How Risk Management Generates Returns by Stephen Duneier
Conduct a post-mortem of your performance for 2016 and you’ll quickly understand what I mean when I assert that improvement comes not from doing more or adding things, but from making fewer mistakes. Maybe you stayed in a trade longer than was appropriate. Perhaps you shouldn’t have jumped back into a losing idea, should have been more patient or even more aggressive when everything lined up. You should have fired that manager or ramped up your investment. Everything seems so simple and straightforward with the benefit of hindsight. We use phrases like “I knew it!” to beat ourselves up over the decisions that didn’t work out well. Truth is, we didn’t know it. Sure, we may have entertained the possibility, but in that moment, we didn’t know it. The difference between a bad year and a phenomenal one comes down to mistakes. The decisions we tend to identify as mistakes are the ones that have a direct negative consequence attached to them, but that’s reflective of a shortcoming in the analysis. During the interview process with a new client, I asked him to tell me about his weaknesses. “Stubborness” was his response. “Sometimes I stay with trades too long.” He then described a few times when it had recently gotten him into trouble. We went on to discuss his approach and his track record. He tends to be early to trends, considers himself a longer term investor than most of his competitors and his track record is also comparatively impressive. I asked if his investment approach and the resulting performance required internal fortitude, a certain courage of his conviction. “Absolutely!” The question then is, how do you make a distinction between actions that are defined in that way versus those that are framed as “stubborness”? The simple answer is, only with the benefit of hindsight. In other words, when it results in a loss, it is stubborness. When connected to a gain, it is courage of conviction and a longer term approach. In order to improve, we must find a way to properly define that decision in the moment it is made. The only way to do that is to develop a consistent, proactive, evidence-based decision making process. If every decision follows the same process, and is made proactively, based on the evidence using a systematic, repeatable approach, thereby reducing the influence of emotion, all decisions must be described in the same manner. The only question that remains then is, “Is this investment process profitable?”
Of course, the process isn’t comprised of a singular component. It is made up of many factors. Even if the process has been converted into an algorithm with no input from a human being, meaning we needn’t worry about emotion or inconsistent application, that algorithm is based on factors that may or may not be positively contributing to performance in their current state. How we gather information, weight it, and factor it in can all be adjusted with the equivalent of levers and dials. By moving the levers and dials we can then see the impact on our performance over time. However, if we don’t break our decisions into their components, separate and distinct from each other, along with a way to adjust and monitor their impact, we have just one data point (p&l) from which to draw an incredible number of conclusions, all of which are then likely based on weak conjecture and emotionally charged beliefs.
One of the implications of not breaking our decisions down into their components as well as tracking the impact of each, is that we have ignored one of the most important contributors to p&l, particularly over the last couple of years. I’m talking about risk management, and the role of the CIO. Instead of thinking of proper risk management as a contributor to p&l, we have framed its function solely as one of containing the downside. However, when you realize that performance gains are a function of making fewer mistakes, and that risk management is the source of a significant number of them, you come to understand that proper risk management can be the difference between poor and phenomenal performance. Unfortunately, most firms don’t track the p&l impact of their risk management decisions (or any management decisions for that matter), perhaps because they don’t know how. In this edition, I will provide a step-by-step analysis of a few common management decisions related to risk management, showing why they are mistakes, how it can be proven and eradicated, thereby improving returns.
A Common Risk Management Mistake
Jim is a portfolio manager with a positive track record over the past 4 years that he’s worked for your fund. He began 2016 managing $400 million with a 1% VaR limit and by the end of August was up 10% for the year (+$40 million profit). Given his track record and recent performance, you doubled his allocation to $800 million beginning September 1. In other words, he could now build a portfolio with as much as $8 million in VaR. Unfortunately, he’s run into a rough patch and as of November 1 is down 5% from his August peak (-$40 million from the peak; $0 million on the year). As a result, according to your risk management rules, he must cut his risk in half, which he does. As of December 31, he’s back to his high watermark (+$40 million profit). Here is what Jim’s track record looks like for 2016.
