Getting to Zero!

Authorship

by on Jul.19, 2021, under About

Books on Strategy, Finance & Risk (based on data modeling)

The primary purpose of modeling is to be able to make good decisions. Having spent a decade in CMBS stress testing, in this monograph, I examine credit and default risk, that is, take it apart and deconstruct these concepts and show that defaults are easy to define but difficult to measure, but more importantly default risk cannot be used to determine credit risk. That pre-payers cannot be lumped together with defaulters as they belong to different population demographics. I then reconstruct the credit and default risk models to provide a much better model for forecasting securitized mortgage portfolio defaults. Vasicek is replaced by the Default Covered-Call Model which consists of the Sustainable Cash Flow Line, Lender ’s Default Function and the Borrower ’s Default Function. While the securitized mortgage industry addresses the Dodd-Frank risk-retention from a lenders perspective, from an investor’s perspective, Dodd-Frank is wanting. Therefore, Dodd-Frank is vague and needs much rework. Finally, a new bank product is proposed which will substantially reduce the impact of mortgage risk in a contracting economy. This is expected to reduce bank capital requirements.

The primary purpose for examining data is to be able to make good decisions. This requires the ability to build, not just a model, but a knowledge domain from the data. Data analysis is primarily concerned with how to extrapolate (out of sample?) data to provide business information. This book is about data modeling with examples from complex real-world data, expansion of the universe, Multiple Sclerosis, COVID-19, and the new Collated Distributions, Wilcoxon Regression and Levy Distribution. How does one construct the knowledge domain for a problem at hand from the data? It requires building the context structure which consists of several individual cooperating models. Determining these models requires an understanding of how to partition the data which is not the same as stratification or machine learning. The partitioned data consists of individual processes which are evidenced as different probability distributions. Beware, much of statistical testing requires that specific axiomatic requirements be met which are usually not. This monograph teaches by way of complex data examples how to read this complex data and why the data is expecting us to ask much more questions than we do.

In 2008, Wall St. crashed, and with it took down the US economy. The incoming Secretary of the Treasury, Tim Geithner, reported that the Great Recession wiped out $15 trillion in household wealth, lost 9 million jobs, caused 5 million homeowners to lose their homes, and brought 9 million Americans below the poverty line. What a legacy! How was this possible? Modern finance teaches us that unsystematic risk is fully diversified away when one constructs portfolios. Therefore, supposedly, how bankers, analyst and corporate managers behave will not affect the markets as the unsystematic risk they create is fully diversified away. Not correct. This book shows that unsystematic risk cannot be fully diversified away. By laying the groundwork for financial management which includes unsystematic risk, new analyses and tools are provided to quantitatively monitor equities markets, portfolios and risk scoring.

The Holistic Business Model identifies, in a structured manner, the 48 structural positions and 32 strategies your company can effect, resulting in 2 million variations in your company’s strategic environment. This complexity is handled by three layers, consisting of the Operations Layer, the Revenue Transaction Layer and the Business Management Layer. Strategy is the migration from one structural position to another in the Business Management Layer. Therefore, the Model prevents investors, business owners and corporate managers from making incorrect moves, while both, enabling them to see their future options, and enhancing the quality of their management decisions. The Operations Layer explains why lean manufacturing (JIT and Kanbans) works when it does, when it does not, and the important considerations when setting up a manufacturing operation using lessons learned from the semiconductor and Fast Moving Consumer Goods industries. The Revenue Transaction Layer identifies how your company generates its revenue. Based on 20+ years in manufacturing and management consulting in multinational, large, medium & small companies, Solomon invented the Holistic Business Model that only requires public information to determine your company’s and your competitors’ strategies. Four case studies are presented: a manufacturing operation, a home builder, a non-profit and a sea port.

Books of the Foundations of Physics (based on data modeling)

Since the discovery of the massless formula for gravitational acceleration g=τc^2 in 2007, this is the third book in the series rewriting the foundations of physics, This book covers the origins of gravitational fields, gravity modification engine design, black holes and their black particles. It provides an alternative explanation to the Universe expansion red shift data. Hand in hand with this is the rewrite of photon probabilities, how Nature implements probabilities, a better handle on randomness versus probabilities, probability control, probability as a field theory and how gravitational fields deform probability fields.

This book, Unifying Gravity with the Atomic Scale, proposes that a new physics exists. The findings are based on 18 years of extensive numerical modeling with empirical data, and therefore, both testable and irrefutable. In 2012 Prof. Nemiroff, using NASA’s Fermi Gamma-ray Space Telescope photographs, showed that quantum foam cannot exists. In the same year Solomon showed that both exotic matter and strings could not exists. In 2015 the Kavli Foundation, with Prof. Efstathiou, Prof. Pryke, Prof. Steinharrd discussed the issues with the Planck Space Telescope findings of a Universe that is significantly simpler than our theories. Therefore the need for new physics. The replacement of the Schrödinger Wave Function with the simpler Probabilistic Wave Function, results in a new electron shell model based on the Rydberg equation giving exact results with quantum mechanics; leading to a new Standard Model and the unification of photon shielding, transmission and invisibility as the same phenomenon. Solomon’s inference is that any current or future stealth technology can be neutralized.

An Introduction to Gravity Modification, Second Edition is the result of a 12-year (1999-2011) study into the theoretical and technological feasibility of gravity modification, that presents the new physics of forces by replacing relativistic, quantum and string theories with process models. Gravity, electromagnetism and mechanical forces are unified by Ni fields, and obey a common equation g = (tau)c^2.

Gravity modification is defined as the modification of the strength and direction of the gravitational acceleration without the use of mass as the primary source of this modification, in local space time. It consists of field modulation and field vectoring. Field modulation is the ability to attenuate or amplify a force field. Field vectoring is the ability to change the direction of this force field .

