Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

Jie Wang · Niu Hongli

98 citations0/0 files storedComputational Intelligence and Neuroscience

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Neural Networks and Applications Forecasting Techniques and Applications

Abstract

In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 98 citations exist upstream.

References

2003A theory of power-law distributions in financial market fluctuations 2010A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems 2006A fusion model of HMM, ANN and GA for stock market forecasting 2005The application of neural networks to forecast fuzzy time series 2004A dynamic artificial neural network model for forecasting time series events 2006Forecasting the volatility of stock price index 2006A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets 2003Neural network forecasts of Canadian stock returns using accounting ratios

Cited by

2016Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend 2021Portfolio management system in equity market neutral using reinforcement learning 2020RETRACTED ARTICLE: Grey Wolf optimization-Elman neural network model for stock price prediction 2019Presentation of a new hybrid approach for forecasting economic growth using artificial intelligence approaches

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.