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Alpha Theory Blog - News and Insights

June 1, 2019

Increasing the Probability of Success - Part 2

 

This article is a continuation of Increasing the Probability of Success - Part 1.

 

2. PROBABILITY BUCKETS

In many ways, this is the easiest of all the methods to implement. Predetermine as a firm how many price target scenarios you’re going to forecast per position. For example, let’s say you’re going to do three: Reward, Base, Risk. And for each position, the analyst can choose Low, Medium, or High Probability and you preset the probability distribution. For instance:

 

Screen Shot 2019-05-29 at 9.12.06 AM

 

In this case, probabilities are constrained within a range but allow for some flexibility. What you should expect from your analysts is a normal distribution of probability ranges. Mostly Mediums with a few Lows and Highs. No analyst should have more Highs than anything else. The way to explain this to your team is that Low isn’t bad. It’s just less likely than the average (Medium) name. And vice versa for a High.

 

Probability Buckets are the most common probability recommendation we’ve made for clients. They are a good combination of flexibility and practicability, easy to explain to the team, and an effective audit to determine if analysts are being too aggressive.

 

3. FLEXIBLE PROBABILITIES

 

The book Superforecasting explains how individuals can improve their forecasting skill (if you are a forecasting practitioner you should read the book – see our blog post about it here). One primary trait of Superforecasters is micro-updates. Superforecasters change their probability estimates in small increments with new information. For example, changing their probability of Trump winning the 2016 election from 43% to 45% after getting the latest polling data. These small updates accrue to better forecasting accuracy.

 

Given that fact, you’d think my recommendation would be for Alpha Theory clients to make micro-updates and have lots of flexibility with price targets. My answer is, well, it depends. The incentive for Superforecasters was to maximize their accuracy. The incentive for most analysts is P&L, not accuracy, making them reward seekers. This encourages behavior that increases position size (ie. inflated price targets and probabilities – case in point, the average hit rate for all Alpha Theory clients is 51% however analyst assume they're going to make money 72% of the time).

 

The culture and workflow of the firm determine what makes the most sense. If analysts come up with price targets and probabilities with little to no input from the Portfolio Manager then use Fixed Probabilities and Probability Buckets to reduce the likelihood of them “gaming the system.” If the opposite is true and price targets and probabilities are a collaborative exercise with the Portfolio Manager, then Flexible Probabilities is more than likely ideal.

 

One last suggestion as it relates to setting probabilities: know your funds' historical hit rate. What percentage of positions made money on an absolute basis? And on an alpha basis? What percentage hit their upside price target? What percentage hit their downside price target? Use these to set a baseline for the probabilities of the firm. For example, if the historical batting average of the firm is 51%, then the average probability of hitting the reward target should not be 72%. Keep your probabilities realistic and the portfolio you build will be a more accurate forecast of what you will receive (see the “Probability Inflation” blog post).

 

There is no question that setting probabilities is one of the trickiest parts of scenario-based forecasting. We hope this overview gives you a starting point for how to implement an effective probability setting framework.

 

May 1, 2019

Increasing the Probability of Success - Part 1

 

I was discussing with a new client how analysts should approach probabilities. Probabilities are used in calculating probability-weighted returns by multiplying them by the client’s scenarios of price forecasts to come up with a probability-weighted return.

 

The probability piece is the most subjective part of the probability-weighted return (see our “False Precision” blog post that discusses why it is important to set probabilities), so we came up with several approaches to see what fit best for their firm. I thought I’d share them with anyone that may be struggling with probabilities:

 

1. Fixed Probabilities (Distribution)

Analysts come up with price targets that match the part of the forecast distribution associated with the probabilities. In this example, all positions have a “fixed” 20%/60%/20% probability framework. The goal is to come up with price targets that match those buckets (i.e. what is the 20% risk price target?).

Probability of Success-1

 

This method pulls price targets associated that reflect the probability-weighted outcomes associated with a broad range of outcomes associated with different probability “buckets”. An analyst would iterate the assumptions in their financial model to estimate the extreme outcomes (two 20% probability buckets at the end) and the higher probability outcomes (60% probability bucket in the middle). The result is a price target that blends the possible outcomes in each bucket by their associated probability. Another way to think of this is a cumulative probability distribution.

Normal CDF

 

For example, the analyst may associate-5% sales growth and 10% EBITDA margins as the 20% cumulative probability outcome, 25% sales growth and 40% EBITDA margins as the 80% cumulative probability outcome, and 60% growth and 55% margins as the 99% cumulative probability. There would be many other points in between (represented by the green dots) where the analyst would apply different assumptions in their model.

 

The benefits of this method are that the probabilities are fixed and require no subjective assessment. This method also allows for highly-sensitive models with extreme outcomes to be reflected in the resultant probability-weighted return. The downside of this method is that it is time-intensive and allows no flexibility in the probabilities.