Deep Learning for Financial Applications : A Survey (2002.05786v1)

Abstract: Computational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have been published resulting in various models. Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models. Lots of different implementations of DL exist today, and the broad interest is continuing. Finance is one particular area where DL models started getting traction, however, the playfield is wide open, a lot of research opportunities still exist. In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today. We not only categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. In addition, we also aimed at identifying possible future implementations and highlighted the pathway for the ongoing research within the field.

Deep Learning in Finance: A Comprehensive Review

The paper "Deep Learning for Financial Applications: A Survey" by Ozbayoglu, Gudelek, and Sezer offers a detailed overview of how deep learning (DL) models are applied within various subfields of finance. This survey categorizes and analyzes existing DL studies across several financial applications, providing insights into current trends and future opportunities for researchers and practitioners.

Overview of Deep Learning Models

Deep learning, a subset of ML, leverages neural networks with multiple layers to model complex data patterns. The survey focuses on several prominent DL architectures: Deep Multilayer Perceptrons (DMLPs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), and Autoencoders (AEs). These models have demonstrated efficacy in predictive and classification tasks involving financial data, with LSTMs and CNNs being particularly popular due to their suitability for time-series and image-like data structures.

Applications in Finance

The survey classifies financial applications of DL into distinct categories:

1. Algorithmic Trading: A significant focus area, DL models, particularly LSTM networks, are prominently utilized for predicting stock prices and generating trading signals. The ability of these models to capture temporal dependencies in time-series data is particularly advantageous for developing high-frequency trading systems.
2. Risk Assessment: DL models are increasingly applied for credit scoring, bankruptcy prediction, and bank distress classification. These studies indicate that DL offers enhanced accuracy in risk assessment tasks compared to traditional ML or expert-rule systems.
3. Portfolio Management: The integration of DL in portfolio selection and management is highlighted, with approaches such as Deep Reinforcement Learning (DRL) showing promise in dynamically adjusting portfolio weights based on market conditions.
4. Fraud Detection: Leveraging DL's anomaly detection capabilities, financial institutions apply these models to identify fraudulent transactions, with studies reporting success using LSTMs and CNNs for various fraud detection tasks.
5. Financial Derivatives and Asset Pricing: Although relatively nascent, some exploratory studies apply DL to options pricing and derivative markets, suggesting potential for future growth in applying neural networks to these complex financial products.
6. Cryptocurrencies and Blockchain: The rise of cryptocurrencies has spurred research into forecasting their market dynamics using DL. However, applications of DL within blockchain technology remain underexplored.
7. Text Mining and Sentiment Analysis: The proliferation of social media and online financial news has resulted in a surge of interest in using DL to gauge market sentiment. Combining text mining with DL models has been shown to improve the accuracy of financial forecasting.

Discussion and Future Directions

The survey highlights several trends and provides perspectives on future research directions:

• Hybrid Models: The combination of different DL architectures, such as integrating CNNs with LSTMs, has shown to improve performance by capturing both spatial and temporal data features.
• Emerging Techniques: Newer DL paradigms like GANs and Capsule Networks, though underutilized, present promising opportunities for financial applications.
• Expanding Areas: Blockchain technology, though not heavily covered currently, could benefit significantly from DL research, particularly in areas such as transaction verification and fraud prevention.

Conclusion

This survey serves as a crucial resource for understanding the current landscape and capabilities of DL in finance. Although significant progress has been made, the field still has substantial room for advancement. Researchers are encouraged to exploit the potential of DL in untapped areas, thereby contributing to its evolving role within the financial industry. The detailed insights provided by the authors set the stage for ongoing innovation and discovery in applying deep learning methodologies to complex financial challenges.