The ongoing financial market theory tells that over a longer time horizon, stocks with a higher degree of risk (beta or volatility) should outperform less risky ones. The main reason for that theory is the fact that a rational investor would only accept a higher level of risk if it is offset by a higher degree of return. Basically, this theory makes a lot of sense, since in equilibrium the return of a security is determined by the risk-free interest rate and its inherent risk premium.

However, the popularity of low-volatility ETFs has risen quite significantly over the last couple of years. According to this ongoing theory, in the long run, all those low-volatility investors should yield lower returns than the overall market portfolio (NYSEARCA:SPY). In real, the popularity of low-volatility ETFs is mostly driven by the fact that those products have clearly outperformed the overall market (SPY), in most of the times.

Obviously, the well-known Capital Asset Pricing Model (higher risk equals higher returns) does not hold in a real market environment. How can it be possible that such a well-known and widespread theory is not valid at all? In order to evaluate the reasons for the so called low-volatility abnormally, we looked at the relationship between risk and return for 308 American stocks in the period from January 1995 to January 2013. The sample consisted of all current stocks in the S&P 500 (SPY) with at least an eighteen years price history.

We divided the data into one eighteen and nine two-year periods, to examine and compare the effects in different market environments. First, we calculated the annualized return, as well as the volatility for each stock using daily data, over the ten different periods mentioned above. The charts below show the returns and the volatilities of all 318 American stocks in different sub periods. Each blue point represents a stock. The red line is the resulting regression.

Subperiods:

Surprisingly, the regression had a positive slope over the whole time period, meaning that riskier stocks delivered higher returns than less riskier ones. Another interesting fact is that in 6 out of 9 sub periods, we have received similar results. Only during the tech- and financial crisis, as well as from 2011 until 2013, the slope was negative and therefore more risk led to lower returns. (Worth mentioning is the fact that we have received similar results by using the individual betas of each stock instead of their volatilities).

Anyhow, the results are kind of odd, since the outcome of our study does not comply with the fact that the S&P 500 Low Volatility Index ETF (NYSEARCA:SPLV) was able to deliver almost as much as performance during bull markets and was clearly outperforming the overall market during difficult market environments.

The answer gets pretty clear, if we have a closer look at the methodology of the S&P 500 Low Volatility Index ETF (SPLV). This ETF mimics the performance of the 100 least volatile stocks in the S&P 500 (SPY). Constituents are weighted relative to the inverse of their corresponding volatility, with the least volatile stocks receiving the highest weights. According to our **latest research article**, the inverse volatility portfolio construction technique is definitely a better way to construct an index compared to the capitalization-weighted methodology, which involves some form of momentum. Therefore, most of the performance from the S&P 500 Low Volatility Index ETF (SPLV) was clearly coming from the advanced index construction methodology, instead of the underlying low-volatility stocks, as the CAPM model still holds in a real market environment.

This result is not as surprising as it may sound. According to the overall finance theory, in the long run, most of the performance of a wide spread portfolio strongly depends on the strategic asset allocation (portfolio construction technique), rather than picking the right stocks!

**Disclosure:** I have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.