Facebook Surpasses Google in Domestic Website Visits for September 2010

Facebook.com which is presently the #2 ranked internet domain in the world behind Google.com (GOOG) according to Alexa.com site rankings, just surpassed Google.com in the number of Website visits from within the USA according to data just released by Compete.com. Compete.com estimates that Americans visited Facebook.com approximately 3.43 billion times during the month of September compared to 3.21 billion visits to Google.com in the same month.

These data further highlight Facebook’s meteoric rise to become the #2 ranked internet property on the web over the last few years. It was only three years ago when Facebook was ranked lower than its social networking competitor MySpace.com, but things have certainly changed over the last 3 years, and while MySpace has definitely lost market share, Facebook seems unstoppable in its quest for more users and web-traffic.

If and when Facebook will eclipse Google to become the most popular website on the internet still remains to be seen, but if the trend in the chart below continues, then it is only a matter of time. What does this mean for Google’s stock and Facebook’s private placement stock which trades on Sharespost.com?

US Job Website Visits by Month – August 2010

The number of unique visits to US job websites has been on a steady rise over the last few months, but it is still well off of the highs that it reached last year. In August 2010, US job sites had about 130 million unique visits which represented a 1.7% increase over July 2010, but still well below the highs of 170 million visits per month which was seen throughout 2009.

The data in the chart below come from Compete.com, which is a website analytics platform, and are an aggregate of views to careerbuilder.com, hotjobs.yahoo.com, indeed.com, and monster.com.  Could these data be a sign that the job market is beginning to tighten again? Or does it simply mean that people were so discouraged that they stopped looking for a job at the beginning of the year, and now they are beginning to look again? The monthly jobs numbers should let us know soon.

Top Semiconductor Stocks by Dividend Yield

Semiconductor stocks were on fire on Friday. Is this the beginning of a big rally for the sector or a dead cat bounce after the sector fell hard after Intel’s announcement to buy McAfee? The yield on many of these stocks has become really high so perhaps this rally in the semis could have legs.

I ran a screen and here is the list of the top semiconductor stocks by dividend yield.

Finding the free lunch: A review of diversification and correlation

It is often said that diversification is the only free lunch in investing. However, the ability to manage risk in the stock market through diversification depends on the level of correlation of stock prices to each other, i.e. how correlated stocks are to one another. If all stocks were perfectly correlated and moved up and down together in tandem, then diversification would offer no risk management advantage over a non-diversified portfolio holding a single stock or an index basket of stocks. Risk management in such a market would be reduced to market timing methods for how to scale in and out of positions as the market moves up and down. If, on the other hand, the return on any given stock were random and independent of the movement of all other stocks, then diversification would offer investors a nearly perfect method for reducing risk. Asset allocation would become a problem of betting on the right stocks. Some stocks would rise while others would fall, and the portfolio manager’s job would be simplified to picking the winners and avoiding or shorting the losers.

In the real world, however, investing follows neither of these contrasting scenarios. Stock returns are not perfectly correlated but they are not independent of one another.  Some days the market goes up sharply and 9 out of 10 listed issues rise. Other days the broad indices are practically flat and some stocks rise while others fall. Thus, the utility of diversification depends on how correlated stocks are to each other, and this is why practitioners have developed many methods for quantifying the level of correlation of stock returns to each other. Knowledge of this correlation level is important to asset managers because it assists them in making decisions such as whether it is worth the effort to build a diversified portfolio of stocks rather than to simply invest in index funds.

Time series analysis offers the most widely applied method for quantifying the global level of correlation in a group of stocks. This method begins with a fixed time interval and stock returns at regular intervals. For example, five years of monthly stock return data could be used to compute the global correlation of a group of country specific stock indices as in the paper by Solnik and Roulet. The same approach could just as easily be applied to long time periods such as years to tick level data on an intraday chart. Pairwise correlation values are computed across each pair of stocks in the group over the fixed interval and then these pairwise correlation values are averaged to arrive at the global level of correlation among the stocks in the group. The average value is usually referred to as the cross sectional correlation and can be estimated over distinct fixed intervals or over a rolling window period. For example, one could compute the 90-day cross sectional correlation of the daily returns of all stocks in the S&P 500 by computing the trailing 90-day correlation of returns for each pair in the S&P 500 and then averaging the pairwise values to find the cross sectional value.

