Essentially, I have two questions when investing in any Chilean business. First and foremost, where is the company in the capital structure? It is true that some managers and investors like me, in the name of prudence, think that less debt is always better than more debt, and no debt is optimal. And second, what do we think about the company’s excess return on invested capital?

The risk of investing in bonds varies across the world, resulting in higher risk premiums in some markets than others. What investors need to do to make money in bonds portfolio is no different from any other period.

Essentially, I have two questions when investing in any Peruvian business. First and foremost, where is the company in the capital structure? It is true that some managers and investors like me, in the name of prudence, think that less debt is always better than more debt, and no debt is optimal. And second, what do we think about the company’s excess return on invested capital?

ChatGPT is a watershed moment for artificial intelligence (AI) and its adoption. We are witnessing the start of a major investment boom and technological advance that may fundamentally affect all economic sectors. However, use cases need to develop further to justify these investments. In the early innings of the AI era, I do recommend investors to focus on vertically integrated players across the AI value chain, as those businesses combine good visibility on monetization and strong competitive positioning. In this screener, I share an investment framework to identify AI opportunities. A popular saying, sometimes attributed to Warren Buffet, is: "The best time to invest was 20 years ago. The second-best time is now."

Online analytical processing is a very common way to analyze raw transaction data by aggregating along different combinations of dimensions.

Dashboard

The purpose of this dashboard is to review which sectors** are trend up vs down to better manage risk against SPY. **What are the 11 sectors in the S&P 500? The order of the 11 sectors based on size is as follows: Information Technology, Health Care, Financials, Consumer Discretionary, Communication Services, Industrials, Consumer Staples, Energy, Utilities, Real Estate, and Materials.

The mean-variance is the wrong starting point in fixed income portfolio construction. The problem is better formulated as, in essence, a linear programming problem. Maximize expected return subject to constraints and risk being less than some prescribed limit. What are the constraints? In fixed income, we want to look at: high-level constraints such as CS01 (sensitivity to 1bp move in spreads) and CSW10 (sensitivity to 10% relative increase in spreads). Also interest rate duration (IR01); high-level constraints specifying losses in certain stress scenarios, either rates or credit or both; sector-level constraints; credit-level constraints; tax-level constraints; and maturities. And with constraints and risk limits taken care of, we can focus on getting the buy or sell decision in single names correct.

The discounted cash flow model is a useful tool to frame the question of what the fair value is for the S&P500, and what the market is currently discounting.

Dashboard

En la República Dominicana los alumnos se enfrentan a verdaderos retos a la hora de interpretar números, ecuaciones y lógicas que conducen al razonamiento.

For any option trader, implied volatility (IV) is one of the most important considerations because it has a direct impact on pricing. It has been our experience that IV increases before an earnings announcement and that this increase is due to option writers who want to ensure adequate protection of their portfolios from significant price fluctuations in the market. It is even more important now as IV spreads have grown significantly wider, and the concept of a volatility crush has become an increasingly viable options trading strategy. A fast, sharp drop in IV will create a volatility crush in the value of an option. This often happens after a major event for the stock, like financial reports, regulatory decisions, new product launches, or quarterly earnings announcements. A volatility crush is an opportunity to take advantage of a pattern of predictable price movement across the options market. When we understand premium rates increasing during a substantial event (like earnings) followed by the decrease in IV, we can make smarter trades, informed positions, and better moves for the overall trading account.

Here I share the updated database of excess returns to equity and the firm for publicly traded UAE companies across sectors. Starting with how the market is pricing risk (both country and equity risk premiums), and then moving on to the cost of equity at each debt ratio, and then estimated the interest coverage ratio, synthetic rating, and cost of debt, taking care to ensure that if the interest expenses exceed the operating income, tax benefits would be lost. These hurdle rates also represent benchmarks that businesses have to beat to create value. When we invest capital in risky businesses, we need to not just make money, but make enough to cover what we could have earned on investments of equivalent risk.

