The recent advances in computational and financial technology and resultant
financial innovation have created the possibility of a new perspective on
indexes, indexation, and the distinction between active and passive investing.
Dynamic indexes in this context have many advantages in terms of meeting
investor objectives, but they also require greater sophistication in evaluation
and risk management.
The author discusses the origin and construction of traditional indexes and
introduces the concept of dynamic indexes. These indexes are not necessarily market
cap weighted or passive in the traditional sense. For example, strategy indexes
embody a particular investment strategy. The author cautions that smart beta
strategies may lead to unintended risks that will not earn a positive risk premium.
Using the example of a volatility-adjusted dynamic index, the author elaborates
further on the benefits and potential pitfalls of dynamic indexes.
How Is This Research Useful to Practitioners?
Given technology-leveraged financial innovation, the author asserts a need for a new
perspective on indexes and the distinction between active and passive investing. In
particular, passive investing need not, and should not, imply passive risk
Traditionally, there has been inertia around moving too far away from
market-cap-weighted constructs because they have worked well in the past. Given the
recent advances in computational, communication, and financial technology and the
resultant technology-leveraged financial innovation, however, a new perspective is
needed on indexes, indexation, and the distinction between active and passive
In these times of high volatility, passive strategies may not be as appropriate as
they were once considered. For example, the recent maximum drawdown (MDD) of the
S&P 500 between 9 October 2007 and 9 March 2009 was 56.8%, which led to
significant investor losses, especially for long-term investors who do not regularly
monitor their portfolios.
One simple way to manage such volatility risk is to create a dynamic index that
contains no alpha but actively manages the volatility to a target level. This
strategy reduces the market exposure when short-term volatility is high. Given the
inverse relationship between volatility and prices, the strategy effectively reduces
equity exposure in declining markets and increases market exposure when volatility
is lower. The author finds that, over 1926–2014, such a volatility-adjusted
index has cumulative returns that are 4× higher than those of the benchmark;
it also has lower MDD (–72% versus –84%) and less excess kurtosis (4.85%
versus 16.97%). The results vary for various subperiods but are generally better for
the volatility-adjusted index than for the raw index.
The author cautions that with increasing innovations and a wide range of available
products, professional fund managers need a higher level of sophistication in terms
of strategy evaluation. The backtest bias is especially problematic for smart beta
strategies because the number of managers/models is rapidly growing, the
signal-to-noise ratio is declining, and the performance measurement for these
strategies depends on simulated statistics with no track record.
How Did the Author Conduct This Research?
The author constructs a volatility-controlled index with target volatility and no
alpha to evaluate and demonstrate the use of dynamic indexes. When the expected
volatility is below the target volatility, the dynamic index uses leverage to
increase exposure. When expected volatility is above target volatility, it invests a
portion of the fund in cash. The leverage/deleverage factor is based on the target
level of volatility relative to the estimated volatility of the index. The target
level of volatility can be chosen with the investor’s risk tolerance in
The author uses various parameters to compare the results of the
volatility-controlled index with those of the CRSP NYSE Value-Weighted Market Index
over the period, and rolling subperiods, between January 1926 and December 2014.
Smart beta and factor investing, quasi-active index strategies, recently have been
extremely popular with fund managers. Historically, selected factors have
outperformed the market indexes. These funds suffer from backtest bias and can carry
higher risk as well as higher fees and higher valuations because of increased
popularity. Although the arguments in favor of smart beta and factor investing are
quite compelling, they require sophisticated evaluation and risk management