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Skill Versus Luck

Paul DePodesta, a former baseball executive and one of the protagonists in Michael Lewis’s Moneyball, shares a story about playing blackjack in Las Vegas when a guy to his right, sitting on 17, ask for a hit. Everyone at the table is surprised and even the dealer asks if he is sure. The player nods yes, and the dealer produces a four and says, “Nice hit.” But was it really a smart play? Of course not (unless you work for the casino). The probability of going bust with a 17 is 69%. The player was simply lucky. If he continued to hit on every 17 he had been dealt, he would lose most of the time.

On the other side of the coin, sometimes decisions that may have seemed sound at the time they were made result in unfavorable outcomes. Consider an example highlighted by poker champion Annie Duke in her book Thinking in Bets: Making Smarter Decisions When You Don’t Have All the Facts. It involves one of the most infamous play calls in the history of football: the decision by Seattle Seahawks coach Pete Carroll to throw the ball at the one-yard line rather than hand the ball off to star running back Marshawn Lynch to end Super Bowl XLIX. The pass was intercepted in the end-zone and the decision cost the Seahawks not only the game, but the NFL championship. Carroll was widely ridiculed for the decision but perhaps the hate is undeserved considering that an incomplete pass would have stopped the clock giving the Seahawks some much needed time to score. Additionally, according to Duke, only 2% of passes from the 1-yard line within the previous 15 years had been intercepted. In all likelihood, the play should have either resulted in a touchdown or a clock stoppage giving the Seahawks more chances to run the ball. Because of the outcome, football fans perceive the play call as an atrocious one.

These anecdotes highlight a cognitive bias known as outcome bias. This has to do with one of the most fundamental and underappreciated concepts in sporting events, gambling and investing: process versus outcome (or skill versus luck). Just like the gambler in the hit-on-17 story, investors tend to make similar errors by placing too much weight on the outcome of a decision rather than the process by which the decision was made. The focus on the outcome is to some degree understandable because the outcome is what ultimately matters. However, it could be due to pure luck or some other random factors. People are not good at understanding the interplay between luck and skill. Luck is the force that brings good fortune by chance and not as a result of effort or ability. Skill, on the other hand, is the ability to do something competently due to learned power or acquired knowledge.

Some activities involve more luck than skill (e.g. playing roulette versus investing). There is, in fact, a simple way of determining whether an activity is based on skill proposed by Wall Street investment strategist, author, and professor Michael Mauboussin – just ask if the player can lose on purpose. If they can, it is a skill-based game (to a degree). If they cannot, luck plays a major role (again, to a degree).

A process is simply a methodology utilized to achieve a goal. It could be a simple checklist, or it could be a more sophisticated approach. Processes concentrate on the specific actions that must be followed in a discipled and systematic way, regardless of results. In investing, a traditional process might involve analyzing the last five years of financial statements for a company before buying their stock. Alternatively, more quantitatively oriented investors might develop intricate models with pre-defined rules to decide which stocks to buy and sell.

But investors often are mistaken by associating good outcomes with skill. Often, outsized gains are the result of a lucky stretch for a particular style of investing with the eventuality of that luck running out. Over time, abnormal returns are bound to revert to the mean, or average, as dictated by the law of large numbers. On the other hand, the best long-term money managers all emphasize a systematic and informed process. They are not concerned with failure in the short run due to factors outside of their control (e.g. global pandemic; shifting market sentiment; sudden outbreak of geopolitical events; etc.)

Jay Russo and Paul Schoemaker emphasize the importance of this process-versus-outcome decision making in their book Winning Decisions: Getting It Right the First Time (see Graph below). Their main point is that an individual who relies on process-based decision making deserves praise regardless of the outcome. Likewise, a person who uses a poor process but is met with a good outcome deserves neither praise nor promotion. This fortunate individual is simply the recipient of dumb luck. This is why it is so essential that investors shun activities such as chasing hot stocks or mutual funds that would make them vulnerable to the influence of psychological biases such as herding behavior or short-term oriented speculative trading. Instead, they should follow a more process-driven system to make informed decisions with a long-term perspective. As the co-founder and CEO of Twitter Jack Dorsey has opined, success is never accidental.