Lets look at how we can use R to automate the process of extracting relevant data from the Singapore capital allocation database.

Here I share the updated database of excess returns to equity and the firm for ~529 publicly traded Singaporean companies across sectors. Starting with how the market is pricing risk (both country and equity risk premiums), and then moving on to the cost of equity at each debt ratio, and then estimated the interest coverage ratio, synthetic rating, and cost of debt, taking care to ensure that if the interest expenses exceed the operating income, tax benefits would be lost. These hurdle rates also represent benchmarks that businesses have to beat to create value. When we invest capital in risky businesses, we need to not just make money, but make enough to cover what we could have earned on investments of equivalent risk.

Lets look at how we can use R to automate the process of extracting relevant data from the Saudi Arabia capital allocation database.

Here I share the updated database of excess returns to equity and the firm for ~254 publicly traded Saudi companies across sectors. Starting with how the market is pricing risk (both country and equity risk premiums), and then moving on to the cost of equity at each debt ratio, and then estimated the interest coverage ratio, synthetic rating, and cost of debt, taking care to ensure that if the interest expenses exceed the operating income, tax benefits would be lost. These hurdle rates also represent benchmarks that businesses have to beat to create value. When we invest capital in risky businesses, we need to not just make money, but make enough to cover what we could have earned on investments of equivalent risk.

Success in the money management business is mostly down to avoiding common mistakes such as over complicating financial decision making, being too optimistic about likely returns, taking excessive risks, and trading too often. Fortunately, there is a solution. The answer is to fully, or partly, systematize our financial decision making. This will give us the best of both worlds – our human ability to interpret and process information, combined with a system giving the correct amount of risk.

A treasure trove of data science recipes that help students achieve operational excellence, and develop competitive edge in the private equity industry. Whether they collect it themselves or acquire it from external sources, the amount of data available to private equity firms today is increasing exponentially. And firms that choose not to use data science data to create value risk hastening their own obsolescence or, at the very least, losing competitive advantage.

Implementation of two relative value models used in fixed income trading algorithms.

Professor Aswath Damodaran suggest that we treat all of R&D expense as tax-deductible capital expenditures, for purposes of valuation, and this can have significant effects on operating income, capital and expected growth measures for firms with substantial research expenses.

Separate, clean, upload and analyze host and listing data taken from Airbnb for Singapore.

This dashboard was created to help people users stats, listings and reviews. The data used for this dashboard is specifically from Singapore.

Download the letters from Berkshire Hathaway, Warren Buffett’s company, and then implement a sentiment analysis.

In this post, text analysis is conducted on the Fed Beige Book to compare the sentiments of reports across time. We will be focusing on the use of the sentometrics package in performing the said analysis.

How to best allocate business responsibilities and risks and how to design tariff adjustment and other rules to achieve the desired allocation in a public-private-partnership.

In this example I mimics someone closing positions that have started to lose money. The back-test shows how profitable this rule would have been if it had been run in the past. By using stop losses we are trading somewhat like an early loss taking trend follower.

In this lecture we reconstructs all dplyr F1 queries, then compares them with the SQL related queries. Students will check if the dplyr output matches using sql statement. For each section, we will repeat this checking process.

In this lecture, I covered how to create a SQLite database image version of the Ergast Developer API containing historical Formula 1 racing data. I also discuss how to use the dplyr package to interface with databases and to make window functions available to SQLite files.

A successful crypto trading operation does not arrive out of the ether!

Our goal is to extract the commentary from each workbook as well as valuation summary tables and enter them into a named list of values, lists, and tibbles.

Our goal is to extract all info pertaining to calculating 'wacc' and performing 'dcf' analysis from each workbook and enter pulled tables and calculations into a named list.

Our goal is to extract the public comparables table from each workbook and transform it into a list of vectors that each contain all the observations corresponding to a heading.

Our goal is to extract the M&A comparables table from each workbook and transform it into a list of vectors that each contain all the observations corresponding to a heading.

