A neural network that explains as well as predicts financial market behavior

2002 ◽  
Author(s):  
C. Ornes ◽  
J. Sklansky
Keyword(s):  
Neural Network ◽  
Financial Market ◽  
Market Behavior

scholarly journals Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Tianle Zhou ◽  
Chaoyi Chu ◽  
Chaobin Xu ◽  
Weihao Liu ◽  
Hao Yu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Financial Market ◽  
Financial Time Series ◽  
Influential Factors ◽  
Series Data ◽  
Phase Point ◽  
Price Fluctuations ◽  
Financial Time

In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data.

Financial market trend analysis based on autoregressive conditional heteroscedasticity model and BP neural network prediction

2021 ◽  
pp. 002072092098849
Author(s):  
Xinzhe Yin ◽  
Jinghua Li
Keyword(s):  
Neural Network ◽  
Trend Analysis ◽  
Financial Market ◽  
Prediction Method ◽  
Good Effect ◽  
Trend Prediction ◽  
Bp Network ◽  
Conditional Heteroscedasticity ◽  
Bp Model ◽  
Autoregressive Conditional Heteroscedasticity

Many experts and scholars at home and abroad have studied this topic in depth, laying a solid foundation for the research of financial market prediction. At present, the mainstream prediction method is to use neural network and autoregressive conditional heteroscedasticity to build models, which is a more scientific way, and also verified the feasibility of the way in many studies. In order to improve the accuracy of financial market trend prediction, this paper studies in detail the neural network system represented by BP and the autoregressive conditional heterogeneous variance model represented by GARCH. Analyze its structure and algorithm, combine the advantages of both, create a GARCH-BP model, and transform its combination structure and optimize the algorithm according to the uniqueness of the financial market, so as to meet the market as much as possible Characteristics. The novelty of this paper is the construction of the autoregressive conditional heteroscedasticity model, which lays the foundation for the prediction of financial market trends through the construction of the model. However, there are some shortcomings in this article. The overall overview of the financial market is not very clear, and the prediction of the BP network is not so comprehensive. Finally, through the actual data statistics of market transactions, the effectiveness of the GARCH-BP model was tested, analyzed and researched. The final results show that model has a good effect on the prediction and trend analysis of market, and its accuracy and availability greatly improved compared with the previous conventional approach, which is worth further study and extensive research It is believed that the financial market prediction model will become one of the mainstream tools in the industry after its later improvement.

scholarly journals Investor Sentiment In The Financial Market: A Survey

2021 ◽  
Author(s):  
Charles A. Aziegbemhin
Keyword(s):  
Neural Networks ◽  
Risk Management ◽  
Financial Market ◽  
Investor Sentiment ◽  
Management Tool ◽  
Market Behavior ◽  
Market Returns ◽  
Wide Acceptance ◽  
Comprehensive Survey ◽  
Alternative Markets

Many techniques like technical analysis, fundamental analysis, neural networks etc are used to forecast market behavior but none of these methods has been consistently acceptable forecasting tool. This thesis surveys more than 200 related published articles that study investor sentiment techniques as derived and applied to forecasting equity, debt and alternative markets. From the literatures, it shows that the application of investor sentiment for evaluating market behavior is gaining wide acceptance. Changes in investor sentiment can trigger changes in the valuation and pricing of assets, therefore offering the ability to forecasting market directions more accurately than other techniques. This study is the most comprehensive survey on investor sentiment techniques and its impact on forecasting a panel of assets in the equity, debt, derivative and other alternative investment markets. It examines forecasting as it affects sentiment, investor sentiment, it influence on market returns, news analytics and its use as profit and risk management tool.

scholarly journals Evaluation of Deep Neural Network in Directional Prediction of Forex Market

2020 ◽  
Author(s):  
Hamid Hassanpour
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Software Package ◽  
Deep Neural Network ◽  
Behavior Prediction ◽  
Market Behavior ◽  
Learning Techniques

This is a paper regarding application of deep neural network in prediction of Forex market. It utilized advanced deep learning techniques and software package in order ti evaluate capability of deep neural network in market behavior prediction.

Higher Order Neural Network for Financial Modeling and Simulation

2016 ◽  
pp. 761-788
Author(s):  
Partha Sarathi Mishra ◽  
Satchidananda Dehuri
Keyword(s):  
Neural Network ◽  
Financial Market ◽  
Lower Number ◽  
Stock Index ◽  
Financial Model ◽  
Functional Link ◽  
Swarm Optimization ◽  
Financial Modeling ◽  
For Profit ◽  
Financial Gain

Financial market creates a complex and ever changing environment in which population of investors are competing for profit. Predicting the future for financial gain is a difficult and challenging task, however at the same time it is a profitable activity. Hence, the ability to obtain the highly efficient financial model has become increasingly important in the competitive world. To cope with this, we consider functional link artificial neural networks (FLANNs) trained by particle swarm optimization (PSO) for stock index prediction (PSO-FLANN). Our strong experimental conviction confirms that the performance of PSO tuned FLANN model for the case of lower number of ahead prediction task is promising. In most cases LMS updated algorithm based FLANN model proved to be as good as or better than the RLS updated algorithm based FLANN but at the same time RLS updated FLANN model for the prediction of stock index system cannot be ignored.

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