Demand for energy resources has been soft in recent years, due to economic problems in much of the developed world. This brought world demand growth for crude oil negative in 2008 and 2009 (see chart below). In addition, the general drive toward energy efficiency has been slow but steady, including more natural-gas power plants, efficient appliances, fuel-efficient cars, and 100% electric cars. Sluggish demand was interrupted by a spike in demand from emerging markets (in particular, China) in 2010, but that reversed in 2011-12 as China slowed down.

The world supply capacity growth, however, has been below demand growth in 2010-11, making for a tight market and contributing to elevated prices.

Sources: IEA, Model Capital Management LLC

Two pieces of information just surfaced recently that, in our view, are critical pieces of the puzzle for future crude oil prices and other energy resources.

North American Supply Shock

The

Gold (GLD) has become a staple in many investor portfolios. What are the reasons people want to own gold? As a real asset, gold is sometimes viewed as protection against inflation. However, since the 1970s, the price of gold has been a poor hedge against inflation - the correlation between the CPI and gold price is close to zero. The rally in gold of roughly 140% over five years from 2007 to 2011 had nothing to do with inflation, which was very low during this period.

Rather, being synonymous with stability, gold has seen investment demand as a hedge against "really bad things" happening. The debt bubble bursting in 2007 created a severe financial crisis in the U.S. and in Europe, and governments coped with the crisis by flooding their financial systems with unprecedented amounts of liquidity. Naturally, people were concerned that all this newly-created money might put fiat currencies

Our PAR Model six month forecast for the S&P 500 fell to 0.4% at the end of March, from 5.5% just a month ago.

A low positive return forecast for the S&P, between 0% and 2% (which doesn't happen often), means that our Tactical Strategy allocations match their balanced benchmark. Low expected returns on both stocks and bonds don't justify taking risk over benchmark in either asset class. Accordingly, we are changing the stocks-bonds allocation in our U.S. Tactical ETF Strategies to 60% stocks, 40% bonds.

The equity rally continued in full force so far in March, with the S&P 500 adding another 3% for a year-to-date return of 6.7%, and 10.2% from the end of November of 2012 - the month of the last moderate downturn in the market. Having been positive since June of 2012, our model produced a very high return forecast in November (19.2%), which we

The six-month S&P 500 (SPY) return forecast by our Performance Analytics Return Model (PAR Model) increased once again this month, to 19.2%. This is only the second time in a decade of backtesting that the model gave such a high return forecast. The last time this happened before was in 1H-2009. We consider this to be a tremendous buying opportunity that comes across only a couple of times in a decade.

The PAR Model is the proprietary factor model that we use to forecast equity returns over a six-month period. The model is based on a dynamic multi-factor regression of S&P 500 returns over economic, valuation, and market variables. Factors are chosen automatically each month based on their statistical significance from the initial set of 22 factors that have proven to be significant over time. Forecasts are revised twice a month.

The model provides the answers that tactical asset allocation

An interesting new white paper written by Alexander Melnikov, PhD., Professor of Mathematical Finance at the University of Alberta speaks to the relationship between alpha and "information coefficient" (IC), as defined by Grinold and Kahn:

"Portfolio returns in excess of an index can be achieved through active investment management in two ways: security selection, and active (or tactical) asset allocation. Research shows that about 90% of a typical balanced stock-bond portfolio risk and return comes from Policy asset allocation - see Brinson et.al. (1986, 1991), Ibbotson & Kaplan (2000). Clearly, potential for adding value through actively managing asset allocation is greater than from active security selection. However, while active security selection is widely practiced, tactical asset allocation (TAA) has been largely overlooked or out of favor. Here, we discuss some of the reasons for this, and describe the process that should be followed in order to successfully perform TAA.

Our model continues to forecast positive 6-month expected return for the S&P 500, currently 4.3%. Accordingly, asset allocation recommendation is still Overweight public equities.

The PAR Model is a factor model that estimates expected equity return over a six-month period. The model is based on a dynamic multi-factor regression of S&P 500 returns over economic, valuation, and market variables. Factors are chosen automatically each month based on their statistical significance from the initial set of 22 factors that have proven to be significant over time.

Significant Factors - Summary

Valuation: Net Positive

The effect of valuation factors on the model's expected return is net positive, though slightly lower due to the rally in equities so far in August. The P/E ratio continues to extend a significant positive contribution at 0.7 standard deviations ("SD" in this report), followed by the Price to Book ratio (0.2 SD), and partly offset by the

Our quantitative forecasting model continues to point to a positive 6-month expected return for the S&P 500 (SPY) of 3.8%, despite economic headwinds. Accordingly, we recommend that investors are overweight equities relative to their benchmark weight.

"Active management is forecasting" - Grinold and Kahn, Active Portfolio Management (1999). In order to achieve alpha from active asset allocation, the manager has to have a model that forecasts expected returns with reasonable degree of accuracy. Many authors provide an opinion on where stocks are going to go next, but not many have a model that does such forecasting accurately. What follows is a general description of how our model works, followed by the description of some of the most important factors that are responsible for this positive result of 3.8%.

Our forecasting model (the "PAR Model" as we call it) is a factor model that estimates expected equity return

The Performance Analytics' PAR Model changed to a positive stance in June, with the six-month expected return for the S&P 500 (SPY) of 3.1%.

Accordingly, we are changing our recommendation to an Overweight to public equities - a change from our Underweight recommendation that was in effect in the last three months.

The PAR Model is a factor model designed to estimate the expected equity return over a six-month period. The model is based on a dynamic multi-factor regression of the S&P 500 returns over economic, valuation and market variables. The factors are chosen each month as part of the model run, based on their statistical significance, from the set of 15 factors that have proven to be significant over time.

S&P 500 6-m expected return: 3.1%

Recommended allocation: Overweight

Prior month -4.4%

Change 7.5%

Significant Factors

The P/E ratio is one of the key measures of index valuation in

The PAR (Performance Analytics Return) Model* changed to a less negative position in May.

Our model is a factor model designed to estimate the expected equity return over a six-month period. The model is based on a dynamic multi-factor regression of the S&P 500 returns over economic, valuation and market variables. The factors are chosen each month as part of the model run, based on their statistical significance, from the set of 15 factors that have proven to be significant over time.

The model provides answers that tactical asset allocation managers need, such as:

- What's the expected return for equities right now?

- What are the factors that we should be looking at, that really affect equities?

S&P 500 (SPY) 6-m expected return: − 4.4%

Recommended allocation: Underweight

Prior month -6.8%

Change 2.3%

The model had predicted a 6-month market drop, at -5.2% in March, confirmed by -6.8% forecast