Quantocracy’s Daily Wrap for 12/02/2019

When working with time series financial data, stationarity (or lack thereof!) is going to be a defining aspect of how you conduct your analyses. In this article, we're going to give you a quick refresher of what these terms mean and how they affect your data. Let's start with importing the basics! import numpy as np import pandas as pd import statsmodels import statsmodels.api as sm from

As readers would know, discussing ways to combine quant and fundamental investing has been a topic I care about (see some prior posts here, here and here). Perhaps Im biased. But I believe that proper collaboration between quant and fundamental approaches is still a largely unexplored area. Yes, many fundamental shops rely on quant screens and many quant shops rely on fundamental justifications

There are various Python projects which require sorting but not the ones that employ a default alphanumeric functionality. We talk about manually specified order or human-order, in short. One of such examples is the case study presented below. Imagine that your risk system provides you with a list of various risk factors and the corresponding risk tenors. The latter may differ among risk factors

Since the global financial crisis, the financial press has periodically asserted that the Federal Reserves actions were the driving force behind rising stock prices. This study investigates this assertion by asking the following research question: Is there a relationship between stock prices and the level of central bank influence in interest rates (where influence= difference between the

ESG ETFs underperformed the stock market since 2005 Likely explained by higher fees, a constrained stock universe, and sector bets Financially-impaired investors like public pension funds and Millennials should avoid ESG investing INTRODUCTION If investors would be looking for the ETF flavor of the year, then it would likely be ESG as highlighted by the large number of product launches in 2019. In