Mullooly Asset Management

This is my favorite time of the year to watch sports. The NFL is back in action, but more importantly, October brings us the MLB Playoffs. We’re huge baseball fans here at Mullooly Asset Management, and this year is particularly exciting for us because our New York Mets are back in the post season after […]

This is my favorite time of the year to watch sports. The NFL is back in action, but more importantly, October brings us the MLB Playoffs. We’re huge baseball fans here at Mullooly Asset Management, and this year is particularly exciting for us because our New York Mets are back in the post season after nine long years.

I’ve always found the playoffs to be funny in any sport though. We often hear the cliche during post game interviews that, “The better team won”, but is that always true? It depends on what time frame we’re talking about and how we choose to define “better”. In a playoff series, the team who wins was undoubtedly the better performer over however many games they played. Are they really the better team as defined by longer term statistical evidence though? We’ve all seen the regular season juggernauts crumble in the postseason. Does that make them statistically worse than the team that got hot and beat them in the playoffs?

If we’re measuring superiority in a statistical sense, the better team does not always win in the playoffs; just ask Billy Beane. Billy Beane has been the GM of the Oakland A’s since 1998, and most people recognize his name from Michael Lewis’ book Moneyball that was made into a movie, starring Brad Pitt. Billy Beane, Bill James, and Sandy Alderson are a few of the biggest names who have been credited with introducing statistical player analysis to the MLB.

Although each practitioner has their own personal input regarding which statistics are the most significant determinants of baseball prowess, they’re all attempting to accomplish the same goal: objectively measure a player’s worth. They want to eliminate emotions, feelings, and other conflicts from the equation, and it’s certainly caught on. Bill James said in 2008 that every team was using some form of statistical analysis, and Billy Beane is on the record saying:

“The people that we’re hiring and other baseball teams are hiring, we’re competing with the Apples and the Googles of the world.”

These evidence based methods of player selection remind me of rules based factor investing. Implementing evidence based investment factor strategies in a systematic fashion have shown impressive results over time. Common investment factors that people like to target include value, momentum, and low volatility. Over at GestaltU they recently wrote about these factors, stating:

“Factor tilt portfolios are consistent with the systematic, rules-based nature of passive indexing, but are constructed to take advantage of well-known and intuitive market inefficiencies to yield excess returns over time. These excess returns are highly significant; more significant even than the equity risk premium itself. Factors appear to exist because of universal behavioural or risk based phenomena across global markets, and have persisted since the dawn of markets.”

In addition to offering excess returns above the equity risk premium, they most certainly offer better returns than relying on the star mutual fund manager and their “secret sauce” or “great intuition” for picking securities. While we need experts to create systematic models and help us understand why factors ...

This week, we mark the seven-year anniversary of the fall of Lehman Brothers back in 2008.  In the podcast, we quickly review how Bear Stearns and Lehman both collapsed from 2007 to 2008.  We also remarked how leaders at several banks, brokerage firms, and other financial companies noted their organizations had little-to-no exposure to sub-prime […]

This week, we mark the seven-year anniversary of the fall of Lehman Brothers back in 2008.  In the podcast, we quickly review how Bear Stearns and Lehman both collapsed from 2007 to 2008.  We also remarked how leaders at several banks, brokerage firms, and other financial companies noted their organizations had little-to-no exposure to sub-prime mortgages.  But yet, as the contagion spread, these same organizations began taking large reserves for losses discovered in their operations.

The point of rehashing all this is to point out fundamental analysis should not be written in stone — things change, all the time.  While many of the financial companies may indeed had little to no exposure in sub-prime mortgages, the virus spread to many other types of investments on the books.

Earnings per share — for so many companies — are very hard to predict and even harder to project.  And ultimately the earnings of many of these financial companies vanished in 2008.  And in some cases, the companies themselves vanished and are no longer around.

This WHY we include the use of point and figure charting in our work.

The excellent work of our friends at Dorsey Wright and Associates, reinforces over and over that price IS the ultimate indicator.  Price — what someone is willing to buy or sell an asset is an accurate indicator of the value.  With that in mind, it does not matter if the company earnings are good or bad, if the economy is good or bad, if the company itself is good or bad, or even if folks are trading with inside information — ALL of that is reflected in the price.

