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检索条件"任意字段=IEEE Symposium on Computational Intelligence for Financial Engineering and Economics"

共 698 条记录，以下是71-80 订阅

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The Superiority of Direct Neuro Volatility Forecasts Over GARCH and Machine Learning Forecasts for financial Assets

The Superiority of Direct Neuro Volatility Forecasts Over GA...

引用

computational intelligence for financial engineering and economics Companion (CiFer Companion), ieee symposium on

作者： Sulalitha Bowala Aerambamoorthy Thavaneswaran Avanthi Saumyamala Joy Dip Das Ruppa Thulasiram Alex Paseka Department of Statistics University of Manitoba Winnipeg Manitoba Department of Computer Science University of Manitoba Winnipeg Manitoba Department of Accounting and Finance University of Manitoba Winnipeg Manitoba

ISBN: (数字)9798331519704

ISBN: (纸本)9798331519711

Traditional volatility forecasting often relies on generalized autoregressive conditional heteroskedasticity (GARCH) models. Moreover, conditional variance forecasts are first obtained, and then the volatility forecasts are obtained as the square root of conditional variance forecasts. However, these methods assume that log returns are either normally distributed or $t$ distributed and are less efficient for heavy-tailed $t$ distributions with excess kurtosis having infinite asymptotic variance. Unlike the existing work, the novelty of this paper is to use recently proposed data-driven neuro volatility models to obtain direct volatility forecasts utilizing neural network models (NNAR, RNN, LSTM, and GRU). The proposed approach eliminates the need to model conditional variance, thereby addressing inefficiencies in traditional methods. The forecast accuracies of single-layer, twolayer, and bidirectional neural networks are compared against GARCH-based models using the CBOE Volatility Index (VIX) and Bitcoin (BTC). The results demonstrate that the data-driven neuro volatility models significantly outperform the GARCH models.

关键词： Accuracy Biological system modeling computational modeling Machine learning Predictive models Kurtosis Indexes Forecasting Long short term memory Testing

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Modeling Investor Sentiment Jumps Using Deep Reinforcement Learning with a Hawkes Cross-Excitation Modeling Approach

Modeling Investor Sentiment Jumps Using Deep Reinforcement L...

引用

Proceedings of the ieee/IAFE computational intelligence for financial engineering (CIFEr)

作者： Yangyang Yu Steve Y. Yang School of Business Stevens Institute of Technology Hoboken USA

ISBN: (数字)9798350354836

ISBN: (纸本)9798350354843

News sentiment is different from the true investor sentiment, and there is a conductive process of information flow from news sentiment to the latent investor sentiment and vice versa. This study aims to develop a methodology to estimate the latent effect between the investor sentiment jumps and the market return jumps using a multivariate Hawkes process along with a deep reinforcement learning algorithm. We achieve this goal through a three-step process: (i) identify the baseline intensity among the events of news sentiment and market return by a multivariate Hawkes process; (ii) estimate the hidden effect that drives the movement of events of news sentiment and market return from the baseline intensity via deep reinforcement learning; (iii) reveal the interaction mechanism among the true investor sentiment and the market return that is responsible to the latent investor sentiment. This approach can be broadly applied to analyzing many phenomena in finance and economics where latent events are non-stationary and can not be observed directly.

关键词： economics Noise Finance Deep reinforcement learning Information filters computational intelligence

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Attribution Methods in Asset Pricing: Do They Account for Risk?

Attribution Methods in Asset Pricing: Do They Account for Ri...

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Proceedings of the ieee/IAFE computational intelligence for financial engineering (CIFEr)

作者： Dangxing Chen Yuan Gao Zu Chongzhi Center for Mathematics and Computational Sciences Duke Kunshan University Kunshan China Charlotte U.S.

ISBN: (数字)9798350354836

ISBN: (纸本)9798350354843

Over the past few decades, machine learning mod-els have been extremely successful. As a result of axiomatic attribution methods, feature contributions have been explained more clearly and rigorously. There are, however, few studies that have examined domain knowledge in conjunction with the axioms. In this study, we examine asset pricing in finance, a field closely related to risk management. Consequently, when applying machine learning models, we must ensure that the attribution methods reflect the underlying risks accurately. In this work, we present and study several axioms derived from asset pricing domain knowledge. It is shown that while Shapley value and Integrated Gradients preserve most axioms, neither can satisfy all axioms. Using extensive analytical and empirical examples, we demonstrate how attribution methods can reflect risks and when they should not be used.

