Advancing Financial Risk Prediction Through Optimized LSTM Model Performance and Comparative Analysis

• URL: http://arxiv.org/abs/2405.20603v1
• Date: Fri, 31 May 2024 03:31:17 GMT
• Title: Advancing Financial Risk Prediction Through Optimized LSTM Model Performance and Comparative Analysis
• Authors: Ke Xu, Yu Cheng, Shiqing Long, Junjie Guo, Jue Xiao, Mengfang Sun,
• Abstract summary: This paper focuses on the application and optimization of LSTM model in financial risk prediction. The optimized LSTM model shows significant advantages in AUC index compared with random forest, BP neural network and XGBoost.
• Score: 12.575399233846092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper focuses on the application and optimization of LSTM model in financial risk prediction. The study starts with an overview of the architecture and algorithm foundation of LSTM, and then details the model training process and hyperparameter tuning strategy, and adjusts network parameters through experiments to improve performance. Comparative experiments show that the optimized LSTM model shows significant advantages in AUC index compared with random forest, BP neural network and XGBoost, which verifies its efficiency and practicability in the field of financial risk prediction, especially its ability to deal with complex time series data, which lays a solid foundation for the application of the model in the actual production environment.

Related papers

• EUR/USD Exchange Rate Forecasting incorporating Text Mining Based on Pre-trained Language Models and Deep Learning Methods [0.0]
  This study introduces a novel approach for EUR/USD exchange rate forecasting that integrates deep learning, textual analysis, and particle swarm optimization (PSO) By incorporating online news and analysis texts as qualitative data, the proposed PSO-LSTM model demonstrates superior performance compared to traditional econometric and machine learning models.
  arXiv  Detail & Related papers  (2024-11-12T05:28:52Z)
• Diffusion Models as Network Optimizers: Explorations and Analysis [71.69869025878856]
  generative diffusion models (GDMs) have emerged as a promising new approach to network optimization. In this study, we first explore the intrinsic characteristics of generative models. We provide a concise theoretical and intuitive demonstration of the advantages of generative models over discriminative network optimization.
  arXiv  Detail & Related papers  (2024-11-01T09:05:47Z)
• Optimization of Actuarial Neural Networks with Response Surface Methodology [0.0]
  This study utilizes a factorial design and response surface methodology (RSM) to optimize CANN performance. By dropping statistically insignificant hyper parameters, we reduced runs from 288 to 188, with negligible loss in accuracy, achieving near-optimal out-of-sample Poisson deviance loss.
  arXiv  Detail & Related papers  (2024-10-01T15:45:41Z)
• Deep Analysis of Time Series Data for Smart Grid Startup Strategies: A Transformer-LSTM-PSO Model Approach [0.8702432681310401]
  Transformer-LSTM-PSO model is designed to more effectively capture the complex temporal relationships in grid startup schemes. Model achieves lower RMSE and MAE values across multiple datasets compared to existing benchmarks. The application of the Transformer-LSTM-PSO model represents a significant advancement in smart grid predictive analytics.
  arXiv  Detail & Related papers  (2024-08-22T04:52:02Z)
• Embedded feature selection in LSTM networks with multi-objective evolutionary ensemble learning for time series forecasting [49.1574468325115]
  We present a novel feature selection method embedded in Long Short-Term Memory networks. Our approach optimize the weights and biases of the LSTM in a partitioned manner. Experimental evaluations on air quality time series data from Italy and southeast Spain demonstrate that our method substantially improves the ability generalization of conventional LSTMs.
  arXiv  Detail & Related papers  (2023-12-29T08:42:10Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• Latent Variable Representation for Reinforcement Learning [131.03944557979725]
  It remains unclear theoretically and empirically how latent variable models may facilitate learning, planning, and exploration to improve the sample efficiency of model-based reinforcement learning. We provide a representation view of the latent variable models for state-action value functions, which allows both tractable variational learning algorithm and effective implementation of the optimism/pessimism principle. In particular, we propose a computationally efficient planning algorithm with UCB exploration by incorporating kernel embeddings of latent variable models.
  arXiv  Detail & Related papers  (2022-12-17T00:26:31Z)
• LSTM based models stability in the context of Sentiment Analysis for social media [0.0]
  We present various LSTM models and their key parameters. We perform experiments to test the stability of these models in the context of Sentiment Analysis.
  arXiv  Detail & Related papers  (2022-11-21T08:31:30Z)
• When to Update Your Model: Constrained Model-based Reinforcement Learning [50.74369835934703]
  We propose a novel and general theoretical scheme for a non-decreasing performance guarantee of model-based RL (MBRL) Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns.
  arXiv  Detail & Related papers  (2022-10-15T17:57:43Z)
• On Effective Scheduling of Model-based Reinforcement Learning [53.027698625496015]
  We propose a framework named AutoMBPO to automatically schedule the real data ratio. In this paper, we first theoretically analyze the role of real data in policy training, which suggests that gradually increasing the ratio of real data yields better performance.
  arXiv  Detail & Related papers  (2021-11-16T15:24:59Z)
• Genetically Optimized Prediction of Remaining Useful Life [4.115847582689283]
  We implement LSTM and GRU models and compare the obtained results with a proposed genetically trained neural network. We hope to improve the consistency of the predictions by adding another layer of optimization using Genetic Algorithms. These models and the proposed architecture are tested on the NASA Turbofan Jet Engine dataset.
  arXiv  Detail & Related papers  (2021-02-17T16:09:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.