Meeting on 6/25/17 in San Francisco, 5:00pm

Our next meeting is scheduled for 6/25/17 (Sunday) at 5pm in San Francisco on the 2nd floor lounge of the Marriott Marquis (780 Mission Street).  See here for more information about the venue. Once you enter the hotel, go to the 2nd floor using the escalator and you will see a lounge with sofas and tables. Feel free to send an email if you have trouble finding it.

We will be having a discussion on Mandelbrot’s market model for momentum effects. See this post.

Mandelbrot and momentum effects

Excerpt:

In Mandelbrot’s view, “markets were fractal and much wilder than classical theory suggests…Recently, we were able to show that the scaling behavior of trends, as defined by a specific trend decomposition using wavelets, are the root cause for the momentum effect…

In this work, we will revisit Mandelbrot’s vision of fractal markets… We will show that the momentum effect discussed heavily in literature can be modeled by the so-called Mandelbrot Market-Model.”

Link to paper here.

Meeting on 6/11/17 in San Francisco, 5:00pm

Our next meeting is scheduled for 6/11/17 (Sunday) at 5pm in San Francisco on the 2nd floor lounge of the Marriott Marquis (780 Mission Street).  See here for more information about the venue. Once you enter the hotel, go to the 2nd floor using the escalator and you will see a lounge with sofas and tables. Feel free to send an email if you have trouble finding it.

We will be having a discussion on mean-reversion. See this post for details about the article.

Mean reversion-based contrarian investment strategies

Summary:

“For U.S. stock prices, evidence of mean reversion over long horizons is mixed, possibly due to lack of a reliable long time series. Using additional cross-sectional power gained from national stock index data of 18 countries during the period 1969 to 1996, we find strong evidence of mean reversion in relative stock index prices. Our findings imply a significantly positive speed of reversion with a half-life of three to three and one-half years. This result is robust to alternative specifications and data. Parametric contrarian investment strategies that fully exploit mean reversion across national indexes outperform buy-and-hold and standard contrarian strategies.”

“Mean Reversion across National Stock Markets and Parametric Contrarian Investment Strategies.”, Ronald Balvers, Yangru Wu, and Erik Gilliland, Journal of Finance, April 2000.

See here for article.

Meeting on 6/1/17 in Palo Alto

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!

Is high leverage the secret behind the low beta anomaly?

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

Link here

Notes from 5/21 Meeting

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.