关键词： economics Biological system modeling Finance Pricing Machine learning Risk management computational intelligence

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Generalized Groves of Neural Additive Models: Pursuing Transparent Machine Learning Models in Finance

Generalized Groves of Neural Additive Models: Pursuing Trans...

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Proceedings of the ieee/IAFE computational intelligence for financial engineering (CIFEr)

作者： Dangxing Chen Weicheng Ye Zu Chongzhi Center for Mathematics and Computational Sciences Duke Kunshan University Kunshan Jiangsu China

ISBN: (数字)9798350354836

ISBN: (纸本)9798350354843

While machine learning methods have significantly improved model performance over traditional methods, their black-box structure makes it difficult for researchers to interpret results. For highly regulated financial industries, model trans-parency is equally important to accuracy. Without understanding how models work, even highly accurate machine learning methods are unlikely to be accepted. We address this issue by introducing a novel class of transparent machine learning models known as generalized groves of neural additive models. The generalized groves of neural additive models separate features into three categories: linear features, individual nonlinear features, and interacted nonlinear features. Additionally, interactions in the last category are only local. A stepwise selection algorithm distinguishes the linear and nonlinear components, and interacted groups are carefully verified by applying additive separation criteria. Through some empirical examples in finance, we demon-strate that generalized grove of neural additive models exhibit high accuracy and transparency with predominantly linear terms and only sparse nonlinear ones.

关键词： economics Additives Accuracy Machine learning algorithms computational modeling Finance Closed box Machine learning financial industry computational intelligence

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Motion planning and modeling for isothermal parallel chemical reactions 17

Motion planning and modeling for isothermal parallel chemica...

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17th ieee International symposium on Applied computational intelligence and Informatics, SACI 2023

作者： Viragh, Eszter Drexler, Daniel Andras Kiss, Balint University Research and Innovation Center Óbuda University Physiological Controls Research Center Budapest Hungary Budapest University of Technology and Economics Department of Control Engineering and Information Technology Budapest Hungary

ISBN: (纸本)9798350321104

For several chemical reactions, the intermediate states of the system has to be known. They can be given, e.g., by sampling, measurements, or motion planning. By motion planning, we can determine the time function of the concentrations and the temperature, using the mathematical model of the system. From this, we can conclude the state of the system at every moment. We provide a motion planning method for isothermal parallel structural chemical reactions. We simulate the behavior of the three-step parallel reaction. The results of the motion planning can help to decide, if it is necessary to intervene from outside, thus we can ensure that the concentration is in a specified interval. © 2023 ieee.

关键词： Motion planning

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Identifying Representation Bias in Large Language Models Used in financial Sentiment Analysis

Identifying Representation Bias in Large Language Models Use...

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2025 ieee symposium on computational intelligence for financial engineering and economics, CiFer 2025

作者： Sabuncuoglu, Alpay Maple, Carsten The Alan Turing Insitute United Kingdom University of Warwick The Alan Turing Institute United Kingdom

ISBN: (纸本)9798331508319

financial sentiment analysis is the task of evaluating and quantifying the emotions and opinions expressed in financial news, reports, or social media to help investors and institutions make informed decisions. financial institutions have been actively exploring the use of large language models (LLMs) to analyse market sentiment signals for a more nuanced understanding of a broader context. However, issues such as the scale of training data, model complexity, and the potential for human oversight can introduce or even amplify bias in these systems. Representation bias is a common challenge for LLMs as training data fail to properly represent the target groups, hence causes harmful bias in general-purpose use. Therefore, replacing current solutions with LLMs in financial organisations requires a robust evaluation methodology to ensure fairness. This paper investigates a three-level bias evaluation approach that specifically focuses on representation bias and presents a baseline evaluation of the FinBERT model. Step 1 uses a synthetic dataset that explicitly reveals sources of bias, structured as probability- and embedding-based evaluation recipes. Step 2 evaluates the model against data released by another country (e.g. Indian News dataset) to assess its performance in relation to more implicit biases. Step 3 examines individual problematic samples using token-based interpretability methods (e.g. integrated gradients). This paper presents the application of this structured bias evaluation process and its results on the FinBERT model. The evaluation code and dataset are available on GitHub (https://***/asabuncuoglu13/faid-test-financial-sentiment-analysis). © 2025 ieee.

关键词： Investments

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Identifying Representation Bias in Large Language Models Used in financial Sentiment Analysis

Identifying Representation Bias in Large Language Models Use...

