Risk Model

ANTICIPATION and RISK TAKING

Beating the market has long been the investor's quest. In this era of robo investing and algo-based trading, what can we do to give ourselves an edge? Can we develop a model to tell us when is it prudent to take more risk?

Simply put, beating the market necessitates we anticipate the anticipations of others. This concept, coined by John Maynard Keynes, academic, speculator, and successful investor can be an investor's guiding principle; a secret sauce for success. The hard part is implementation. We need an objective and repeatable system. Developing a simple risk model is a good place to start. Sentiment and momentum based rules are helpful in developing such a model.

The model I developed during my 35 years as a portfolio manager for some of the largest pension funds in Canada, is simple and effective. This version consists of 3 sentiment rules and 2 momentum rules. By using the model consistently we can control the risk taking that is necessary to do better than a passive index. I use this model weekly as a tool to guide my risk appetite.

DEVELOPING A RISK MODEL

SENTIMENT RULES

We know from behavioural studies about bias in financial decision-making. Risk taking is asymmetric; people are much more afraid of losing money than greedy of making it. Hence the shape of financial charts; up an escalator, down an elevator. We also know about overconfidence and confirmation bias that often leads active investors to chase trends and make suicide switches - buying high and selling low. It are these tendencies that offer us an opportunity to profit. These sentiment rules allow us to quantify investor expectation.

1. AAII BULL/BEAR RATIO:

The American Association of Individual Investors does a weekly survey of Investor opinion. It asks a simple question of its members and publishes the results weekly. These data can form the basis for quantifying anticipations. A contrary rule can be developed from

the Warren Buffett dictum; "Be greedy when others are fearful and be fearful when others are greedy"

From this data, my back-tested rule for to reduce risk when the ratio of bulls to bears is below its 30 week moving average. I like this rule because the group of investors it surveys is usually wrong at the extremes . But individual investors are the fuel that drives the market direction. When they are bullish they buy and when they get scared they stop buying . The rule will kick into "beta down" mode and the markets usually sell off or consolidate until the ratio reverses. The contrarian element in this rule is that the ratio always kicks in at the beginning of a rally as investors chase the rally after being bearish. You just have to wait for the signal to switch from red to green.

2. VIX:

Another useful measure is the Volatility Index. This is a measure of the premiums option investors and hedgers are willing to pay for protection from abrupt price movements. I have found risk taking is not rewarded when this measurement is above the 50 day moving average.

3. COPPER/GOLD RATIO:

A third way of getting at sentiment is to measure the ratio of the price of copper to the price of gold. Gold is a form of protection during periods of excess and duress. Lately we have know more of the latter and this ratio has been driven by fear of political or financial upset. I can recall periods in which gold reflected the inflationary excesses that built up during prolonged periods of monetary ease. Either way, when gold outperforms the price of copper, a metal whose price reflects periods of strong economic growth and prosperity, it is never a great time to be taking excess risk. Reduce risk when this ratio is below the 50 day moving average.

MOMENTUM RULES

Now we need rules that relate to momentum of price. When markets are trending strongly in one direction it can be helpful to go with the flow. Fighting the tape is never advisable and trends can take on a life of their own. I believe this is the hardest lesson to learn. We as investors are always confident, (over-confident) in our current position. We attribute virtues to our holdings and denigrate assets which we do not own. My momentum based rules do not help us with either valuation or quality. The only thing they accomplish is to keep us from being on the wrong side of the trade for too long. The market can stay "irrational" longer than you can stay solvent.

4. RSI:

For a shorter term indicator I use a common measure of price momentum, the RSI Index. In works like a thermostat, not too hot, not too cold. Reduce risk when this measure is below 45 or above 70. This keeps you from bottom fishing or chasing an overbought market.

5. 200 DMA:

Reducing portfolio risk in an overbought market is an obvious tactic but this rule also works when the market falls. This rule, developed through backtesting, says to lower portfolio risk when the market is 10% above the 200 day moving average and When it is 5% below of the 200 DMA. In a falling market it acts as a stop loss.

IMPLEMENTING THE MODEL

The rules developed in the above model are intended to control the risk of a portfolio with weekly rebalancing. The range of riskiness in a given portfolio is up to the investor.

The weekly model results are calculated and scored from 0 to 5. The operative rule is to position the portfolio in above market risk (beta) when there are 3, 4 or 5 rules in the "risk on" position. When the number of "risk on" readings drops below 3 the beta of the portfolio is reduced. The range of beta is for the investor to decide as it varies with risk tolerance of the individual.

Concluding Thoughts

This variation of the AURION risk model that I developed in my days in the pension fund industry is only one tool of many.

Notice the model does not included fundamental elements such as valuation or quantitative approaches such as estimate revision or surprise. These tools are best used to optimizing the selection of stocks used to construct the portfolio.

Ultimately, the monetary backdrop, usually defined by the shape of the yield curve determines whether or not you should even be playing the game. But in most inverted curve environments the model tends to get stuck on "SELL"

It has been my experience that no approach has an unblemished track record, but the intent here is to measure the expectations of others as expressed in market based variables that can be translated into a rules-based model. In my future Tues@11 blogs, I will preface all commentary with a comment on the status of the risk model and any changes I observe.