Stock Trend Prediction
10 papers with code • 1 benchmarks • 2 datasets
Latest papers with no code
Microstructure-Empowered Stock Factor Extraction and Utilization
To address these challenges, we propose a novel framework that aims to effectively extract essential factors from order flow data for diverse downstream tasks across different granularities and scenarios.
Support for Stock Trend Prediction Using Transformers and Sentiment Analysis
However, due to the limitations of RNNs, such as gradient vanish and long-term dependencies being lost as sequence length increases, in this paper we develop a Transformer based model that uses technical stock data and sentiment analysis to conduct accurate stock trend prediction over long time windows.
Stock Trend Prediction: A Semantic Segmentation Approach
However, semantic segmentation and its well-designed fully convolutional networks have never been studied for time-series dense classification.
Factor Investing with a Deep Multi-Factor Model
Modeling and characterizing multiple factors is perhaps the most important step in achieving excess returns over market benchmarks.
Dynamic Inference
How the optimal estimation strategy works is illustrated through two examples, stock trend prediction and vehicle behavior prediction.
FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance
Unfortunately, the steep learning curve and the difficulty in quick modeling and agile development are impeding finance researchers from using deep reinforcement learning in quantitative trading.
Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network
However, most of these models have two common drawbacks, including (i) current methods are not sensitive enough to abrupt changes of stock trend, and (ii) forecasting results are not interpretable for humans.
Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies
Stock trend prediction is a challenging task due to the market's noise, and machine learning techniques have recently been successful in coping with this challenge.