Bayesian Neural Networks: Bayes’ Theorem Applied to Deep Learning

  The article was written by Amber Zhou, a Financial Analyst at I Know First.

Highlights:

  • Vanilla Deep Learning Method: Multilayer Perceptron (MLP)
  • Introduction to Bayes’ Theorem
  • Application of Bayes’ Theorem: Bayesian Inference
  • Combination: Bayesian Neural Networks
  • How I Know First Utilizes Bayesian Neural Networks for Forecasting

premiumRead The Full Premium Article

Subscribe to receive exclusive PREMIUM content here

Deep Reinforcement Learning: Building A “Self-Driving Car” In Financial World.

  The article was written by Hieu Nguyen, a Financial Analyst at I Know First.

     

Summary:

  • What makes Deep Reinforcement Learning different?
  • Markov Decision Process and its application
  • How to find Optimal Policy using Q-learning?
  • Building a “Self-Driving Car” in financial world
When talking about deep learning, many seems to refer it to only computer science. Today, we will talk about a part of deep learning that utilize many areas of both natural science and social science: Deep Reinforcement Learning. Deep Reinforcement Learning has been changing our world every day. This learning method helps scientists to explore new disease treatments, help engineers to develop self-driving cars, and help investors to maximize their profit. To subscribe today and receive exclusive AI-based algorithmic predictions, click here

Deep Learning Finance: Revolutionizing The Market Today

This article was written by David Shabotinsky, a Financial Analyst at I Know First, and enrolled at the undergraduate Finance program at the Interdisciplinary Center, Herzliya.

Deep Learning Finance

Summary
  • How Deep Learning developed from AI
  • The evolution of Deep Learning in the market
  • How the finance sector has begun to further take advantage of Deep learning
  • I Know First implementation of Deep Learning to better forecast financial markets

premiumRead The Full Premium Article

Subscribe to receive exclusive PREMIUM content here

Deep Learning Algorithms: The Future of Financial Investment?

    The article was written by Harry Chiang, a Financial Analyst at I Know First.

Deep Learning & Finance

Summary:
  • What is Deep Learning?
  • Deep Learning Applications Today
  • Deep Learning & Financial Markets
  • I Know First's Role in the Machine-Learning World

Outlook For Gold PricesRead The Full Premium Article

Subscribe to receive exclusive PREMIUM content here

Deep Learning Algorithms: Deep Learning Through TensorFlow

This article was written by David Berger, a Financial Analyst at I Know First.

Deep Learning Algorithms: Deep Learning Through TensorFlow

Summary:
  • What is Deep Learning?
  • TensorFlow- Google's Open Source Software Library
  • TensorFlow Compared to Other Libraries
  • Early Results from TensorFlow

premiumRead The Full Premium Article

Subscribe to receive exclusive PREMIUM content Here

I Know First at the AI In Finance Summit New York 2018: Video + Interview

I Know First Presents at the AI In Finance Summit

I Know First was honored to be selected to present as one of the leaders of innovation through incorporation of machine learning & deep learning into the financial sector. The event took place in New York City beginning on September 6th. In the presentation delivered by CEO & Co-Founder Yaron Golgher, he explains how modeling the market as a complex chaotic system allows algorithms like I Know First’s to accurately predict the stock market. Co-Founder & Partner Dr. Lipa Roitman further elaborates and dissects a more technical view of the algorithm and how it was founded. The presentation  labeled: Investment Selection By Combining Chaos Theory with Artificial Intelligence, can be viewed in it's entirety above...

An Overview of How To Use I Know First’s AI Forecasts for ETF Trading

In the following we give an overview of the construction and performance of ETF Portfolios constructed using I Know First’s Algorithmic Forecasts for the SPDR Sector ETFs. We present the construction and performance of:
  1. Portfolios which directly use the algorithmic signals generated to select the sector ETFs to invest in and to rebalance the portfolio
  2. Portfolios which use sector-level predictions computed by aggregating our forecasting algorithm’s daily forecasts for individual S&P 500 stocks
  3. Portfolios which combine the algorithmic forecasts with an equally invested benchmark to create long only strategies which allow investors to target desired alpha and beta statistics
We show that these portfolios register very good performance statistics over the analyzed time-horizon outperforming the benchmark.
Pages:1234»