Papers Stock Prediction
“Stock Prediction” 태그가 달린 논문 102편 · 필터 해제
FINN-GL: Generalized Mixed-Precision Extensions for FPGA-Accelerated LSTMs
Recurrent neural networks (RNNs), particularly LSTMs, are effective for time-series tasks like sentiment analysis and short-term stock prediction. However, their computational complexity poses challenges for real-time de…
Sentiment AnalysisStock PredictionDeep Learning Enhanced Multi-Day Turnover Quantitative Trading Algorithm for Chinese A-Share Market
This paper presents a sophisticated multi-day turnover quantitative trading algorithm that integrates advanced deep learning techniques with comprehensive cross-sectional stock prediction for the Chinese A-share market. …
Stock PredictionExAnte: A Benchmark for Ex-Ante Inference in Large Language Models
Large language models (LLMs) face significant challenges in ex-ante reasoning, where analysis, inference, or predictions must be made without access to information from future events. Even with explicit prompts enforcing…
PredictionQuestion AnsweringStock PredictionYour Offline Policy is Not Trustworthy: Bilevel Reinforcement Learning for Sequential Portfolio Optimization
Reinforcement learning (RL) has shown significant promise for sequential portfolio optimization tasks, such as stock trading, where the objective is to maximize cumulative returns while minimizing risks using historical …
Offline RLPortfolio OptimizationReinforcement Learning (RL)Stock PredictionEvaluating Financial Sentiment Analysis with Annotators Instruction Assisted Prompting: Enhancing Contextual Interpretation and Stock Prediction Accuracy
Financial sentiment analysis (FSA) presents unique challenges to LLMs that surpass those in typical sentiment analysis due to the nuanced language used in financial contexts. The prowess of these models is often undermin…
BenchmarkingSentiment AnalysisStock PredictionStock Price PredictionTransformer Encoder and Multi-features Time2Vec for Financial Prediction
Financial prediction is a complex and challenging task of time series analysis and signal processing, expected to model both short-term fluctuations and long-term temporal dependencies. Transformers have remarkable succe…
feature selectionStock PredictionTime SeriesTime Series AnalysisA Causal Perspective of Stock Prediction Models
In the realm of stock prediction, machine learning models encounter considerable obstacles due to the inherent low signal-to-noise ratio and the nonstationary nature of financial markets. These challenges often result in…
Causal DiscoveryDomain GeneralizationEconometricsPrediction+2Large language models in finance : what is financial sentiment?
Financial sentiment has become a crucial yet complex concept in finance, increasingly used in market forecasting and investment strategies. Despite its growing importance, there remains a need to define and understand wh…
Algorithmic TradingDecision MakingSentiment AnalysisSentiment Classification+2Multi-Agent Stock Prediction Systems: Machine Learning Models, Simulations, and Real-Time Trading Strategies
This paper presents a comprehensive study on stock price prediction, leveragingadvanced machine learning (ML) and deep learning (DL) techniques to improve financial forecasting accuracy. The research evaluates the perfor…
Stock PredictionStock Price PredictionA Distillation-based Future-aware Graph Neural Network for Stock Trend Prediction
Stock trend prediction involves forecasting the future price movements by analyzing historical data and various market indicators. With the advancement of machine learning, graph neural networks (GNNs) have been extensiv…
Graph Neural NetworkPredictionStock PredictionStock Trend PredictionChinese Stock Prediction Based on a Multi-Modal Transformer Framework: Macro-Micro Information Fusion
This paper proposes an innovative Multi-Modal Transformer framework (MMF-Trans) designed to significantly improve the prediction accuracy of the Chinese stock market by integrating multi-source heterogeneous information …
Graph AttentionStock PredictionStock Type Prediction Model Based on Hierarchical Graph Neural Network
This paper introduces a novel approach to stock data analysis by employing a Hierarchical Graph Neural Network (HGNN) model that captures multi-level information and relational structures in the stock market. The HGNN mo…
Graph Neural NetworkStock PredictionType predictionGRU-PFG: Extract Inter-Stock Correlation from Stock Factors with Graph Neural Network
The complexity of stocks and industries presents challenges for stock prediction. Currently, stock prediction models can be divided into two categories. One category, represented by GRU and ALSTM, relies solely on stock …
Graph Neural NetworkPredictionStock PredictionA Stock Price Prediction Approach Based on Time Series Decomposition and Multi-Scale CNN using OHLCT Images
Recently, deep learning in stock prediction has become an important branch. Image-based methods show potential by capturing complex visual patterns and spatial correlations, offering advantages in interpretability over t…
Stock PredictionStock Price PredictionStock Trend PredictionTime SeriesLeveraging Fundamental Analysis for Stock Trend Prediction for Profit
This paper investigates the application of machine learning models, Long Short-Term Memory (LSTM), one-dimensional Convolutional Neural Networks (1D CNN), and Logistic Regression (LR), for predicting stock trends based o…
Decision MakingPredictionSentiment AnalysisStock Prediction+1FLAG: Financial Long Document Classification via AMR-based GNN
The advent of large language models (LLMs) has initiated much research into their various financial applications. However, in applying LLMs on long documents, semantic relations are not explicitly incorporated, and a ful…
Abstract Meaning RepresentationDocument ClassificationStock PredictionStock Trend Prediction+3MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU
As financial markets grow increasingly complex in the big data era, accurate stock prediction has become more critical. Traditional time series models, such as GRUs, have been widely used but often struggle to capture th…
PredictionStock PredictionDouble-Path Adaptive-correlation Spatial-Temporal Inverted Transformer for Stock Time Series Forecasting
Spatial-temporal graph neural networks (STGNNs) have achieved significant success in various time series forecasting tasks. However, due to the lack of explicit and fixed spatial relationships in stock prediction tasks, …
Stock PredictionTime SeriesTime Series ForecastingEnhancing Few-Shot Stock Trend Prediction with Large Language Models
The goal of stock trend prediction is to forecast future market movements for informed investment decisions. Existing methods mostly focus on predicting stock trends with supervised models trained on extensive annotated …
DenoisingPredictionStock PredictionStock Trend PredictionStockGPT: A GenAI Model for Stock Prediction and Trading
This paper introduces StockGPT, an autoregressive ``number'' model trained and tested on 70 million daily U.S.\ stock returns over nearly 100 years. Treating each return series as a sequence of tokens, StockGPT automatic…
Stock Prediction