Keynote 2

Phayung Meesad

... currently is an Associate Professor at the Faculty of Information Technology and Digital Innovation, King Mongkut’s University of Technology North Bangkok (KMUTNB), Thailand. He also serves as the Director of Central Library at KMUTNB. Phayung received Bachelor of Science in Technical Education (Teaching in Electrical Engineering), from KMUTNB in 1994. He received Master of Science (MS) and Doctor of Philosophy (Ph.D.) in Electrical Engineering from School of Electrical and Computer Engineering, Oklahoma State University (OSU), Stillwater, USA, in 1998 and 2002, respectively. His research of interests are Artificial Intelligence, Big Data Analytics, Business Intelligence and Analytics, Computational Intelligence, Data Analytics, Data Mining, Data Science, Deep Learning, Digital Signal Processing, Image Processing, Machine Learning, Metaheuristics Optimization, Natural Language Processing, and Time Series Analysis.

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Stock Analysis based on Deep Reinforcement Learning

Artificial Intelligence (AI) applications have been growing recently based on Reinforcement learning (RL), a type of machine learning where an agent learns to behave in an environment by trial and error. The agent receives a reward for actions that lead to desired outcomes and a penalty for actions that lead to undesired results. Deep reinforcement learning (DRL) uses deep learning to represent the agent's state and the environment, allowing DRL agents to learn in complex environments with states and actions to get optimal rewards. Several domains apply DRL: game playing, robotics, and finance. In finance, DRL has been used to develop trading algorithms that can automatically buy and sell stocks. One of the challenges of using DRL for stock trading is that the stock market is a very complex environment. Several factors can affect the price of a stock, and it is difficult to predict how these factors will change in the future. A challenge is that the stock market is a very competitive environment. Many other traders are also trying to make money by buying and selling stocks. This means that it is important for DRL agents to be able to learn quickly and adapt to changes in the market. DRL agents can learn to identify patterns in the market that humans may not be able to see. They can also learn to adapt to changes in the market much faster than humans. DRL is a promising new technology for stock trading. It can be a very powerful tool for making money in the stock market. However, it is still a relatively new technology requiring improvement to overcome existing problems before being applied to real applications. This talk reviews state-of-the-art related to deep reinforcement learning and stock time series prediction with multivariate stock technical analysis.