facebook twitter instagram linkedin google youtube vimeo tumblr yelp rss email podcast blog search brokercheck brokercheck

Understanding Forecast's global macro process

Download our Brochure

Our Global Macro Process

How do we thrive in today's markets?


We’re not your father’s Wall Street investment firm. That’s by design—we do things differently here.

There is a massive opportunity for investors to use modern day risk management tools to peer into the future and see where markets are likely headed next. While the future is obviously uncertain, the primary goal of any successful investor (and our investing research) is simple: Be more right than wrong

Growth, inflation, policy (GIP) Model


Our Growth, Inflation, Policy (GIP) model is the hallmark of our fundamental research process.

We find two factors to be most consequential in forecasting future financial market returns: economic growth and inflation. We track both on a year-over-year, rate of change basis to better understand the big picture then ask the fundamental question: Is growth and inflation heating up or cooling down?

From there, we get four possible outcomes, each of which is assigned a “quadrant” in our Growth, Inflation, Policy (GIP) model and the typical government response as a result (neutral, hawkish, in-a-box or dovish):

  • Growth accelerating, Inflation slowing (QUAD 1);
  • Growth accelerating, Inflation accelerating (QUAD 2);
  • Growth slowing, Inflation accelerating (QUAD 3);
  • Growth slowing, Inflation slowing (QUAD 4)

After building this base of knowledge, we select what we like (and don’t like) based on our historical back-testing of the different asset classes that perform best in each of the four quadrants.

In QUAD 1, for instance, where growth is accelerating and inflation is slowing, that has historically been really positive for both equity and credit data across all sectors of the U.S. economy. Whereas when you think about QUAD 4, in which growth and inflation are slowing concomitantly, that has historically been quite negative for both equities and credit.

If this regime-based framework sounds familiar, it’s because billionaire investor and Bridgewater founder Ray Dalio employs a similar risk management process also focused on growth and inflation.

I knew which shifts in the economic environment caused asset classes to move around, and I knew that those relationships had remained essentially the same for hundreds of years. There were only two big forces to worry about: growth and inflation.
-Bridgewater Founder Ray Dalio 

Our primary goal is to help our clients avoid getting blown up by the big stuff.

We can help steer you into our preferred factor exposures on the long and short side. We make this point often, differentiated processes lead to differentiated perspectives and differentiated perspectives help investors generate alpha.

Quantamental Risk Management

What is "Quantamental risk management"


The rise of "quants" – investors who use algorithms and program-based mathematical models to trade markets – has changed the game for investors. Ever-increasing computer processing power and the proliferation of "machine learning" tools will only escalate this arms race.

For "fundamental" investors – investors diligently modeling companies to gain an edge on stock valuation – all is not lost. At Forecast, we think investors can gain an edge on Wall Street consensus by marrying both quantitative and fundamental investing.

We call this "quantamental."

What does a "quantamental" risk management process look like?

Our hybrid investing approach combines:

  1. Proprietary quantitative analysis
  2. Bottom-up sector research
  3. Top-down macro research with an emphasis on duration

The end result is an intelligent, high-octane investment process that draws on insights from over 40 research analysts. These analysts cover everything from Global Macro and Retail, to Energy, Restaurants and Washington Policy research. 

Quantitative risk ranges

The quantitative trading range model was developed by our research firm Hedgeye It was specifically designed to risk manage the reflexive nature of markets.

This risk range model is utilized to augment the 40+ person Hedgeye research team's fundamental views. Think about it. All investors have some basket of core investing ideas (stocks, bonds, ETFs or all of the above). Identifying those investing ideas is tough enough, then you have to deal with the uncertainty of markets.

When the proprietary Risk Range model was created the aim was simple: Create a quantitative risk management tool to help investors actually buy low and sell high.

The model uses three core inputs - price, volume and volatility - to determine the likely daily trading range for any publicly-traded asset class. These risk ranges are dynamic. They are designed to change as the data changes. At its core, you sell at the top end of the range and buy at the low end.

Bottom-up sector research

Together, the Hedgeye team of 40+ research analysts cover 19 different Sectors - from Housing to Industrials to Technology - and have an unparalleled understanding of what's driving specific stocks, sectors, policies, global markets and economies.

Top-down macro research

In addition to a deep bench of 18 fundamental equity and Washington policy research teams, the Macro team measures and maps economic data for the top 50 economies around the world, covering 90% of global GDP. Hedgeye runs predictive tracking algorithms for both growth and inflation for each of these economies to forecast the likely path for financial markets.

Bottom Line: The Macro team is focused on generating investable ideas based on this research that combines their deep study of market history, the tracking of Wall Street consensus positioning and the volatility signals embedded in futures and options markets.

Our Multi-Factor, Multi-Durational Model

Multi-factor (Price, volume, volatility)


This risk management model is multi-factor meaning it’s based on the price, volume and volatility of a publicly-traded security. This model is what drives our daily alerts and throws off Bullish/Bearish risk management signals throughout the course of the trading day.

price

What does a security’s last price tell you?

The most popular price-based answers to that question are the 50 and 200-day moving averages, based on closing prices of an index or an individual stock. 

Other systems, such as Candlestick Charts, track daily open, high, low and closing prices. But they still work off only a single factor, and thus do not present a full risk management picture. 

Price charting is based on the assumption that forces beyond mere Supply and Demand set the price of goods or securities. We don’t disagree with that. We disagree with how consensus tools contextualize it.

