Market Sentiments and Artificial Intelligence Neural Network Algorithms in Taiwan Derivatives Markets
Advances in Pacific Basin Business, Economics and Finance
ISBN: 978-1-80117-313-1, eISBN: 978-1-80117-312-4
Publication date: 15 March 2022
Abstract
This chapter derives sentiment indicators (implied volatility and implied skewness) from the option pricing models of Corrado and Su (1996), Bakshi, Kapadia, and Madan (2003), and Zhang, Zhen, Sun, and Zhao (2017), and then integrates these sentiment indicators with artificial intelligence deep neural network (AIDNN) for developing the behavioral finance AIDNN (BFAIDNN) algorithms. We apply the BFAIDNN algorithms to daily derivatives data of Taiwan Futures and Options markets from 2015 to 2017. Our results demonstrate that the trading strategies established by the BFAIDNN algorithms can generate positive rewards.
Keywords
Acknowledgements
Acknowledgment
This research is partially supported by the Ministry of Science and Technology through Pervasive Artificial Intelligence Research (PAIR) Labs in Taiwan.
Citation
Lin, C.-G., Yu, M.-T., Chen, C.-Y. and Hsu, P.-H. (2022), "Market Sentiments and Artificial Intelligence Neural Network Algorithms in Taiwan Derivatives Markets", Lee, C.-F. and Yu, M.-T. (Ed.) Advances in Pacific Basin Business, Economics and Finance (Advances in Pacific Basin Business, Economics and Finance, Vol. 10), Emerald Publishing Limited, Leeds, pp. 145-157. https://doi.org/10.1108/S2514-465020220000010007
Publisher
:Emerald Publishing Limited
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