Stock Prediction
26 papers with code • 0 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Stock Prediction
StockNet
Latest papers with no code
Stock Index Prediction with Multi-task Learning and Word Polarity Over Time
We adopt BERT with multitask learning which additionally predicts the worthiness of the news and propose a metric called Polarity-Over-Time to extract the word polarity among different event periods.
Online Prediction With History-Dependent Experts: The General Case
We consider the problem with history-dependent experts, in which each expert uses the previous $d$ days of history of the market in making their predictions.
A PDE Approach to the Prediction of a Binary Sequence with Advice from Two History-Dependent Experts
Compared to other recent applications of partial differential equations to prediction, ours has a new element: there are two timescales, since the recent history changes at every step whereas regret accumulates more slowly.
A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News
This paper proposes to use sentiment analysis to extract useful information from multiple textual data sources and a blending ensemble deep learning model to predict future stock movement.
Experimental evaluation of quantum Bayesian networks on IBM QX hardware
Bayesian Networks (BN) are probabilistic graphical models that are widely used for uncertainty modeling, stochastic prediction and probabilistic inference.
News-Driven Stock Prediction With Attention-Based Noisy Recurrent State Transition
Thanks to the use of attention over news events, our model is also more explainable.
Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction
To address this challenge, we propose the second-order identical distribution assumption, where the data distribution is assumed to be fluctuating over time with certain patterns.
From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations
We present a method to automatically identify financially relevant news using stock price movements and news headlines as input.
Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction
In this paper, we propose a novel deep neural network Mid-LSTM for midterm stock prediction, which incorporates the market trend as hidden states.
Neural Network Models for Stock Selection Based on Fundamental Analysis
Application of neural network architectures for financial prediction has been actively studied in recent years.
