Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

A project using deep learning to forecast stock prices and covariance. Uses multiple threads to gather data and optimize portfolio using numerous APIs.

Stock markets are an essential component of the economy. Their prediction naturally arouses afascination in the academic and financial world. Indeed, financial time series, due to their widerange application fields, have seen numerous studies being published for their prediction. Some ofthese studies aim to establish whether there is a strong and predictive link between macroeconomicindicators and stock market trends and thus predict market returns. Stock market prediction howeverremains a challenging task due to uncertain noise. To what extent can macroeconomic indicatorsbe strong predictors of stock price ? Can they be used for stock trends modeling ? To answer thesequestions, we will focus on several time series forecasting models. We will on the one hand usestatistical time series models, more specifically the most commonly used time series approachesfor stock prediction : the Autoregressive Integrated Moving Average (ARIMA), the GeneralizedAutoregressive Conditional Heteroscedasticity (GARCH) and the Vector Autoregressive (VAR)approach. On the other hand, we will be using two deep learning models : the Long-Short TermMemory (LSTM) and the Gated Recurrent Unit (GRU) for our prediction task. In the final section ofthis paper, we look directly at companies to detect trends

Google Stock Price Prediction using Long Short-Term Memory (LSTM) is a deep learning-based approach to forecasting stock prices using historical data. LSTM is a type of recurrent neural network (RNN) that is well-suited for sequential data like stock prices

Predict future stock prices using a pre-trained LSTM deep learning model. Upload a CSV file with historical stock data or use a sample to visualize trends and forecast closing prices.

"Exploring the Dynamics of Stock Price Prediction: Harnessing the power of LSTM neural networks, this project demonstrates the application of deep learning techniques to forecast Apple's stock prices using historical data from Yahoo Finance."

Analysis of market trend using Deep Learning is project that forecasts stock prices using historical data and ML models. Leveraging data collection, feature engineering, and model training. Primarily designed for the Indian stock market, it is adaptable for international markets, providing valuable insights for investors and analysts.

A deep learning-based stock price prediction model using LSTM and GRU networks. It uses historical stock data to forecast future prices, focusing on time-series analysis and evaluation metrics like RMSE and MAE to assess model accuracy.

Stock close price forecasting using deep learning (TensorFlow)

A hybrid deep learning approach for stock price prediction using CNN and LSTM models, leveraging multi-scale historical data to improve forecast accuracy.

forecast stock prices using advanced deep learning models such as Transformers, LSTMs, and TCNs, comparing their performance against traditional models like ARIMA and SARIMA.

A Flask-based web app that predicts future stock prices using an LSTM deep learning model. Users can register, log in, and view visual forecasts, comparisons, and analytics on stock trends. Includes admin dashboard, feedback system, and live data from Yahoo Finance.

📈 Predict and forecast Apple stock prices using a Stacked LSTM model for accurate stock market insights and decision-making.

Amazon stock prices using a Long Short-Term Memory (LSTM) neural network implemented with PyTorch. This project leverages deep learning techniques for accurate time series forecasting.

Stock Price Prediction and Time Series Forecasting using Deep Learning And Ensemble methods.

S&P 500 Stock Price Forecast Using Machine Learning and Deep Learning

Leverages Long Short-Term Memory (LSTM) neural networks to forecast stock prices using historical data. This project showcases how deep learning can be applied to time series analysis for technical stock market prediction, using one year of data from TataGlobal (NSE) as a case study.

The Stock Price Forecasting system uses a deep learning model to predict future stock prices based on historical data. It takes in historical price data and technical indicators, preprocesses the data, trains a deep neural network, and generates predictions for future stock prices

Stock Market Behavior Analysis and Forecasting of prices to better understand the seemingly arbitrary nature of stock movements, using Deep Learning, Natural Language Processing, and other Data modeling techniques.

This repository showcases the implementation of a Neural Network for stock price forecasting using historical market data. Dive into the world of deep learning and explore how neural networks can contribute to predicting stock prices based on historical trends.

A deep learning framework for time series forecasting using attention mechanisms. This repository provides an implementation of state-of-the-art neural architectures designed for accurate and interpretable time series prediction tasks such as energy consumption, stock price, weather data, and more.

Developed deep learning models (LSTM & Transformer) to forecast FAANG stock prices using historical data. Integrated a DQN-based trading agent using technical indicators like RSI and volatility. The model achieved higher returns than traditional strategies, highlighting adaptive AI in trading.

This project applies both deep learning models (LSTM and CNN-LSTM) and a classical econometric model (GARCH(1,1)) to the task of 5-day volatility forecasting using historical stock price data. It serves as a practical demonstration of how traditional and modern approaches can be integrated in financial time series analysis.

This project aims to forecast stock prices using a deep learning model based on historical data. The model utilizes a sequence-to-sequence architecture with attention mechanisms for time series forecasting. The implementation is done using TensorFlow.

A comprehensive MLOps pipeline for predicting Vestas stock prices using historical market data and macroeconomic indicators. The system leverages LSTM deep learning models to forecast stock prices at multiple time horizons (1, 3, and 7 days ahead).

Harbinger is a time series forecasting model built using the Burn deep learning framework in Rust. It leverages LSTM (Long Short-Term Memory) networks to predict stock prices based on historical data.

Explore NVIDIA's stock dynamics with this project, using a mix of traditional and deep learning models to forecast stock prices and analyze the influence of market sentiment. Integrates ARIMA, LSTM, and more to provide deep insights.

A machine learning system that forecasts stock prices using LSTM deep learning models. Features technical indicator analysis (RSI, MACD), Flask web interface, and performance metrics tracking. Achieves 78% prediction accuracy in backtesting.

This project demonstrates advanced time-series forecasting using a Deep Learning approach (Stacked LSTM) to predict the closing prices of a major stock (Microsoft/MSFT). The model learns sequential dependencies in historical data to forecast future values, a critical task in quantitative finance

An end-to-end Deep Learning project to predict Tesla (TSLA) stock prices using recurrent neural networks (RNN & LSTM). This project includes a comprehensive data analysis pipeline, technical indicator engineering, and an interactive Streamlit dashboard for real-time forecasting.