With or Without Skill Image
The views expressed represent the opinion of Passage Global Capital Management, LLC. The views are subject to change and are not intended as a forecast or guarantee of future results. This material is for informational purposes only. It does not constitute as investment advice and is not intended as an endorsement of any specific investment. Stated information is derived from proprietary and nonproprietary sources that have not been independently verified for accuracy or completeness. While Passage Global Capital Management, LLC believes the information to be accurate and reliable, we do not claim or have responsibility for its completeness, accuracy, or reliability. Statements of future expectations, estimates, projections and other forward-looking statements are based on available information and Passage Global Capital Management, LLC’s views as of the time of these statements. Accordingly, such statements are inherently speculative as they are based on assumption that may involve known and unknown risks and uncertainties. Actual results, performance or events may differ materially from those expressed or implied in such statements.

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A Scientific Approach to Investing

Many areas of modern life rely on scientific research and are guided systematically by well-defined rules. When designing self-driving cars or developing surgical robots, fields such as physics, math, statistics, and computer science are all relied upon. Finance and investing are no different. Finding ways to maximize gains while minimizing downside risks is the goal of investment analysis and portfolio management. There are various schools of thought on how to achieve this, but two popular examples are 1.) traditional fundamental analysis and 2.) a more quantitative approach. The former is the classic way to examine company financials and evaluate investments. Quantitative investing, on the other hand, is based on identifying reasonable, repeatable, and measurable hypotheses regarding behaviors of financial instruments and markets. It has advanced to a highly specialized discipline, which has offered quantitative investors additional tools and insights as well as speed thanks largely to a series of developments:

Computational power has roughly doubled every two years since the 1970s

There has been an exponential increase in data availability accompanied by a decrease in storage costs due mostly to cloud computing

Powerful new algorithms in AI (“Artificial Intelligence”) and its subset of machine learning have been developed from more traditional techniques in fields such as computer science and statistics

The views expressed represent the opinion of Passage Global Capital Management, LLC. The views are subject to change and are not intended as a forecast or guarantee of future results. This material is for informational purposes only. It does not constitute as investment advice and is not intended as an endorsement of any specific investment. Stated information is derived from proprietary and nonproprietary sources that have not been independently verified for accuracy or completeness. While Passage Global Capital Management, LLC believes the information to be accurate and reliable, we do not claim or have responsibility for its completeness, accuracy, or reliability. Statements of future expectations, estimates, projections, and other forward-looking statements are based on available information and Passage Global Capital Management, LLC’s views as of the time of these statements. Accordingly, such statements are inherently speculative as they are based on assumption that may involve known and unknown risks and uncertainties. Actual results, performance or events may differ materially from those expressed or implied in such statements.

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What is Factor Investing?

Of the specialized investment strategies popular across the board today, factor investing is one of the most interesting. Popular with both quantitative investors and those with a more traditional approach to money management, factor investing is a discipline heavily focused on research. With its roots in the investors of the 1970s, factor investing seeks to identify distinct factors that drive stock returns.

By quantifying and identifying these factors and their presence in other instruments, an investor can theoretically make smart strategic decisions to grow their assets. What are some of the things investors look for that hint at future success?

Understanding the “Factors” in Factor Investing

The popularity of factor investing can be credited to Eugene Fama and Kenneth French, creators of the Fama-French Three Factor Model. This model identified three factors that drove stock returns:

Market Risk

Size: Smaller-cap stocks tended to outperform larger ones

Value: Cheap stocks, as measured with the book to market ratio, tended to outperform expensive ones

Other factors that have been added to the model over the years include liquidity, quality, and momentum. Ultimately, the investments that perform well are typically those in which you can identify the presence of multiple favorable factors.

What Risks Does a Factor-Based Approach Entail? Every strategy involves some level of risk, and choosing investments based on market factors is no different. First, it is not possible to fully assess all the factors that drive returns. Second, market volatility or a sudden downswing may bring new factors into play. Just as there are attributes that may make your investments smart choices, there are also some that can negatively impact your investments in the same degree. When market conditions change rapidly, what made your selections successful could work against them instead.

