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AI
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AI in Finance: Leveraging AI to Transform Financial Services
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AI in Finance: Leveraging AI to Transform Financial Services

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: 28 February 2025 | Viewed by 14799

Special Issue Editor

Dr. Xianrong (Shawn) Zheng

E-Mail Website
Guest Editor
Information Technology & Decision Sciences Department, Old Dominion University, Norfolk, VA 23529, USA
Interests: AI; cloud computing; FinTech
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on “AI in Finance”. AI is concerned with intelligent behavior in artifacts. Deep learning and generative AI are the recent innovations in artificial intelligence. AI has a wide range of applications, including healthcare, transportation, and finance. It will have transformative impacts on financial sectors, including banking, insurance, investments, securities, etc. Now, AI is used for lending, investing, risk analysis, fraud detection, customer service, etc.

This Special Issue calls for papers on AI in Finance. It welcomes research articles that present novel theory, algorithms, systems, and applications of AI in financial sectors and encourages submissions from multiple disciplines, including artificial intelligence, computer science, information systems, finance, etc. Topics of interest include, but are not limited to, AI in Finance, ML in Finance, algorithmic trading, robo-advisor, risk management, etc.

Dr. Xianrong (Shawn) Zheng
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI in finance
  • ML in finance
  • algo trading
  • robo-advisor

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

Jump to: Review

23 pages, 4581 KiB  
Article
Seeing Beyond Noise: Improving Cryptocurrency Forecasting with Linear Bias Correction
by Sibtain Syed, Syed Muhammad Talha, Arshad Iqbal, Naveed Ahmad and Mohammed Ali Alshara
AI 2024, 5(4), 2829-2851; https://doi.org/10.3390/ai5040136 - 8 Dec 2024
Viewed by 1240
Abstract
Cryptocurrency is recognized as a leading digital currency by its peer-to-peer transfer capabilities and secure features. Accurately forecasting cryptocurrency price trends holds substantial significance for investors and traders, as they inform critical decisions regarding the acquisition, divestment, or retention of cryptocurrencies, guided by [...] Read more.
Cryptocurrency is recognized as a leading digital currency by its peer-to-peer transfer capabilities and secure features. Accurately forecasting cryptocurrency price trends holds substantial significance for investors and traders, as they inform critical decisions regarding the acquisition, divestment, or retention of cryptocurrencies, guided by expectations of value, risk assessment, and potential returns. This study also aims to identify a resourceful technique to efficiently forecast prices of cryptocurrencies such as Bitcoin (BTC), Binance (BNB), Ripple (XRP), and Tether (USDT) using optimal data-driven models (LSTM, GRU, and BiLSTM models) using bias correction. The proposed methodology includes collecting cryptocurrency data and precious metal data from Coindesk and BullionVault, respectively, and then finding the optimal model input combination for each cryptocurrency by lag adjustment and correlating feature selection. Hyperparameter tuning was performed by trial-and-error technique, and an early stopping function was applied to minimize time and space complexity. Bias correction (BC) is applied to model-forecasted price trends to reduce errors in evaluation and to enhance accuracy by adjusting model outputs to reduce prediction bias, providing a refined alternative to traditional unadjusted deep learning methods. GRU-BC outperformed other models in forecasting Bitcoin (with MAE 25.291, RMSE 31.266, MAPE 2.999) and USDT (with MAE 0.0006, RMSE 0.0012, MAPE 0.0622) price trends, while BiLSTM-BC was superior in predicting XRP (with MAE 0.0129, RMSE 0.0171, MAPE 2.9013) and BNB (with MAE 2.2759, RMSE 2.8357, MAPE 1.9785) market price flow. Full article
(This article belongs to the Special Issue AI in Finance: Leveraging AI to Transform Financial Services)
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Figure 1

