Skip to content
An evaluation of stock price prediction algorithms using analyst ratings
Jupyter Notebook
Branch: master
Clone or download
Louis Schlessinger
Louis Schlessinger update readme
Latest commit 58ee945 May 9, 2018
Type Name Latest commit message Commit time
Failed to load latest commit information.
Ratings add files May 8, 2018
data add files May 8, 2018
.gitignore Initial commit May 8, 2018
A Bayesian approach to predicting stock performance using analyst reviews.pdf add files May 8, 2018 update readme May 9, 2018
bayesian-analysis.ipynb add files May 8, 2018


An evaluation of stock price prediction algorithms using analyst ratings

The problem of whether a stock will outperform the US stock market (as measured by S&P 500 Index, or another major index) is an important problem in quantitative finance pertaining to modern portfolio theory (i.e. trying to build a high-return low-risk portfolio) that many people have been working on for years with mixed results.

The unique approach that is taken here is to incorporate analyst ratings into a prior.

Research professionals at trading brokerages provide ratings on companies to help investors choose stocks. The vocabulary varies between companies, but almost all use a 1-5 scale, where a 5 indicates a “strong buy”, 1 indicates “strong sell” and 3 indicates hold. Access to aggregate analyst rating is provided as a service from the trading brokerage Morningstar. This data lists a company and how many analysts provide each rating now and across the past 3 months. For example, Amazon (NASDAQ: AMZN) currently has 13 strong buy (5) ratings, and 1 hold (3) rating, for an average rating of 4.86, and had the same rating 3 months ago.

You can’t perform that action at this time.