The time series approach has many limitations. First, each pairwise correlation value is computed separately, and second, it is difficult to estimate the change in the correlation because each pairwise value is computed from a moving window in which only one data point changes at each successive time step. This problem can be rectified somewhat if the pairwise correlations are computed using formulae that give more weight to recent data such as exponentially weighted data. However, this solution is somewhat ad hoc and each pairwise value still depends on many historical data points. It is for this reason that people began to look for a method of estimating the global level of correlation in a way such that new estimates are independent of the historical data.

Cross sectional dispersion is a model of global market correlation published by Bruno Solnik and Jaques Roulet in 2000. This model offers an alternative approach for quantifying the level of correlation of stocks. The premise of their model is to compute the standard deviation of the returns of a group of stocks at regular intervals, every month, for example, and to use the standard deviation, which they call the “dispersion”, as a means to derive an estimate for the “instantaneous correlation” of the group of assets. They present empirical data that shows that the long term mean of the cross sectional correlation computed in this manner is very close to the long term value of cross sectional correlation computed using traditional time series approaches.

The math behind the cross sectional dispersion approach is arguably simpler than that of the time series approach. To calculate the cross sectional dispersion of a group of stocks, simply compute the standard deviation of the returns of all stocks in the group over a fixed interval, such as a month. In their paper, Solnik and Roulet use country specific stock indices at monthly intervals as an example, but the method can easily be applied to a group of stocks within a sector or a major index at daily or even intraday periodicities. Now compute the historical value of the volatility for the group of stocks. Let  σc denote the cross sectional dispersion and  σw denote the volatility of the group of stocks. Then the global level of correlation is given by the formula:

where ρ is the “instantaneous” global level of correlation. Solnik and Roulet derive the formula above using a series of approximations and assumptions. I will not reproduce the derivation here, but I will comment on their results and the utility of their model.

Solnik and Roulet use empirical data from country specific stock indices to show that an estimate of the global level of correlation from their model has a long term average that is approximately equal to the value of the cross sectional average correlation computed with the time series approach. This is comforting, given that their approach makes so many approximations and assumptions. Their data demonstrates cross sectional correlation estimates derived from their dispersion method change more frequently than a value computed using a rolling window with the time series approach.

The greatest advantage of the cross sectional dispersion approach is that it provides more frequent estimates of the current global level of correlation in a group of securities than the time series approach. One of its greatest drawbacks, however, is that the method only offers information about the global level of correlation in a group of stocks and says nothing about the correlation between different components within the group. In fact, one of the assumptions of the model is that every stock has the same correlation to every other stock in the group. As crude as this assumption may appear, the empirical data suggest that it is still a very useful way to estimate the level of co-movement in a group of stocks.

The cross sectional dispersion model offers investors another tool for identifying the utility of diversification in managing the risk of a portfolio. As up to the minute information becomes more readily available to people worldwide, and globalization causes world economies to become more interconnected, it is logical to expect that global stock returns will become more correlated. Investors now need better tools to identify new opportunities for improving the diversification of their portfolios. A combination of the traditional time series approach supplemented with the frequent correlation estimates offered by the dispersion model could be a great way to begin looking for such opportunities. These quantitative methods are no substitution for old fashioned bottoms up analysis, however. Investors should always examine the who, what, where, why and how of the underlying businesses into which they are placing their money and seek to diversify themselves across the fundamental business dimensions as well.

What is market neutral trading?

Market neutral trading or investing is a portfolio management strategy that aims to reduce the risk of a portfolio to fluctuations in asset prices in the market of a specific asset class while achieving positive returns in the portfolio as a whole. In the stock market this is usually achieved by constructing long-short portfolios by taking long positions in some stocks and short positions in others so that the correlation of the portfolio’s returns to the return of a particular stock index such as the S&P 500 is close to zero.

