Why it Takes More Than Just Good Timing to Succeed in the Market

Combining Factor Investing and Market Timing For the Best Trading

Dec 17, 2020
As Featured In 
Review of Financial Studies

Some traders know how to time the market, others know what signs to look for to get the most bang for their buck. But what’s really the best method between the two of them? The answer is both, according to new research from Maryland Smith.

It’s an idea known as factor timing, a combination of long-short factor investing and market timing. It turns out that putting the two strategies together is very valuable, even more so than market timing and factor investing alone, says professor Serhiy Kozak from the University of Maryland’s Robert H. Smith School of Business.

The research, written with two co-authors from the University of California, Los Angeles, and the University of Colorado, Boulder, was recently published in the Review of Financial Studies.

It is well known that timing individual stocks is extremely difficult due to extreme volatility on stock returns. One can reformulate the problem of timing individual stock returns in terms of timing the largest components of long-short factor investing strategies. It turns out that the latter are much easier to predict due to their favorable statistical properties. Once the predictions are made, they can be translated back to implied predictions for individual stocks, which, Kozak finds, are sizable.

The sum of their research efforts, Kozak says, shows market-neutral equity factors, as well as individual stock returns, are predictable. The “trick” is to look for predictability in the right place. That predictability, he says, can help traders make a positive jump in their portfolio performance in comparison to other strategies.

Read the full research, “Factor Timing,” published in the Review of Financial Studies.


About the Author(s)

Serhiy Kozak is an assistant professor of finance at the Robert H. Smith School of Business at the University of Maryland. His current research focuses on embedding economic and asset pricing restrictions into machine learning methods to study the dynamics of asset prices.

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