Paper Info
Title
How Adaptive Agents in Stock Market Perform in the Presence of Random News: A Genetic Algorithm Approach
Abstract
The effect of random news on the performance of adaptive agents as investors in stock market is modelled by genetic algorithm and measured by their portfolio values. The agents are defined by the rules evolved from a simple genetic algorithm, based on the rate of correct prediction on past data. The effects of random news are incorporated via a model of herd effect to characterize the human nature of the investors in changing their original plan of investment when the news contradicts their prediction. The random news is generated by white noise, with equal probability of being good and bad news. Several artificial time series with different memory factors in the time correlation function are used to measure the performance of the agents after the training and testing. A universal feature that greedy and confident investors outperform others emerges from this study.
Year
DOI
Venue
2000
10.1007/3-540-44491-2_74
IDEAL
Keywords
Field
DocType
herd effect,artificial time series,confident investor,random news,simple genetic algorithm,genetic algorithm,time correlation function,adaptive agents,adaptive agent,bad news,stock market perform,genetic algorithm approach,correct prediction,white noise,technical analysis,investment strategies,economic model,human nature,rule based,correlation function,trading strategy,time series
Stock price,Computer science,Self-organization,Portfolio,White noise,Artificial intelligence,Correlation function,Adaptive agents,Stock market,Machine learning,Genetic algorithm
Conference
ISBN
Citations 
PageRank 
3-540-41450-9
8
1.54
References 
Authors
1
2
Name
Order
Citations
PageRank
Kwok Yip Szeto16421.47
L. Y. Fong281.54