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Alpha Theory Blog - News and Insights

June 24, 2022

8 Data Science Resources for Investment Management Professionals

 

Investment managers are increasingly interested in using data science and artificial intelligence to improve their processes and outcomes. Recently, a few clients have asked our team how they can sharpen their skills in the subject.  

 

The problem is that most entry-level data science material is not very useful for finance, and the material useful for finance is not entry-level by any means. Not to worry, our team has shared their top books and articles for investment professionals eager to learn about solving problems with data. Read on for a list of our top eight picks. 

 

Algorithms to Live By- The Computer Science of Human Decisions 

 

Authors Brian Christian and Tom Griffiths show how algorithms developed for computers also untangle very human questions by explaining, in layman’s terms, algorithmic solutions for real-world decision making. If you like problem-solving and decision theory, you’ll love this book. 

 

Recommended by Cameron Hight, Alpha Theory CEO 

 

Big Data: A Revolution That Will Transform How We Live, Work, and Think 

 

Viktor Mayer-Schönberger and Kenneth Cukier, two leading experts in data science, wrote this non-technical book that discusses what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. It’s a great place to start for those who wish to get into a data-oriented mindset, but do not have direct experience. 

 

Recommended by Aaron Hirsch, Data Scientist at Alpha Theory 

 

CRISP-DM – a Standard Methodology to Ensure a Good Outcome 

 

CRISP-DM is a framework for applying data science to business problems. This article gives a solid explanation on how to approach a project before getting started. For those getting started in practicing data science, it will save them time by helping to avoid rabbit holes. 

 

Recommended by Billy Armfield, Data Scientist at Alpha Theory 

 

The 7 Steps of Machine Learning 

 

This article, written by a google developer, outlines in broad strokes the steps in a typical machine learning problem. It walks through a basic example to describe the process of getting answers from data using machine learning. Readers will gain a foundational framework to think through the problem and the language to articulate each step. 

 

Recommended by Billy Armfield, Data Scientist at Alpha Theory 

 

Machine Learning: An Applied Mathematics Introduction by Paul Wilmott 

 

This slim book by uber-quant Paul Wilmott gives clear and detailed explanations of the machine learning models most used in quant finance, along with pointers to further reading. While the book assumes basic calculus and linear algebra skills, it is an approachable resource for those who desire a deeper understanding of machine learning models without dense textbook reading. 

 

Recommended by Ross Fabricant, Director of Data Science at CenterBook Partners 

 

Statistical Methods for Machine Learning- Learn How to Transform Data into Knowledge with Python 

 

Machine learning specialist Jason Brownlee provides a thorough hands-on introduction to statistics and hypothesis testing with step-by-step instructions through Python-based projects. The book builds a solid foundation for future discovery and assumes little prior knowledge of statistics and coding. 

 

Recommended by Chris White, Head of Portfolio Implementation & Risk at CenterBook Partners 

 

Machine Learning Mastery with Python- Understand Your Data, Create Accurate Models, and Work Projects End-to-End   

 

Also, by Jason Brownlee, this step-by-step guide helps the reader master foundational techniques in machine learning, using Python with scikit-learn, pandas, tensorflow and other helpful libraries. It is written in an engaging and accessible style, without assuming much prior knowledge. 

 

Recommended by Chris White, Asia CEO & Head of Risk & Portfolio Implementation at CenterBook Partners 

 

An Introduction to the Bootstrap 

 

Bradley Efron and Robert J. Tibshirani arm scientists and engineers with computational techniques to analyze and understand complicated data sets, without relying on an understanding of advanced mathematics. But be warned- this dense academic textbook is no-nonsense. Fancy charts and descriptions of tooling are few and far between.  

 

Recommended by Ake Kullenberg, Head of Execution Trading at CenterBook Partners 

 

Were there any books that have been helpful to you as you begun learning about data science? We’d love to know. 

 

Get in Touch with Alpha Theory 

 

If you have questions about the resources mentioned above, our in-house data science team, or our leading portfolio construction platform and services for investment managers, please do not hesitate to reach out.  

