“The narrative fallacy addresses our limited ability to look at sequences of facts without weaving an explanation into them, or, equivalently, forcing a logical link, an arrow of relationship upon them. Explanations bind facts together. They make them all the more easily remembered; they help them make more sense. Where this propensity can go wrong is when it increases our impression of understanding.” – Nassim Nicholas Taleb – Black Swan
I recently had a discussion on Facebook with two financial advisors about risk tolerance questionnaires. The advisors expressed their frustrations with managing client behaviour. In one advisor’s words “there is an emotional element that is difficult to quantify” in questionnaires. Pocket Risk helps here but we agreed a certain level of client education is necessary to ensure people “stay the course”.
The academic and practice literature supports this. The University of Michigan’s Health and Retirement Study has shown that people with lower levels of education are less likely to take investment risk. This means they are at risk of forgoing the gains in holding equities and missing their retirement goals. Practice professionals such as Harold Evensky in his book “The New Wealth Management” have stressed the importance of client education before building a financial plan and investing.
So what do your clients need to know?
1. Your Clients Must Understand The Risk-Return Trade-off
In order to be comfortable with risk your clients need to understand what risks they are taking. At the beginning they should understand the risk return trade-off. That is the idea that as an investor you are expected to get higher returns for taking more risk.
Here is a real life example you can share with your clients. In 2004 Google IPO’ed and became a public company. The shares traded at $54 (accounting for stock splits). There was a lot of uncertainty about the company’s business model and investors were largely reluctant to invest because of the dot com collapse four years earlier. Most investors did not want to take the risk.
However, today, Google’s shares have been trading at over $750 per share. Those investors who were willing to take the risk (on a company that was eventually very successful) have received high returns. This is how the risk-return tradeoff works in action.
Conversely, people who invested in Twitter which IPO’ed in 2013 at $41 have seen their value drop to $18 dollars. These investors took a risk most people were not willing to make and they lost money.
Your clients should know, that if they wish to make more money they are probably risking greater losses.
2. Your Clients Must Understand Time Horizon
Your clients should appreciate that assets can go up and down in value but over the long term they have a trend. In the short term (0-3 years) volatility can be wild but over longer periods of time there tends to be a pattern. Therefore, the longer you invest the more certain you can be about how much money you will make.
Below is a real life example you can share with your clients with historical returns of the MSCI World Stock Market Index 1970-2015 (dividends reinvested, before fees and taxes). This diagram shows your clients that investing over a longer time period means less chance of loss.
By jumping in and out of investments when assets drop in value, they will likely miss the gains on the upside. As proven by Geoffrey Friesen and Travis Sapp in their paper – “Mutual fund flows and investor returns: An empirical examination of fund investor timing ability” – 2007. We recommend you use diagrams like the below to explain key concepts to clients.
3. Your Clients Must Become Comfortable With Uncertainty
Once your clients understand the risk return trade-off and the importance of time horizon there is one last thing they must understand…
Sometimes great plans don’t hit the mark. There is a luck factor in everything that we do as humans. Your clients must understand there is no certainty in investing. However, we can use probabilities to increase our chances of achieving our goals.
Communicating this to clients can be difficult. They are paying you for results and you have to explain to them why there is a small chance you won’t deliver.
But if you use simple analogies, it can be explained. For example there is a high probability of getting into a car accident if you run red lights. There is a low probability if you follow all the rules but you can still get into an accident. It’s your job to explain how you will increase the chances of them hitting their goals.
I recommend you read a book by Nick Murray called “Simple Wealth, Inevitable Wealth”. Focus on the epilogue titled “Optimism Is The Only Realism”. Nick Murray has been a financial advisor for 50 years and a coach to thousands of advisors over the last two decades.
He believes that the dominant determinant of long-term, real-life return is not investment performance but investor behavior. Second, that behavior modification ought to be an advisor’s true value proposition, because great behavioral advice is at critical moments in an investor’s life, worth so much more. I completely agree.
