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stanford-project-predicting-stock-prices-using-a-lstm-network

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).

statistical-learning

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

string_grouper

Super Fast String Matching in Python

structest_w19

MACS 40200 (Winter 2019): Structural Estimation

technological-innovation-resource-allocation-and-growth-replication-kit

This repository provides the replication code and data for Kogan, L., Papanikolaou, D., Seru, A. and Stoffman, N., QJE 2017.

time-series-forecasting-with-python

A use-case focused tutorial for time series forecasting with python

time_series_modeling

Modeling a noisy, high dimension time series data set

today-i-found

A personal archive for tutorials, articles, blog posts, StackExchange, Quora, etc...

torch-rnn

Efficient, reusable RNNs and LSTMs for torch

transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

value_return_predictability_across_asset_classes_and_commonalities_in_risk_premia

This repository contains data and replication code for the paper "Value Return Predictability Across Asset Classes and Commonalities in Risk Premia" forth coming "Review of Finance".

volatility-study

• Conducted a volatility study to develop pairs trading strategy by writing web crawlers that automated extracting 30 equity and ETF spot and options prices data from CBOE and Yahoo Finance • Utilized NumPy, Pandas, and SciPy packages to calculate implied volatility, realized volatility, and risk premiums to measure how the market prices risk • Gathered and plotted daily VIX futures data with Selenium, Seaborn and Matplotlib to study volatility term structure • Examined volatility clustering and built forecasting tools for market risk using correlations of daily absolute returns and volatility at different time lags

vpin

Order flow toxicity; Volume-Synchronized Probability of Informed Trading

zz2585.github.io

Accounting PhD at UCLA