Sep 24, 2020
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Forget What You’ve Learned: The Real Way To Pick Tech Stocks

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Nvidia chips can solve Feynman Diagrams like this one—here s why investors should care. Traditional stock analysis has lost its edge. As a way to make money in stock picking you must go well past the ancient tools of fundamental analysis. Over the last 50 years there were all kinds of stories told by money managers to impress their clients in regards to the detailed company analysis they perform to beat the market


A lot of them focused on obsessive data analysis with increasingly finer details. Typical stories would involve analysts tracing input prices and their effects on a firm s earnings. One of the earlier examples I remember was the study of timber prices and the way they impact the earnings of a match manufacturer for instance


Now because of computers and spread sheets everyone can answer those questions and more–to whatever ridiculous decimal accuracy you want. And somehow many of us think that more decimal point accuracy means more confidence inside the outlook. How misleading! Most of these questions while important to the mechanics of a firm s financial statements and earnings modeling are not nearly enough to find undervalued stocks anymore


Yet many still seem impressed by an analogous kind of outdated approaches. We rattle off stock statistics like kids that recite a memorized poem for sophistication thinking that it ends up in insightful analysis—or at least demonstrates our capabilities for intellectual insight. Let s not conflate the flexibility to memorize facts with intelligent insight a couple of company


That old model is obsolete. Active management in accordance with that process has failed to accomplish over the years. Some have realized the sense of false security in excel spreadsheets and have sought better ways of gaining insight. In attempting to find another lens to view the market Wall Street looked toward engineers


The phrase rocket scientist involves mind and makes me laugh. What value can an individual that is able to calculate rings around anybody on Wall Street bring to a stock valuation? True they are able to mine for hidden correlations and orchestrate impressive quant strategies but I’m more drawn to fundamental analysis here


For stock analysis the fabled rocket scientist is overbought. And I will say that because I have advanced degrees in physics–but not rocket science. In graduate school I knew a large number of these types. They knew the price of everything but the value of nothing and didn’t enjoy deep thinking


The deep thinkers were in my area–quantum field theory and mathematical physics. We spent loads of time studying abstract mathematical methods that at the time had no application to reality. Lucky for me that a long time later these abstract tools have become valuable to understanding certain technology industries! PROMOTED Grads of Life BRANDVOICE | Paid Program
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An Important Step To Success For Young People Starts With #WhyApply On September 18 The main point here is this: Many emerging technology industries are very difficult to understand for the layperson and professional


Traditional fundamental analysts quant analysts and rocket scientists are not capable of truly understating the products anymore. It takes some special people who have actually studied the science and made career changes to money management to understand. It really is true in other areas together with patent law. I once had a lawyer tell me I will turn an engineer right into a patent lawyer but a cannot turn a patent lawyer into an engineer


I totally agree. When you like to appreciate new industries like cryptology or quantum computing and are looking to buy stocks in those areas consider this: These fields are well past the ancient analysts capabilities. Look for someone that has a background inside the field but is now inside the investment management area


These individuals are hard to find but their opinions in regards to the product would be very credible. If you re still not convinced let me give you some examples from my very own world. Listed below are a number of the tools that I learned in physics which are relevant to portfolio management and economic analysis: The Modified Dietz Method for presenting total returns of portfolios is the most suitable for the investment industry


I was surprised to see that the strategy uses a tool I learned in physics from perturbation theory in quantum mechanics that approximates small changes in a mathematical expression. Most users of the Dietz method are blind to this clever mathematical tool that simplifies calculations universal across asset managers. The current interest in risk allocation in portfolios is in accordance with Euler s Homogeneous Function Theorem that I learned in a mathematical physics class


I found this out after I desired to know the way risk contributions are calculated. Spread sheets make this calculation easy which blunts the significance of this elegant 18th century theorem. I’d guess that almost all users of this tool are totally unaware that Mr. Euler made it possible. The sudden Trump metal tariffs have raised the potential for making a trade war


