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

共 687 条记录，以下是1-10 订阅

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

2025 IEEE Symposium on Computational Intelligence for Financ...

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

ISBN: (纸本)9798331508319

The proceedings contain 11 papers. The topics discussed include: novel financial network models using neuro correlations and applications;the superiority of direct neuro volatility forecasts over GARCH and machine learning forecasts for financial assets;innovative pattern extraction and synthetic high-frequency data generation in European carbon emission markets using GAN networks;robust European call option pricing via linear regression;simulating illiquid markets: insights from fractional ownership trading and agent-based models;enhancing forecasting with a 2D time series approach for cohort-based data;stock prediction by signal decomposition-driven multivariate feature extractor and executor-based mixture of experts;a deep ensemble learning approach for imbalanced data in bankruptcy prediction;and leveraging large language models and retrieval-augmented generation for enhanced multi-asset portfolio construction.

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2024 ieee symposium on computational intelligence for financial engineering and economics, CIFEr 2024

2024 IEEE Symposium on Computational Intelligence for Financ...

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2024 ieee symposium on computational intelligence for financial engineering and economics, CIFEr 2024

ISBN: (纸本)9798350354836

The proceedings contain 31 papers. The topics discussed include: FedPNN: one-shot federated classifier to predict credit card fraud and bankruptcy in banks;attribution methods in asset pricing: do they account for risk?;financial risk disclosure return premium: a topic modeling approach;generalized groves of neural additive models: pursuing transparent machine learning models in finance;JaFIn: Japanese financial instruction dataset;Fed-RD: privacy-preserving federated learning for financial crime detection;evidential reasoning in the calculation of individual injury claims reserve;and towards enhanced information access in finance: a dataset for table structure understanding in annual securities reports.

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2022 ieee symposium on computational intelligence for financial engineering and economics, CIFEr 2022

2022 IEEE Symposium on Computational Intelligence for Financ...

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2022 ieee symposium on computational intelligence for financial engineering and economics, CIFEr 2022

ISBN: (纸本)9781665442343

The proceedings contain 26 papers. The topics discussed include: a deep learning-based high-order operator splitting method for high-dimensional nonlinear parabolic PDEs via Malliavin calculus: application to cva computation;a performance study of multiobjective particle swarm optimization algorithms for market timing;an empirical comparison of cross-validation procedures for portfolio selection;applying sentiment analysis, topic modeling, and XGBoost to classify implied volatility;balancing profit, risk, and sustainability for portfolio management;comparison of fuzzy risk forecast intervals for cryptocurrencies;concept and practice of artificial market data mining platform;construction of real-time manufacturing industry production activity estimation models using high-frequency electricity demand data;customized stock return prediction with deep learning;high-dimensional stock portfolio trading with deep reinforcement learning;impact of false information from spoofing strategies: an ABM model of market dynamics;and information retrieval from alternative data using zero-shot self-supervised learning.

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Fed-RD: Privacy-Preserving Federated Learning for financial Crime Detection

Fed-RD: Privacy-Preserving Federated Learning for Financial ...

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2024 Conference on computational intelligence for financial engineering and economics

作者： Khan, Md Saikat Islam Gupta, Aparna Seneviratne, Oshani Patterson, Stacy Rensselaer Polytech Inst Dept Comp Sci Troy NY 12180 USA Rensselaer Polytech Inst Lally Sch Management Troy NY USA

ISBN: (纸本)9798350354843;9798350354836

We introduce Federated Learning for Relational Data (Fed-RD), a novel privacy-preserving federated learning algorithm specifically developed for financial transaction datasets partitioned vertically and horizontally across parties. Fed-RD strategically employs differential privacy and secure multiparty computation to guarantee the privacy of training data. We provide theoretical analysis of the end-to-end privacy of the training algorithm and present experimental results on realistic synthetic datasets. Our results demonstrate that Fed-RD achieves high model accuracy with minimal degradation as privacy increases, while consistently surpassing benchmark results.

关键词： Anomalous transactions financial fraud federated learning data partition privacy analysis

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Large Language Model in financial Regulatory Interpretation

Large Language Model in Financial Regulatory Interpretation

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2024 Conference on computational intelligence for financial engineering and economics

作者： Cao, Zhiyu Feinstein, Zachary Stevens Inst Technol Sch Business Hoboken NJ 07030 USA

ISBN: (纸本)9798350354843;9798350354836

This study explores the innovative use of Large Language Models (LLMs) as analytical tools for interpreting complex financial regulations. The primary objective is to design effective prompts that guide LLMs in distilling verbose and intricate regulatory texts, such as the Basel III capital requirement regulations, into a concise mathematical framework that can be subsequently translated into actionable code. This novel approach aims to streamline the implementation of regulatory mandates within the financial reporting and risk management systems of global banking institutions. A case study was conducted to assess the performance of various LLMs, demonstrating that GPT-4 outperforms other models in processing and collecting necessary information, as well as executing mathematical calculations. The case study utilized numerical simulations with asset holdings including fixed income, equities, currency pairs, and commodities - to demonstrate how LLMs can effectively implement the Basel III capital adequacy requirements.

关键词： Large Language Models Prompt engineering LLMs in Finance Basel III Minimum Capital Requirements LLM Ethics

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financial Risk Disclosure Return Premium: A Topic Modeling Approach

Financial Risk Disclosure Return Premium: A Topic Modeling A...

