Tag Info

Hot answers tagged

8

If $Q$ is your covariance matrix, and $r$ is a vector of your expected returns, then the maximum Sharpe ratio is given by the following math program. $${\rm maximize} \frac{r^t x}{\sqrt{0.5 x^t Q x}}$$ subject to $$ 1^t x = m$$ $$ x \in \{0,1\}^n$$ Where $x$ is a vector of indicators of which of the $n$ assets are part of the $m$ selected assets. While the ...


3

I wouldn't put too much faith in IBES forecasts. You may remember this situation: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=889322 (In case the above link doesn't work, Google "Rewriting History Alexander Ljungqvist"). You'll find lots of excuses for worthless forecasts: http://www.princeton.edu/~hhong/rje-analyst.pdf Below is a graph that I ...


3

There are a couple components to this problem: Construct a portfolio incorporating relative views where weights shrink towards a default policy 1a. Some views are in currency-hedged terms 1b. Relative views are on a 5-point scale Maximize a mean-variance utility function incorporating a penalty for transactions costs and requiring that weights sum to ...


3

I would use something similar to Black Litterman where both confidence of manager views as well as dynamic correlations are used to re weight asset classes. For a good look at how transaction costs affect long term allocation decisions with changing parameters, you may be interested in Balduzzi and Lynch (1999). Another approach to consider is to look at ...


3

Take a look at Campbell's 2008 paper "Predicting Excess Stock Returns out of Sample". This paper is in response to Goyal & Welch's paper which argued that excess returns cannot be predicted out of sample. Also see Baekart and Ang's paper "Stock Return Predictability: Is it there?". A good theoretical framework that ties stock return predictability to ...


3

Check out: "Universal hedging: Optimizing currency risk and reward in international equity portfolios," Fischer Black - Financial Analysts Journal, 1989. as well as many of the subsequent research that references this article (via Google Scholar, for instance). Good luck.


2

For such a problem ("selecting n out of m") you can use optimisation heuristics. These algorithms work well even for large n and m, and they are flexible: you may as well select a portfolio that minimises some other function, for instance, the portfolio's drawdown. The downside is that you may have to do some programming yourself. An example very similar ...


2

There are a number of issues here. First, there are a number of methodologies called “performance attribution” each providing answers to different questions. So I am not sure what type of question you wish to address. I will here assume that you wish to evaluate the effects of investment decisions as opposed to the effects of market factors. I will also ...


2

Here's an approach by Kritzman et al on Tactical Asset Allocation using Markov regime switching models. Here's another approach that uses relative strength in TAA.


1

Not Purely Tactical however some of this should answer your question: I included the minimum variance portfolio as a "active" (with lots of rebalancing) Value and Momentum: http://pages.stern.nyu.edu/~lpederse/papers/ValMomEverywhere.pdf A tl;dr: long undervalued stocks (book/market) that have strong up momentum, short overvalued stocks (book/market) that ...


1

I would normalize valuation variables over the business cycle. These normalized variables exhibit mean-reversion. For example, use price-to-peak earnings rather than P/E. Here is a good illustration of the idea.



Only top voted, non community-wiki answers of a minimum length are eligible