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

Stock price trend prediction with news sentiment analysis using deep learning

Price Prediction Case Study predicting the Bitcoin price and the Google stock price using Deep Learning, RNN with LSTM layers with TensorFlow and Keras in Python. (Includes: Data, Case Study Paper, Code)

Stock price prediction and automated trading using Deep Reinforcement Learning and Machine Learning

FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysis

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

Financial time series analysis and prediction have become an important area of re- search in today's world. Designing and pricing securities, construction of portfolios and other risk management strategies depends on the prediction of financial time se- ries. A financial time series often involve large dataset with complex interaction among themselves. A proper analysis of this data will give the investor better gains, but the existing methodologies focus on linear models (AR, MA, ARMA, ARIMA) and non- linear models (ARCH, GARCH, TAR). These models are not capable of identifying the complex interactions and latent dynamics existing within the data. Applying Deep learning methods to these types of data will give more accurate results than the existing methods. Deep learning architectures can identify the hidden patterns in the data and is also capable of exploiting the interactions existing within the data, which is, at least not possible by the existing financial models. The proposed work uses four different deep learning architectures (RNN, LSTM, CNN, and MLP) for predicting the minute wise stock price for NSE listed companies and compares the performance of the mod- els. The proposed method uses a sliding window based approach for predicting future values on a short-term basis. The performance of the models was quantified using error percentage.

"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."

Stock Price Predictions from Financial Statements using Machine Learning and Deep Learning algorithms augmented with Knowledge Graph Embeddings

Predicting how the stock market will perform is one of the most difficult things to do. There are so many factors involved in the prediction – physical factors vs. psychological, rational and irrational behavior, etc. All these aspects combine to make share prices volatile and very difficult to predict with a high degree of accuracy. Using Time Series Forecasting, which is the process of analyzing time series data using statistics and modelling to make predictions and inform strategic decision-making, I have tried to predict the future stock price based on its historical data.

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.

🔮 Stock Price Prediction using LSTM, GRU, and CNN A deep learning project to predict future stock prices using historical data of Apple Inc. (AAPL), built with LSTM, GRU, and 1D CNN models. The project includes data fetching via yfinance, time series preprocessing, training, evaluation, and visualization of model predictions.

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_Market_Prediction using textual analysis : Applying the NLP processing and the Sentiment analysis to the textual dataset(news headlines) * Numericel analysis : Applying the numerical analysis to the historical stock prices dataset * merging the two datasets : Create a hybrid model for stock price/performance prediction using deep learning LSTM for time series forcasting with the avoidance of overfitting

Stock Price Prediction using deep learning (lstm,bilstm and gru) on apple,tesla and google stock prices

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

This project is an interactive web application that uses a Long Short-Term Memory (LSTM) deep learning model to predict stock prices. It features automated hyperparameter optimization with Keras Tuner and a real-time dashboard built with Streamlit for visualizing predictions against live data from Yahoo Finance.

Stock price prediction using deep learning LMST

No description available

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

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

⚡ STOCK MARKET PREDICTION is a Deep Learning based web application using LSTM model and that is used to predict the future stock prices based on 10 years historical data

Stock Price Prediction Using Neural Networks: How deep learning models improve predicting stock prices by using technical and fundamental indicators?

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

This Thesis work is based on Prediction of Financial Stock price prediction using Deep learning techniques

Predicting Stock Price using Machine Learning & Deep Learning. Taking Google trends value in addition to the stock price for a particular day to get more accurate predictions.

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.

stock price prediction using deep learning algorithms. The models will be trained using Kiwoom api+ and IEX data set.