A Study on LLMs for Financial Statement Analysis
In this article, we'll review the findings of a study on large language models (LLMs) for financial statement analysis.
By
Peter Foy
Machine Learning for Finance
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By
Peter Foy
100+ Prompts to Analyze Earnings Calls
In this guide, we provide 100+ prompts that you can use to analyze earnings call transcripts using large language models (LLMs).
By
MLQ
GPT-4 Passes Bridgewater's Investment Associate Test
Earlier this month, the Co-CIO of Bridgewater revealed that OpenAI's GPT-3.5 has successfully passed Bridgewater’s investment associate test.
By
Mike Sak
GPT-4 for Crypto On-Chain Analysis
In this guide, we'll discuss how we can use GPT-4 to summarize and analyze on-chain trading signals of crypto assets.
By
Peter Foy
GPT-4 for Financial Ratios: Academy
In this video tutorial, we'll walk through how to use GPT-4 to analyze financial ratios, including liquidity, profitability, valuation, and other key metrics.
By
Peter Foy
GPT-4 for Finance: Turning Financial Ratios into Insights
In this guide, we'll discuss how to use GPT-4 to summarize financial ratios and provide insightful analysis of how the data changed over the chosen time period.
By
Peter Foy
GPT-4 for Financial Statements: Academy
In this video tutorial, we'll walk through how to use GPT-4 to summarize and analyze financial statements, including income, balance sheet, and cash flow statements of public companies.
By
Peter Foy
Featured
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GPT-4
GPT-4 for Financial Statements: Building an AI Analyst
In this guide, we discuss how build an AI analyst that uses GPT-4 to analyze financial statements, including income statements, balance sheets, and cash flow of public companies.
By
Peter Foy
Time Series with TensorFlow: Prediction Intervals for Forecasting
In this article, we discuss the concept of prediction intervals, also known as uncertainty estimates, which give a range of prediction values with upper and lower bounds.
By
Peter Foy
Time Series with TensorFlow: Building an Ensemble Model for Forecasting
In this article, we discuss how to use ensemble learning for the task of time series forecasting and combine their predictions to improve performance.
By
Peter Foy
Time Series with TensorFlow: Replicating the N-BEATS Algorithm
In this article, we'll expand on our previous time series forecasting models and replicate the N-BEATS algorithm, which is a state-of-the-art forecasting algorithm.
By
Peter Foy
Time Series with TensorFlow: Building a multivariate time series forecasting model
In this Time Series with TensorFlow article, we create a multivariate dataset, prepare it for modeling, and then create a simple dense model for forecasting.
By
Peter Foy
Machine Learning for Finance: Price Prediction with Linear Regression
In this project we'll look at linear regression for price prediction, specifically the relationship between historical data and future price prediction.
By
Peter Foy
Time Series with TensorFlow: Building an LSTM (RNN) for Forecasting
In this Time Series with TensorFlow article, we build a recurrent neural network (LSTM) model for forecasting Bitcoin price data.
By
Peter Foy
Time Series with TensorFlow: Building a Convolutional Neural Network (CNN) for Forecasting
In this Time Series with TensorFlow article, we build a Conv1D (CNN) model for forecasting Bitcoin price data.
By
Peter Foy
Time Series with TensorFlow: Building dense models with larger windows & horizons
In this article, we build two dense models with larger window & horizon sizes.
By
Peter Foy
Time Series with TensorFlow: Building a dense model for Bitcoin price forecasting
In this article, we're going to create our first deep learning model for time series forecasting with Bitcoin price data.
By
Peter Foy
Time Series with TensorFlow: Formatting Data with Windows & Horizons
In this article, we format our time series data with windows and horizons in order to turn the task of forecasting into a supervised learning problem.
By
Peter Foy
Time Series with TensorFlow: Common Evaluation Metrics
In this article, we discuss several common evaluation metrics to evaluate our time series forecasting models.
By
Peter Foy
Time Series with TensorFlow: Building a Naive Forecasting Model
In this article, we discuss the various modeling experiments we'll be running and then build a naive forecasting model for our Bitcoin price data.
By
Peter Foy
Time Series with TensorFlow: Downloading & Formatting Historical Bitcoin Data
In this article, we'll start a new time series with TensorFlow project by importing historical Bitcoin data, visualizing it, and preparing it for modeling.
By
Peter Foy
Deep Reinforcement Learning for Trading: Deploying the Algorithm at Interactive Brokers
In this section we'll finish our initial deep reinforcement learning trading algorithm by deploying it at a simulated account at Interactive Brokers.
By
Peter Foy
Deep Reinforcement Learning for Trading: Using Gradient Ascent to Maximize Sharpe Ratio
In this section, the objective is to use reinforcement learning to maximize the Sharpe ratio using gradient ascent.
By
Peter Foy
Deep Reinforcement Learning for Trading: Building a CNN for Probability
In this section, we're going to add another deep learning model to our trading algorithm and build a convolutional neural network (CNN).
By
Peter Foy
Deep Reinforcement Learning for Trading: Building an LSTM for Price Prediction
In this guide we build an LSTM for price prediction in our deep reinforcement learning trading algorithm.
By
Peter Foy
Deep Reinforcement Learning for Trading: Imports, Inputs, & Helper Functions
In this section we'll start with the imports, model and trading logic inputs, and helper functions that we'll need for this deep reinforcement learning for trading project.
By
Peter Foy
Deep Reinforcement Learning for Trading: Project Overview
In this project we're going to build a deep reinforcement learning trading agent and deploy it in a simulated trading account at Interactive Brokers.
By
Peter Foy
Nvidia Beats Q2 Earnings by 62%: Using AI to Analyze the Stock
Nvidia reported Q2 earnings after the close today, beating earnings by a staggering 68 percent from last year. In this article, we look at several takeaways from the quarter and look at ML-based estimates.
By
MLQ
8 Companies Working on AI for Trading & Investing
In this article, we've put together a list of 8 companies are that are helping investors improve their research process with AI and machine learning.
By
Peter Foy
The Rise of Alternative Data & Machine Learning in Finance
In this guide, we'll discuss what alternative data is, examples and challenges of alt-data, and how machine learning can be used to extract insights and signals from the noise.
By
Peter Foy
Machine Learning for Multiday Stock Estimates
In this guide, we're going to review an interesting application of AI for trading and investing: machine learning for multiday stock estimates.
By
Peter Foy
Classification-Based Machine Learning for Finance
In this article, we discuss various applications of classification-based machine learning in finance, including logistic regression for predicting asset returns.
By
Peter Foy
Deep Reinforcement Learning for Trading: Strategy Development & AutoML
In this guide, we discuss the application of deep reinforcement learning to the field of algorithmic trading.
By
Peter Foy
Deep Reinforcement Learning for Trading with TensorFlow 2.0
In this article we look at how to build a reinforcement learning trading agent with deep Q-learning using TensorFlow 2.0.
By
Peter Foy
An Overview of Deep Reinforcement Learning for Trading
In this article we provide an overview of deep reinforcement learning for trading. Reinforcement learning is the computational science of decision making.
By
Peter Foy
Machine Learning for Finance
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Synthetic Financial Data with Generative Adversarial Networks (GANs)
In order to overcome the limitations of data scarcity, privacy, and costs, GANs for generating synthetic financial data may be essential in the adoption of AI.
By
Peter Foy
Natural Language Processing
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Introduction to Natural Language Processing (NLP) with Python
In this guide we introduce the core concepts of natural language processing, including an overview of the NLP pipeline and useful Python libraries.
By
Peter Foy