Rational Decisions: The Challenge
More often than any of us would like to believe, we make decisions by employing mental shortcuts known as heuristics. Even decisions of great consequence, where money and people's lives are at risk, are often made by relying on little more than intuition and gut feel. According to decades of research, the result is often suboptimal decisions that lead to unnecessary losses.
We devised this quiz to test your ability, and willingness, to employ cognitive strain over mental shortcuts, even when the answer appears obvious. See if your intuition leads to rational decisions, or just choices that appeal to your gut. The difference affects the returns you generate, the business you run and even the life you lead.
Enjoy!
We devised this quiz to test your ability, and willingness, to employ cognitive strain over mental shortcuts, even when the answer appears obvious. See if your intuition leads to rational decisions, or just choices that appeal to your gut. The difference affects the returns you generate, the business you run and even the life you lead.
Enjoy!
Question 1
Jim is planning a huge outdoor party in Isla Vista (IV) for Thursday. On average, IV experiences 14 days of rain per year. Unfortunately, his weather app is predicting rain for Thursday. When it actually rains, the app correctly forecasts rain 95% of the time. When it doesn’t rain, the app correctly forecasts that it won’t rain, 90% of the time. What is the probability that it will rain on Jim’s party?
Question 2
Olive University has an acceptance rate of just 9%. 75% of those who apply to Olive U have a high school GPA greater than 4.0. Every single applicant who is accepted has a high school GPA greater than 4.0. What is the probability that an applicant with a high school GPA greater than 4.0 will be accepted?
Question 3
A cab was involved in a hit-and-run accident at night. Two cab companies, the Green and the Blue, operate in the city. 85% of the cabs in the city are Green and 15% are Blue. A witness identified the cab involved as Blue. The court tested the reliability of the witness under the circumstances that existed on the night of the accident and concluded that the witness correctly identified each one of the two colors 80% of the time and failed 20% of the time. What is the probability that the cab involved in the accident was Blue rather than Green?
Question 4
Since December 1927, the S&P 500 index has had 1,053 rolling twelve month periods of which 68% have been positive. Over that same time frame, the S&P 500 was up 7 years in a row just 6% of the time. Having rallied 6 straight years, something that occurs 9% of the time, what is the probability the S&P 500 will be up next year?
Question 6
A pension fund has identified a specific investment style which delivers results that are widely dispersed. 40% of the hedge funds focused on the space generate small positive returns in a given year, but what makes it so compelling for investors is that on average, 3% of the hedge funds will generate outlier returns, often in excess of 50%. Unfortunately, those who don’t generate positive results can underperform significantly. In order to improve their chances of finding the right manager to allocate to in the space, one pension fund has devised a checklist that delivers an “Upside Outlier” reading 90% of the time when the fund actually does deliver those types of returns, and predicts that same “upside outlier” reading just 5% of the time when they don’t. Using their checklist, what is the probability that a hedge fund in the space delivers positive outlier returns given that the model predicted it?
Question 8
If you were attempting to predict the outcome of the very next at-bat for a major league baseball player, which of the following do you believe would offer the most predictive value?
- His batting average over the last five plate appearances
- His batting average over the last five games
- His batting average over the last month
- His batting average over the season so far
- His batting average over the previous two seasons
Question 11
An option pays out $10 for every $1 paid in premium if XYZ is trading above 30 at expiration. Although it has never traded above 30, you are bullish on the company’s prospects and believe there is a 10% chance that the option pays off. Alternatively, you could purchase an option that pays out $0.25 in profit for every $1 paid in premium if XYZ is trading above 10 at expiration. XYZ hasn’t traded below 15 since it went public 10 years ago. Given your expectations and the historical data, which is the better investment?
Question 12
Year after year you underperform your expectations, and even your views. In order to generate better results you should...
- Do things exactly as you've always done them.
- Do things exactly as you've always done them, but in greater volume.
- Make marginal adjustments to your process by improving your understanding of how your brain works, how we approach problems and make decisions, and working with a coach to ensure that you make the right adjustments.
Answer to Question 5
B) Cut his AuM
Reason: The fundamental purpose of a "speed bump" is to reduce the impact that a portfolio manager is likely to have on the overall fund's returns, at a time when the CIO believes the PM isn't thinking clearly. Effectively, the cumulative losses to date are thought to be negatively impacting the PM's decision making. There is no scientific evidence that the speed bump works in that respect, but there is statistical evidence which proves that speed bumps reduce the returns of good PM's and extend the life of those who should be fired.
Effectively, implementing and enforcing a speed bump is a judgment call, and as such ownership of that decision should reside with the decision maker. In most cases, that person is the CIO. In order to track the value of the decision to cut the PM's allocation, as well as continue tracking the PM's own unadulterated decisions, and prove/disprove the effectiveness/necessity of the speed bump, the CIO must cut the PM's AuM rather than reduce their VaR.
Reason: The fundamental purpose of a "speed bump" is to reduce the impact that a portfolio manager is likely to have on the overall fund's returns, at a time when the CIO believes the PM isn't thinking clearly. Effectively, the cumulative losses to date are thought to be negatively impacting the PM's decision making. There is no scientific evidence that the speed bump works in that respect, but there is statistical evidence which proves that speed bumps reduce the returns of good PM's and extend the life of those who should be fired.