Figure 1
<p>Graphical representation of Blockchain behavioral architecture.</p> Full article ">Figure 2
<p>Long short-term memory cell: illustrated representation for the current study.</p> Full article ">Figure 3
<p>Gated Recurrent Unit (GRU): illustrated representation for current study.</p> Full article ">Figure 4
<p>Graphical representation of bidirectional long short−term memory (BiLSTM) of the current study.</p> Full article ">Figure 5
<p>Graphical representation of the current study.</p> Full article ">Figure 6
<p>Graphical representation of the correlation between cryptocurrencies and precious metals.</p> Full article ">Figure 7
<p>Graphical representation of training and validation loss for all models on different sets of cryptocurrency.</p> Full article ">Figure 8
<p>Graphical scheme of actual, predicted, and bias-corrected price flow series for each cryptocurrency (i.e., BTC, XRP, BNB, and USDT).</p> Full article ">Figure 9
<p>Correlation matrix for actual, predicted, and bias-corrected price of cryptocurrency (i.e., BTC, XRP, BNB, and USDT).</p> Full article ">
19 pages, 2134 KiB  
Article
Utilizing Genetic Algorithms in Conjunction with ANN-Based Stock Valuation Models to Enhance the Optimization of Stock Investment Decisions
by Ying-Hua Chang and Chen-Wei Huang
AI 2024, 5(3), 1011-1029; https://doi.org/10.3390/ai5030050 - 1 Jul 2024
Cited by 1 | Viewed by 1615
Abstract
Navigating the stock market’s unpredictability and reducing vulnerability to its volatility requires well-informed decisions on stock selection, capital allocation, and transaction timing. While stock selection can be accomplished through fundamental analysis, the extensive data involved often pose challenges in discerning pertinent information. Timing, [...] Read more.
Navigating the stock market’s unpredictability and reducing vulnerability to its volatility requires well-informed decisions on stock selection, capital allocation, and transaction timing. While stock selection can be accomplished through fundamental analysis, the extensive data involved often pose challenges in discerning pertinent information. Timing, typically managed through technical analysis, may experience delays, leading to missed opportunities for stock transactions. Capital allocation, a quintessential resource optimization dilemma, necessitates meticulous planning for resolution. Consequently, this thesis leverages the optimization attributes of genetic algorithms, in conjunction with fundamental analysis and the concept of combination with repetition optimization, to identify appropriate stock selection and capital allocation strategies. Regarding timing, it employs deep learning coupled with the Ohlson model for stock valuation to ascertain the intrinsic worth of stocks. This lays the groundwork for transactions to yield favorable returns. In terms of experimentation, this study juxtaposes the integrated analytical approach of this thesis with the equal capital allocation strategy, TAIEX, and the Taiwan 50 index. The findings affirm that irrespective of the Taiwan stock market’s bullish or bearish tendencies, the method proposed in this study indeed facilitates investors in making astute investment decisions and attaining substantial profits. Full article
(This article belongs to the Special Issue AI in Finance: Leveraging AI to Transform Financial Services)
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<p>Research framework.</p> Full article ">Figure 2
<p>Chromosome encoding.</p> Full article ">Figure 3
<p>Chromosome crossover.</p> Full article ">Figure 4
<p>Chromosome mutation.</p> Full article ">Figure 5
<p>LSTM neural network architecture to predict stock price chart.</p> Full article ">Figure 6
<p>The chromosome coding for simple genetic algorithm.</p> Full article ">Figure 7
<p>The crossover for SGA’s chromosome.</p> Full article ">Figure 8
<p>Chromosome mutations for SGA.</p> Full article ">Figure 9
<p>Average rate of return for 2008~2015.</p> Full article ">
19 pages, 598 KiB  
Article
Generative Adversarial Networks for Synthetic Data Generation in Finance: Evaluating Statistical Similarities and Quality Assessment
by Faisal Ramzan, Claudio Sartori, Sergio Consoli and Diego Reforgiato Recupero
AI 2024, 5(2), 667-685; https://doi.org/10.3390/ai5020035 - 13 May 2024
Cited by 4 | Viewed by 5152
Abstract
Generating synthetic data is a complex task that necessitates accurately replicating the statistical and mathematical properties of the original data elements. In sectors such as finance, utilizing and disseminating real data for research or model development can pose substantial privacy risks owing to [...] Read more.
Generating synthetic data is a complex task that necessitates accurately replicating the statistical and mathematical properties of the original data elements. In sectors such as finance, utilizing and disseminating real data for research or model development can pose substantial privacy risks owing to the inclusion of sensitive information. Additionally, authentic data may be scarce, particularly in specialized domains where acquiring ample, varied, and high-quality data is difficult or costly. This scarcity or limited data availability can limit the training and testing of machine-learning models. In this paper, we address this challenge. In particular, our task is to synthesize a dataset with similar properties to an input dataset about the stock market. The input dataset is anonymized and consists of very few columns and rows, contains many inconsistencies, such as missing rows and duplicates, and its values are not normalized, scaled, or balanced. We explore the utilization of generative adversarial networks, a deep-learning technique, to generate synthetic data and evaluate its quality compared to the input stock dataset. Our innovation involves generating artificial datasets that mimic the statistical properties of the input elements without revealing complete information. For example, synthetic datasets can capture the distribution of stock prices, trading volumes, and market trends observed in the original dataset. The generated datasets cover a wider range of scenarios and variations, enabling researchers and practitioners to explore different market conditions and investment strategies. This diversity can enhance the robustness and generalization of machine-learning models. We evaluate our synthetic data in terms of the mean, similarities, and correlations. Full article
(This article belongs to the Special Issue AI in Finance: Leveraging AI to Transform Financial Services)
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<p>The architecture of GAN.</p> Full article ">Figure 2
<p>Flowchart of the proposed methodology.</p> Full article ">Figure 3
<p>Comparison of cumulative distributions per feature between continuous data distributions: Real (Blue) versus Synthetic (Orange).</p> Full article ">Figure 4
<p>Comparison of the distributions per feature in the original (blue) and synthetic (orange) continuous datasets.</p> Full article ">Figure 5
<p>This plot represents the comparison between the two distributions Real and Synthetic.</p> Full article ">

