A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

By Arash Bahrammirzaee. 2010

529 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Management Science and Operations Research, Statistical Learning Theory, Economics & Operations Research

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “[6] surveyed financial prediction and planning studies along with other financial applications using various Artificial Intelligence (AI) techniques like ANN, Expert Systems, hybrid models.”
Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward (2020, background): “…elds, such as (precision) agriculture (Sennaar, 2019), air combat and military training (Gallagher, 2016; Wong, 2020), education (Sears, 2018), ﬁ nance (Bahrammirzaee, 2010), health care (Beam and Kohane, 2018), human resources and recruiting (Hmoud and Laszlo, 2019), music composition (Cheng,…”
Deep Learning for Financial Applications : A Survey (2020, background): “[2] compared Artificial Neural Networks (ANNs), Expert Systems and Hybrid models for various financial applications.”
Stock trend forecasting in turbulent market periods using neuro-fuzzy systems (2015, methodology): “A common approach among state of the art techniques (Bahrammirzaee 2010) is the use of artiﬁcial neural networks (ANNs), in combination with other techniques, e.g. neuro-fuzzy systems.”
A literature review on the application of evolutionary computing to credit scoring (2013, background): “From the seminal reference to credit scoring in the introductory paper by Altman (1968), many other developments have been subsequently proposed (Baesens et al , 2003; Bahrammirzaee, 2010; Abdou and Pointon, 2011).”
Bio inspired computing - A review of algorithms and scope of applications (2016, background): “Further a lot of the applications of these matured algorithms have been witnessed in the domain of information systems, focusing on decisions at the operational level, tactical level, functional level strategic decisions, business level strategic decisions and corporate level strategic decisions acr”
AI in Finance: Challenges, Techniques, and Opportunities (2021, background): “The data-driven AI for smart FinTech needs to tackle the characteristics and complexities of EcoFin data and extract the potential and intelligence from the data for smart decision-support and making [6, 14, 27, 28, 56, 62, 158].”
Can Deep Learning Techniques Improve the Risk Adjusted Returns from Enhanced Indexing Investment Strategies (2017, result): “In the context of portfolio management, Bahrammirzaee (2010) undertakes a review of the work of a number of researchers and notes that all of the research reviewed points to superior results being achieved using neural networks, and in particular backpropagation neural networks, as compared to the r”
Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models (2016, methodology): “The empirical results of this research are in support of previous academic literature [11, 12] that provides evidence of successfully forecasting future price movements using machine learning methods like ANNs, genetic programming, and wavelet analysis.”
Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks (2014, background): “Indeed, a large number of computing methods for stock prediction have been proposed in the literature (see Atsalakis and Valavanis (2009) and Bahrammirzaee (2010) for surveys).”
Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches (2014, unknown): “By considering researches in this category, Bahrammirzaee [10] conducted a comparative review of three famous intelligent techniques for financial applications, i.e., artificial neural networks, expert systems and hybrid intelligent systems.”
Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects (2014, background): “NNs are considered a relatively new technology in Finance, but with high potential and an increasing number of applications (Lam (2004) and Bahrammirzaee (2010)).”
Artificial neural networks applications in economics and management research: An exploratory literature review (2018, background): “Bahrammirzaee [15] presented a comparative survey of artificial intelligence applications in finance.”