4.7 Article

Stock Market Analysis Using Time Series Relational Models for Stock Price Prediction

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Fuzzy time-series prediction model based on text features and network features

Zeguang Liu et al.

Summary: The prediction of time-series data is a challenging issue. This article proposes a prediction model based on network topology information and text information, and uses stock data as an example to demonstrate its effectiveness. The experimental results show that the model outperforms other fuzzy time-series prediction methods in predicting multiple stocks' time-series.

NEURAL COMPUTING & APPLICATIONS (2023)

Article Computer Science, Artificial Intelligence

DAFA-BiLSTM: Deep Autoregression Feature Augmented Bidirectional LSTM network for time series prediction

Heshan Wang et al.

Summary: In this study, a novel deep autoregression feature augmented bidirectional LSTM network (DAFA-BiLSTM) is proposed for time series prediction. It effectively extracts the transient characteristics of long interval sequential datasets and demonstrates good adaptive performance and robustness even in noisy environments through comparative experiments and statistical analysis.

NEURAL NETWORKS (2023)

Article Computer Science, Artificial Intelligence

Novel optimization approach for stock price forecasting using multi-layered sequential LSTM

Md Abdul Quadir et al.

Summary: Stock markets are volatile and predicting stock prices accurately is a complex task. This study proposes an optimization approach for stock price prediction using a Multi-Layer Sequential Long Short Term Memory (MLS LSTM) model with the adam optimizer. The results show high prediction accuracy and outperformance compared to other machine learning and deep learning algorithms.

APPLIED SOFT COMPUTING (2023)

Article Computer Science, Artificial Intelligence

Stock market index prediction using deep Transformer model

Chaojie Wang et al.

Summary: This paper discusses the applications of deep learning in financial market prediction and introduces the use of the Transformer framework for stock market index prediction. The experiments demonstrate that Transformer outperforms other classic methods significantly and can generate excess earnings for investors.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

Article Computer Science, Artificial Intelligence

GCN-based stock relations analysis for stock market prediction

Cheng Zhao et al.

Summary: Most stock price predictive models neglect the correlation effects between stocks, while this article proposes a unified time-series relational multi-factor model that can automatically extract relational features and integrate them with other multiple dimensional features, resulting in significantly improved prediction accuracy and stability.

PEERJ COMPUTER SCIENCE (2022)

Article Computer Science, Artificial Intelligence

China?s commercial bank stock price prediction using a novel K-means-LSTM hybrid approach

Yufeng Chen et al.

Summary: This study proposes a novel hybrid deep learning approach that utilizes an improved clustering algorithm and LSTM neural network model to predict stock prices more accurately. Experimental results demonstrate that this method outperforms traditional statistical models in terms of prediction ability and accuracy.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

Article Computer Science, Artificial Intelligence

Construction of stock portfolios based on k-means clustering of continuous trend features

Dingming Wu et al.

Summary: This paper proposes a portfolio construction method based on the continuous trend characteristics of the market to address the challenges of selecting qualified stocks, calculating weights, and considering risk aversion in investment evaluation. By using the k-means clustering algorithm, the method divides stock pools and revises the calculation of returns for the Sharpe ratio, while combining inverse volatility weighting, risk parity, and Markowitz's portfolio theories. Experimental results confirm the superiority of this proposed method in optimizing portfolio construction.

KNOWLEDGE-BASED SYSTEMS (2022)

Article Computer Science, Artificial Intelligence

Multi-scale local cues and hierarchical attention-based LSTM for stock price trend prediction

Xiao Teng et al.

Summary: This paper proposes a multi-scale local cues and hierarchical attention-based LSTM model for stock price trend prediction. Experiments confirm the superior performance of the proposed model compared to existing models.

NEUROCOMPUTING (2022)

Article Computer Science, Information Systems

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Theyazn H. H. Aldhyani et al.

Summary: The creation of trustworthy models of the equities market is important for enabling investors to make better choices. This study proposes the development of a robust time series model based on deep learning for forecasting future stock market values. The research shows that artificial intelligence models, especially deep learning strategies, have been effective in predicting market behavior.

