This is a summary of links featured on Quantocracy on Monday, 12/18/2017. To see our most recent links, visit the Quant Mashup. Read on readers!
Value 2.0 [Flirting with Models]Traditional value strategies simply sort the investment universe based on one or more valuation metrics (e.g. book-to-market, price-to-earnings, etc.) and purchase the securities that look the cheapest. However, this process is often prone to structural sector bets, which are uncompensated sources of risk within a strategy. By comparing the value of stocks within each sector along with the value
Factor Returns: Year-End Calendar Effects [Factor Research]Value & Size generate abnormally large positive returns in January, Momentum negative returns Abnormal returns are limited to the last week of December and first week of January Difficult to harvest these returns efficiently due to illiquidity of markets at these times INTRODUCTION At this time of the year investors tend to receive market outlooks for 2018 from a variety of service providers.
Machine Learning Classification Strategy In Python [Quant Insti]In this blog, we will step by step implement a machine learning classification algorithm on S&P500 using Support Vector Classifier (SVC). SVCs are supervised learning classification models. A set of training data is provided to the machine learning classification algorithm, each belonging to one of the categories. For instance, the categories can be to either buy or sell a stock. The