Incorporating Advanced Strategies into Your Investment Portfolio

With a substantial volume of trades on the market every day and new businesses entering the arena all the time, identifying the factors outlined above often proves challenging. Separating the signal from the noise can require an immense amount of research and continual re-evaluation of your assumptions about the business. There is thankfully a better way to tackle this problem.

At Passage Global Capital Management, we believe that the underlying principles of factor investing can be a vital contributor to a sound strategy. We rely on quantitative investment methods powered by our rich insights into the market and unique algorithmic models. By peering into the data with the power of modern technology, we work together with our clients to produce results that align with your investment goals. Contact us today for further details.

The views expressed represent the opinion of Passage Global Capital Management, LLC. The views are subject to change and are not intended as a forecast or guarantee of future results. This material is for informational purposes only. It does not constitute as investment advice and is not intended as an endorsement of any specific investment. Stated information is derived from proprietary and nonproprietary sources that have not been independently verified for accuracy or completeness. While Passage Global Capital Management, LLC believes the information to be accurate and reliable, we do not claim or have responsibility for its completeness, accuracy, or reliability. Statements of future expectations, estimates, projections, and other forward-looking statements are based on available information and Passage Global Capital Management, LLC’s views as of the time of these statements. Accordingly, such statements are inherently speculative as they are based on assumption that may involve known and unknown risks and uncertainties. Actual results, performance or events may differ materially from those expressed or implied in such statements.

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An Introduction to Quantitative Investing

If timely and accurate information is a vital factor in analyzing stocks and key to smart investments, today’s investors face nothing short of a tidal wave of data. In the face of thousands of data points about every public company, from their financial ratios to news breaking headlines to social media postings, it is often difficult to make sense of all the noise. This complexity makes one thing abundantly clear: no human can analyze it all on his or her own. A powerful computer system, on the other hand, may have everything we need to try to identify and understand where the best investments might hide.

Thus, we have the heart of quantitative investing, one of the growing investment strategies around the world today. More and more investors have been gravitating toward algorithm-driven quantitative strategies at the expense of traditionally managed funds. In this discipline, experienced individuals with data and computer science backgrounds called “quants” rely on high-powered computers, vast data sets, and sophisticated algorithms to extract correlations and insights in order to systematically exploit patterns in securities prices and markets trends. What is this strategy all about, and what can it mean for your investments?

Understanding What Quants Do: What’s the Quantitative Model All About?

Almost everything about a stock’s performance and the overall marketplace is quantifiable — we can express it in some form with numbers. Analyzing these numbers, determining their relationships to one another, deciphering patterns, and using them to make predictions are the core activities involved in quantitative investing. By interpreting past events and current trends, quants hope to draw conclusions that lead them to generate higher returns with lower risk.

There are several disciplines within quantitative analysis such as factor investing and statistical arbitrage. Data sources also range widely from publicly accessible stock prices to unique alternative data sets. Examples of alternative data include satellite pictures of retailers’ parking lots and sentiment of companies’ financial statements.

Why Quantitative Investing Remains the Wave of the Future

The earliest quants decades ago did not have access to the same level of computing power, advanced techniques, and big data available today. That made large-scale analysis more difficult and necessitated more narrow focuses. Today, many of those limits are gone. Quants have access to extremely powerful computers, and with the advent of cloud computing, the sky is the limit when it comes to analyzing the data produced by the market.

There’s no shortage of information, either. Where once it was a problematic barrier to effective analysis, today the amount of data is a blessing. A well-crafted algorithm can comb through millions of data points in a fraction of the time it would take for a human to reach the same conclusion. With so much activity, market inefficiencies become harder and harder to spot. With advances in technology such as machine learning, more patterns will likely emerge over time. These advances can make quantitative investing appealing to everyone from the high-flying trader to the retiree with a moderate appetite for risk, and it may explain why quantitative investing accounts for $1 trillion in market value.

Are There Any Issues with Quantitative Investing Models?

