Abstract
Deep learning has been widely applied in computer vision, natural language processing, and audio-visual recognition. The overwhelming success of deep learning as a data processing technique has sparked the interest of the research community. Given the proliferation of Fintech in recent years, the use of deep learning in finance and banking services has become prevalent. However, a detailed survey of the applications of deep learning in finance and banking is lacking in the existing literature. This study surveys and analyzes the literature on the application of deep learning models in the key finance and banking domains to provide a systematic evaluation of the model preprocessing, input data, and model evaluation. Finally, we discuss three aspects that could affect the outcomes of financial deep learning models. This study provides academics and practitioners with insight and direction on the state-of-the-art of the application of deep learning models in finance and banking.
Highlights
Deep learning (DL) is an advanced technique of machine learning (ML) based on artificial neural network (NN) algorithms
As we focus on the application rather than theoretical DL aspect, this study will not consider other popular DL algorithms, including convolutional neural networks” (CNN) and reinforcement learning (RL), as well as Latent variable models such as variational autoencoders and generative adversarial network
The results reveal that CNN is more efficient than decision trees (DT), support vector machine (SVM), linear discriminant analysis, multi-layer perceptron (MLP), and AdaBoost
Summary
Deep learning (DL) is an advanced technique of machine learning (ML) based on artificial neural network (NN) algorithms. This section briefly reviews the basic concept of DL, including NN and deep neural network (DNN) All of these models have greatly contributed to the applications in F&B. Application of DL models in F&B domains Based on our review, six types of DL models are reported They are FNN, CNN, RNN, RL, deep belief networks (DBN), and restricted Boltzmann machine (RBM). Galeshchuk and Mukherjee (2017) conduct experiments and claim that a single hidden layer NN or SVM performs worse than a simple model like moving average (MA) They find that CNN could achieve higher classification accuracy in predicting the direction of the change of exchange rate because of successive layers of DNN.
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