Our goal is to extract the 'trading levels of company securities' table from each workbook and transform it into a list containing lists that detail the properties of each security.

Our goal is to extract the 'Long Term Financial Performance' table from each workbook and transform it into a vector of two vectors detailing financial performance and covenant ratios.

Our goal is to extract the capitalization table from each workbook and transform it into a vector detailing each observation and its variables for the different capitalization periods. It is also to perform and compile summary calculations to be stored in the vector.

Our goal is to extract the security description table from each workbook and transform it into a list detailing each observation and its variables and values.

Our aim in this exercise is to extract the business description elements from each workbook and transform it into a named list.

One of the lessons from the COVID-19 crisis was to move away from static approaches for computing risk premiums, dependent on looking at long periods of history. The price of risk changes on a day-to-day basis based on a variety of variables, including uncertainty around future economic growth, political stability, worries about catastrophes/disasters, investor risk aversion, and information availability/reliability.

Now that we have hit the midway point of the MLB season, it's time to take a look at the batting ranking. This ranking is based on a combination of career performance and single-season offensive performance, based on the Offensive Wins Above Replacement (oWAR) stat.

For the past fifteen years I have been collecting company level data and performing bottom-up analysis of leading economic industry companies. The idea here is to use the stock market conditions as signals for what is about to happen in the economy. Stocks usually lead fundamentals by about 6 to 12 months, thus it has a remarkably prescient message about future economic activity.

* What is the total annual Dominican import and export? * Who are the main trading partners? * What type of goods are traded from China?

If we accept the proposition that the interest rate in a currency is the sum of the expected inflation in that currency and a real interest that stands in for real growth, it follows that risk free rates will vary across currencies.

While much of the discussion about the election has been about its impact on the overall economy and equity values, the effect of any change to the U.S. corporate tax codes will likely be redistributive, with some sectors gaining and other losing. To identify the sectors that will benefit the most from any tax code change under Biden, I looked at effective tax rates, capital expenditures/sales and debt ratios across all U.S. non-financial service sectors.

With reference from Free Range Statistics’ text analysis of Hamlet, there were 2 main issues that arose in class: (i) incomplete stage directions, and (ii) differing speeches for Hamlet. In this post my students made some changes to solve these problems.

Sports fans frequently argue over who is the greatest of all time or the GOAT of their sport. Elo ratings are one of the more objective ways to measure a player’s ability. The Elo rating gives us a number of a player’s strength over time in a way that accounts for the ability of the players they have won and lost to over their career.

Isn't math beautiful? Here I invoke Euler's formula to perform a simple substitution on the classical notation of the Fourier Series, and create a beautiful and truthful description of the sine and cosine waves that allow a regression to detect seasonal patterns.

Methods for calculating private equity performance as a public market equivalent.

As part of evaluating an investment program in private markets, you will inevitably be asked, “Did you receive value for the fees you paid?” In negotiating private equity or real estate deals, we are faced with the dilemma of how to negotiate fees.

Applying some brain gesticulation to the various interpretations people might have regarding the Internal Rate of Return (IRR) and the well known Reinvestment Assumption in private equity (PE) investments.

The sine of 1,234,567,890 degrees is 1

Formula x = 1 + 1/ x expanded recursively to obtain a continued fraction approximation for 1.62.

There is almost nothing of use to investors from poring over current macroeconomic data, which is one reason why markets have started ignoring them

Tidying the value of the S&P500 to its cashflows, growth and risk.

The S&P 500 numbers are pre-tax whereas the Berkshire numbers are after-tax. Data source: Warren Buffett's 2019 annual letter.

Saudi Aramco has set a price range for its listing that implies the oil giant is worth between USD$1.6 trillion and US$1.7 trillion, making it potentially the world's biggest IPO. The numbers that are laid out in the prospectus are impressive, painting a picture of the most profitable company in the world, with almost unassailable competitive advantages. In this post, I valued Saudi Aramco between US$1.69$ and US$1.83$ trillion using the the tidy data principles.