Headlines like Michael Kitces’ “The Sorry State Of Risk Tolerance Questionnaires” disappoint me but they also motivate. Criticism is how we improve and a new version of Pocket Risk is launching soon. Advisor technology has its challenges. Yet I rarely hear voices from the other side. The founders, CEOs and CTOs of advisor tech companies giving their perspective. This is an honest post about building technology for financial advisors from an advisor tech CEO.
1. Advisors vs Clients – Most advisor tech is designed to benefit the client, but occasionally, the interests of the advisor and the client conflict.
A simple example is disclosures. Should technology providers put them at the top of the page with a large font or bury them at the bottom, just meeting the minimally regulated font size? Technology providers walk a tightrope between what is best for the client and what the advisor requests.
I’ve had to make dozens of these decisions over the years and since advisors “pay the salaries” there is no pretending they have a loud voice. But first we have ethics and refuse to work with advisors who seek to maximally manipulate features to their advantage.
99.9% of the time advisors just want to be efficient and regulations have them contorted in the most uncomfortable ways.
As a technology provider, having to constantly think about advisors AND clients, makes for slower technology development. Just imagine trying to build a product that simultaneously satisfies an advanced practitioner and a complete novice.
2. Data Security and Integrations – After the defense industry, financial services firms host the most private data. This makes companies reluctant to share and integrate. Most integrations happen because a relationship has developed between CEOs of two companies (usually beginning at Technology Tools For Today). But that relationship can take years, while advisors sit and wait. Very few companies have open APIs. Redtail is one of the exceptions.
3. Fractured Industry – The term “financial advisor” is too broad. We routinely find that broker-dealers want something different from independent RIAs. Not to mention the issues involved serving advisors internationally.
Creating advisor tech to satisfy all advisors is impossible, because the constituents want many different things. Using Pocket Risk as example, we have demands to turn our product into a sales tool, a compliance check, a client behavioral management tool and sundry other services.
So looking forward, I see a few things have to happen for advisors to get better tech. Firstly, tech firms will have to specialize and only serve certain parts of the market (e.g. broker-dealers vs independent RIAs). Whether this can be done profitably remains to be seen. Secondly, advisor tech firms need to establish a security standard so they can easily share data. Orion has done some work in this area. Lastly, ultimately all advisors will be working in a client’s best interest and that will make technology development simpler.
In my previous article “What Is Risk Profiling – Part 1”, I showed how you can’t talk about risk profiling, without understanding risk. And that the definition of risk has changed over the centuries due to academic research and human experience.
The modern consensus is that risk is a mathematical and psychological construct.
This consensus is built on over 64 years of research. It starts with the mathematical Harry Markowitz and his followers who gave us Modern Portfolio Theory and the Capital Asset Pricing Model. Most recently Friesen and Sapp have added to the psychological by publishing mutual fund data showing that “investor underperformance due to poor timing” is consistent with “return-chasing behavior”. And return-chasing behavior is primarily driven by psychological biases and a lack of financial education.
So for financial advisors, risk profiling is the process of understanding your clients’ mathematical and psychological situation in order to give good advice. Take a look at the diagram below.
The mathematical and psychological situation of the client has been further broken down in order to translate into how financial advisors do their work. See diagram below.
According to the Ontario Securities Commission who recently undertook a study into risk profiling and risk profile questionnaires, the terms are defined as follows.
“Risk Tolerance: The willingness of the client to take on risk. It can be defined through their attitude towards risk and is often described as a high/low risk tolerance.” Risk Tolerance is also regarded as the opposite of loss aversion. This is backed up by research from Rozkowski, Grable, Kahneman and Tversky.
“Risk Capacity: The financial ability of a client to endure any potential financial loss. Does the client have the financial ability and can they afford to take on the risk?” This is backed up by research from Hanna, Chen, Waller and Finke.
“Risk Need: Refers to the amount of risk that should be expected in order for a client to meet specific financial goals. Larger goals may require higher returns on investment that comes at the cost of higher risk.” This is backed up Markowitz and his followers.
“Risk Perception: A judgment that the client feels towards the severity of risk in association with the broader economic environment. This perception can be heavily influenced by the media and/or through lack of understanding of the risks. The influence of ‘risk perception’ and ambiguity aversion may be reduced by greater financial literacy, education or experience.” This is backed up by research from Friesen and Sapp.
