Our next meeting is scheduled for 6/1/17 at 7:00pm at Paris Baguette on 383 University Avenue, Palo Alto (see here for directions).
We will discuss the paper “Leverage as a Weapon of Mass Shareholder-Value Destruction; Another Look at the Low Beta Anomaly.”
I’d also like to give a quick demo of the Quantopian backtesting platform to show how we might test some of the results in these papers that we discuss.
If you find other papers or topics that you’d like to suggest in future meetings, please mention it in the comments.
Let me know if you have any questions and see you soon!
A few weeks ago, we covered the Frazzini-Pedersen paper describing how low-beta stocks outperform those with high beta. They theorized that this was because many investors (i.e., most large funds) tend to be constrained against making levered bets, and therefore compensate for this limitations by crowding into high-beta stocks. This in turn lowered the future return of those stocks.
But something didn’t seem to make sense about this explanation to me. If high-beta stocks were being chased by investors, would this not drive the price up even more?
A paper that I recently came across poses a possible resolution to this question. In “Leverage as a Weapon of Mass Shareholder-Value Destruction; Another Look at the Low Beta Anomaly” author Andricopoulos suggests that the pool of high beta stocks are mixed in with companies that lever up (i.e,, have high debt to equity ratios) as a way to disguise their underperformance. Measures such as ROE and P/E can be made to look attractive with changes in capital structure. Over time, rather than correcting their core challenges, these management teams become accustomed to using leverage, and also become risk-averse since they must meet their debt obligations. Risk aversion leads to long-term under-investment in new products, which in turn causes them to underperform.
“Leverage as a Weapon of Mass Shareholder-Value Destruction; Another Look at the Low Beta Anomaly”, Ari D. Andricopoulos. Dacharan Advisory, November 2016
We had a great discussion on the paper “Striking Oil: Another Puzzle” by Driesprong, Jacobsen, and Maat.
To recap, the central point of this paper is that stock markets take time to react to changes in oil prices in ways that cannot be explained by reasons consistent with efficient markets, such as time-varying risk premia, or transitory effects due to changes in the oil market itself. The main reasons the authors give is that it take time for information about oil markets to reliably diffuse to broader market investors; hence there is an under-reaction; this effect is supposedly also the reasons for momentum factors elsewhere in the stock market (See Hong-Harrison).
However, someone at the meeting pointed out that Ben Bernanke recently (2016) wrote how this effect might be complicated than claimed above. Specifically, there are times when oil prices and stocks are *positively* correlated rather than negatively. Bernanke writes:
“Much of this positive correlation can be explained by the tendency of stocks and oil prices to react in the same direction to common factors, including changes in aggregate demand and in overall uncertainty and risk aversion.”
In other words, if there is a reduction in predicted world economic output , there will be a lower demand for oil, which in turn will lower the price of oil and stock prices together.
This is by no means the end of the story. This effect continues to be an active area of research. For example see Chiang, who “explores stock return predictability by exploiting the cross-section of oil futures prices. Motivated by principal component analysis, we find the curvature factor of the oil futures curve predicts monthly stock returns: a 1% per month increase in the curvature factor predicts 0.4% per month decrease in stock market index return.”
As a test to see if I could build a crude trading model, I took relatively small datasets of USO and SPY (period of 1 year) and auto-regressed them with a lag of 1-10 days. There was a negative relationship when a lag of 3-5 days was introduced similar to what the paper described, but it’s not clear how persistent this is across different periods, as Bernanke predicts.
We should revisit these topics in future blog posts.
Our next meeting is scheduled for 5/21/17 at 5pm in San Francisco at Workshop Cafe (180 Montgomery Street). See here for more information about the venue.
We will be discussing an observed anomaly regarding a lag between oil prices and the broader stock market. See this post for details about the article.
In terms of a suggested agenda:
- Paper review: hypothesis, data and analysis methods, results, and robustness of paper linked above [60 minutes]
- Demo of Quantopian platform for backtesting [10 minutes]
- Possible software test using oil price anomaly: use oil futures to predict returns in a major world index, measure the resulting alpha, volatility, and Sharpe ratio. [30-60 minutes]
“This paper investigates whether changes in oil prices predict stock returns.Stock returns tend to be lower after oil price increases and higher if the oil price falls in the previous month. We find no evidence that our results can be explained by time varying risk premia. Even though oil price shocks increase risk, investors seem to under-react to information in the price of oil.
Our findings are consistent with the hypothesis of a delayed reaction by investors to oil price changes. In line with this hypothesis the relation between monthly stock returns and lagged monthly oil price changes becomes substantially stronger once we introduce lags of several trading days between monthly stock returns and lagged monthly oil price changes.”
“Striking Oil: Another Puzzle?” , Gerben Driesprong , Ben Jacobsen, Benjamin Maat. Journal of Financial Economics ,Volume 89, Issue 2, August 2008, Pages 307–327
Free link here
Funny but true: “If your investment firm has a marketing department, you’re probably not that good an investor.”
Source: “Proprietary trading: truth and fiction”, Peter Muller, Quantitative Finance Journal.
Our next meeting is scheduled for 5/10/17 at 6:30pm at Paris Baguette on 383 University Avenue, Palo Alto (see here for directions).
We will discuss the Black Litterman model and possible ways to build a small test implementation. See here for article and feel free to add your questions in the comments section below. I have also added more helpful links and demo in the comments section of the original post, which I am copying here:
Step By Step Guide to Implementing Black Litterman: https://corporate.morningstar.com/ib/documents/MethodologyDocuments/IBBAssociates/BlackLitterman.pdf
Black Litterman.org, site focusing on the technique:
Please let me know if you have any questions and see you soon!