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!
Our next meeting is scheduled for 5/10/17 at 6:30pm in Palo Alto (exact location TBD- check back here in a few days).
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.
The Black Litterman model was developed in 1990 at Goldman Sachs by Fischer Black and Robert Litterman and deals with two problems in classic portfolio optimization:
- First, a standard optimization model requires as inputs the expected returns for all assets and currencies. Thus investors must augment their views with a set of auxiliary assumptions, and the historical returns they often use for this purpose provide poor guides to future returns.
- Second, the optimal portfolio asset weights and currency positions of standard asset allocation models are extremely sensitive to the return assumptions used.
This article describes an approach that provides an intuitive solution to the two problems that have plagued quantitative asset allocation models. The key is combining two established tenets of modern portfolio theory-the mean-variance optimization framework of Markowitz and the capital asset pricing model (CAPM).
“Global Portfolio Optimization” by Fischer Black and Robert Litterman, Financial Analysts Journal, September 1992.
“Betting against beta”, by Andrea Frazzini , Lasse Heje Pedersen, AQR Capital Management, April 2013
“We present a model with leverage and margin constraints that vary across investors and time. We find evidence consistent with each of the model’s five central predictions: (1) Because constrained investors bid up high-beta assets, high beta is associated with low alpha, as we find empirically for US equities, 20 international equity markets, Treasury bonds, corporate bonds, and futures. (2) A betting against beta (BAB) factor, which is long leveraged low-beta assets and short high-beta assets, produces significant positive riskadjusted returns. (3) When funding constraints tighten, the return of the BAB factor is low. (4) Increased funding liquidity risk compresses betas toward one. (5) More constrained investors hold riskier assets.”