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- W4313402616 abstract "Recent developments in Machine Learning have attracted the attention of the financial community for the development of an optimal strategy in stock markets. The majority of existing approaches introduced a model to maximize return. However, these methods often fail to account to adapt to the constantly changing market circumstances. This paper develops an efficient learning model based on the unsupervised learning and deep reinforcement learning technique to determine an optimal stock portfolio. An unsupervised algorithm is employed to select suitable stocks in the portfolio. An LSTM (Long Short-Term Memory) based agent mechanism is proposed to address the weight assignment problem to each stock in order to minimize the risk and increase portfolio value. The study also presents an intelligent trading algorithm that is flexible to trade on any Indian stock portfolio. The experimental analysis showcases the proposed scheme performs better than the baseline and secured portfolio." @default.
- W4313402616 created "2023-01-06" @default.
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- W4313402616 date "2023-01-01" @default.
- W4313402616 modified "2023-10-16" @default.
- W4313402616 title "A Modelling of Novel Approach for Stock Portfolio Management Using Unsupervised Algorithm and Deep Reinforcement Technique" @default.
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- W4313402616 doi "https://doi.org/10.1007/978-3-031-21435-6_28" @default.
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