The beauty of point and figure charting is they cut through all the noise.  The lesson we take away when we see collapses in stocks (like Lehman Brothers) is the patterns and price action we see on the charts give us real-time indications.  Many investors who rely strictly on fundamental analysis sometimes get surprised when companies adjust earnings (earnings in the future and, sometimes, earnings in the past).  We’ll continue to fold technical analysis (like point and figure charting) into our work here at Mullooly Asset Management.

The NFL season is about to get underway. I know I’m anxiously anticipating next Thursday’s showdown between the Patriots and Steelers. Can you imagine either of these teams not planning for this matchup? We all know Bill Belichick will have something up his sleeve, and Mike Tomlin isn’t one to be caught flat-footed. Like football, […]

The NFL season is about to get underway. I know I’m anxiously anticipating next Thursday’s showdown between the Patriots and Steelers. Can you imagine either of these teams not planning for this matchup? We all know Bill Belichick will have something up his sleeve, and Mike Tomlin isn’t one to be caught flat-footed. Like football, investing also requires a disciplined game plan.

Even more integral than having a defined investment strategy is actually sticking with it. There are plenty of investment strategies that work over time, but the results aren’t pretty when they’re prematurely abandoned. If you can’t follow your investment rules, results are going to disappoint. This is especially true when the market, or a particular strategy, experiences inevitable periods of volatility or underperformance. Volatile time periods are challenging because they’re often when you’ll want to abandon an investment strategy the most. However, they also tend to be the worst possible time you could bail.

The 2015 DALBAR Quantitative Analysis of Investor Behavior shares some interesting data about the average mutual fund investor. Over the last 20 years, the average equity fund investor earned annualized returns of 5.19%. Over that same time period, the S&P 500 earned annualized returns of 9.85%. Why the gap of 4.66% between investors and the index? They don’t call it the behavior gap for nothing! The main reason the S&P 500 outperforms most mutual fund investors is because it cannot deviate from its strategy. The S&P 500 will own the 500 largest US stocks weighted by their market capitalization regardless of market conditions. These are its rules and it must abide them. We should all take a note out of its book and become that loyal to our own strategies.

We’ve been told time and time again that rules-based, statistically driven models outperform humans, but when underperformance or market volatility rears its ugly head, our brains continue to tell us that “we know better than the stupid broken model”.

Our preference for human intuition over rules-based models has been dubbed “algorithm aversion”. Hat tip to the guys of Alpha Architect for bringing an excellent paper on the subject to my attention. The paper states that despite proven research showing the superiority of evidence-based algorithms to human forecasters, people continue to show more willingness to trust an emotional human-being over a set of emotionless rules. Here’s an excellent example of this behavior from the paper:

“Imagine that you are driving to work via your normal route. You run into traffic and you predict that a different route will be faster. You get to work 20 minutes later than usual, and you learn from a coworker that your decision to abandon your route was costly; the traffic was not as bad as it seemed. Many of us have made mistakes like this one, and most would shrug it off. Very few people would decide to never again trust their own judgment in such situations. Now imagine the same scenario, but instead of you having wrongly decided to abandon your route, your traffic-sensitive GPS made the error. Upon learning that the GPS made a mistake, many of us would lose confidence in the machine, becoming reluctant to use it again in a similar situation. It seems that the errors that we tolerate in humans become less tolerable when machines make them.”

Simply put, we’re less apt to trust a system of rules than our ...

On the podcast, recorded Wednesday August 26, 2015, Tom, Tim and Casey had a discussion on basics: Stop orders, Limit orders and Market orders.

On the podcast (recorded Wednesday August 26, 2015), Tom, Tim and Casey had a discussion on basics: Stop orders, Limit orders and Market orders.

That is because on Monday morning, August 24, 2015, we saw the Dow Jones Industrial Average trade down more than 1000 points. At that time we saw the S&P 500 also trade down a like amount on a percentage basis.  We also saw many exchange-traded funds (ETF’s) trade down on Monday morning – some moved down thirty percent or more in very early trading.

How could that happen?

To help answer that, let’s walk through a few different types or orders which can be entered on stock exchanges:

Market order: you will be buying or selling your shares at what the current price is right now.

Market at Open: can be entered nearly right up until the market opens. You will get the open price.

Limit order:

Instead of buying or selling “at the market” you identify the price for the trade to happen.

But please understand the order may not happen right away – or ever!

Stop Orders: Two types of stop orders —

Stop order: this becomes activated when the stock or ETF moves through that stop price. THEN it becomes a MARKET order.

