Where is the Light by Storm Thorgerson

What is Data Governance, and why is it so popular? How does Data Science play a role in Data Governance? Why have organizations changed their view of data assets? Below, I provide answers from a Data Scientist’s perspective.


From Classical to Contemporary Models for Stock Valuation

Demand and Supply by Erik Johansson

I have been following a linear regression model for my investments for two decades, but things have changed. Forty years ago, when the original paper came out, the financial markets were different. There were more stocks, less volatility, and fewer people involved. Today, the core argument still holds in the Fama — French Three-Factor Market, and I continue to make money, but there are fundamental changes.

(1) THE CLASSIC MULTIPLE LINEAR REGRESSION MODEL

(2) RANDOM FOREST MODEL WITH GRID SEARCH

(3) SUPPORT VECTOR MACHINE (SVM)

(4) BAYESIAN RIDGE REGRESSION

(5) ENHANCEMENT TO THE MODEL WITH MOMENTUM

(6) MODEL SUMMARIES


In Defense of the US Dollar

Millennium Watchman Image by Vladimir Kush

Introduction

About 3,500 publicly listed companies can trade in the US Stock Market, but more than 80% of the daily price moves are algorithmic. Financial trading has evolved as a traditional brain-driven rationalism to a data-driven empiricism. Below I walkthrough a thorough justification of selecting the US Dollar for Algorithmic Trading with Artificial Intelligence because the reserve currency of the US Dollar is widely misunderstood. Let’s first begin with operational definitions.


The Yield Curve with Python Programming

Image by Storm Thorgerson

One of the most critical data structures in the Global Financial Markets is a Yield Curve. Whether you are not taking a risk on the sell-side such as a big bank . . . or taking a risk on the buy-side such as a fund manager . . . the Yield Curve is a compound business object that prices many types of products and is a market indicator.

Starting in the 1980s, a new product in London called Interest Rate Swaps exchanged two sets of cash flows and relied on a series of rates for pricing. The motivation was the…


Image by Erik Johansson

Bonds are complicated, especially non — conventional ones. One of the most popular metrics in the Fixed Income industry is the Option Adjusted Spread or OAS. Bonds, as a generic product of the framework of Fixed Income, are all Debt.

Debt is an increase in current spending in exchange for a decrease in future spending. The process of debt has many products, which do vary by the components that roll up to its value and risk.

OAS is a checkpoint metric before a debt deal ends, specifically for non — conventional debt. Generically, the OAS calculation is the difference between…


A Bond Trading Desk Perspective with Monetary Data Science

Venice from Hüseyin Şahin

There are plenty of articles and talk about a potential crash in the US Financial Markets. The majority of the knowledge base of people and their methodology is flawed, and most financial market traders are on the wrong side of the market. Below I will show how Speculators who are betting against the US Dollar will eventually cover their trading positions by selling their US Stock Market positions for cash and therefore cause a US Stock Market crash. My central premise is the betting against the US Dollar without knowing the estimated number of US Dollars and its sources is…


Image by Unknown Artist

There are always some unknowns in sales that are accepted. Salespeople progress forward in time with different types of uncertainty, like the customer type, the environment, competitors, and so much more. The lack of visibility and partial knowledge is disquieting unless you were able to predict. Data Science has plenty of predictive analytics, and XGBoost is a fascinating model that can fill knowledge gaps in sales. I first begin with data analysis with three important data attributes and then proceed with a description and implementation of XGBoost.


The blackbird represents the US Central Bank, the Federal Reserve, the buildings are businesses, the houses below are households, and the planes are foreign central banks. (Image by Rustam QBic and Reinterpretation by John Foxworthy)

Today is the 4th of September 2020, and I am waiting for the U.S. Financial Markets to crash in 2020 or 2021. My domain knowledge of Financial Economics, coupled with my Data Science experience, has developed an approach to explore datasets and use indicators to forecast a downfall in economic markets. Below, we will follow this outline to my conclusion with a bias to keep it short.


An Attempt to Categorize Supervised Learning Models (1.0)

Image by Rustam QBic

There are many types of Predictive Models for forecasting and it is a struggle to find a short write up on the subject. Below is a walkthrough, of several major types of forecasting with discussions on methodology and data descriptions below . . . whether you are predicting a category, a number, a text or an image.


If you want to know where you are going, then you need to know where you came from. Initial conditions matter, but datasets that rhyme are easier to predict future values than secular trends. Below is a contemporary model called LSTM.

In the early 1990’s, two German computer scientists, Sepp Horchreiter and Jurgen Schmidhuber created a Recurrent Neural Network for Deep Learning called Long Short — Term Memory or LSTM.

To elaborate, Machine Learning can be defined as software program that makes decisions without explicit programming, . . . so a layered version is called Deep Learning. The phrase Neural…

John T Foxworthy

Data Scientist looking for a new role

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