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 deep learning method for event driven stock market prediction. Deep learning is useful for event-driven stock price movement prediction by proposing a novel neural tensor network for learning event embedding, and using a deep convolutional neural network to model the combined influence of long-term events and short-term events on stock price movements

Comparative Analysis of Conv1D-LSTM with CNN , LSTM for Stock Price Prediction

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

This project focuses on predicting stock prices using Recurrent Neural Networks (RNNs), a type of deep learning model well-suited for sequential data. The provided Jupyter notebook includes all the steps necessary for data preprocessing, model training, evaluation, and making predictions on stock market data.

This paper proposes a novel hybrid approach for stock price prediction. Stock price prediction implies telling whether a particular stock price will go up or down on a certain day. The model proposed in this paper uses Gated Recurrent Units to take into account the sequential nature of days, which is not handled by CNNs. However, the CNN is used to capture relationships between long term and short term stock patterns. Finally we have a simple feedforward neural network which learns the weights to be given to the CNN prediction and the weight to be given to the GRU prediction.

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

This project provides an overview of utilizing deep learning techniques, specifically LSTM neural networks, for stock price prediction through time series analysis. It outlines the significance, proposed solution, challenges, and references, setting the stage for further exploration and implementation in the financial domain.

Comprehensive project focused on stock price prediction for Tesla stocks, leveraging advanced deep learning techniques. This project involved developing and comparing three different models: Long Short-Term Memory (LSTM), 1D Convolutional Neural Network (1D CNN), and Gated Recurrent Unit (GRU).

Final team project was a Stock Price Prediction Model using Deep Learning neural networks and Python. Deliverables included pulling data from Yahoo Finance Library, processing the data for database storage and retrieval, connecting PgAdmin database and AWS cloud storage to store and retrieve the data; retrieving data from the database for preprocessing and use in an LSTM Machine Learning model for prediction. Also, prepared data visualization describing the project and displaying our project deliverables on an active website using JavaScript.

Stock values is very valuable but extremely hard to predict correctly for any human being on their own. This project seeks to solve the problem of Stock Prices Prediction by utilizes Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict future stock values.

A deep learning model for predicting stock prices based on historical data. Using LSTM neural networks to analyze patterns and make future price predictions.

The main objective of this project is the analysis and prediction of stock prices. As far as it concerns the predictions, a variety of classic machine learning algorithms and deep neural networks are used for: One-step price forecast, Long-term price forecast and Prediction of stock price movements.

Bulbea is a Python library for stock market prediction and modeling using deep learning. It uses recurrent neural networks to predict stock prices and the Twitter API for sentiment analysis.

Stock price prediction project using LSTM (Long Short-Term Memory) neural networks. This project applies deep learning techniques on historical stock market data for time-series forecasting, data preprocessing, model training, and visualization of actual vs predicted prices using Python and TensorFlow.

This project implements and compares multiple deep learning models for stock price prediction using various neural network architectures and optimization algorithms. The system predicts stock prices using historical data and evaluates performance across different optimizers and model architectures.

Stock price prediction project using LSTM neural networks trained on real market data fetched via the Fyers API. The pipeline covers data collection, preprocessing, sequence generation, and deep learning–based time-series forecasting, with support for long-term future price prediction. Built with Python, NumPy, and TensorFlow.

This project builds a stock price prediction system using Long Short-Term Memory (LSTM) networks, a type of recurrent neural network (RNN) designed for time-series forecasting. It utilizes Yahoo Finance (yfinance) for stock data, TensorFlow/Keras for deep learning, and Streamlit for an interactive web-based interface.

The project aims to develop and evaluate various deep learning models for time series forecasting using historical stock price data of the Apple (AAPL) stock. It employs Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) models, and a Multivariate LSTM model to make predictions.

Can I Earn Money with this :?

A concept of using Deep Neural Networks for Stock Price Prediction

Neural Network : Deep learning model for stock price prediction using TensorFlow

Stock Market Prediction Using Machine Learning This project predicts stock market prices using traditional machine learning models instead of deep learning or neural networks. The project includes data preprocessing, feature selection, model training, and evaluation for stock price prediction.

This project implements a deep learning model for stock price prediction using Long Short-Term Memory (LSTM) neural networks. The model analyzes historical stock market data and learns temporal patterns to predict future stock prices.

This report explores the application of deep learning for stock price prediction. It investigates the effectiveness of GRU neural networks in analyzing historical data to forecast closing prices for multiple companies.

Stock Price Prediction project using TensorFlow. Stock Market price analysis and can be performed using a Recurrent Neural Network. famously known for its Deep Learning and Machine Learning functionalities. Building Neural Networks becomes easy by writing just a few lines of Tensorflow code.

This project implements a stock price forecasting system using LSTM (Long Short-Term Memory) neural networks, a deep learning architecture particularly effective for time-series prediction.

The Stock Prediction by using LSTM (Long Short-Term Memory) project employs advanced deep learning techniques to analyze historical stock data, leveraging LSTM neural networks for capturing complex patterns and dependencies, ultimately providing accurate predictions of future stock prices.

A comprehensive deep learning solution for stock price prediction using LSTM neural networks, with real-time data fetching from Yahoo Finance and an interactive Dash dashboard for visualization.