AI in Finance: Challenges, Techniques and Opportunities

@article{Cao2021AIIF,
  title={AI in Finance: Challenges, Techniques and Opportunities},
  author={Longbing Cao},
  journal={Banking \& Insurance eJournal},
  year={2021}
}
  • Longbing Cao
  • Published 2021
  • Computer Science, Economics
  • Banking & Insurance eJournal
AI in finance broadly refers to the applications of AI techniques in financial businesses. This area has attracted attention for decades with both classic and modern AI techniques applied to increasingly broader areas of finance, economy and society. In contrast to either discussing the problems, aspects and opportunities of finance that have benefited from specific AI techniques and in particular some new-generation AI and data science (AIDS) areas or reviewing the progress of applying… Expand
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References

SHOWING 1-10 OF 207 REFERENCES
AI and Machine Learning in Real Estate Investment
The topic of artificial intelligence (AI) is seen frequently in popular media, academic journals, and corporate statements. However, real estate is an industry notoriously slow to adopt newExpand
Deep Learning for Financial Applications : A Survey
TLDR
This paper tried to provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today, and categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. Expand
Computational Intelligence and Financial Markets: A Survey and Future Directions
TLDR
An overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others, are given. Expand
Reinforcement learning in financial markets - a survey
TLDR
The present paper draws insights from almost 50 publications, and categorizes them into three main approaches, i.e., critic-only approach, actor- only approach, and actor-critic approach, which help identify recurring design decisions as well as potential levers to improve the agent's performance. Expand
A review of machine learning experiments in equity investment decision-making: why most published research findings do not live up to their promise in real life
TLDR
A literature review of 27 academic experiments spanning over two decades and contrasted them with real-life examples of machine learning-driven funds to try to explain this apparent contradiction in the picture of real-world AI-driven investments. Expand
Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey
TLDR
It is shown that machine learning algorithms tend to outperform most traditional stochastic methods in financial market forecasting and there is evidence that, on average, recurrent neural networks outperform feed forward neural networks as well as support vector machines which implies the existence of exploitable temporal dependencies in financial time series across multiple asset classes and geographies. Expand
The Impact of Machine Learning on Economics
  • S. Athey
  • Computer Science
  • The Economics of Artificial Intelligence
  • 2019
TLDR
An assessment of the early contributions of machine learning to economics, as well as predictions about its future contributions, and some highlights from the emerging econometric literature combining machine learning and causal inference. Expand
Adaptive Quantitative Trading: An Imitative Deep Reinforcement Learning Approach
TLDR
An adaptive trading model, namely iRDPG, is proposed, to automatically develop QT strategies by an intelligent trading agent and is enhanced by deep reinforcement learning (DRL) and imitation learning techniques. Expand
Deep Learning Based on Generative Adversarial and Convolutional Neural Networks for Financial Time Series Predictions
TLDR
This paper proposes the implementation of a generative adversarial network (GAN), which is composed by a bi-directional Long short-term memory (LSTM) and convolutional neural network(CNN) referred as Bi-L STM-CNN to generate synthetic data that agree with existing real financial data so the features of stocks with positive or negative trends can be retained to predict future trends of a stock. Expand
Multimodal deep learning for finance: integrating and forecasting international stock markets
  • S. Lee, S. Yoo
  • Computer Science, Economics
  • The Journal of Supercomputing
  • 2019
TLDR
This study develops stock return prediction models that can jointly consider international markets, using multimodal deep learning and conclusively demonstrates that the early and intermediate fusion models achieve a significant performance boost in comparison with the late fusion and single-modality models. Expand
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