## Friday, January 30, 2015

### Financial Data for Statistical Learning

There are plenty of financial and economic data which can be used in Machine Learning and Data Mining exercises. Here is list of mine:

FRED - Federal Research Economic Data.
CRSP - Center for Research in Security Prices
Ken-French - Fama-French 3-factor model data
John Cochrane - Data and Programs, Liquidity factor, Grumpy Economist.
Robet Shiller - Online data, and other research data.

Web Scraping is the new way to get the free "real-time" data.:)

Here is introduction given by Christopher Reeves.

Python (urllib, re, scrapy) and R(quantmod) are my favorite languages for FIN/ECON data scraping.

## Saturday, January 24, 2015

### ffn

Intruction to ffn - Financial Functions for Python

ffn is a Python library for quantitative finance. It stands on the shoulders of giants (Pandas, Numpy, Scipy, etc.) and provides a vast array of utilities, from performance measurement and evaluation to graphing and common data transformations.

Its APIs support data retrieval, data manipulation, performance measurement, numerical routines and financial functions.

### Statistical Significance vs. Economic Significance

 Statistical Significance Economic significance Is it fitted well? Is it an important factor? [large] t-stat or [small] p-value [large] bj values Low t-stat => need more sample data? Small bj values => Multicollinearity?

This is a sample regression output from fitlm() in Matlab:

Linear regression model:
y ~ 1 + x1

Estimated Coefficients:
Estimate     SE           tStat     pValue
(Intercept)    0.0028283    0.0023652    1.1958    0.23514
x1             0.91903      0.045009     20.419    2.5487e-34

Number of observations: 86, Error degrees of freedom: 84
Root Mean Squared Error: 0.0143
R-squared: 0.832,  Adjusted R-Squared 0.83
F-statistic vs. constant model: 417, p-value = 2.55e-34