Category Archives: Education

Solution to the ‘Introduction to Portfolio Theory: Part 13 Hard’

Plot the feasible set of the portfolios consisting of a risky portfolio (defined as Portfolio R) consisting of 35% of AT&T, 40% of Ford, and 25% of Dell public stocks and a risk-free asset, which is the 1-Y US Treasury Bill in this case.

Show the points of the portfolios of 70% in the risk-free asset and the rest in Portfolio R, 35% in risk-free asset and the rest in Portfolio R, and a riskless borrowing case in which you borrow 40% of your original capital and combine the amount borrowed with your original capital to invest solely in Portfolio R.

Also investigate the feasible set of portfolios for which you borrow at 7% and invest all your funds in the Portfolio R, together with the case in which you borrow 40% and invest all your funds in Portfolio R.

For the risk-free asset, use the info on 5th of February, 2024 and for the stocks, use the info for the last 3 years to estimate the necessary input values.

Predictive Power of VIX: Is it reliable?

Apart from traditional economic indicators that will help investors to monitor business cycles, VIX is also touted as a measure that may help investors foresee what is about to befall them.
Previously, I wrote about reliability issues with respect to complex financial models and measures here: https://bit.ly/3M8Lp0S
Is VIX any different from such models and measures? Does VIX really have such a predictive power? Is it reliable? Can VIX be manipulated, too as is the case with other financial securities?
In this article, I will answer these questions.

Focusing on understanding and predicting risks rather than calculating them Part 2: Examples from Equities, ETFs and Cryptocurrencies

In the second part, I provide examples from equities, ETFs, and cryptocurrencies. These financial instruments are deemed inherently riskier than some types of fixed income securities are on which, as the name suggests, absolute returns are somewhat fixed. However, within each asset class except for cryptocurrencies, there are equities and ETFs for which perceived risks are lower. An equity that has both a diversified business model and an impeccable track record against downturns may be deemed less risky. By the same token, ETFs containing highly diversified (i.e. in very broad terms fewer perfectly correlated) stocks may be less risky. Obviously, these risks profiles will yield disparate returns in different future events such as upturns and downturns.
My focus will be again more diverted to understanding risks rather than calculating them.

Focusing on understanding and predicting risks rather than calculating them Part 1: Examples from FX and bond markets

In finance, the so-called plain vanilla models to calculate the risk of a financial instrument are sometimes more reliable than the complex models claimed to be “accurate”. Plain vanilla models are criticized because of their backward looking fundamentals; however, complex models allegedly benefitting from “cutting-age” machine learning and/or deep learning still cannot predict the future as the models based on these methods are also being trained on the backward looking data. To that end, for investors who do not have complex tools at their disposal, understanding risks prove to be more practical than calculating them accurately. Furthermore, behavioral aspects of finance, as well as centralization within the financial markets, will distort your trades and investments, more so for short-term than for long-term.
In this first part, I will cover FX and bond markets. In the second part, I will provide additional examples from equities, ETFs, and cryptocurrencies.

Valuation of cash-flow bearing long-lived assets: Focus on Real Estate Property

In this video, I will explain how to value a real estate property. The method I employ is similar to that used by investment bankers in their enterprise and equity valuations. The complexity of valuing any cash flow bearing asset lies partly in the uncertainties related to cash flows and required rate of return representing the risk profile of the asset. I will show you how to mitigate the impact of these unknowns on the valuation model. With such a tool in your hand, you will be able to make your sell and buy decisions in a more informed and accurate way.

GameStop (GME) Part 2: Concluding remarks on GameStop: Further fact checking and demystifying illusions they want you to believe in & Lessons learned for retail investors

In this paper, I will share some additional facts helping you understand why GME case was hardly a landmark in the financial markets. Similar phenomena happened in the past and it is very likely that they will happen in the future. However, GME case is significant in that it has presented great lessons ranging from how to expect and spot financial irregularities and how to manage your risk capital to recent digitization in finance and its ramifications for retail investors.

GameStop (GME) Part 1: Financial Mechanics explained

In this paper, I will explain why such a pricing irregularity concerning the value of the GameStop’s stock happened and it happened fast although the buildup was manifest to the trained eye. Obviously, GameStop may not be on your radar considering the current state of the company, the size of its market cap and shares floating, and disruptions faced by retailers alike; however, the financial mechanics here are relatively complex and understanding them will make you more prepared for similar cases that very likely will happen in the future.
In the 2nd part of this series, against the backdrop of part 1, I will elaborate further on lessons that must be learned by retail investors and how this saga may end.