Backpropagation and recurrent neural networks in financial analysis of multiple stock market returns

Román, Jesús Ángel; Jameel, A.

doi:10.1109/hicss.1996.495431

Cited by 69 publications

(30 citation statements)

References 1 publication

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“…Most of previous studies utilize historical time-series prices to predict the future prices of instruments in financial market and make predictions with various models [37,42,41,6,23,8,9,18]. An intelligent decision support system combines influence digram generator, probability assessor, value function generator to help decision-makers make better investment decisions [39].…”

Section: Related Workmentioning

confidence: 99%

Enhancing stock market prediction with extended coupled hidden Markov model over multi-sourced data

Zhang

Wang

et al. 2018

Knowl Inf Syst

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Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market fluctuations can be impacted by various other information sources such as stock related events. Although some recent works propose event-driven prediction approaches by considering the event data, how to leverage the joint impacts of multiple data sources still remains an open research problem. In this work, we study how to explore multiple data sources to improve the performance of the stock prediction. We introduce an Extended Coupled Hidden Markov Model incorporating the news events with the historical trading data. To address the data sparsity issue of news events for each single stock, we further study the fluctuation correlations between the stocks and incorporate the correlations into the model to facilitate the prediction task. Evaluations on China A-share market data in 2016 show the superior performance of our model against previous methods.

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Section: Related Workmentioning

confidence: 99%

Enhancing stock market prediction with extended coupled hidden Markov model over multi-sourced data

Zhang

Wang

et al. 2018

Knowl Inf Syst

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“…The well-known statistical models proposed include autoregressive, moving average, autoregressive moving average (ARMA), and autoregressive integrated moving average (ARIMA) models. The widely used machine-learning approaches include the neural network (NN) based models (Quah and Srinivasan 1999;Rabiner 1989;Roman and Jameel 1996), support vector machines (De Gooijer and Hyndman 2006;He et al 2008;Shen et al 2010;Tkacz 2001), fuzzy systems (Kandel 1991), linear regression, Kalman filtering (Ma and Teng 2004), and hidden Markov models (Rabiner 1989). All of these approaches were used for learning the forecasting models.…”

Section: Introductionmentioning

confidence: 99%

A Rolling Grey Model Optimized by Particle Swarm Optimization in Economic Prediction

Liu

Wang

et al. 2014

Computational Intelligence

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Grey system theory has been widely used to forecast the economic data that are often nonlinear, irregular, and nonstationary. Current forecasting models based on grey system theory could adapt to various economic time series data. However, these models ignored the importance of the model parameter optimization and the use of recent data, which lead to poor forecasting accuracy. In this article, we propose a novel forecasting model, called particle swarm optimization rolling grey model (PSO-RGM(1,1)), based on a rolling mechanism GM with optimized parameters by using the particle swarm optimization algorithm. The simple model is shown to be very effective in forecasting the tertiary industry data sequences, which are short and noisy but regular in secular trend. The experimental results show that PSO-RGM(1,1) outperforms other commonly used forecasting models on three real economic data sets. Our empirical study shows that PSO is found to be the best overall algorithm to optimize the parameter of RGM compared with other well-known metaheuristics. Furthermore, we evaluated other variant PSOs and found that single particle PSO outperforms others overall in terms of prediction accuracy, convergence speed, and degree of certainty.

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“…Regression models have been traditionally used to model changes in the stock markets. However, those models can predict linear patterns only [13]. Typically, the performance of a model in classification problems such as stock prediction is measured by prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Magic of Fibonacci Sequence in Prediction of Stock Behavior

Kumar¹

2014

IJCA

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Predicting the return of a financial product is a very risky task. It involves subjectivity and experts knowledge. In the development of an expert system, domain knowledge is one of the important component. For a software to be artificial intelligent, some heuristics are required, which can help in decision making. It is admitted by the technical experts of financial sectors that in predicting the support or resistance backtracking is required when prediction of support or resistance fails. In this paper ,an attempt has been made to restrict the back tracking of support and resistance to a maximum of two attempts. Proposed model can be further used in machine learning to remove the subjectivity.

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Backpropagation and recurrent neural networks in financial analysis of multiple stock market returns

Cited by 69 publications

References 1 publication

Enhancing stock market prediction with extended coupled hidden Markov model over multi-sourced data

Enhancing stock market prediction with extended coupled hidden Markov model over multi-sourced data

A Rolling Grey Model Optimized by Particle Swarm Optimization in Economic Prediction

Magic of Fibonacci Sequence in Prediction of Stock Behavior

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