This book reaches out to a wider audience, and not just to the theoretical physicist; to engineers and technologist who have the funding to experiment; just as Arno Penzias and Robert Woodrow Wilson experimented with the Holmdel Horn Antenna and discovered the microwave background radiation. The mathematics is easier than that taught in theoretical physics and therefore accessible to a wider audience such as these engineers and technologists.

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Recent Bio

by on Jul.19, 2021, under About

Having spent a year (during the COVID-19 lockdown) writing journal quality papers of my experience in credit and default risk, in November 2020 I started my FinTech (S-corp.) company, Business AIR Models Inc (BAM). BAM was set up to deliver SaaS financial, credit and default risk products. I am able to do this because I have 40+ years working with data at GMAC Commercial Holdings, UMB Bank, Key Bank, Texas Instruments, PwC, Unilever, etc. in finance, credit risk, decision theory, strategy, manufacturing, medicine and physics. I have invented Asymmetric Information Resolution (AIR) Models (to infer private information from public data), Wilcoxon Regression, Collated Distributions, Default Covered-Call, Economic & Funding Statements, the Capital Premium Model and Infectious Disease Lifecycle (https://www.busairmod.com/images/IDS-Covid-19-LifeCycle.svg). I have written two monographs on credit risk and data modeling and have published 7 technical books in total.

My IQ is 164 (i.e., less than 1 in a 100,000 people have this IQ), and therefore, I think differently from most people, as some of you already know. We are the people who disrupt markets. Having said that, in my monograph “A Critique of Dodd-Frank” I proposed a new bank product HELSIS, to minimize the financial and economic impact of future financial meltdowns (https://www.youtube.com/watch?v=mZVfZLt_P5E). This is an alternative to, and equivalent to, the bricks of Geithner’s “Wall of Money”, that is, a distributed wall. HELSIS will reduce bank capital requirements by 34% to 58% and reduce borrower defaults by 70%.

A few years ago, I read the Fed’s bank stress test documentation, or at least more than half of it. It’s all about Dodd-Frank and nothing about the actual methods to implement stress testing. I found only 2 pages out of about 150 that actually mentioned Loss Given Defaults (LGD) and that was a repeat of industry definitions, which are wanting. So, the Fed’s bank stress test is an opaque methodology that probably cannot stand up to public scrutiny. This is a serious problem as, if you read the IHME’s models on infectious disease spread, it is very clear and open. Even though I do not agree with the IHME/Imperial College reproduction models, the IHME documentation provides us with a benchmark on how public methodologies need to be documented.

The question I’ve always asked myself, during these past 40+ years, is “how do we make decisions?” Since, having spent a decade in CMBS stress testing, influenced by Kaizen, I have narrowed this question to “how do we prevent bad decisions?” That is, since life, economy and the market environment is always in a flux, the “optimization” problem cannot be “what is the best solution, today?” It should be “what is a good solution, today, that allows us to be successful tomorrow, too?”, because tomorrow is indeterminate for non-trivial problems and real world business problems are “cycling”.

Therefore, I have developed a proprietary methodology that is based on AIR models that informs the user of a specific company’s financial and credit risk. Not just a number but a set of maps that explains why the company is in the position it is and alternative paths the company can take to improve its financial condition, in minutes. Therefore, BAM’s SaaS financial products are suitable for C&I lending, investor stock pickings, corporate managers, hedge funds and M&A participants.

Different people respond differently. In 2016, I spoke to a retired Bank of America SVP. She confirmed my and the Bank of International Settlements, working paper that banks have a risk problem in spite of the level of sophistication of their risk management tools. In 2019, I spoke to another bank VP, and she was furious about my opinions – her husband had to calm her down. In my opinion companies fail because their staff are so identified with what to think that they are unable to practice how to think. See https://youtu.be/KjAfRbva3Xs. Group-based scenario analysis is an approach to getting around the ‘what versus how to’ think problem.

Oh, yes, I almost forgot. I have an (i) MBS (Banking and Finance), University College Dublin, Ireland, 1995, the oldest graduate finance program in Europe, (ii) MA (Operations Research), Lancaster University, UK, 1982, a UK ivy league, (iii) BSc (Elec. Eng.), Aston University, UK, 1979.

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HELSIS – Home Equity Line for Self-Insurance Servicing

by on Jul.16, 2021, under Mortgages

I invented HELSIS. It is based on my decade long work on how Commercial Mortgage Backed Securities (CMBS) losses behave, and some experience in residential mortgages. This video explains how to reduce mortgage defaults while at the same time reducing banks’ unproductive risk capital requirements. Please reach out to your congressional representatives to make HELSIS law. Enjoy.

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Are Ivy Leagues Worth It?

by on Jul.16, 2021, under Life

First I must say that I am a graduate of a British Ivy League, Lancaster University, where I completed my 2-year Master of Arts in Operations Research.
The question is, Are Ivy Leagues Worth It? The answer? That depends.
The prior question should have been, what do you want to get out of it? Not “expect” but “want”.
Let me tell a story . . .

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Play it again, Sam

by on Mar.27, 2021, under About

Welcome to Getting to Zero! These (video) blogs discuss problems I have encountered with analyzing my data and those of others. Having worked 40-years in many different industries, I provide the solutions to overcome these problems in this blog.

Yes, it is vital to determine unbiased parameter estimates but many times statistical methods do not provide unbiased parameter estimates, even though the theory claims that they are. If you didn’t know that then . . .

It is not sufficient to just examine the data once, and so the title Play it again, Sam from Casablanca.

I hope you will enjoy this blog and learn a lot from it.

(continue reading…)
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