Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system

Abstract

The increasing reliance on Computational Intelligence techniques like Artificial Neural Networks and Genetic Algorithms to formulate trading decisions have sparked off a chain of research into financial forecasting and trading trend identifications. Many research efforts focused on enhancing predictive capability and identifying turning points. Few actually presented empirical results using live data and actual technical trading rules. This paper proposed a novel RSPOP Intelligent Stock Trading System, that combines the superior predictive capability of RSPOP FNN and the use of widely accepted Moving Average and Relative Strength Indicator Trading Rules. The system is demonstrated empirically using real live stock data to achieve significantly higher Multiplicative Returns than a conventional technical rule trading system. It is able to outperform the buy-and-hold strategy and generate several folds of dollar returns over an investment horizon of four years. The Percentage of Winning Trades was increased significantly from an average of 70% to more than 92% using the system as compared to the conventional trading system; demonstrating the system's ability to filter out erroneous trading signals generated by technical rules and to preempt any losing trades. The system is designed based on the premise that it is possible to capitalize on the swings in a stock counter's price, without a need for predicting target prices.

Artificial Intelligence: The New Frontier?

“…By gaining access to resources that support and enhance the decision-making process, AI assists healthcare specialists by making suggestions [31]. According to Tan et al [32], AI is being applied in stock trading areas and is multiplying returns and boosting the likelihood of winning trades.…”

Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions

“…RQ3: to conduct more research using a combination of other themes for stock market prediction, also because the existing artificial neural networks have not been able to provide effective results [137] and this calls for more advanced techniques such as neuro-fuzzy methods, genetic algorithm, option pricing models, machine learning techniques, and component analysis models Neuro-fuzzy systems: [104,105,[138][139][140][141]] Genetic algorithm and linear representation methods: [28,93,[142][143][144][145][146][147][148]] Linear regression models: [21,116,122,149] Option pricing model, machine learning techniques and hybrid combinations with neural networks: [34,91,[150][151][152][153] Component analysis model: [22,46,62,137,154] Gap4: more studies are required to maintain consistency throughout the time period since the inception of these three topics, viz., AI, neural networks, and stock market forecasting RQ4: the majority of the study is concentrated on longer periods. Very few studies have conducted an experiment or a case study on the trading prices spread over the previous 6 months or less [30,38,50,75,81,91,95,98,…”

Improving return using risk-return adjustment and incremental training in technical trading rules with GAPs

“…• Technical analysis: This considers that the historical evolution of the prices of each asset permits the prediction of its future behaviour. Within this line there are two different approaches: -Based on the study of price graphs (chartism): the trader subjectively analyzes the figure of the evolution of the asset price, searching for trends, support lines, resistences and different figures in order to try and anticipate its future behaviour [3]. -Based on the study of technical indicators: these are mathematical formulas which are applied to historical asset prices and try to detect trends, divergences, averages, etc.…”

“…3.4). 3 In the case of the mutation operator a similar scheme has been employed. The GP part or the GA part will be mutated randomly.…”