Example: Pairs Trading

Pairs trading is a simple example of market neutral trading. Suppose that a trader would like to take a position in the telecom sector but would like to reduce her exposure to the overall performance of the sector because most of the stocks in the sector tend to trade together based on the idea that all of the underlying companies have exposure to the earnings growth and performance of the telecom industry. One way to achieve this would be to place a bet on the relative performance of a pair of stocks in the industry such as Verizon (VZ) and AT&T (T).

Suppose that our trader expects AT&T to outperform Verizon. She could have many reasons for making this prediction. For example, she may believe that AT&T will take market share away from Verizon because iPhones are only available on AT&T’s service and she expects many Verizon subscribers to switch to AT&T so they can use their iPhones.  In this case she would take a long position in AT&T and a short position in Verizon. This pairs trade is not a riskless a position, however, far from it, but it does enable her to reduce her exposure to the aggregate performance of the telecom sector.

The risk in this position lies in the relative performance of AT&T versus Verizon. If she were to place half her money in her long AT&T position and the other half in her short Verizon position, then if AT&T outperforms Verizon, she will make money, but if Verizon winds up outperforming AT&T, then she will lose money. However, she could place different portions of her capital in each position.

Zero Beta Risk Management

A common method of risk management for market neutral portfolios is called zero beta risk management. The goal of zero beta risk management is to try and completely eliminate a portfolio’s risk with respect to the performance of a particular group of assets. In this case our investor would like to eliminate her risk with respect to the aggregate performance of the telecom sector. She can accomplish this by weighting her long and short positions such that the beta of her portfolio relative to the performance of the telecom sector is zero.

Here we speak of the beta from the Capital Assets pricing model (CAPM), which is calculated by performing a linear regression. I will not go through the details of this computation in this article, but if you are unfamiliar with CAPM, you can read about it at many places on the web, and a great place to start is the Wikipedia article (http://en.wikipedia.org/wiki/Capital_asset_pricing_model).

An equally dollar weighted portfolio generally does not have zero correlation to the underlying index. This is because the components of the index generally do not have the same volatilities. For example, Verizon has historically been a bit more volatile than AT&T. Thus, if the telecom sector rises sharply she is likely to lose money in an equally weighted portfolio because Verizon will rise more than AT&T because Verizon trades in a more volatile fashion.

To solve this problem she can compute the beta of each stock with respect to a telecom sector index such as the Dow Jones US Telecom Index. She will then choose the weights for each position by looking at the relative size of the betas for each stock. For example, if Verizon has a beta of 1.2 with respect to the telecom index while AT&T has a beta of 0.8, then she would place 60% of her capital in her long AT&T position and 40% of her capital in her short Verizon position. This follows from solving the set of simultaneous equations below:

WT + WVZ = 1

WTβT + WVZ βVZ = 0

where WT, WVZ, βT, βVZ are the weights for AT&T and Verizon and their corresponding betas, respectively.

This analysis can be extended to more complex portfolios consisting of more than two stocks. One important thing to consider, however, is that the betas used in this analysis are usually the historical values for the beta and not the future values. The beta of a stock is not a static value but rather, dynamic just as the stock’s price and can change wildly over time. Thus, this analysis leaves one with the problem of predicting the future values of beta. Using the historical value of beta is often a great place to start, but there is no guarantee the future beta will be close to the historical value.

Market Neutral Portfolios

A portfolio is referred to as “market neutral” with respect to an index if its beta is zero with respect to that index and such portfolios are called market neutral portfolios. Market neutral portfolios are not without risk, but they aim to eliminate risk with respect to a certain market, which in our example, is the portfolio’s risk with respect to the performance of the telecom sector as a whole.

In the hedge fund industry there is an entire group of strategies and funds that fall under the market neutral category, each of which aims to reduce risk. Pairs trading individual stocks, long short sector rotation, long short countries, and merger risk arbitrage are common examples. While not all of these funds use zero beta risk management, research indicates that they have the lowest beta of all of the major groups of hedge funds except for short funds which have a negative beta.