This paper describes a heuristic forecasting model based on neural networks for stock decision-making. Some heuristic strategies are presented for enhancing the learning capability of neural networks and obtaining better trading performance. The China Shanghai Composite Index is used as case study. The forecasting model can forecast the buying and selling signs according to the result of neural...

Applications of Machine Learning (ML) to stock market analysis include Portfolio Optimization, Investment Strategy Determination, and Market Risk Analysis. This paper focuses on the problem of Investment Strategy Determination through the use of reinforcement learning techniques. Four techniques, two based on Recurrent Reinforcement Learning (RLL) and two based on Q-learning, were utilized. Q-l...

This paper describes how cooperative coevolution can be used for GP of technical trading rules. A number of different methods of choosing collaborators for fitness evaluation are investigated. Several of the methods outperformed, at a statistically significant level, a buy-and-hold strategy for the S&P500 on the testing period from 1990-2002, even taking into account transaction costs.

We propose a genetic ensemble of recurrent neural networks for stock prediction model. The genetic algorithm tunes neural networks in a two-dimensional and parallel framework. The ensemble makes the decision of buying or selling more conservative. It showed notable improvement on the average over not only the buy-and-hold strategy but also other traditional ensemble approaches.

An algorithm for managing a portfolio of stocks using a trading agent is presented. A simulation game inspired by history-based Parrondo games is described. A performance measure is defined, with which various strategy mixes can be judged. Even when transaction costs are taken into account, active portfolio management (as opposed to Buy and Hold) is shown to be profitable.

working papers are produced by the Bradford University School of Management and are to be circulated for discussion purposes only. Their contents should be considered to be preliminary. The papers are expected to be published in due course, in a revised form and should not be quoted without the author's permission. ABSTRACT This paper studies the economic significance of stock and bond return p...

The proposed stock market prediction system is comprised of two preprocessing components, two specialized neural networks, and a decision rule base. First, the preprocessing components determine the most relevant features for stock market prediction, remove the noise, and separate the remaining patterns into two disjoint sets. Next, the two neural networks predict the market’s rate of return, w...