RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction

• URL: http://arxiv.org/abs/2402.10760v1
• Date: Fri, 16 Feb 2024 15:34:07 GMT
• Title: RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction
• Authors: Jingyi Gu, Wenlu Du, Guiling Wang
• Abstract summary: Many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making. We propose RAGIC, which introduces sequence generation for stock interval prediction to quantify uncertainty more effectively. RAGIC's generator includes a risk module, capturing the risk perception of informed investors, and a temporal module, accounting for historical price trends and seasonality.
• Score: 4.059196561157555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efforts to predict stock market outcomes have yielded limited success due to the inherently stochastic nature of the market, influenced by numerous unpredictable factors. Many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making and often overlooking market risk. To bridge this gap, we propose a novel model, RAGIC, which introduces sequence generation for stock interval prediction to quantify uncertainty more effectively. Our approach leverages a Generative Adversarial Network (GAN) to produce future price sequences infused with randomness inherent in financial markets. RAGIC's generator includes a risk module, capturing the risk perception of informed investors, and a temporal module, accounting for historical price trends and seasonality. This multi-faceted generator informs the creation of risk-sensitive intervals through statistical inference, incorporating horizon-wise insights. The interval's width is carefully adjusted to reflect market volatility. Importantly, our approach relies solely on publicly available data and incurs only low computational overhead. RAGIC's evaluation across globally recognized broad-based indices demonstrates its balanced performance, offering both accuracy and informativeness. Achieving a consistent 95% coverage, RAGIC maintains a narrow interval width. This promising outcome suggests that our approach effectively addresses the challenges of stock market prediction while incorporating vital risk considerations.

Related papers

• Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework [77.45983464131977]
  We focus on how likely it is that a RAG model's prediction is incorrect, resulting in uncontrollable risks in real-world applications. Our research identifies two critical latent factors affecting RAG's confidence in its predictions. We develop a counterfactual prompting framework that induces the models to alter these factors and analyzes the effect on their answers.
  arXiv  Detail & Related papers  (2024-09-24T14:52:14Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network [2.1163070161951865]
  We propose IndexGAN, which includes deliberate designs for the inherent characteristics of the stock market. We also utilize the critic to approximate the Wasserstein distance between actual and predicted sequences.
  arXiv  Detail & Related papers  (2023-02-27T21:56:56Z)
• Adaptive Risk-Aware Bidding with Budget Constraint in Display Advertising [47.14651340748015]
  We propose a novel adaptive risk-aware bidding algorithm with budget constraint via reinforcement learning. We theoretically unveil the intrinsic relation between the uncertainty and the risk tendency based on value at risk (VaR)
  arXiv  Detail & Related papers  (2022-12-06T18:50:09Z)
• RAP: Risk-Aware Prediction for Robust Planning [21.83865866611308]
  We introduce a new prediction objective to learn a risk-biased distribution over trajectories. This reduces the sample complexity of the risk estimation during online planning.
  arXiv  Detail & Related papers  (2022-10-04T04:19:15Z)
• Probable Domain Generalization via Quantile Risk Minimization [90.15831047587302]
  Domain generalization seeks predictors which perform well on unseen test distributions. We propose a new probabilistic framework for DG where the goal is to learn predictors that perform well with high probability.
  arXiv  Detail & Related papers  (2022-07-20T14:41:09Z)
• Price Interpretability of Prediction Markets: A Convergence Analysis [6.623653707600985]
  We show that the limiting price converges to the geometric mean of agent beliefs in exponential utility-based markets. We also show that the limiting price can be characterized by systems of equations that encapsulate agent beliefs, risk parameters, and wealth.
  arXiv  Detail & Related papers  (2022-05-18T13:15:11Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Quantifying Uncertainty in Deep Spatiotemporal Forecasting [67.77102283276409]
  We describe two types of forecasting problems: regular grid-based and graph-based. We analyze UQ methods from both the Bayesian and the frequentist point view, casting in a unified framework via statistical decision theory. Through extensive experiments on real-world road network traffic, epidemics, and air quality forecasting tasks, we reveal the statistical computational trade-offs for different UQ methods.
  arXiv  Detail & Related papers  (2021-05-25T14:35:46Z)
• Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks [0.0]
  This article explores the use of machine learning models to build a market generator. The underlying idea is to simulate artificial multi-dimensional financial time series, whose statistical properties are the same as those observed in the financial markets. The article proposes then a new approach for estimating the probability distribution of backtest statistics.
  arXiv  Detail & Related papers  (2020-07-09T14:37:45Z)

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.