Effectively, implementing and enforcing a speed bump is a judgment call, and as such ownership of that decision should reside with the decision maker. In most cases, that person is the CIO. In order to track the value of the decision to cut the PM's allocation, as well as continue tracking the PM's own unadulterated decisions, and prove/disprove the effectiveness/necessity of the speed bump, the CIO must cut the PM's AuM rather than reduce their VaR.
Answer to Question 7
As soon as we see an image like this, our brains immediately set about the task of explaining why it is that the healthy counties appear to be mainly rural. Perhaps it is a result of breathing in unpolluted air, consumption of fresh food delivered straight from the farm to the table, or maybe it’s the availability of clean water delivered by tranquil streams.
Perhaps some additional information would be helpful. Image 2 shown here, shows the counties in the top decile of the kidney cancer distribution. In other words, the counties across the US with the HIGHEST 10% age-standardized death rates for cancer of the kidney/ureter for US males (1980-89). Once again, rural areas dominate. If you had been presented with this image first, you would likely have jumped to the conclusion that the high rates might be due to higher poverty rates, limited access to proper medical care, greater propensity for smoking and drinking alcohol, or perhaps diets that tend to be higher in fats.
The truth is, there is no valid narrative that can accurately explain the phenomenon. It is merely a function of studying a small sample set, but rather than chalk it up to the random, highly variable nature of small sample sets, we intuitively set about the task of generating a story that can explain it. Unfortunately for us, regardless of what we desire, small towns represent small sample sets and small sample sets typically exhibit greater variability and so tend to be overrepresented in the tails, both of them. It really is that simple.
Perhaps some additional information would be helpful. Image 2 shown here, shows the counties in the top decile of the kidney cancer distribution. In other words, the counties across the US with the HIGHEST 10% age-standardized death rates for cancer of the kidney/ureter for US males (1980-89). Once again, rural areas dominate. If you had been presented with this image first, you would likely have jumped to the conclusion that the high rates might be due to higher poverty rates, limited access to proper medical care, greater propensity for smoking and drinking alcohol, or perhaps diets that tend to be higher in fats.
The truth is, there is no valid narrative that can accurately explain the phenomenon. It is merely a function of studying a small sample set, but rather than chalk it up to the random, highly variable nature of small sample sets, we intuitively set about the task of generating a story that can explain it. Unfortunately for us, regardless of what we desire, small towns represent small sample sets and small sample sets typically exhibit greater variability and so tend to be overrepresented in the tails, both of them. It really is that simple.
Answer to Question 8
If you’re like most people, you would order the predictive power exactly as it is presented in the question above. However when Moskowitz and Wertheim* studied all MLB hitters over an entire decade, it was the batting average of the previous two seasons that offered the most predictive value. In fact, if you wanted to order the list above from most valuable to least in predicting the outcome of a batter’s next time at the plate, you’d need to flip it completely. Interestingly, they found the same results when applied to the NBA, NFL, NHL and European Football.
* Scorecasting: The Hidden Influences Behind How Sports Are Played and Games Are Won
* Scorecasting: The Hidden Influences Behind How Sports Are Played and Games Are Won
Answer to Question 10
C. She does not have a college degree
Women with a PhD account for 0.62% of the US population over the age of 25.
Women without a college degree account for 68% of the US population over the age of 25.
Since all we know about the reader is that she's female, the odds are overwhelmingly in favor of her not having a college degree, despite what our intuition may tell us.
Women with a PhD account for 0.62% of the US population over the age of 25.
Women without a college degree account for 68% of the US population over the age of 25.
Since all we know about the reader is that she's female, the odds are overwhelmingly in favor of her not having a college degree, despite what our intuition may tell us.
Answer to Question 11
The option that pays out just 25 cents for every dollar at risk is 2.4x more attractive than the one that pays out $10 for every dollar at risk.
Despite Paul Tudor Jones mandate that, "No trade can be entered unless the risk/reward ratio is greater than 4-to-1.” and other similarly silly mantras that have become widely accepted in the industry, a proper risk/reward must account for probabilities. In this case, it looks like this.
Option 1: Expected Return = (0.1 x $10) + (0.9 x -$1) = $0.10
Option 2: Expected Return = (0.99 x $0.25) + (0.01 x -$1) = $0.24
After doing a sensitivity analysis, Option 2 would be preferable so long as the odds of XYZ stock remaining above $10 were greater than or equal to 88%.
Despite Paul Tudor Jones mandate that, "No trade can be entered unless the risk/reward ratio is greater than 4-to-1.” and other similarly silly mantras that have become widely accepted in the industry, a proper risk/reward must account for probabilities. In this case, it looks like this.
Option 1: Expected Return = (0.1 x $10) + (0.9 x -$1) = $0.10
Option 2: Expected Return = (0.99 x $0.25) + (0.01 x -$1) = $0.24
After doing a sensitivity analysis, Option 2 would be preferable so long as the odds of XYZ stock remaining above $10 were greater than or equal to 88%.
Answer to Question 12
What many fail to appreciate is that investment management is the business of decision making. Your results are a function of your ability to make good decisions, consistently. Due to the continuous and compounding nature of decisions, even a marginal improvement in the decision making process can have a huge impact on your results (see Novak Djokovic stats below). Therefore, the goal is to nudge the odds of a successful decision in your favor, each and every time. Luckily, we have a wealth of valuable research in the field of decision theory to draw upon in our efforts to enhance performance. While few do, clients of Bija Advisors take full advantage of what we know about how the brain works, how we approach problems and how we make decisions. Through better decisions, they achieve better results.
