Two Ingredients for Being a Risk Aware Advisor

There is a reason we insisted on including RISK, a four letter word if there ever was one, in our company name. We believe that our patented risk fingerprint technology is the only way available to quantitatively turn risk into an investor’s ally.

There is no potential gain in life without taking on the right amount of risk. We know that all meaningful monetary decisions depend on the actual dollar amounts a specific investor is willing to place at risk. Importantly, those invested outside their tolerance for risk make stupid and emotional decisions when they feel the pain of feared or actual loss.

Ingredient Number 1: Risk Questionnaire

Humans are not programmed to simply “play the odds.” When offered a 50-50 bet with a $1,000,000 payoff most people would not pay $200,000 to play the game. Yet, change the game’s payoff to $10 for $2 at risk and many would not hesitate to play. Amounts at risk make all the difference. Human bias is to limit pain. And each of us, individually, has our own threshold for financial pain.

Our risk tolerance assessment technology can protect people from the common emotional response to financial loss, by protecting them from taking on more risk than their individual risk tolerance can endure. Advisors can hope they know how much risk a client can tolerate…or they can know. We believe that it’s better to know a client’s pain threshold prior to the next crash and allocate accordingly, rather than hoping you can control the client’s reaction during the next crash.

Our quantitative risk assessment process pinpoints individual risk tolerance and generates specific investment allocations that are unique to the individual’s current financial position and natural aversion to monetary risk.

Ingredient Number 2: Data Model

Calculating the risk in portfolios requires the use of an underlying data model to determine the probable range of returns, volatility and correlations. While we provide a default data model in Riskalyze, our technology is data model-agnostic and can work with any market outlook.

Our default data model, detailed in this article on our knowledge base, was designed, tested and evaluated by a talented team of investors and mathematicians and designed to take both systemic and non-systemic risk into account. In 2012, users built more than $2 billion in portfolios on that model, and its accuracy rates were off the chart — especially during May 2012 when markets were choppy and down, and with all of the Apple-heavy portfolios during the fall.

Riskalyze works best when we marry the individual’s risk tolerance with the advisor’s beliefs about the expectations of the investments under consideration when building a portfolio. It’s within the choice of data model that the advisor gets to shine.

Whether looking for complimentary investments, building efficient models, targeting a favorite sector or stress testing portfolios, advisors get to include their beliefs into the process. The advisor gets to control the data model by either selecting a data model from our menu and/or clicking on each investment to add an individual “best case” and “worst case” belief for any of the investments over the next six months.

An advisor can even control the data for selected holdings if desired. Think China is the place to be? Assign your “best case” and “worst case” accordingly, and watch the risk in the portfolio change. Think interest rates will rise? Turn on the “Interest Rate Stress Test” and the portfolio analytics will instantly take rising rates into account. Want to see how much potential risk, reward or diversified risk is associated with each position in a portfolio? Turn on the Risk/Reward Heatmap and know instantly.

With both an underlying data model and a risk target, Riskalyze can even determine the most optimal portfolio to fit a client’s needs, maximizing the investor’s expected monetary utility over a specific time horizon. Said another way, Riskalyze is able to accurately balance the relationship between risk and reward for each investor.