Stock Trend Prediction

10 papers with code • 1 benchmarks • 2 datasets

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Latest papers with no code

Microstructure-Empowered Stock Factor Extraction and Utilization

no code yet • 16 Aug 2023

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

no code yet • 18 May 2023

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

no code yet • 9 Mar 2023

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

no code yet • 22 Oct 2022

Modeling and characterizing multiple factors is perhaps the most important step in achieving excess returns over market benchmarks.

Dynamic Inference

no code yet • 29 Nov 2021

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

no code yet • 7 Nov 2021

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

no code yet • 13 May 2019

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

no code yet • 24 May 2018

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.