 

Any communication regarding CenterBook Partners LP (“CenterBook”) is for information purposes only and should not be regarded as advice, a solicitation of an offer to buy or sell any CenterBook financial product, or as an official statement of CenterBook. 

May 26, 2022

Turtle Creek: The Discipline Behind 21% Compound Returns

We recently spoke with a portfolio manager who runs a concentrated Canadian long/short fund. He said that they aspire to be like Turtle Creek, a firm that embodies the Alpha Theory philosophy of refining the investment process to generate better outcomes. Turtle Creek has a remarkable return stream, with compounded capital at 21% per year for almost 25 years. 

After reading a few of their letters, it’s obvious that Turtle Creek has been running the Alpha Theory strategy for 10 years longer than Alpha Theory has existed. Their performance is no surprise given our findings that the most process-oriented managers outperform. 

In Turtle Creek’s Third Quarter Investor Letter, they discuss their portfolio construction and continuous portfolio optimization. I’m going to include a rather long excerpt (slightly edited for brevity) because it is a great explanation of how and why this is the best way to manage a fundamental portfolio. 

Our investment approach comprises four steps: 1. Finding the right kind of companies; 2. Valuation; 3. Portfolio Construction; and, 4. Continuous Portfolio Optimization.  

Our final two steps – the initial sizing of a position (Portfolio Construction) and then adding to or trimming the position in reaction to changing share prices (Continuous Portfolio Optimization) – can be understood as one step. But years ago, we decided to break them into two separate steps in order to better explain our process. By doing so, we can focus people on our initial sizing without the distraction of talking about future fluctuating share prices and how we continuously revise the size of a position. Then, once the logic of how we size a new holding is understood, we can introduce step four: Continuous Portfolio Optimization.  

In explaining Portfolio Construction, we often start with a simple example. Suppose that we only know two companies and that we have built a balanced, long term financial forecast for each. The present value of our forecasted cash flows represents our view of Business Value. If both companies are trading at the same discount to our Business Value estimate, we could buy their shares and, if our forecasts turn out to be correct, we would earn an annual return that is somewhat better than our discount rate – say 15% [this is akin to Alpha Theory Probability Weighted Return]. Next, assume that we know both companies equally well and assess management quality, business risk, etc. to be the same [this is akin to the Alpha Theory Confidence Checklist]. In this simple example, we would invest half of the fund in one company and half in the other, since we expect both to earn a 15% return.  

But of course, no two companies are the same. Some trade at bigger discounts to our view of Business Value than others. Discount to Business Value is one of the largest drivers of our portfolio weightings since tilting towards cheaper companies both minimizes risk and, almost always, enhances expected returns. Away from the discount, we also consider other factors when determining portfolio weightings. For example, every company has a range of future outcomes – some fall within a relatively tight band, while others have a much broader range. In developing our financial forecast, we think about the probability of different future outcomes and consider risk to be the potential downside scenarios from our expected outcome, not the chance that the share price will fluctuate in the short term. So, in our simple example, we might decide that, while the long term expected return is 15% per annum for both companies, by the time we account for other factors – dispersion of future outcomes, relative strength of management, how long we have known the company, etc. – we might initially size one holding at 60% and the other at 40%, rather than 50% for each. Think of it as one company having a better risk-adjusted 15% expected return than the other.  

Of course, we don’t own just two companies; we own 25 to 30 in each of our funds. Things become a lot more complicated, but the ideas behind our Portfolio Construction described above still drive our portfolio weightings.  

And once we have sized a position, we don’t just sit back and simply wait for some stock price target to be reached. The sizing exercise we undertake when adding a position essentially assumes a static portfolio. But portfolios are far from static – stock prices are constantly moving around and so, in a way, we must continuously re-construct the portfolio. This ongoing re-construction process is something we call Continuous Portfolio Optimization (“CPO”). Typically, this process entails selling small amounts of positions that have seen share price appreciation and, in turn, buying small amounts of positions that have seen their share prices fall.  