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computational intelligence for financial engineering and economics (CiFer), ieee symposium on

作者： Alpay Sabuncuoglu Carsten Maple The Alan Turing Insitute University of Warwick The Alan Turing Institute

ISBN: (数字)9798331508319

ISBN: (纸本)9798331508326

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Explainable Artificial intelligence and Causal Inference Based ATM Fraud Detection

Explainable Artificial Intelligence and Causal Inference Bas...

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Proceedings of the ieee/IAFE computational intelligence for financial engineering (CIFEr)

作者： Yelleti Vivek Vadlamani Ravi Abhay Mane Laveti Ramesh Naidu Institute for Development and Research in Banking Hyderabad Telangana India Center for Development of Advanced Computing Bangalore Karnataka India

ISBN: (数字)9798350354836

ISBN: (纸本)9798350354843

Gaining the empathy and trust of customers is paramount in the financial domain. However, the recurring occurrence of fraudulent activities undermines both of these factors. ATM fraud is a prevalent issue faced in today's banking landscape. The critical challenges in fraud datasets are highly imbalanced datasets, evolving fraud patterns, and lack of explainability. In this study, we handled these techniques on an ATM transaction dataset collected from India. In binary classification, we investigated the effectiveness of various over-sampling techniques, such as the Synthetic Minority Oversampling Technique (SMOTE) and its variants, Generative Adversarial Networks (GAN), to achieve oversampling. Gradient Boosting Tree (GBT), outperformed the rest of the techniques by achieving an AUC of 0.963, and Decision Tree (DT) stands second with an AUC of 0.958. In terms of complexity and interpretability, DT is the winner. Among the oversampling approaches, SMOTE and its variants performed better. We incorporated explainable artificial intelligence (XAI) and Causal Inference (CI) in the fraud detection framework and studied them via various analyses. Further, we provided managerial impact.

关键词： economics Explainable AI Banking Generative adversarial networks Boosting Fraud Complexity theory Decision trees computational intelligence

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Finite-Time Consensus of Discrete-Time Multi-Agent Systems 8

Finite-Time Consensus of Discrete-Time Multi-Agent Systems

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8th International symposium on Computer Science and Intelligent Control, ISCSIC 2024

作者： Liang, Zhihong Ding, Sanbo Hebei University of Technology School of Artificial Intelligence Control Engineering Technology Innovation Center of Hebei Province Tianjin China

ISBN: (纸本)9798350380286

This paper studies the finite-time consensus of discrete-time multi-agent systems (MASs). First, a regionconditional switching controller is developed to realize finitetime consensus problem of the discrete-time system. Under this scheme, the controller is divided into two modes of operation, and whether or not the controller makes switching depends on a predetermined region condition. The study of finite-time consensus can achieve consistency by reducing the consumption of communication and computational resources. Compared with the existing results, the scheme realizes the complete consensus of the discrete-time system. It improves control performance and reduces excessive consumption of control resources. Then, the concept of finite-time consensus is introduced in discrete-time MASs to describe the control performance by the settling time function. The finite-time consensus criterion is obtained by constructing the Lyapunov function, and the settling time is obtained by solving difference fractional and linear matrix inequality. Finally, the validity of the method is verified through numerical simulation. © 2024 ieee.

关键词： Discrete time control systems

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Machine Learning-Based Malicious URLs Detection 7th

Machine Learning-Based Malicious URLs Detection

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7th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2022

作者： Hublikar, Shivaraj Kalginkar, Adishree Shet, N.S.V. Department of Electronics and Communication Engineering National Institute of Technology Surathkal Karnataka Mangalore India School of Electronics and Communication KLE Technological University Hubballi Karnataka Dharwad India

ISBN: (纸本)9789811993039

Malicious URL’s also known as harmful website which is a serious and common thread to network security. World Wide Web (WWW) (Vanhoenshoven et al. in 2016 ieee symposium series on computational intelligence (SSCI). ieee, 2016 [1]), which encourages a wide range of illicit actions like financial fraud, malware distribution and e-commerce. It is of vital importance to discover and act on different ventures on time. Several procedures like blacklisting have been carried out to detect malicious Uniform Resource Locators (URLs) (Catak et al. in Malicious URL detection using machine learning. IGI Global, 2021 [2]). To advance the majority of harmful Uniform Resource Locator’s, different machine learning algorithms are executed in the modern years. Here, in this research paper, we are addressing malicious URLs detection as a problem of classification and also to understand the working and functioning of known machine learning classifiers, namely support vector machines (SVMs), K-nearest neighbors (KNNs), random forest (RF) and Naive Bayes (Vanhoenshoven et al. in 2016 ieee symposium series on computational intelligence (SSCI). ieee, 2016 [1]). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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