Stock Price Prediction
26 papers with code • 1 benchmarks • 2 datasets
Stock Price Prediction is the task of forecasting future stock prices based on historical data and various market indicators. It involves using statistical models and machine learning algorithms to analyze financial data and make predictions about the future performance of a stock. The goal of stock price prediction is to help investors make informed investment decisions by providing a forecast of future stock prices.
Astock
Latest papers with no code
BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights
This paper explores the intersection of Natural Language Processing (NLP) and financial analysis, focusing on the impact of sentiment analysis in stock price prediction.
Tweet Influence on Market Trends: Analyzing the Impact of Social Media Sentiment on Biotech Stocks
This study investigates the relationship between tweet sentiment across diverse categories: news, company opinions, CEO opinions, competitor opinions, and stock market behavior in the biotechnology sector, with a focus on understanding the impact of social media discourse on investor sentiment and decision-making processes.
Natural Language Processing and Multimodal Stock Price Prediction
In the realm of financial decision-making, predicting stock prices is pivotal.
Fine-tuning and Utilization Methods of Domain-specific LLMs
In the practical application of LLM fine-tuning, the study outlines the procedure and implementation for generating domain-specific LLMs in finance.
ResNLS: An Improved Model for Stock Price Forecasting
Stock prices forecasting has always been a challenging task.
Boosting Stock Price Prediction with Anticipated Macro Policy Changes
Results from this strongly support the inclusion of future economic policy changes along with current macroeconomic information.
Feature selection and regression methods for stock price prediction using technical indicators
The RISF method also improved 72. 82 % of Ridge regression.
Generalized Mixture Model for Extreme Events Forecasting in Time Series Data
Specifically, we propose a Deep Extreme Mixture Model with Autoencoder (DEMMA) for time series prediction.
A Generalization Bound of Deep Neural Networks for Dependent Data
Existing generalization bounds for deep neural networks require data to be independent and identically distributed (iid).
The Potential of Quantum Techniques for Stock Price Prediction
Through these experimental simulations, we shed light on the potential advantages and limitations of Quantum Algorithms in stock price prediction and contribute to the growing body of knowledge at the intersection of Quantum Computing and Finance.
