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ST-Trader:A Spatial-Temporal Deep Neural Network for Modeling stock market Movement

IEEE/CAA Journal of Automatica Sinica 2021年第5期8卷 1015-1024页

作者： Xiurui Hou Kai Wang Cheng Zhong Zhi Wei the Department of Computer Science New Jersey Institute of TechnologyNewarkNJ 07102 USA the School of Business Stevens Institute of TechnologyHobokenNJ 07030 USA

stocks that are fundamentally connected with each other tend to move *** such common trends is believed to benefit stock movement forecasting ***,such signals are not trivial to model because the connections among stocks are not physically presented and need to be estimated from volatile *** by this observation,we propose a framework that incorporates the inter-connection of firms to forecast stock *** effectively utilize a large set of fundamental features,we further design a novel ***,we use variational autoencoder(VAE)to reduce the dimension of stock fundamental information and then cluster stocks into a graph structure(fundamentally clustering).Second,a hybrid model of graph convolutional network and long-short term memory network(GCN-LSTM)with an adjacency graph matrix(learnt from VAE)is proposed for graph-structured stock market *** on minute-level *** market data demonstrate that our model effectively captures both spatial and temporal signals and achieves superior improvement over baseline *** proposed model is promising for other applications in which there is a possible but hidden spatial dependency to improve time-series prediction.

关键词： Graph convolution network long-short term memory network stock market forecasting variational autoencoder(VAE)

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Estimating stock closing indices using a GA-weighted condensed polynomial neural network

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Financial Innovation 2018年第1期4卷 311-332页

作者： Sarat Chandra Nayak Bijan Bihari Misra Department of Computer Science and Engineering CMR College of Engineering&Technology(Autonomous)501401HyderabadIndia Department of Information Technology Silicon Institute of Technology751024BhubaneswarIndia.

Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable ***,predicting the closing prices of stock indices remains a challenging task because stock price movements are characterized by high volatility and *** paper proposes a novel condensed polynomial neural network(CPNN)for the task of forecasting stock closing price *** developed a model that uses partial descriptions(PDs)and is limited to only two layers for the PNN *** outputs of these PDs along with the original features are fed to a single output neuron,and the synaptic weight values and biases of the CPNN are optimized by a genetic *** proposed model was evaluated by predicting the next day’s closing price of five fast-growing stock indices:the BSE,DJIA,NASDAQ,FTSE,and *** comparative testing,the proposed model proved its ability to provide closing price predictions with superior ***,the Deibold-Mariano test justified the statistical significance of the model,establishing that this approach can be adopted as a competent financial forecasting tool.

关键词： stock market forecasting Polynomial neural network Partial description Genetic algorithm Multilayer perceptron

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Survey of feature selection and extraction techniques for stock market prediction

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Financial Innovation 2023年第1期9卷 667-691页

作者： Htet Htet Htun Michael Biehl Nicolai Petkov Bernoulli Institute for Mathematics Computer ScienceArtificial IntelligenceUniversity of GroningenGroningenThe Netherlands

In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price *** review papers in the literature have focused on various ML,statistical,and deep learning-based methods used in stock market ***,no survey study has explored feature selection and extraction techniques for stock market *** survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market *** conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–*** review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the *** also describe the combination of feature analysis techniques and ML methods and evaluate their ***,we present other survey articles,stock market input and output data,and analyses based on various *** find that correlation criteria,random forest,principal component analysis,and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.

关键词： Feature selection Feature extraction Dimensionality reduction stock market forecasting Machine learning

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A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

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Financial Innovation 2019年第1期5卷 645-678页

作者： Sarat Chandra Nayak Bijan Bihari Misra Department of Computer Science and Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Information Technology Silicon Institute of TechnologyBhubaneswar 751024India

Accurate prediction of stock market behavior is a challenging issue for financial *** neural networks,such as multilayer perceptron have been established as better approximation and classification models for this *** study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock *** input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this *** was chosen because it requires fewer control parameters and has a faster convergence *** statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock *** performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be *** conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be *** model can be used as a promising tool for financial forecasting.

关键词： Artificial neural network Neuro-fuzzy network Multilayer perceptron Chemical reaction optimization stock market forecasting Financial time series forecasting

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