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2024 Conference on computational intelligence for financial engineering and economics

作者： Zhang, Beichen Yang, Steve Stevens Inst Technol Sch Business Hoboken NJ 07030 USA

ISBN: (纸本)9798350354843;9798350354836

We examine the risk factors disclosed in the 10K financial statement section 1A across 9 years with over 500 hundred companies. We propose a financial disclosure risk factor to extend the Fama-French 3 factor model and Fama-MacBeth cross-section regression. Using the risk factors data from 2015 to 2023, we find the average risk-return premium across nine sectors is significant after controlling for other risk factors from the Fama-French 3-factor model. The premium is measured by monthly return series on risky-minus-less risky stocks or by the coefficient of stock risk factor estimated from cross-section Fama-MacBeth regressions. These text risk factors can potentially be used to construct portfolios that can generate significant returns across different sectors.

关键词： risk factors financial statement Fama-French 3 factor model Fama-McBeth cross-section regression

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JaFIn: Japanese financial Instruction Dataset

JaFIn: Japanese Financial Instruction Dataset

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2024 Conference on computational intelligence for financial engineering and economics

作者： Tanabe, Kota Suzuki, Masahiro Sakaji, Hiroki Noda, Itsuki Hokkaido Univ Fac Informat Sci & Technol Sapporo Hokkaido Japan Univ Tokyo Sch Engn Tokyo Japan

ISBN: (纸本)9798350354843;9798350354836

We construct an instruction dataset for the large language model (LLM) in the Japanese finance domain. Domain adaptation of language models, including LLMs, is receiving more attention as language models become more popular. This study demonstrates the effectiveness of domain adaptation through instruction tuning. To achieve this, we propose an instruction tuning data in Japanese called JaFIn, the Japanese financial Instruction Dataset. JaFIn is manually constructed based on multiple data sources, including Japanese government websites, which provide extensive financial knowledge. We then utilize JaFIn to apply instruction tuning for several LLMs, demonstrating that our models specialized in finance have better domain adaptability than the original models. The financial-specialized LLMs created were evaluated using a quantitative Japanese financial benchmark and qualitative response comparisons, showing improved performance over the originals.

关键词： financial Natural Language Processing Large Language Model Instruction Dataset Instruction Tuning Domain Adaptation

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financial Semantic Textual Similarity: A New Dataset and Model

Financial Semantic Textual Similarity: A New Dataset and Mod...

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2024 Conference on computational intelligence for financial engineering and economics

作者： Yang, Shanshan Yang, Steve Mai, Feng Stevens Inst Technol Sch Business Hoboken NJ 07030 USA Univ Iowa Dept Business Analyt Iowa City IA USA

ISBN: (纸本)9798350354843;9798350354836

We introduce FinSTS, a novel dataset for financial semantic textual similarity (STS), comprising 4,000 sentence pairs from earnings calls and SEC filings. To improve models for the financial STS task, we propose an active learning (AL) algorithm that efficiently selects informative sentence pairs for annotation by GPT-4 and creates high-quality training data. Using this approach, we train FinSentenceBERT, a model that generates semantic embeddings specifically for financial text. FinSentenceBERT establishes a new performance benchmark on FinSTS, outperforming models that use basic pooling strategies or are fine-tuned on general datasets. Surprisingly, a general SBERT model trained using our AL approach surpasses even models based on FinBERT, a language model pre-trained on financial text. Our research contributes a specialized dataset, model, and methodology that advance semantic understanding in the financial domain, with potential applications to other specialized domains.

关键词： BERT Representation learning Active learning Text similarity

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Experimental Analysis of Deep Hedging Using Artificial Market Simulations for Underlying Asset Simulators

Experimental Analysis of Deep Hedging Using Artificial Marke...

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2024 Conference on computational intelligence for financial engineering and economics

作者： Hirano, Masanori Preferred Networks Inc Tokyo Japan

ISBN: (纸本)9798350354843;9798350354836

Derivative hedging and pricing are important and continuously studied topics in financial markets. Recently, deep hedging has been proposed as a promising approach that uses deep learning to approximate the optimal hedging strategy and can handle incomplete markets. However, deep hedging usually requires underlying asset simulations, and it is challenging to select the best model for such simulations. This study proposes a new approach using artificial market simulations for underlying asset simulations in deep hedging. Artificial market simulations can replicate the stylized facts of financial markets, and they seem to be a promising approach for deep hedging. We investigate the effectiveness of the proposed approach by comparing its results with those of the traditional approach, which uses mathematical finance models such as Brownian motion and Heston models for underlying asset simulations. The results show that the proposed approach can achieve almost the same level of performance as the traditional approach without mathematical finance models. Finally, we also reveal that the proposed approach has some limitations in terms of performance under certain conditions.

关键词： deep hedging artificial market simulation financial markets derivative hedging

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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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2024 Conference on computational intelligence for financial engineering and economics

作者： Vivek, Yelleti Ravi, Vadlamani Mane, Abhay Naidu, Laveti Ramesh Inst Dev & Res Banking Hyderabad Telangana India Ctr Dev Adv Comp Bangalore Karnataka India

ISBN: (纸本)9798350354843;9798350354836

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 oversampling 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： Fraud detection XAI Causal Inference SMOTE GAN

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