Risk Composure: This is the likelihood that in a perceived crisis the client will behave fundamentally different to their rational self and may take action that could crystalize losses. It can be measured based on a client’s past decisions.
Risk Profile: The aggregate of all of these factors to arrive at an overall determination of a ‘sweet spot’ for a client, such that it maximizes their ability to achieve their goals but is consistent with the level of risk they are willing and can afford to take.
At Pocket Risk we agree with these definitions and the academic literature supports it.
These definitions are the culmination of decades of experiments by academics, the practical experience of advisors and increasingly the support of regulators in Canada, the UK, Australia, and India.
In order for advisors to best help their clients, they need clear definitions from regulators and the academic community. Now we have them, it’s time to build tools and practices that allow advisors to better serve their clients.
If you have any thoughts on this article, I’d love to hear them.
Risk profiling is the process of understanding how much risk is necessary for a client to achieve their financial goals.
But this only begs the question – What is risk?
The challenge for financial advisors is that the definition of risk keeps changing as research expands. In order to understand risk profiling, you have to start with the history of risk.
17th Century – Expected Value
In the 17th century Blaise Pascal (on the left) and Pierre de Fermat exchanged letters attempting to solve a mathematical puzzle and they gave us Expected Value. This is the idea that by multiplying the possible outcomes of an event by the likelihood of each outcome and summing those values we could know whether to proceed with a bet. Here is an example.
Imagine investing $50,000 in a biotech stock. You calculate your returns as…
90% chance the value drops by $25,000
10% chance the value increases by $300,000
The Expected Value = (0.9 * -$25,000) + (0.1 * +$300,000)
Expected Value = +$7,500
According to Expected Value, we should invest in the biotech stock because even though the odds are terrible, we have a positive expected value with a potentially huge payoff.
18th Century – Marginal Utility
In the 18th century Daniel Bernoulli rejected this approach and published an article stating, “The determination of the value of an item must not be based on the price, but rather on the utility it yields…. There is no doubt that a gain of one thousand ducats is more significant to the pauper than to a rich man though both gain the same amount.” – Exposition of a new theory on the measurement of risk – 1738
Essentially Bernoulli said risk depends on an individual’s circumstance not just the odds of a potential payout. His theory became known as Marginal Utility Theory and is closely linked to the modern idea of risk need.
20th Century – Volatility
In the mid 20th century Harry Markowitz birthed Modern Portfolio Theory. Markowitz’s model defined risk as the variability of returns (also known as standard deviation). From the 1950’s to the early 2000’s risk has been primarily been measured in volatility.
Unfortunately, Markowitz’s ideas have been twisted. He clearly stated in his seminal work “Portfolio Selection – 1952” that his models were about “choice of portfolio” not about the “experience” of the investor. I doubt he would state that the primary risk to a financial plan is the variability of portfolio returns. He would say the primary risk is the investor making bad choices.
Starting in the 1970’s and gaining rapid attention today is the idea that risk is mathematical AND psychological construct.
21st Century – Behavioral Finance
In the last 30 years there has been an explosion of interest in risk. Look at the graph below from Google Books. It shows the popularity of the word “risk” in English language publications from 1700 to 2016. We are now 6x more likely to write about risk than God.
Behavioral finance led by Kahneman, Tversky, Thaler, Ariely, Rozkowski, Grable, Lytton, Finke and others shows us that risk lies in the actions of the investor not just security selection and financial planning. Psychological biases like anchoring, loss aversion and social proof demonstrate we are not rational utility maximizers. Risk is not volatility.
I’ll end Part 1 of this post by going back to definition set out earlier. I said, “Risk profiling is the process of understanding how much risk is necessary for a client to achieve their financial goals.“
Therefore, in order to understand how much risk is necessary for a client to achieve their financial goals you must understand the mathematical AND psychological situation of the client. You must know your client.
In Part 2 we will go into detail and discuss the mathematical and psychological elements of risk profiling.