Systematic Rebalancing Drives ‘Smart’ Beta
Implementing a scientific and disciplined risk-managed investment process can capture natural stock price volatility to generate an excess return above the benchmark over time, according to Richard Yasenchak, client portfolio manager at quantitative investment firm Intech.
“We do not forecast stock alphas; instead we attempt to construct a more efficient portfolio by estimating the correlations and volatilities of stocks,” Yasenchak said at a press briefing last week. “Periodically rebalancing the portfolio back to optimal target weights, as stock prices move up and down, provides the potential to add return by beneficially capturing the natural volatility of stocks.”
It is this rebalancing, which can be applied to any equity benchmark that is necessary for preserving diversification and potentially achieving higher portfolio compound returns.
“In essence, what we’re saying at Intech is that smart beta is not a buy-and-hold strategy, it requires constant rebalancing in order to maintain the exposure you’re seeking, or desiring, within that particular strategy,” said Yasenchak. “It’s that rebalancing mechanism that’s driving the excess returns from a portfolio-centric viewpoint.”
Prior to joining Intech in 2014, Yasenchak was a portfolio manager at Russell Investments, where he managed the U.S. equity quantitative fund and the U.S. defensive equity fund.
Beta itself is a proxy for the market, for market exposure, a cap-weighted benchmark for example. “You can think of a cap-weighted benchmark as a very inexpensive way to get exposure passively to something,” said Yasenchak.
“There’s also benefit that a cap weighted benchmark has limitless capacity, and therefore you can actually just get the beta exposure you’re seeking and perhaps that beta exposure might be even more attractive in times when there’s less risk in the system.”
The big drawbacks associated with cap weighting is that there might be unintended exposures within a cap-weighted benchmark. “By buying passively, you’re taking on a size bet,” said Yasenchak. “In essence, academia and others have shown that there’s some kind of risk premium tied to valuation, and perhaps size and other characteristics.”
The excess returns relative to whatever benchmark Intech is following have to deal with rebalancing. “In essence, you can think of rebalancing as simply selling high and buying low back to some kind of target weight, and doing that over and over again across numerous stocks,” Yasenchak said. “That is really the fuel, the driver, of our smart beta process.”
It is often difficult to predict the actual return of a stock over the next day, month, quarter or year, but a stock’s return will typically be within a range that is consistent with its historical volatility. Therefore, without having to forecast the future returns of stocks, a proper use of statistics provides a reliable estimate of the most likely range of outcomes for a stock’s return.
Through rebalancing and based on estimates of volatility and correlation, Intech’s process can capture stock price volatility and potentially outperform the market or a benchmark, over time, according to Yasenchak.
Excess returns, or alpha, consist of strategic and tactical components. “To express the strategic component, if you firmly believe that valuation pays off over the long term you would tilt your fund to engage in a strategic valuation exposure,” said Yasenchak. “At other points in time you may take on a tactical view to actually express something that you believe will happen in the short term and then shift the exposures of your portfolio, either through your managers in a multi-manager framework, or within exposure to a group of stocks such as a smart beta portfolio.”
Algorithms have become more prevalent in the spot FX market.
QB’s Algo Suite for futures market trade execution is also being co-located to HKEX.
Breaking data silos is key to deploying automation beyond 'nuisance' orders.
They can be used on quantum hardware expected to be available in 5 to 10 years.
Streaming blocks change the basis of matching and price discovery so institutions can find new liquidity.