296 research outputs found
An empirical methodology for developing stockmarket trading systems using artificial neural networks
Creating short-term stockmarket trading strategies using artificial neural networks:A case study
Abstract — Developing short-term stockmarket trading systems is a complex process, as there is a great deal of random noise present in the time series data of individual securities. The primary difficulty in training neural networks to identify return expectations is to find variables to help identify the signal present in the data. In this paper, the authors follow the previously published Vanstone and Finnie methodology. They develop a successful neural network, and demonstrate its effectiveness as the core element of a financially viable trading system. Index Terms—stockmarket trading, neural networks, trading system
Stock Market Prediction via Deep Learning Techniques: A Survey
The stock market prediction has been a traditional yet complex problem
researched within diverse research areas and application domains due to its
non-linear, highly volatile and complex nature. Existing surveys on stock
market prediction often focus on traditional machine learning methods instead
of deep learning methods. Deep learning has dominated many domains, gained much
success and popularity in recent years in stock market prediction. This
motivates us to provide a structured and comprehensive overview of the research
on stock market prediction focusing on deep learning techniques. We present
four elaborated subtasks of stock market prediction and propose a novel
taxonomy to summarize the state-of-the-art models based on deep neural networks
from 2011 to 2022. In addition, we also provide detailed statistics on the
datasets and evaluation metrics commonly used in the stock market. Finally, we
highlight some open issues and point out several future directions by sharing
some new perspectives on stock market prediction
Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction
This paper examines the forecasting performance of ARIMA and artificial neural networks model with published stock data obtained from New York Stock Exchange. The empirical results obtained reveal the superiority of neural networks model over ARIMA model. The findings further resolve and clarify contradictory opinions reported in literature over the superiority of neural networks and ARIMA model and vice versa
Reconstructing the Emergent Organization of Information Flows in International Stock Markets: A Computational Complex Systems Approach
In this paper we study the interdependences between the dynamics of the stock market indexes of 30 different stock markets across 29 different countries to analyze the nonlinear dynamics of their information flows. We find that the system exhibits complex dynamic properties that go beyond what has been generally found in the previous literature, suggesting that the structure of information flows is regulated by subtle homeostatic forces that cause the roles of the single markets in the whole network to evolve in unexpected ways. We present a toolkit of ANN-based methods that can be systematically deployed to analyze different aspects of such dynamics
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