A Financial Trading Decision-making Support Model Using Deep Learning and Signal Decomposition

Abstract

Abstract: This paper presents a decision support model for algorithmic trading in the financial market, which utilizes a novel hybrid model to make automatic trading decision. The proposed model hierarchically represents the one-dimensional non-stationary time series into multi-dimensional stationary sub-series, and then restructures them into a two-dimensional matrix to represent the daily market state. Then, the neural network, which has a powerful features learning ability, is used to capture the optimal entry and exit points of the fluctuating stock prices. Finally, the actual predicted results are evaluated by two different ways: statistical performance evaluation and financial performance evaluation. The results show that the proposed decision support model in this paper has strong applicability and adaptability which can bring positive profits in multiple environments.

Key words: sequence decomposition, neural network, decision support system, algorithmic trading

Liu Min, Zhang Fan, Wang Lin, Zhu Qing. A Financial Trading Decision-making Support Model Using Deep Learning and Signal Decomposition[J]. Management Review, 2022, 34(9): 14-26.

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