A sparse regression and neural network approach for financial factor modeling

Anis, Hassan; Kwon, Roy H.

doi:10.1016/j.asoc.2021.107983

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…In the practical application of factor models, the factor selection process based on regression methods is prone to overfitting problems. To solve this problem, neural networks are applied to detect interactions between factors in [156], while this method can cope with different degrees of nonlinearity in historical financial data. Among the classic financial market prediction techniques, of particular note are the following: technical analysis with indicators calculated from past prices to indicate bearish or bullish trends [157] and fundamentalist analysis, which seeks economic factors that influence market trends [158].…”

Section: ) Stock Pricingmentioning

confidence: 99%

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Artificial Intelligence in Accounting and Finance: Challenges and Opportunities

Yi,

Cao,

Chen

et al. 2023

IEEE Access

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The rapid expansion of artificial intelligence (AI) technologies presents novel technical solutions to traditional accounting and finance problems. Despite this, scholars in accounting and finance frequently encounter difficulties navigating the extensive and intricate domain knowledge of AI and its continuously evolving literature. To address this gap, this paper conducts a qualitative survey of the implementation of AI methods in accounting and finance. The paper is structured into four sections. Firstly, we examine the conventional accounting and finance issues and their requirement for AI techniques. Secondly, to inform accounting and finance researchers about the potential of AI, we present broad categories of AI applications. Thirdly, we explore recent research on AI solutions to conventional problems. Finally, we highlight emerging trends and possible research directions.

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Section: ) Stock Pricingmentioning

confidence: 99%

“…Stock pricing [156] The method can obtain the price-dividend function precisely, quickly, and feasibly.…”

Section: Improved Trigonometric Neural Networkmentioning

confidence: 99%

Artificial Intelligence in Accounting and Finance: Challenges and Opportunities

Yi,

Cao,

Chen

et al. 2023

IEEE Access

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“…In recent times many researchers are using neural networkbased approaches to build regression models for trend prediction. [57][58][59][60]…”

Section: Machine Learning Analysismentioning

confidence: 99%

Ultra‐Wideband, Polarization‐Independent, Wide‐Angle Multilayer Swastika‐Shaped Metamaterial Solar Energy Absorber with Absorption Prediction using Machine Learning

Patel

Surve

Jadeja

et al. 2022

Advcd Theory and Sims

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This paper proposes a double layer of gold multipattern swastika (DLMP) resonator based on SiO2 substrate. The average absorption of 95% is achieved for the DLMP metasurface‐based solar absorber in the spectrum (0.1–3 μm) covering the ultraviolet, visible, near‐infrared (NIR), and some range of mid‐infrared regions which makes proposed solar energy absorber ultra‐wideband. The absorptance rate of more than 90% is achieved for the bandwidth of 2516 nm, in absorptance spectrum of 0.314 to 2.830 μm. Shape analysis is also carried out for proposed structure with simulations of five variations and comparative analysis in terms of absorptance response under solar radiation is also presented to check the effect of shape variation on absorption. Furthermore, the influence of several structural parameters on absorptance spectra is also investigated. It is also observed that the absorptance spectrum of proposed solar absorber is angle insensitive for the range of 0° to 70° and is also polarization insensitive. General regression neural network is used to build regression models which can learn and predict the behavior of absorbers in assorted conditions. Experimental results prove that these models can predict the absorber behavior with high accuracy and can reduce the simulation time, resource requirements by 80%.

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Explainable artificial intelligence (XAI) in finance: a systematic literature review

Černevičienė,

Kabašinskas

2024

Artif Intell Rev

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As the range of decisions made by Artificial Intelligence (AI) expands, the need for Explainable AI (XAI) becomes increasingly critical. The reasoning behind the specific outcomes of complex and opaque financial models requires a thorough justification to improve risk assessment, minimise the loss of trust, and promote a more resilient and trustworthy financial ecosystem. This Systematic Literature Review (SLR) identifies 138 relevant articles from 2005 to 2022 and highlights empirical examples demonstrating XAI's potential benefits in the financial industry. We classified the articles according to the financial tasks addressed by AI using XAI, the variation in XAI methods between applications and tasks, and the development and application of new XAI methods. The most popular financial tasks addressed by the AI using XAI were credit management, stock price predictions, and fraud detection. The three most commonly employed AI black-box techniques in finance whose explainability was evaluated were Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGBoost), and Random Forest. Most of the examined publications utilise feature importance, Shapley additive explanations (SHAP), and rule-based methods. In addition, they employ explainability frameworks that integrate multiple XAI techniques. We also concisely define the existing challenges, requirements, and unresolved issues in applying XAI in the financial sector.

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A sparse regression and neural network approach for financial factor modeling

Cited by 7 publications

References 19 publications

Artificial Intelligence in Accounting and Finance: Challenges and Opportunities

Artificial Intelligence in Accounting and Finance: Challenges and Opportunities

Ultra‐Wideband, Polarization‐Independent, Wide‐Angle Multilayer Swastika‐Shaped Metamaterial Solar Energy Absorber with Absorption Prediction using Machine Learning

Explainable artificial intelligence (XAI) in finance: a systematic literature review

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