In a previous post, we discussed ideas generated by a Timely Portfolio post about Linear Models on Stock. I wanted to see if there was a relationship between the window length of the running mean of the linear regression slope estimate and the running mean of the correlation between fitted and observed values. The parameters
Timely Portfolio has a nice post about linear models sytems for stock. The idea follows from the steps below: Get the weekly closing values of the S&P 500. Choose a time window (i.e. 25 weeks) and for each window, linearly regress the subset of closing values Choose an investment strategy based on the residuals, the
I’ve been reading The Big Picture, and again, there was a discussion about seasonality in stock markets (see Fourth Quarter is Da Bomb). I’ve already discussed the two seasonal investment scenarios (Nov. to Apr VS May to Oct) in this post, and was wondering if one could break it down further into a monthly analysis.
Once you become addicted to chess game analysis, it becomes very easy to swamp yourselves with questions regarding different aspects of the game. Testing out different hypothesis like preference of mobility versus positional advantage requires a bit of manual chess game mining, which could potentially be analyzed using R. With the help of websites like
Dataspora recently analyzed Lending Club‘s data in a geographical way using the data distributed by the site. Lending Club is an online financial community that brings together creditworthy borrowers and savvy investors so that both can benefit financially. We replace the high cost and complexity of bank lending with a faster, smarter way to borrow
Bubbles. I’m no expert in behavioral economics, but bubbles seem to be well understood (after they occur) although they seem hard to detect (at least in the eyes of outsiders and late bubble participants). This post won’t tell you how to avoid bubbles, but might give you some insight. I came across Minsky’s explanation of
In “A tale of 2 Seasonal Investors“, the Big Picture discusses the simple idea of comparing two simple investment approaches: being exposed to the market 6 months every year (from November to April), as opposed to investing in the other 6 months of every year (from May to October). Going back 50 years in the
I stumbled upon this chart in the R Graph Gallery, which got me thinking someone could come up with a Volume by Price chart using R. Such charts can be useful to determine support and resistance levels, as they illustrate amount of volume for different price ranges. Below is my first attempt at this. Note
Arthur Charpentier used R to denote a broken record of the CAC 40 when it went 11 consecutive days with negative returns. Question: What happens to the market after runs of positive or negative returns? Will the market tank or soar after n days of gains/losses? First, a little dissection of historical data (S&P 500
In this post, Portfolio Probe explores a way to decide whether market kurtosis and skewness are predictable. Market skewness, in naive financial modeling, is some kind of measure of (as-)symmetrical distribution of (daily) returns around the average market return. A higher skewness would tend to indicate a denser distribution of higher returns, compared to lower
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