While very complicated debate I feel game theory particularly the concept of a Nash Equilibrium can help us know the way nations may react to the tariff. (In simple terms a Nash equilibrium describes how all parties would act given infinite choices to maximize their individual situations. ) I never thought that game theory once reserved for educational engagement would find this sort of compelling usefulness in today s world affairs


In terms of stock picking listed below are tools that I have used to appreciate the products of a few companies in emerging technologies: Number Theory is the muse of cryptography. Many companies together with Palo Alto Networks (PANW) Cyber-Ark (CYBR) and others are founded at the role that top numbers play in keeping our e-mails and other data strictly private


To appreciate this business one cannot treat the industry as just another widget maker. Traditional measures of corporate hierarchy and structure will bring about wrong conclusions. These companies are filled with the main unlikely kinds of corporate professionals you may imagine. In the past they’d work at universities only pursuing prime number theorems for the pure intellect of it


The appearance of the hacker has changed this and a number of the most abstract thinkers populate PANW and CYBR. I think of these companies as for-profit math departments where prestige is as important as profits. As the rate of hacking continues to grow picking winners is in accordance with recognizing the finest minds inside the business and which companies they opt to work


Sure you continue to need a complete understanding of earnings generation etc. but that’s merely the beginning. A superficial understanding of this science is dangerous. In my opinion the finest minds work at PANW and CYBR. It really is all relevant to stock analysis because it supplies additional insight that few Wall Street analysts understand


It would be extremely rare for more than a few theory expert to work on Wall Street. But knowing the product and the way the business truly works offers you the additional edge in picking the finest stocks inside the group. Most analysts focus at the driverless car marketplace for Nvidia (NVDA)


I feel that segment is overstated and intensely speculative. There are way more important (but less exotic) applications that NVDA will lead. For example I never thought I’d ever see a Feynman Diagram (like the one pictured at the top of this article) since my graduate physics days but leave it to Nvidia to resurrect them


In physics these simple diagrams represent a series of devilishly complicated calculations that might take years to compute by hand. NVIDA s graphic processing units have made many of those calculations almost trivial. I remember reading in regards to the Fadeev Many Body Scattering Feynman Diagrams in my day and thinking the array of 260 million complex integrals could never be computed


Recently a team of Russian physicists solved them using Nvidia s graphic processing units in15 minutes!This has implications for most computing tasks together with plasma physics geophysics medicine and many other areas of science. Wall Street has no idea because this rather obscure news is barely appreciated by theoretical physicists at the present


But the applications will come faster than the driverless cars will. NVDA leads this race. Investor interest in quantum computing has been growing over the last years. Like understanding Feynman Diagrams quantum mechanics takes years to master. The investing public reads about quantum entanglement spooky action at a distance (thank you Einstein) the Heisenberg Uncertainty Principle and many other strange terms


These are all important features to appreciate the feasibility of the business. I’m certain that almost all investment professionals using those terms have no idea what they mean! Any physics major realizes that the sensible application of quantum computing is further away than self-driving cars. If you choose to invest in early stage developers of quantum computing now you must accept the possibility that your investment may match to zero


Physics has helped me in other investment circumstances too. When Mr. Market pushes Nvidia down because there is negative news about self-driving cars or competitors gaining I don t worry. I do know that there are a number of more latent applications waiting inside the wings. Generally if some of my stocks dump suddenly I feel back to my physics days


Physics taught me that often the first explanation is often not the best. Initial explanations tend to be more complicated but most analysts accept that answer and make flawed decisions. I learned if you keep at it commonly a less complicated and more elegant explanation involves you. That makes the call process much clearer


And a clearer decision process should bring about better money-making possibilities. Disclosure: I own Nvida Palo Alto Networks and Cyber-Ark. — Opinions expressed are subject to alter and past performance would not equal future returns

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