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ORCIDCIFEr 2024: Hoboken, NJ, USA
- IEEE Symposium on Computational Intelligence for Financial Engineering and Economics, CIFEr 2024, Hoboken, NJ, USA, October 22-23, 2024. IEEE 2024, ISBN 979-8-3503-5483-6
- Leonardo Conegundes Martinez, Adriano C. M. Pereira:
A Methodology for Developing Deep Reinforcement Learning Trading Strategies: A Case Study in the Futures Market. 1-10 - Xinpeng Long, Michael Kampouridis:
α-dominance two-objective Optimization Genetic Programming for Algorithmic Trading under a Directional Changes Environment. 1-8 - Kota Tanabe, Masahiro Suzuki
, Hiroki Sakaji, Itsuki Noda:
JaFIn: Japanese Financial Instruction Dataset. 1-10 - Md. Saikat Islam Khan, Aparna Gupta, Oshani Seneviratne, Stacy Patterson:
Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection. 1-9 - Sasan Jafarnejad, François Robinet, Raphaël Frank:
A Risk-Based AML Framework: Finding Associates Through Ultimate Beneficial Owners. 1-7 - Beichen Zhang, Steve Yang:
Financial Risk Disclosure Return Premium: A Topic Modeling Approach. 1-6 - Mohamed Lashuel, Gulrukh Kurdistan, Aaron Green, John S. Erickson, Oshani Seneviratne, Kristin P. Bennett:
LLM - Based Code Generation for Querying Temporal Tabular Financial Data. 1-8 - Takanobu Mizuta, Isao Yagi:
An Interaction Between a Leveraged ETF and Futures in a Crash Investigated by an Agent-Based Model. 1-10 - Shanshan Yang, Steve Yang, Feng Mai:
Financial Semantic Textual Similarity: A New Dataset and Model. 1-8 - Karim Derrick, Xi Liu:
Evidential Reasoning in the Calculation of Individual Injury Claims Reserve. 1-8 - Kazuma Kadowaki, Yasutomo Kimura, Hokuto Ototake:
Towards Enhanced Information Access in Finance: A Dataset for Table Structure Understanding in Annual Securities Reports. 1-6 - Yuanyuan Liu, Yongxin Yang:
Leveraging Stochastic Optimization in Asset Allocation for Enhanced Index Tracking. 1-8 - Dangxing Chen, Weicheng Ye:
Generalized Groves of Neural Additive Models: Pursuing Transparent Machine Learning Models in Finance. 1-8 - Umur Cetin, Andreea Minca:
Model Based Simulation vs Generative Modeling in Limit Order Books. 1-6 - Yelleti Vivek, Vadlamani Ravi, Abhay Anand Mane, Laveti Ramesh Naidu:
Explainable Artificial Intelligence and Causal Inference Based ATM Fraud Detection. 1-7 - Tomoki Ito, Shun Nakagawa:
Analysis of Important Phrases in Bidding Documents Using Table Encoder Joint Extraction. 1-7 - Polaki Durga Prasad, Yelleti Vivek, Vadlamani Ravi:
FedPNN: One-Shot Federated Classifier to Predict Credit Card Fraud and Bankruptcy in Banks. 1-8 - Dimitrios Vamvourellis, Máté Tóth, Snigdha Bhagat, Dhruv Desai, Dhagash Mehta, Stefano Pasquali:
Company Similarity Using Large Language Models. 1-9 - James Lewis-Cheetham, Yuhua Li, Federico Liberatore, Qingwei Wang:
The Impact of Transaction Costs on Forecast-Based Trading Strategy Performance. 1-8 - Maksim Kazadaev, Vitaliy Pozdnyakov, Ilya Makarov:
Time Series Generation with GANs for Momentum Effect Simulation on Moscow Stock Exchange. 1-7 - Dangxing Chen, Yuan Gao:
Attribution Methods in Asset Pricing: Do They Account for Risk? 1-8 - Zhiyu Cao, Zachary Feinstein:
Large Language Model in Financial Regulatory Interpretation. 1-7 - Bolun Namir Xia, Aparna Gupta, Mohammed J. Zaki:
Semantic Graph Learning for Trend Prediction from Long Financial Documents. 1-8 - Rui Fan, Cindy S. H. Wang, Yimeng Xie:
Optimal Timing for Portfolio Adjustment Using Aggregated Time Series Data. 1-8 - Yangyang Yu, Steve Y. Yang:
Modeling Investor Sentiment Jumps Using Deep Reinforcement Learning with a Hawkes Cross-Excitation Modeling Approach. 1-8 - Leandro Maciel, Rosangela Ballini, Fernando A. C. Gomide:
Adaptive Fuzzy Modeling and Forecasting of Financial Time Series. 1-7 - Masanori Hirano:
Experimental Analysis of Deep Hedging Using Artificial Market Simulations for Underlying Asset Simulators. 1-8 - Maruf Ahmed Mridul, Kaiyang Chang, Aparna Gupta, Oshani Seneviratne:
Smart Contracts, Smarter Payments: Innovating Cross Border Payments and Reporting Transactions. 1-8 - Bhaskarjit Sarmah, Nayana Nair, Riya Jain, Dhagash Mehta, Stefano Pasquali:
Learning Embedded Representation of the Stock Correlation Matrix Using Graph Machine Learning. 1-9 - Frederic Voigt, José M. Alcaraz Calero, Keshav P. Dahal, Qi Wang, Kai von Luck, Peer Stelldinger:
Quantitative Market Situation Embeddings: Utilizing Doc2Vec Strategies for Stock Data. 1-10 - Kentaro Hoshisashi, Carolyn E. Phelan, Paolo Barucca:
Whack-a-mole Learning: Physics- Informed Deep Calibration for Implied Volatility Surface. 1-8
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