It’s important to note that quants aren’t all-knowing and all-seeing. The models are only as good as those who design them and the data input. It’s certainly possible for biases and incorrect assumptions to make their way into a model. A quant model tries to tell the future, but it can’t produce the exact details. Historically, quantitative market analysis has tended to focus on past results, adding new data as it becomes available.

A balanced investment approach may rely on quantitative principles while also following the best practices for managing risk. The right solutions are transparent about their results and upfront with what you can expect for your funds.

The views expressed represent the opinion of Passage Global Capital Management, LLC. The views are subject to change and are not intended as a forecast or guarantee of future results. This material is for informational purposes only. It does not constitute as investment advice and is not intended as an endorsement of any specific investment. Stated information is derived from proprietary and nonproprietary sources that have not been independently verified for accuracy or completeness. While Passage Global Capital Management, LLC believes the information to be accurate and reliable, we do not claim or have responsibility for its completeness, accuracy, or reliability. Statements of future expectations, estimates, projections, and other forward-looking statements are based on available information and Passage Global Capital Management, LLC’s views as of the time of these statements. Accordingly, such statements are inherently speculative as they are based on assumption that may involve known and unknown risks and uncertainties. Actual results, performance or events may differ materially from those expressed or implied in such statements.

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The Power of Al: How Does It Work?

Artificial Intelligence (Al) is far from a new concept. Cars, cell phones, fraud detection and prevention technology, ad recommendations that pop on the side of your Facebook page, etc. are all forms of technology that utilize artificial intelligence in one form or another. Within the larger field of AI, there are various subsets such as robotics, computer vision, and perhaps most famously machine learning (ML). Machine learning is a sophisticated extension of Al that is rapidly advancing the efficiency of the modern world, and in fact, you probably use ML-powered technology on a daily basis.

Coined by A.L. Samuel in 1959, machine learning is a process whereby a computer system learns and adapts to data it is fed, essentially training itself. Instead of humans programming the technology to do or know something, the technology is programmed to teach itself based on its experiences

There are three primary categories of ML: :Supervised LearningUnsupervised learningReinforcement learning.

With supervised learning, the model is provided with a known, desired outcome. The model then trains itself to best achieve that outcome. The challenge, of course, is then feeding new data to the existing model. Often, the model is “overfit” on the data that it was trained on and is useless in making decisions with new data.

In unsupervised learning, the model is not provided with any target outcome. Often, unsupervised learning is used to identify similar clusters, or groups, of observations. Recommendation systems are a famous use case of unsupervised learning.

Finally, reinforcement learning is quickly gaining momentum. In this approach, the model learns from feedback it is given throughout the learning process. For example, when training a dog to sit, the dog is rewarded with a treat once it performs the task it is commanded to do. After the dog is praised and rewarded for its accomplishment, the dog will know to do the same thing when asked to execute the task again. Reinforcement learning uses the same idea, but without the dog treat, of course.

You may be wondering what an every-day example of Machine Learning is. The truth is that ML is embedded in multiple forms of technology that you more than likely use regularly. For example, the spam filter in your email is one common application of ML. Based off what you have opened and responded to in the past, ML will sort what it thinks you will find important from unimportant. It is important to consider that the more information it is fed, the more accurate it will be.

Al was created to simplify our lives as well as take on larger and more complex feats that human error inhibits us from accomplishing, and ML is precisely the resource that will continue to innovate the world. Industries all over the globe are beginning to utilize forms of Al to enhance performance, efficiency, and general way of life.

The views expressed represent the opinion of Passage Global Capital Management, LLC. The views are subject to change and are not intended as a forecast or guarantee of future results. This material is for informational purposes only. It does not constitute as investment advice and is not intended as an endorsement of any specific investment. Stated information is derived from proprietary and nonproprietary sources that have not been independently verified for accuracy or completeness. While Passage Global Capital Management, LLC believes the information to be accurate and reliable, we do not claim or have responsibility for its completeness, accuracy, or reliability. Statements of future expectations, estimates, projections, and other forward-looking statements are based on available information and Passage Global Capital Management, LLC’s views as of the time of these statements. Accordingly, such statements are inherently speculative as they are based on assumption that may involve known and unknown risks and uncertainties. Actual results, performance or events may differ materially from those expressed or implied in such statements.