Category Archives: background

Quantitative Investing Beyond Equities

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I recently received a reference request for an alumnus of my class who was seeking employment at a Financial Advisory Firm. It was a very pleasant and productive encounter – my former student advised me via email that I was listed as a reference and I might get a call; I received an email from a pretty high level person at my student’s prospective employer to schedule a call; we had a very productive call.

During the call, I told the employer about some of the quantitative investing stuff we do in in my class. The employer said it would be useful – their firm did similar stuff for a fixed income product. This was my second run-in with a firm that does quant stuff with fixed income. It appears quantitative investing is growing in fixed income, but there may also be issues.  (see https://www.barrons.com/articles/is-fixed-income-ready-for-factors-1530897141 )

Blackrock has a delightful webpage on the space ( https://www.ishares.com/us/strategies/fixed-income-factors ) where they highlight the main factors in fixed income (FI) as value, quality, momentum, carry, and low vol. Very similar to Equities. There’s also academic work in this regard ( https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2516322  for example).

On the other hand, high transactions costs, large minimum investment amounts, minute differences between bonds that broad factors may not pick up (but that may end up making a huge difference), and buy-and-hold-to-maturity investors may prove to be headwinds in the space.

More specifically, there may be additional signals, besides the usual corporate finance and market price signals, that may be informative. The employer I spoke to was in the muni bond space, and was using geographic data ( I imagine micro level data from the various municipalities whose bonds they were considering )  to try and predict future credit moves.

I’d imagine with the wealth of data out there, and the variety of financial instruments traded, there may be some very interesting predictive relationships to be uncovered outside the equity markets.

Python Basics

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I recently learned and started using Python for some of my projects. Python is a high level programming language with a number of pre-programmed packages for a variety of useful tasks. Tasks I’ve used Python for include scraping the web for data (excellent!), machine learning (meh … but that’s more my fault than Python’s), OCR (super meh), and algorithmic name classification, such as gender determination (again, excellent!).

While I will not provide direct code to perform predictive analysis using Python, I will use this post to link to a variety of resources that I have used, along with how I use it.

First, how to get started with Python. I use Jupyter Notebook, along with Anaconda. Both of these are installed when you download and install the latest version of Anaconda – google “download jupyter notebook” and go to the first link. The actual download will be from the Anaconda website.  As of posting, the latest version is Python 3.6. Click “Download,” run the file and choose all the default options and install Python and Jupyter Notebook.

Jupyter Notebook runs inside your browser. Open up Jupyter Notebook, create a folder for coding, and then create a new Notebook. Each Notebook has distinct cells for distinct blocks of code that can be run separately. Once you run the code in a cell, the output is produced right below. Here is an example:

Capture2

 

As you can see, when you run each cell, it simply generates the output right below. One thing I wanted to point out is that variables and variable types are generated dynamically. the code “a=1” first defined a as an integer and then sets it to two. Printing (and other functions) can be applied to integers (e.g. “print(a)”) or strings (e.g. print(‘hello world’)) but not to a mix (see the error in the second cell).

The second thing (and I love this) is the indentation is part of the language.

if 3>2:

print(‘hi’)
print(‘there’)

will return

hi
there

if 3<2:

print(‘hi’)
print(‘there’)

will return nothing

but

if 3<2:

print(‘hi’)

print(‘there’)

will return

there

The indentation controls what is run in the “if” statement. This forces discipline in generating readable (and workable) code.

Once you’ve gotten Python up and running – you’ll need additional packages to do other code.

For webscraping, I’d recommend selenium and chromedriver.

For OCR, I’d recommend Tesseract (Google’s OCR).

For machine learning, I use (but don’t know enough to recommend) tensorflow.

 

 

What is backtesting

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Backtecting is simulating how a strategy would have performed, had we been following it historically

For example, if our strategy were: buy all profitable stocks in equal amounts, and rebalance quarterly ….

  1. Backtesting over 10 years would start in Oct 19th 2005,
  2. divide our money equally between all stocks that were profitable then,
  3. hold them until Jan 19th 2006,
  4. sell them at prices from Jan 19th 2006,
  5. reinvest the proceeds into stocks profitable in Jan 2006
  6. rinse and repeat until today … 40 quarters later

The report for the backtest would present how this strategy would have performed historically, and we could compare this to the index returns or other returns.