To remove such subjectivity, this paper suggests a neural network model for the investors to decide whether buy or sell the shares. The model consists two wings - one, based on technical analysis and the other, on fundamental analysis. The integral part of this model is t...
现代机器学习:with the booming of artificial intelligence technology, machine learning techniques have been introduced to handle complex financial market data and proved to be useful for making stock trendpredictions。 第三段:CNN简介 —— 近些年来使用图像特征的研究 —— 指出现在的不足就是欠缺考虑整个...
Stock markets are dynamic systems that exhibit complex intra-share and inter-share temporal dependencies. Spatial-temporal graph neural networks (ST-GNN) are emerging DNN architectures that have yielded high performance for flow prediction in dynamic sys
作者使用了 Apache Spark 大数据框架用于训练过程,最后利用从 2007 年到 2017 年的数据进行模型测试。结果表明,通过选择最合适的技术指标,在大多数情况下,神经网络模型可以实现比较好的策略结果。此外,微调技术指标和(或)优化策略可以提升整体的交易表现。 2. 数据处理 股票市场上面有许多的技术指标,在这里我们主要选择...
In this study, we propose a stock trading system based on optimized technical analysis parameters for creating buy-sell points using genetic algorithms. The model is developed utilizing Apache Spark big data platform. The optimized parameters are then passed to a deep MLP neural network for buy-se...
Stock market predictionsSummary: The aim of this paper is to develop new neural network algorithms to predict trading signals: buy, hold and sell, of stock market indices. Most commonly used classification techniques are not suitable to predict trading signals when the distribution of the actual ...
In the present study, evolved neural network is applied to construct a new intelligent stock trading system. First, heterogeneous double populations based hybrid genetic algorithm is adopted to optimize the connection weights of feedforward neural networks. Second, a new intelligent stock trading system...
Simulating profitable stock trading strategies with an evolutionary artificial neural network By simulating stock traders 'price forecasts and trading strategies development, the performance surface of Artificial Neural Network with Genetic Algorith... S Hayward - 《International Workshop on Intelligent Finance...
The research results helped to conclude the effectiveness of application of neural networks, as compared to the traditional linear statistical methods for identifying differences of Monday and Friday stock trading anomalies. The effectiveness of the method was confirmed by exploring impact of different ...
In this chapter Marina Resta demonstrates experimentally the great potential of neural networks for design of systems for trading stock markets. She suggests the use of a hybrid neural network architecture that combines the approach of Self-Organizing Maps together with that of Genetic Algorithms. She...