Review

Jump to: Research

26 pages, 809 KiB  
Review
Deep Learning in Finance: A Survey of Applications and Techniques
by Ebikella Mienye, Nobert Jere, George Obaido, Ibomoiye Domor Mienye and Kehinde Aruleba
AI 2024, 5(4), 2066-2091; https://doi.org/10.3390/ai5040101 - 28 Oct 2024
Cited by 1 | Viewed by 4578
Abstract
Machine learning (ML) has transformed the financial industry by enabling advanced applications such as credit scoring, fraud detection, and market forecasting. At the core of this transformation is deep learning (DL), a subset of ML that is robust in processing and analyzing complex [...] Read more.
Machine learning (ML) has transformed the financial industry by enabling advanced applications such as credit scoring, fraud detection, and market forecasting. At the core of this transformation is deep learning (DL), a subset of ML that is robust in processing and analyzing complex and large datasets. This paper provides a comprehensive overview of key deep learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Deep Belief Networks (DBNs), Transformers, Generative Adversarial Networks (GANs), and Deep Reinforcement Learning (Deep RL). Beyond summarizing their mathematical foundations and learning processes, this study offers new insights into how these models are applied in real-world financial contexts, highlighting their specific advantages and limitations in tasks such as algorithmic trading, risk management, and portfolio optimization. It also examines recent advances and emerging trends in the financial industry alongside critical challenges such as data quality, model interpretability, and computational complexity. These insights can guide future research directions toward developing more efficient, robust, and explainable financial models that address the evolving needs of the financial sector. Full article
(This article belongs to the Special Issue AI in Finance: Leveraging AI to Transform Financial Services)
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<p>RNN architecture [<a href="#B4-ai-05-00101" class="html-bibr">4</a>].</p> Full article ">Figure 2
<p>LSTM architecture [<a href="#B29-ai-05-00101" class="html-bibr">29</a>].</p> Full article ">Figure 3
<p>GRU architecture [<a href="#B4-ai-05-00101" class="html-bibr">4</a>].</p> Full article ">Figure 4
<p>CNN architecture [<a href="#B32-ai-05-00101" class="html-bibr">32</a>].</p> Full article ">
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