There’s just one small problem. That isn’t Jim’s track record. It’s the track record of your firm’s decisions as they relate to Jim’s portfolio. Let’s delineate all of the decisions that led to those results.
The CIO made four decisions:
Allocate $400 million to Jim on January 1.
Allocate $750 million to Jim on September 1.
Treat the allocations homogeneously (back to this in a moment).
Mandate that VaR be cut by 50% if a PM experiences a 5% peak-to-trough drawdown.
Risk Management made one decision:
Recommend that VaR limit be cut in half at 5% drawdown.
Jim made all remaining decisions.
Each of those decisions impacted the firm’s track record as it relates to Jim’s portfolio, however because of the way we track and monitor decisions in this industry, only Jim owns them. Since the end result is a good one, he’s unlikely to kick up much of a fuss, nor is anyone else, but if we are to reduce mistakes going forward, a number of changes should be made. Let’s make just one minor adjustment to the above. Instead of cutting Jim’s VaR limit in half, risk management recommends that the CIO take back half of Jim’s allocation, and reallocate it to other portfolio managers. What would Jim’s track record look like then?
Jim makes his decisions as a proportion of the risk he is allocated. How much VaR he can take on as a percentage of AuM, how much AuM he is allocated and how his compensation is determined are all beyond his control. All he controls are the decisions he makes given the rules of the game as determined by the CIO and risk management. Jim’s actual return on AuM for 2016 is 15%. The difference between the 15% Jim delivered and the 10% shown in the previous table should be owned by management. This isn’t a theoretical loss, it is negative alpha and it should be attributed to the CIO.
After all, it is the CIO’s job to maximize the returns on the portfolio of PM’s they have chosen to invest in. Their ability to generate alpha can be broken down into its components. Comparison to a benchmark tells part of the story, but there are multiple factors that go into those results. Which PM’s should they employ? How much should they allocate to each of them? Given the correlation among them, should they overallocate, and if so, by how much? When should they cut, reduce or increase their position size (allocation to a PM)? In effect, the CIO is a portfolio manager of portfolio managers. The value he delivers as a decision maker overall, and as it relates to each of these questions, should be tracked as it is for any other allocator.
Why would a CIO instruct a PM to cut their risk? There are two reasons commonly given. (1) To help the PM get their head straight again. This is invoked because the CIO believes the PM has become affected by the loss itself, leading them to become overly emotional in their decision making. To break the emotional connection and get the PM to invest strictly based on market factors, they reduce the position sizing to something far less consequential. That’s the idea at least. (2) To reduce the impact of the PM on the fund’s performance. In other words, the CIO has lowered the probability that the PM will generate positive results going forward. Naturally, if your expectations for that PM have gone down, you want to reduce their ability to affect your fund returns. Effectively, you cut back on your investment in that PM. That is a trading decision, and like all trading decisions, it should have p&l attributed to it. In order for it to be tracked, it must be clearly recorded as a decision. That is why it is important for management to cut the PM’s AuM rather than cut their VaR. Over time, we can then see if this decision to reduce allocations at a random moment such as the 5% drawdown is optimal. (Of course it isn’t.) However, it is easier to implement speed bumps than the more optimal alternatives of (1) ensuring that all PM’s have proper investment / decision making processes in place to reduce the potential for emotion to creep in and (2) developing a better process for assessing whether a portfolio manager adds value over time. (The Bija Discounting Method can be employed here too.)
An Uncommon Solution
After 8 months, Jim was up $40 million. If his payout is 13%, he was looking at taking home more than $5 million. Then suddenly, without any say in the matter, management allocates another $400 million to his portfolio. It creates a very real quandary for him. The rational thing to do of course is increase his position size proportionally going forward. Well, that’s not entirely accurate. If his objective is to maximize returns on his portfolio over time, then yes, he should make a proportional adjustment to his position sizing. However, if his objective is to maximize his take home pay over time, the rational decision may be to make no adjustment. (Read Why Risk Takers Stopped Taking Risk to learn how time bucketing affects decision making.)
