I took the following code and applied it to aapl and qqqq time series (see attached file):
import numpy as np lstsq = np.linalg.lstsq from numpy import float64, extract aapl_array = np.array([row[0] for row in stock_and_market_values]) qqqq_array = np.array([row[1] for row in stock_and_market_values]) A = np.ones((len(qqqq_array), 2), dtype=float64) A[:,0] = aapl_array result = lstsq(A, qqqq_array) print result The result is: (array([ 0.13851625, 29.57888955]), array([ 144.23291488]), 2, array([ 639.591 08529, 0.94451427])) And the beta comes out to be 0.138 which is a low. It should be closer to 2. Any idea on what I'm doing wrong. Vineet -----Original Message----- From: Vineet Jain (gmail) [mailto:[EMAIL PROTECTED] Sent: Wednesday, June 04, 2008 9:24 PM To: 'Discussion of Numerical Python' Subject: RE: [Numpy-discussion] Is there a function to calculate ecnomic beta coefficient in numpy given two time series data. Thanks Keith! -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Keith Goodman Sent: Wednesday, June 04, 2008 9:04 PM To: Discussion of Numerical Python Subject: Re: [Numpy-discussion] Is there a function to calculate ecnomic beta coefficient in numpy given two time series data. On Wed, Jun 4, 2008 at 5:39 PM, Vineet Jain (gmail) <[EMAIL PROTECTED]> wrote: > Timeseries1 = daily or weekly close of stock a > > Timeseries2 = daily or weekly close of market index (spx, qqqq, etc) > > > > Beta of stock a is what I would like to compute as explained in this article > on Wikipedia: > > > > http://en.wikipedia.org/wiki/Beta_coefficient > > > > I'm trying to compute the beta of entire stock market (about 15,000 > instruments) one stock at a time and would like to use the spiders and qqqq > to represent the overall market. Unless you run out of memory (or if you want to handle missing returns which may occur on different dates in each series) there is no need to do it one stock at a time: >> import numpy.matlib as mp >> mrkt = mp.randn(250,1) # <----- 250 days of returns >> stocks = mp.randn(250, 4) # <---- 4 stocks >> beta, resids, rank, s = mp.linalg.lstsq(mrkt, stocks) >> beta matrix([[-0.01701467, 0.11242168, 0.00207398, 0.03920687]]) And you can use mp.log to convert the price ratios to log returns. It might also be useful to shuffle (mp.random.shuffle) the market returns and repeat the beta calculation many times to estimate the noise level of your beta estimates. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
beta.py
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