Volume

Since most investors care about the price of their holdings, shouldn’t they care about liquidity? 

If the price of your stock goes up, did you make money? The reality is that you only make money when you sell at that higher price, and in order to do that, you need liquidity. If there is not sufficient volume traded, you will not be able to sell at your price – maybe not at all.

Price moves perpetuate TRENDs. Volume either confirms (rising volume) a bullish TREND, or calls it into question (decreasing volume). 

Strong overall volume is generally seen as a sign of health in the markets, though isolated moves in Volume can signal turning points. 

Stocks moving UP on decelerating volume have the potential to create a Liquidity Trap and could signal a coming correction, while an outsized burst of volume on a strong UP move in a stock could signal a breakout to new price levels. 

volatility

If you are combining VOLUME with PRICE, you’re already well ahead of the single-factor technicians. But you’re not quite there yet. Hedgeye’s Model tracks multiple factors in three categories: Price, Volume, and Volatility.

Does it matter? Immensely. But how many people proactively solve for it?

Many Institutional Investors analyze the relationship between Price and Volume. Analyzing volatility is far less trivial – and for some reason, far less common.

Volatility is the statistical dispersion of prices of a security or index; the variance, or the standard deviation of prices for the security. 

The higher the Volatility, the greater the price uncertainty of a position. That doesn’t mean that Volatility is bad. It means that you have to take it into account if you want to make good buy/sell decisions, across durations, in what we call the Risk Range.

If you don’t incorporate an analysis of volatility in your buy/sell decision making process, you will have to get used to “averaging down” to offset your timing mistakes. And as any seasoned trader can tell you, averaging down presupposes two things, both of which are unreasonable: an endless supply of money, and a stock price that finally goes back up.

Volatility is measured against other positions in your portfolio (relative Beta), and against broad market averages (market Beta). A “high Beta” stock is more volatile than the broad averages and is likely to both fall and rise by a greater percentage than the market does. 

Risk-oriented investors are drawn to Volatility because they generally believe they can get in and out at the right time, and they believe they are better at timing Volatility than most other traders. 

This is often based on a small number of lucky trades, or on forgetting unsuccessful trades. This common psychological trap is called Confirmation Bias. There’s also the adrenaline factor, which is great if you want thrills at a casino, but it has no place in a risk management strategy.

Multi-duration (trade, trend, tail)


Since risk is non-linear (it happens fast, and slow, and episodically), it makes sense to attempt to contextualize changes in price and volatility across time.

Mainstream “technical” measures consider time/price relationships using a one-factor price momentum model (like a simple moving average). That doesn’t work.

These consensus metrics have become the Received Wisdom of Wall Street. Every “chart” is crystal clear, in hindsight. But these charts tell you nothing about power laws and/or phase transitions. No single-factor linear model does.

Our Multi-Durational proprietary system breaks the investment time horizon into three core durations:

  • TRADE – the next three weeks or less
  • TREND – the next three months or more
  • TAIL – the next three years or less

At any given moment, every position in your portfolio will exhibit characteristics in each of these three durations, depending on a broad range of factors ranging from company-specific, to sector-specific, to broad market, to the global macro level.

trade

The TRADE duration measures risk over the very immediate-term (3 weeks or less), and it shouldn’t surprise you to realize that an awful lot can happen in that time frame. 

TRADE is the point of departure for measuring risk in the immediate term. And TRADE is the first signal you might look at if you are an active short-term trader. 

The TRADE duration keys off of current events and macro correlations. As an example, a good earnings report may drive a lot of buying, causing the stock to look overbought on an immediate-term TRADE basis. Whereas a bad one might signal immediate-term TRADE oversold.

If you are a longer term holder, you can use the immediate-term TRADE duration to help you risk manage (sell some high, so that you can buy more low) your best ideas. 

trend

The TREND duration in the Multi-durational model measures risk over the intermediate-term (3 months or more) and back-tests as the most manageable in our model.

That shouldn’t surprise you as this is the duration where many investment strategies purport to live. Three-months or more captures Institutional Investors trying to handicap “the quarter.” 

While immediate-term TRADE volatility can present you with buy/sell opportunities, contextualizing TRENDs, and whether the probability that they continue is rising or falling, is the most important skill set within the longer-term risk manager’s game.

tail

The TAIL duration measures risk over the longer-term (3 years or less). After more than 15 years of trial/error developing this model, we’ve been humbled into submission on this front. It’s very difficult to dynamically risk manage investment ideas beyond that time frame.

Not to be confused with the popular definition of Black Swan “tail risks” that can often be qualitatively defined, we’ve submitted ourselves to Mr. Macro Market on this front and decided that TAIL risks are manageable, if you subject yourself to change and uncertainty.

Across all of our core risk management durations, the fulcrum point in the analysis is the rate of change. This is a critical difference compared to other modeling approaches. The key questions aren’t about whether things are good or bad – they are all about probability weighing whether risk factors are getting relatively better or worse.

Mathematically speaking, we’re talking calculus here (2nd derivatives). Physically, you can also think about it in thermodynamic terms. What factors are undergoing “phase transitions” (from one state to another)? Because once something has moved from bullish to bearish TREND, it’s often too late.

Put simply, TRADEs often educates us about the stability/fragility of TRENDs, whereas the direction of longer-term TAILs can be shocked when a TREND undergoes a phase transition.





Sign up for our Newsletter

The Forecast Report

Every month. Directly in your inbox.