On January 1, the firm’s objectives and Jim’s were identical and led to the same actions. As soon as profits or losses are generated though, they begin to diverge. When objectives diverge, rational actions diverge as well. Unless management understands the dynamic, how to monitor it and the appropriate actions to mitigate the impact, it can lead to mistakes that reduce returns. On the other hand, if management can and does take the appropriate actions, the mistakes are reduced and returns improve. To find a simple solution, we need look no further than what happens when an investor allocates more to the fund itself. Typically, it is treated as a series. In other words, the initial $400 million investment is treated as a separate series from the additional $400 million. As of October 31, it looks like this…
If the year were to end here, the fund would be paid a performance fee of $4 million (assuming 2/20). However, Jim would be paid zero. The reason is that Jim’s entire capital allocation is treated homogeneously. There is no distinction between new capital and old. Therefore, when Jim’s allocation is doubled, it becomes twice as easy for him to give back everything he’d made up to that point. That has real implications for Jim’s decision making, likely making him more risk averse with the additional capital. A simple solution is to treat the capital allocated by the fund to their portfolio managers in the same way as capital allocated by investors to the fund.
This edition of Seeds is intended merely as a primer, to help management understand that your actions have implications and that even subtle, seemingly insignificant adjustments can have real p&l consequences. For the alpha generating CIO, properly implemented risk management can deliver a significant competitive advantage. For more on how a deliberate approach to managing a hedge fund can improve returns, go to Building a Better Hedge Fund.
"While everyone else is scrambling to answer who, what, where and when, Duneier is focused on explaining the 'why'."
About the Author For nearly thirty years, Stephen Duneier has applied cognitive science to investment and business management. The result has been the turnaround of numerous institutional trading businesses, career best returns for experienced portfolio managers who have adopted his methods, the development of a $1.25 billion dollar hedge fund and 20.3% average annualized returns as a global macro portfolio manager.
Mr. Duneier teaches graduate courses on Decision Analysis and Behavioral Investing in the College of Engineering at the University of California. His book, AlphaBrain, is due to be published in early 2017 (Wiley & Sons).
Through Bija Advisors' coaching, workshops and publications, he helps the world's most successful and experienced investment managers improve performance by applying proven, proprietary decision-making methods to their own processes.
Stephen Duneier was formerly Global Head of Currency Option Trading at Bank of America, Managing Director in charge of Emerging Markets at AIG International and founding partner of award winning hedge funds, Grant Capital Partners and Bija Capital Management. As a speaker, Stephen has delivered informative and inspirational talks to audiences around the world for more than 20 years on topics including global macro economic themes, how cognitive science can improve performance and the keys to living a more deliberate life. Each is delivered via highly entertaining stories that inevitably lead to further conversation, and ultimately, better results.
His artwork has been featured in international publications and on television programs around the world, is represented by the renowned gallery, Sullivan Goss and earned him more than 50,000 followers across social media. As Commissioner of the League of Professional Educators, Duneier is using cognitive science to alter the landscape of American K-12 education. He received his master's degree in finance and economics from New York University's Stern School of Business.
Bija Advisors LLC In publishing research, Bija Advisors LLC is not soliciting any action based upon it. Bija Advisors LLC’s publications contain material based upon publicly available information, obtained from sources that we consider reliable. However, Bija Advisors LLC does not represent that it is accurate and it should not be relied on as such. Opinions expressed are current opinions as of the date appearing on Bija Advisors LLC’s publications only. All forecasts and statements about the future, even if presented as fact, should be treated as judgments, and neither Bija Advisors LLC nor its partners can be held responsible for any failure of those judgments to prove accurate. It should be assumed that, from time to time, Bija Advisors LLC and its partners will hold investments in securities and other positions, in equity, bond, currency and commodities markets, from which they will benefit if the forecasts and judgments about the future presented in this document do prove to be accurate. Bija Advisors LLC is not liable for any loss or damage resulting from the use of its product.
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