Stop Limit Order: also becomes activated when the stock or ETF moves through that stop price. THEN it becomes a LIMIT order. Like all limit orders, the order may not happen right away – or ever! It’s possible to have a stop price at one price, and a limit price at another (Sell stop at $30, limit $29)

Was it stop orders that drove down the market on Monday?

VERY possible, but not the entire reason.

Some ETF’s traded immediately down huge amounts, down 30% or more.

But how useful was that stop order?  When you have “gap down” openings: a stock or ETF closed the day before at $50, you have a stop order at $40 and the stock or ETF opens at $30, your stop order was activated…and became a market order, and your shares were sold at $30.

Now with the “ease” of trading – you can now buy and sell stocks and ETF’s on a smart phone. It has become so easy to place an order on your laptop, iPad, smart phone. It’s really easy to make trading mistakes, or simply miss HOW the order should be entered.

Something additional to consider with ETF’s: The price of an ETF is based on the price of the underlying holdings in the basket. The price gets updated throughout the trading day every 15 seconds. Now some media folks urge listeners NOT to own ETF’s with little volume. But the volume of the holdings IN the basket are what matter most.

We heard claims like “the ETF system is broken” when some of these ETF’s were down 25%, 30% or more on Monday morning.  But the system worked.

If a market order is placed, and all the components in the basket are not currently open for trading, you will get the value of the components which ARE open for trading.  Additionally, if an investor places an order – especially a market order – to sell at or before the market opens, there is something working against them: all the underlying members of the ETF may not have begun trading!

 

We’ve gained an outstanding education on relative strength investing from our friends at Dorsey Wright and Associates. Between their Point and Figure Institutes and daily research, they’ve taught us an incredible amount about the markets. One of the biggest tools they provide us with is called DALI, or Dynamic Asset Level Investing. DALI evaluates the supply […]

We’ve gained an outstanding education on relative strength investing from our friends at Dorsey Wright and Associates. Between their Point and Figure Institutes and daily research, they’ve taught us an incredible amount about the markets. One of the biggest tools they provide us with is called DALI, or Dynamic Asset Level Investing. DALI evaluates the supply and demand forces of asset classes, and ranks them from strongest to weakest based on their relative strength. It also takes their absolute trend characteristics into account. We strongly believe that relative and absolute asset class trends should be considered in the tactical asset allocation process.

Measuring Asset Class Relative Strength

When it comes to point and figure relative strength charts, buy signals indicate longer term outperformance. Therefore, these signals serve as our central unit of asset class measurement. Each asset class is given a ranking based on the number of relative strength buy signals it’s able to accumulate. DALI displays this score not only as a numerical ranking, but as a percentage of the total possible signals as well. The asset class with the highest number (or percentage) of buy signals has the best relative strength.

Why Not Stop There?

An issue with only considering relative strength is that sometimes relative outperformance means being down 20% when everybody else is down 25%. This is why DALI pairs relative performance with absolute performance.

Measuring the Absolute Performance of Asset Classes

An additional DALI component looks at each asset class’s cash percentile rank. This is done by creating an inventory of the asset class’s underlying components and including a proxy for cash. Cash’s relative rank among the asset class’s inventory is considered. When cash ranks highly in comparison to the majority of an asset class, that’s a clear red flag. Anything that ranks beneath cash on a relative strength basis has been losing money. This is our absolute benchmark.

DALI also looks at something Dorsey Wright and Associates have deemed the “cash bogey check”. This involves a point and figure relative strength chart of each asset class that measures it against a cash proxy. When the relative strength chart is in X’s, the asset class passes its cash bogey check. That means it has been outperforming cash in the near term. When the relative strength chart is in O’s, the asset class fails its cash bogey check. On these charts, column changes tell us about near term activity, so that asset class has been underperforming cash in the shorter term. In order to generate a column change on these relative strength charts, an asset class must reverse by 10%. Failing a cash bogey check means an asset class has reached correction territory. It may not always mean an end to the trend, but it definitely warrants attention.

How Do We Consider the Relative and Absolute Together?

If an asset class has accumulated a strong percentage of the available relative strength buy signals, and cash ranks poorly against its components, that is a trend we would strongly consider participating in. However, if an asset class has strong relative strength characteristics, but cash is outperforming many of its components, we may want to think twice about our level of participation in that trend.

All of DALI’s pieces work together to provide us with guidance in the tactical asset a...