Finding Alphas. Igor Tulchinsky

and death of alphas.

      DATA INPUT

      In order to predict the price movement of financial instruments, alphas need data. This data can be the prices themselves or a historical record of those prices. Most of the time, however, it helps to have more information than just the prices. For example, how many shares of a stock were traded, its volume, etc., can complement the historical price–time series.

      A simple diagram to represent what an alpha is doing is as follows:

DATA (E.G. HISTORICAL PRICES) → ALPHA → PRICE PREDICTION

      Note that data quality can have a large effect on the output of an alpha. So it’s important to evaluate data quality before it is used and address shortcomings then. Issues that may affect data quality can be technical, e.g. hardware problems, or related to human error, e.g. unexpected data format change, extra digits, etc.

      PREDICTIVE OUTPUT

      An alpha model’s output is typically a prediction. In many markets, it’s easier to predict the relative price of a financial instrument than it is to predict the absolute price of a financial instrument. Thus, in stocks, many alpha models predict the movement of the prices of various stocks relative to other similar stocks.

      Typically, alphas are implemented using a programming language like C++, Python, or any other flexible and modern language. In larger organizations, a software environment developed in-house can abstract the alpha developer from many book-keeping and data management issues, letting the developer focus on creative research and modeling.

      EVALUATION

      What is a good alpha? What is a bad one? There is no single metric that will answer that question. In addition, the answer depends in part on how the alpha is going to be used. Certain investment strategies require very strong predictors; others benefit, marginally, from weak ones. Some pointers to alpha evaluation are:

      ● Good in-sample performance doesn’t guarantee good out-of-sample performance.

      ● Just like in academic statistics, outliers can ruin a model and lead to erroneous predictions.

      It takes a lot of in-sample and out-of-sample testing to validate an idea. The more data one has, the more confidence one can have in an alpha. Conversely, the longer the period one considers, the more likely that the alpha will exhibit signs of decay and the more likely fundamental market changes will make the alpha unusable in the future. Thus, there is a natural tension between developing confidence in an alpha and its usefulness. One must strike the right balance.

      LOOKING BACK

      When developing alphas, one has the opportunity to look back in time and evaluate how certain predictive models would have performed historically. And, while evaluating an alpha with backtesting is invaluable (providing a window into both the markets and how the alpha would have performed), there are a few important points to remember:

      ● History never repeats itself exactly, ever. So while an alpha idea may look great on paper, there’s no guarantee it will continue to work in the future. This is because of the perverse power of computation, and the ability of creative modelers to miss the forest for the trees. With computational resources, one can evaluate a very large number of ideas and permutations of those ideas. But without the discipline to keep track of what ideas were tried, and without taking that into account when evaluating the likelihood of a model being a true model versus a statistical artifact only, one will end up mistaking lumps of coal for gold.

      ● It’s easy to look back at history and imagine that the market was easier to trade than it was in reality. That’s because of several effects. First, you didn’t have the insight back then that you have now. In addition, you didn’t have the data back then that you have now. Finally, you didn’t have the computational power and technology to do what you can do now. Ideas that seem simple and were programmed in spreadsheets in the 1990s were actually not so simple back then, especially when one considers the research it took to get there. Every decade has its market and its own unique market opportunities. It’s up to the alpha developer to find market opportunities with the data and technology available. Looking back at previous eras, it is wrong to believe it would have been easy to predict the market and make money.

      STATISTICS! = STATISTICAL ARBITRAGE

      The term statistical arbitrage is another term used to describe quantitative investing. The term conjures a couple of ideas: (1) that the predictions used don’t constitute a pure and risk-free arbitrage, and (2) that statistical models are used to predict prices. While (1) is absolutely correct, (2) requires some explanation. While it is the case that certain models from academic statistics – such as time series analysis and machine learning, or regression and optimization – can be used as part of alpha development, it’s important to realize that most techniques from academia aren’t really aiming to solve the problem that is of interest to a quantitative investment firm (i.e., generating a cash flow and managing the risk of that cash flow). Thus, while looking at mean-square errors when evaluating models has some merit, it is only indirectly related to making money.

      TO SUM IT UP

      The existence of alphas in the market is a result of the imperfect flow of information among market participants with competing objectives. An investor with a long-term horizon may not be concerned with short-term variations in price. Conversely, a trader with a short-term horizon doesn’t need to understand the fundamental factors affecting price movements, and is rewarded for understanding short-term supply-and-demand dynamics instead. Yet these different investors coexist and arguably provide some value to the other. In addition, each investor’s actions in the market coalesce to produce patterns. The goal of an alpha researcher is to find which of these patterns are relevant to predicting future prices, subject to various constraints, like the availability of data and the researcher’s modeling toolkit. While the nature of the alphas changes with time, it is the researcher’s challenge to find which ideas are relevant today, and to implement them in a way that is efficient, robust, and elegant.

      3

      Cutting Losses

      By Igor Tulchinsky

      Man is a creature who became successful because he was able to make sense of his environment and apply rules more effectively than all his other competitors. In hunting and agriculture and, later, physics, the understanding of rules proliferated as man advanced. Today the mastering of rules abounds in every area. From finance to exercise, to relationships and self-improvement, rules describe it all. Man depends on rules.

      There are an infinite number of possible rules that describe reality, and the mind of man is always struggling and working to discover more and more. Yet, paradoxically, there is only one rule governing them all. That rule is: no rule ever works perfectly. We call it the “UnRule.”

      As a matter of fact, it is a scientific principle that no rule can really be proved, it can only be disproved. It was Karl Popper, the great philosopher of science, who pointed this out in 1934. He said it is impossible to verify a universal truth; instead, a single counter-instance could disprove it. Popper stressed that, because pure facts don’t exist, all observations and rules are subjective and theory laden.

      There is good reason why rules can’t be proved. Reality is complicated. People and their ideas are imperfect. Ideas are expressed as abstractions, in words or symbols. Rules are just metaphorical attempts to get at reality. Thus, every rule is flawed and no rule works all the time. No single dogma describes or rules the world. But every rule describes the world a little bit. And every rule works, sometimes.

      We are like artists who gradually paint an image on the canvas. Every stroke brings the image closer to reality, but it never becomes reality and never becomes a perfect interpretation of it. Instead, we get closer and closer to it, never quite reaching it. Examples abound. Newton’s laws, which for centuries seemed to describe motion perfectly, turned out to be flawed, giving way to relativity. Man’s explanation for himself and his place in the universe has been continually evolving – from the belief that earth was the center of everything, to the realization that we are really specks