirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU) with BiLSTM. For our second experimental set we propose a new deep learning topology based on Attention CNN_BiLSTM for pretraining and Light Gradient Bosting Machine (LGBM) as a regressor. The evaluation of experimental results indicates that the last proposed model achieves better performance in predicting stock market when compared to the models proposed in the first experimental set.

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AI in Stock Market Forecasting: A Bibliometric Analysis

N.Dao,

ChuanYuan,

Suzuki

et al. 2024

SHS Web Conf.

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In recent years, the swift progress of artificial intelligence (AI) has significantly influenced trading practices, providing traders with advanced algorithms that improve decision-making and enhance trading strategies, leading to increased profits and reduced risks. The onset of the era of big data has further enriched this field, offering access to extensive financial data, such as historical stock prices, company financial statements, financial news articles, social media sentiments, and macroeconomic indicators—all publicly available. By identifying complex patterns and correlations within this vast data set, deep learning (DL) algorithms have proven their ability to predict stock prices and market trends more accurately than traditional methods. This comprehensive survey aims to provide an insightful examination of various deeplearning models employed in stock market forecasting. The primary objective is to categorize these models into two distinct types: Uni-modal and multimodal models. By exploring the nuances within each category, this literature survey provides a comprehensive understanding of these models’ strengths, applications, and contributions to the constantly evolving research landscape of stock market forecasting. Our survey adopts a systematic approach to categorize and analyze deep-learning models in stock market forecasting. Leveraging established databases and repositories, we will compile a comprehensive dataset comprising academic articles, conference papers, and other scholarly publications related to DL in finance. This dataset will span a defined period, allowing us to capture the temporal evolution of research trends in stock market prediction. The first phase involves extracting and compiling relevant literature from established databases, including but not limited to Scopus, Web of Science, and Google Scholar. This dataset will serve as the foundation for exploring the evolving landscape of DL applications in stock market forecasting. Subsequently, advanced techniques and methodologies will be employed to analyze citation patterns, model co-occurrence, and the intellectual structure of research in this domain. Our research identifies influential authors, collaboration networks, and geographical distribution of research activities to uncover emerging clusters of research excellence. The findings of this survey contribute valuable insights to both academia and industry. By categorizing and examining the strengths of uni-modal and multi-modal deep-learning models, researchers can refine their methodologies, and practitioners can make informed decisions regarding adopting predictive models in financial markets. Furthermore, the survey aims to guide future research directions, enhancing the overall effectiveness of predictive models in the dynamic landscape of stock market forecasting. In conclusion, this survey aims to provide a comprehensive overview of deeplearning models in stock market forecasting. By systematically categorizing and analyzing these models, our study aspires to contribute to the ongoing dialogue on integrating AI in financial practices, fostering a deeper understanding of the field’s evolution and future directions.

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Decision support system for stock trading using multiple indicators decision tree

Cited by 10 publications

References 0 publications

Hybrid Models for Predicting Stock Market Performance

Hybrid Models for Predicting Stock Market Performance

Hybrid Deep Learning Architectures for Stock Market Prediction

AI in Stock Market Forecasting: A Bibliometric Analysis

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