Skip to main content

New Position - Short QIHU...

Here's a new position that I built out using the trade optimization model.  There are quite a few interesting things to note with the model that I'll follow up with later.

It's also important to note that I built this trade out earlier in the day and the stock has moved quite a bit since then.

The trade:

Long:  112 Shares @ $63.87
Long:  3 September $65 Puts @ $3.80
Long: 3 September $62.50 Puts @ $2.75
Long: 2 September $60 Puts @ $2


The breakeven on the downside is roughly $59 and just less than $85 on the upside.  With a max loss of $2,200, the expected value is +$760.

Comments

Popular posts from this blog

Modeling Black-Litterman; Part 2 - Incorporating Manager Views

  "The 'radical' of one century is the 'conservative' of the next." -Mark Twain In this series, I'm going to explore some of the advances in portfolio management, construction, and modeling since the advent of Harry Markowitz's Nobel Prize winning Modern Portfolio Theory (MPT) in 1952. MPT's mean-variance optimization approach shaped theoretical asset allocation models for decades after its introduction.  However, the theory failed to become an accepted industry practice, so we'll explore why that is and what advances have developed in recent years to address the shortcomings of the original model. The Black-Litterman Formula The Black-Litterman formula incorporates two distinct inputs; the first is the Implied Equilibrium Return Vector we constructed in Part 1, the second is a series of vectors and matrices that incorporate a manager's views/forecasts of the market.  The product of the formula is an updated Combined Expected Excess Return ...

Variable Types for Principal Component & Factor Modeling

TRANSFORMING RAW DATA INTO INSIGHT & ACTIONABLE INFORMATION After reading the book Moneyball for the first time, I built a factor model in hopes of finding a way to finally be competitive in my fantasy baseball league - which I had consistently been terrible at.  It worked immediately.  By taking raw data and turning it into actionable information, I was able to solve a problem that had long perplexed me.  It was like discovering a new power.  What else could I do with this? Today, I build models for everything and have come a long way since that first simple spreadsheet but still use a lot of the same concepts. To build a traditional factor model, you would regress a dependent variable against a series of independent variables and use the resulting beta coefficients as the factor weights... assuming your resulting r-squared and t-test showed a meaningful relationship of course. Variables typically fall into one of two categories... continuous or dichotomous....

Convexity as a Technical Indicator - Applications of the Second Derivative...

WHAT'S IN A NAME? In investment parlance, the term 'convexity' is typically reserved for the topic of fixed income risk; especially in regards to debt with embedded optionality where negative convexity is a prominent pricing factor.  However, it is important to recall that 'convexity' (or 'concavity', for that matter) is a mathematical measurement used to describe the second derivative of a continuous, nonlinear function on an interval. The issue with confining the term to a single connotation is that - for better or worse - investment tools have become increasingly nonlinear since the days of the 60/40 model and convexity is present in a number of applications.  The extent to which it has been circumscribed to a single asset class is evident in equity option jargon where the second derivative of the pricing function is called 'gamma' instead of convexity.  Of course, the argument could be made that the principle purpose of option vernacular is to c...