Quantitative Value is now available on Kindle–finally!
Sorry about the delay!
Herbalife (HLF): Is it a Fraud? Not likely.
Herbalife is currently a highly debated stock. On one side you have Bill Ackman claiming the firm is a ponzi scheme. On the other side [...]
Simple Models Beat Experts: Even in Investment!
One of the more fascinating findings from psychology research is the empirical finding that simple models often beat experts. In other words, insights from your [...]
Flexible Asset Allocation
Generalized Momentum and Flexible Asset Allocation (FAA) An Heuristic Approach Wouter J. Keller and Hugo S.van Putten A recent version of the paper can be [...]
Low Beta/Vol Outperformance
There are many blogs/funds/research promoting low beta stocks as a way to get rich: A blog example --Falken A fund example --AQR Defensive Fund A [...]
Make 24bps a week trading skewness?
That is what Amaya, Christofferson, Jacobs, and Vasquez find. We use intraday data to compute weekly realized variance, skewness, and kurtosis for equity returns and [...]
Dividing Value into “Priced” and “Mispriced”
Gerakos and Linnainmaa have a new paper out: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2083166 and http://www.asb.unsw.edu.au/schools/bankingandfinance/Documents/J.Gerakos,%20J.T.%20Linnainmaa%20-%20The%20Unpriced%20Side%20of%20Value.pdf Here is code to perform the decomposition: http://faculty.chicagobooth.edu/juhani.linnainmaa/ValueDecomposition.do Summary Book-to-market (BE/ME) ratios explain variation [...]
Applied Quantitative Value (Part 4 of 4)
I sat down with Wes Gray--who also contributes frequently on this blog under the title Wesley Gray, Ph.D.--and Toby Carlisle--who runs the great blog over [...]
How to Identify High Quality Stocks, Continued (Part 3b of 4)
Suppose you had to sail across the Atlantic and were given a choice between making the crossing in either an 8 foot sailing dinghy, or [...]
How to Identify High Quality Stocks (Part 3a of 4)
What does it mean to measure a business’ “quality” in the context of investing in common stocks? We try to answer this question in Part [...]
How to Identify the Cheapest Stocks (Part 2 of 4)
This is the next installment in our series covering Turnkey Analyst’s approach to the holy grail of investing – how to identify low risk, high [...]
Risk Premia Harvesting Through Momentum
Risk Premia Harvesting Through Momentum Gary Antonacci A version of the paper can be found here. Abstract: "Momentum is the premier market anomaly. It is [...]
Will My Risk Parity Strategy Outperform?
Will My Risk Parity Strategy Outperform? Robert M. Anderson, Stephen W. Bianchi, Lisa R. Goldberg A version of the paper can be found here. Abstract: [...]
Managing the Risks of Permanent Capital Impairment (Part 1 of 4)
This post is the first in a series that will discuss Turnkey Analyst’s approach to finding the holy grail of quantitative finance – a robust, [...]
Quantitative Value vs. Magic Formula Stocks
Here is a quick screen comparison between our Quantitative Value (described here) and the Magic Formula (screen results from here): click to enlarge click to enlarge [...]
Calculating Value Portfolios–Why Details Matter
The Devil in HML's Details Cliff Asness and Andrea Frazzni http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2054749 Key Points: Using a more real-time estimate for book-to-market (B/M) matters. Alphas from using [...]
Do Cash-Adjusted P/E Ratios Work?
A guest speaker in my lecture last week mentioned something interesting: Apple looks like a growth stock on a P/E basis, but when you strip [...]
Predicting anomaly returns with politics, weather, global warming, sunspots, and the stars
Ferson, Sarkissian and Simin (2003) warn that persistence in expected returns generates spurious regression bias in predictive regressions of stock returns, even though stock returns are themselves only weakly auto correlated. Despite this fact a growing literature attempts to explain the performance of stock market anomalies with highly persistent investor sentiment. The data suggest, however, that the potential misspecification bias may be large. Predictive regressions of real returns on simulated regressors are too likely to reject the null of independence, and it is far too easy to find real variables that have “significant power” predicting returns. Standard OLS predictive regressions find that the party of the U.S. President, cold weather in Manhattan, global warming, the El Nino phenomenon, atmospheric pressure in the Arctic, the conjunctions of the planets, and sunspots, all have “significant power” predicting the performance of anomalies. These issues appear particularly acute for anomalies prominent in the sentiment literature, including those formed on the basis of size, distress, asset growth, investment, profitability, and idiosyncratic volatility.
Valueinvestorsclub.com Performance Update
If you are unfamiliar with Valueinvestorsclub.com and you call yourself a "value investor," you've been missing out on the greatest value communities of all time. [...]
Dissecting Net-Net Returns over the past 6-months
Paul Sepulveda has an interesting guest post for us today. Paul dissects the recent performance of net-nets and identifies some interesting tidbits. Below I have [...]