ELECTRONICS (2022)

Article Multidisciplinary Sciences

Dependence between Chinese stock market and Vietnamese stock market during the Covid-19 pandemic

Van Chien Nguyen et al.

Summary: This study aims to investigate the tail dependence between Chinese and Vietnamese stock markets during the Covid-19 pandemic. The results show that the Vietnamese stock market is heavily dependent on the Chinese stock market during the pandemic.

HELIYON (2022)

Article Mathematics

A prediction model for stock market based on the integration of independent component analysis and Multi-LSTM

Hongzeng He et al.

Summary: This research proposes a hybrid model that combines independent component analysis (ICA) and multivariate long short-term memory (Multi-LSTM) neural network to predict stock price variations in a complex market network. Experimental results demonstrate that this hybrid model outperforms benchmark approaches in terms of prediction accuracy.

ELECTRONIC RESEARCH ARCHIVE (2022)

Article Economics

Predicting Stock Price Using Two-Stage Machine Learning Techniques

Jun Zhang et al.

Summary: This study introduces a novel two-stage ensemble machine learning model named SVR-ENANFIS for stock price prediction, which combines features of support vector regression and ensemble adaptive neuro fuzzy inference system. Experimental results demonstrate that the proposed model outperforms other models in terms of prediction performance.

COMPUTATIONAL ECONOMICS (2021)

Article Mathematics, Interdisciplinary Applications

Multivariate CNN-LSTM Model for Multiple Parallel Financial Time-Series Prediction

Harya Widiputra et al.

Summary: The aim is to create a time-series data forecasting model that incorporates the best features of many time-series data analysis models, resulting in a hybrid ensemble model tested during the COVID-19 pandemic. The experimental results show that the multivariate CNN-LSTM model has the highest statistical accuracy and reliability compared to CNN and LSTM, supporting its use for forecasting different stock market indices.

COMPLEXITY (2021)

Article Computer Science, Artificial Intelligence

Mean-variance portfolio optimization using machine learning-based stock price prediction

Wei Chen et al.

Summary: The success of portfolio construction relies on future stock market performance, with recent advances in machine learning offering significant opportunities. This study introduces a novel approach that combines machine learning for stock prediction and the MV model for portfolio selection, showing superior results in returns and risks compared to traditional methods.

APPLIED SOFT COMPUTING (2021)

Article Physics, Multidisciplinary

Financial time series forecasting model based on CEEMDAN and LSTM

Jian Cao et al.

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2019)

Article Business, Finance

Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction

Shun Chen et al.

QUANTITATIVE FINANCE (2019)

Article Computer Science, Artificial Intelligence

Support vector regression with modified firefly algorithm for stock price forecasting

Jun Zhang et al.

APPLIED INTELLIGENCE (2019)

Article Computer Science, Information Systems

Temporal Relational Ranking for Stock Prediction

Fuli Feng et al.

ACM TRANSACTIONS ON INFORMATION SYSTEMS (2019)

Article Computer Science, Artificial Intelligence

Portfolio formation with preselection using deep learning from long-term financial data

Wuyu Wang et al.

EXPERT SYSTEMS WITH APPLICATIONS (2019)

Article Management

Deep learning with long short-term memory networks for financial market predictions

Thomas Fischer et al.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2018)

Article Computer Science, Artificial Intelligence

Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models

Ha Young Kim et al.

EXPERT SYSTEMS WITH APPLICATIONS (2018)

Proceedings Paper Computer Science, Information Systems

Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction

Yingmei Chen et al.

CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT (2018)

Article Business, Finance

International tests of a five-factor asset pricing model

Eugene F. Fama et al.

JOURNAL OF FINANCIAL ECONOMICS (2017)

Article Computer Science, Artificial Intelligence

Incorporating feature selection method into support vector regression for stock index forecasting

Wensheng Dai et al.

NEURAL COMPUTING & APPLICATIONS (2013)

Article Computer Science, Artificial Intelligence

An efficient k-means clustering algorithm:: Analysis and implementation

T Kanungo et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2002)