Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Institution
- Publication
- Publication Type
Articles 1 - 3 of 3
Full-Text Articles in Business
Competitive Models To Detect Stock Manipulation, Jose J. Thoppan, Punniyamoorthy M., Ganesh K.
Competitive Models To Detect Stock Manipulation, Jose J. Thoppan, Punniyamoorthy M., Ganesh K.
Communications of the IIMA
In this paper, data from the Indian stock market is used to study the prediction accuracy of various classification techniques that can be used to identify market manipulation. The data contains information regarding price, volume and volatility of various stocks. Techniques like discriminant analysis, a composite model based on artificial neural network – genetic algorithm (ann-ga) and support vector machine (svm) have been used for classifying stocks into manipulated and non manipulated categories. It is observed that the support vector machine based technique gives the best classification accuracy among the three techniques.
Hedge Fund Replication With A Genetic Algorithm: Breeding A Usable Mousetrap, Brian C. Payne, Jiri Tresl
Hedge Fund Replication With A Genetic Algorithm: Breeding A Usable Mousetrap, Brian C. Payne, Jiri Tresl
Department of Finance: Faculty Publications
This study tests the performance of 14 hedge fund index clones created using parsimonious outof- sample replication portfolios consisting solely of easily accessible assets. We employ a genetic algorithm to integrate two traditional hedge fund replication methods, the factor-based and payoff distribution replication methods, and evaluate over 4500 commonly held stocks, bonds and mutual funds as replicating portfolio components. In-sample performance indicates that hedge funds have return series similar to portfolios of commonly held assets, and out-of-sample results provide evidence that the in-sample relationships can hold with infrequent rebalancing. This hedge fund replication attempt rates well relatively to prior efforts …
Artificial Intelligence – I: Robust Audio Steganography Via Genetic Algorithm, Mazdak Zamani, Hamed Taherdoost, Azizah A. Manaf, Rabiah B. Ahmad, Akram M. Zeki
Artificial Intelligence – I: Robust Audio Steganography Via Genetic Algorithm, Mazdak Zamani, Hamed Taherdoost, Azizah A. Manaf, Rabiah B. Ahmad, Akram M. Zeki
International Conference on Information and Communication Technologies
Steganography is a technique used to transmit hidden information by modifying an audio signal in an imperceptible manner. The transmission must be possible in spite of subsequent imperceptible alterations (attacks) of the modified signal. We propose a novel approach of substitution technique of audio steganography. Using genetic algorithm, message bits are embedded into multiple, vague and higher LSB layers, resulting in increased robustness. The robustness specially would be increased against those intentional attacks which try to reveal the hidden message and also some unintentional attacks like noise addition as well.