Momentum investing

Photo by Pixabay on

When you drive a large vehicle, you’re often told to either not drive too fast or leave some distance between you and the car in front because safe braking distance can be very different from an average vehicle. That’s the idea behind momentum, which in physics can be simply defined as “mass in motion”.

In finance, momentum refers to a phenomenon where assets whose prices have increased in the past tend to continue increasing (in motion), often for a while (and the opposite, as well). This phenomenon is observed across many asset classes in many markets, and is the basis of one of the most popular trading strategies (see, for example, Asness, Moskowitz and Pedersen, 2013)

There are many types of momentum strategies. Here are just 11 of them.

Momentum has been a thorn in the side for many fundamentalists because it seems to violate the assumptions that investors are rational (pay full attention, can process information correctly, are not prone to psychological biases) and markets are efficient (any price/value deviations are quickly arbitraged away and will not persist).

We can classify momentum strategies in many ways. For example, a cross-sectional (CS) momentum strategy would rank across all available investment opportunities and long the ones that show the strongest “signals” and short (if possible) the opposites. Examples of signals include past cumulative returns (e.g. 1-month, 6-month, 12-month) or company fundamentals (e.g. earnings announcements).

There are also time-series (TS) strategies that evaluate investment opportunities one-by-one and use their historical patterns to identify whether one should go long or short. Examples of such signals include past cumulative returns, weighted returns (e.g. EMA) or their combinations (e.g. MACD, RSI). One can view TS momentums as market-timing strategies. TS momentum strategies challenges the notion of efficient market hypothesis, as the implication of weak form efficiency explicitly rules out such trading strategies.

Another classification scheme is by the type of signals, as already discussed above. Signals based on past returns can be viewed as price momentum, and signals based on announcements of financial statements can be viewed as earnings momentum. An example of an earnings momentum is market reactions to earnings announcements, which are essentially “surprises” or unanticipated information. Note that this notion is not inconsistent with market efficiency, as arrival of new information can change asset valuation. However, studies such as Chan, Jegadeesh and Lakonishok (1996) show that price reactions continue beyond the announcement dates, which contradicts the prediction of semi-strong form market efficiency.

Aside from violating efficient market hypothesis on both levels, the momentum phenomenon seems to contradict the notion of rational investors as well. For example, price momentum can arise from overconfidence and self-attribution bias (Daniel, Hirshleifer and Subrahmanyan, 2005), or which month of the year it is (Heston and Sadka, 2008). Earnings momentum can arise from investor sentiment (Barberis, Schleifer and Vishny, 1998) or inattention (Hong and Stein, 1999). Both types of momentum can be exacerbated by the disposition effect (Grinblatt and Han, 2002; Li and Yang, 2013).

In recent years, scholars have tried to disentangle the source of the momentum phenomenon. For example, Novy-Marx (2013) dissects price momentum and finds that much of it is attributable to recent accounting performance, suggesting that earnings momentum is related to price momentum. In Novy-Marx (2015), he takes the idea further and finds that price momentum strategies are, in fact, driven by earnings momentum. Birru (2015) investigates whether the disposition effect causes momentum by using stock splits as events that rebase prices. He finds that the disposition effect is indeed related to but cannot explain price momentum.

I’ve analyzed some of the popular momentum strategies with several holding periods (1-month, 6-month and 12-month) in Thailand between January 2000 and May 2021 for both SET and Mai-listed stocks. The results are striking:

  • 10 out of 11 strategies work. In fact, the only strategy that doesn’t work is standardized earnings surprise (Sue), which is actually calculated as volatility-scaled earnings growth.
  • Short-term strategies (X-1 version) tend to perform better, suggesting that momentum is a short-term phenomenon.
  • In the US, R1 actually doesn’t exist. It’s short-term reversal (S-Rev), where stocks that just experience price increase tend to revert in the next month. In Thailand, it just keeps going.
  • Industry momentum (Im) is constructed based on just the 8 broad industry groups. It longs the industries that perform well and shorts the laggards. This is very broad definition compared to Moskowitz and Grinblatt (1999) who use 20 industries.

The results are perhaps not that surprising because momentum is a very pervasive phenomenon. Asness, Mostkowitz and Pedersen (2013) find it in currencies, government bonds and commodity futures. In fact, Liu and Tsyvinski (2021) find it in cryptocurrencies as well. What causes it, however, is a question that we still don’t fully understand and is a promising venue of research.

In a future post, I’ll be taking a look at other asset pricing anomalies in Thailand. Here’s a hint: the title of the Asness, Mostkowitz and Pedersen (2013) paper is “Value and Momentum Everywhere”. And as you can guess, the same is true for Thailand, too!

References (not in any particular order)

  • Asness, Clifford S., Tobias J. Moskowitz, and Lasse Heje Pedersen. “Value and momentum everywhere.” The Journal of Finance 68, no. 3 (2013): 929-985.
  • Chong, T. T. L., Ng, W. K., & Liew, V. K. S. (2014). Revisiting the Performance of MACD and RSI Oscillators. Journal of Risk and Financial Management7(1), 1-12.
  • Chan, L. K., Jegadeesh, N., & Lakonishok, J. (1996). Momentum strategies. The Journal of Finance51(5), 1681-1713.
  • Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (2005). Investor psychology and security market under-and overreaction. Advances in Behavioral Finance, Volume II, 460-501.
  • Hong, H., & Stein, J. C. (1999). A unified theory of underreaction, momentum trading, and overreaction in asset markets. The Journal of Finance54(6), 2143-2184.
  • Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of financial economics49(3), 307-343.
  • Li, Y., & Yang, L. (2013). Prospect theory, the disposition effect, and asset prices. Journal of Financial Economics107(3), 715-739.
  • Grinblatt, M., & Han, B. (2002). The disposition effect and momentum (No. w8734). National Bureau of Economic Research.
  • Novy-Marx, Robert. “Is momentum really momentum?.” Journal of Financial Economics 103, no. 3 (2012): 429-453.
  • Heston, S. L., & Sadka, R. (2008). Seasonality in the cross-section of stock returns. Journal of Financial Economics87(2), 418-445.
  • Novy-Marx, R. (2015). Fundamentally, momentum is fundamental momentum (No. w20984). National Bureau of Economic Research.
  • Moskowitz, T. J., & Grinblatt, M. (1999). Do industries explain momentum?. The Journal of Finance54(4), 1249-1290.
  • Liu, Y., & Tsyvinski, A. (2021). Risks and returns of cryptocurrency. The Review of Financial Studies34(6), 2689-2727.
  • Birru, J. (2015). Confusion of confusions: a test of the disposition effect and momentum. The Review of Financial Studies28(7), 1849-1873.

Leave a comment

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s