Let’s return to our two-stock portfolio example. After initially constructing the portfolio, let’s then assume that the share price of one company declines by 10% while the share price of the other rises by 10% (a pretty common occurrence in the stock market, as you know), with no change to our long term view of Business Value for either company. Obviously, the portfolio is imbalanced because now the company with the lower long term expected return (because its share price has risen) is a larger weighting and the company with the higher long term expected return (because its share price has declined) is a smaller weighting. To us, it makes no sense to do nothing and so we would sell some of the lower prospective return position (the one which saw its share price rise 10%) and invest the proceeds in the higher prospective return position (the one which saw its share price fall 10%).  

In our previous commentaries we highlighted some of the differences in our approach to company identification and valuation. But in these two final steps – Portfolio Construction and CPO – the entire approach is different. We are often asked to identify the source of our out-performance. It’s a complex question that proves difficult to answer. Turtle Creek Equity Fund is comprised of only 30 names, so clearly identifying great companies, and avoiding not so great ones, coupled with a well thought out view of value is an important contributor to our returns. Yet our approach of overweighting the most attractive positions, on a continuous basis, is also an important contributor. The thing is, these steps are all inter-connected and heavily dependent on each other. Without a view of value that we have confidence in, we would be unable to initially size a position, nor react to changing prices. And arriving at a well thought out view of value is certainly made easier when you are dealing with highly intelligent, shareholder focused organizations.  

The impact of our CPO shows up whether you look at individual holdings or the portfolio overall. On an individual holding basis, for virtually all of them, the return we have generated exceeds that of a ‘buy and hold’ approach and, over time, the difference becomes larger and larger. This is also the case for the overall portfolio 

While CPO has generated incremental positive returns, we don’t do it to boost performance. We engage in CPO to constantly de-risk the portfolio – to maintain one that has the lowest risk or highest margin of safety. But, of course, this also has the inverse impact of constantly fine tuning the portfolio to have the highest long term expected return. For those of you who are interested in reading a more expansive discussion on risk, we would direct you to our Tao of the Turtle, Risk, A Further Discussion.  

We are often asked “why don’t others do CPO?” The answer is complicated. First off, one must do the first three steps really well: finding the right companies, doing fundamental work to have a confident view of the true intrinsic value of each company and having a logical means of initially sizing individual holdings. That is the foundation that enables us to buy more of a holding at lower prices and, equally, have the comfort to trim the position at higher prices. Then there are factors such as temperament that make CPO difficult to put into practice. Recently, in a meeting with a large U.S. family office, they commented that they have some good investment managers but every time those managers try to ‘trade around’ their positions they find that they actually detract from a buy and hold return. And then they observed that, clearly, we have added value. We explained that we are not ‘trading around’ our holdings; instead, we are simply reacting to other people ‘trading around’ and the share price changes that result.  

Looking forward, as we survey the companies in our portfolio today, we would be very surprised if each share price wasn’t higher (frankly, a lot higher) in five to ten years. Think of us as having high confidence in each share price far into the future. But we have very low confidence as to where the share prices will go in the shorter term. If we are lucky, the path to those higher long term share prices will be uneven with lots of ups and downs so that we will be able to apply Continuous Portfolio Optimization to the benefit of our investors.  

And in their 2013 piece, “The Tao of the Turtle” Turtle Creek discusses how portfolio construction requires valuation: 

This brings us to a key point about portfolio construction: without our Edge 2 – Valuation, it would be impossible to construct an optimal portfolio, or even any logical sort of portfolio. You have to understand the value of each of your investments (how much free cash each investment will generate over time) – and most importantly, the relative value among your investments – before you can go about the process of portfolio construction. It is worth reiterating that we think about valuation probabilistically.  In our recent Tao (on valuation), we provided the visual of a bell curve to show how we think about our intrinsic value estimates – while we use the ‘best estimate’ midpoint for each valuation we recognize that, in an uncertain world, value exists over a range. 

The concept of continuous portfolio optimization is simple. Maintaining the discipline to follow it is not simple. Our clients, it should be assumed, would be the most likely to follow continuous portfolio optimization, because they use our platform, which is designed to drive refinement of the investment process. Still, even they leave over five percent of returns on the table from not more closely following the optimal position sizes generated from their research. Our hope is that seeing Turtle Creek’s 25-year track record, which is made possible by continuous portfolio optimization makes sticking to the discipline a little bit easier.