The original article can be found on the Refinitiv Developer Portal.
Refinitiv’s Refinitiv Quantitative Analytics recently released an exciting new alternative data set, LinkUp. LinkUp keeps track of jobs posted and removed by the hiring company in the form of a daily time series. These companies are mapped to Refinitiv’s PermID for easy inoperability between content sets. This article will begin by showing you how to access Refinitiv Quantitative Analytics with Python code, query the LinkUp Active Jobs and load it into a pandas dataframe.
Before LinkUp alternative data was released to our customers, the StarMine Research team had the…
In this article, we will create a Python function that will take the median measure of all (non ‘NaN’) values of a specific field for any index (or list of indices) of choice using Refinitiv’s DataStream Web Services (DSWS).
For full replication, note that the version of libraries used
import sys # ' sys ' is only needed to display our Pyhon…
Gross Domestic Product (GDP) is often thought of and calculated with an Expenditure Approach such that
where C = Consumption, I = Investment, G = Government Spending, & (X–M) = Net Exports.
The United States of America( USA)’s GDP (US GDP) is released quarterly by the Bureau of Economic Analysis, U.S. Department of Commerce. The only aforementioned component of US GDP released on a monthly basis is C, USA’s Consumption Data. …
Only quarterly U.S.A. G.D.P. data is published; this article describes a method of estimating monthly such figures using monthly Total Compensation figures.
Gross Domestic Product (G.D.P.) is often thought of and calculated with an Expenditure Approach such that GDP=C+I+G+(X−M) where C = Consumption, I = Investment, G = Government Spending, & (X–M) = Net Exports, but it is also possible to calculate it via a per worker Income Approach. With this approach, we may estimate G.D.P. Per Worker (G.D.P.P.W.) with Total Compensation Per Worker (T.C.P.W.) figures such that (as per Appendix 1 on GitHub’s Notebook):
Environmental, Social and Governance (ESG) data is difficult to come by. It is also becoming critical for effective investment analysis. It helps you assess the risks — and opportunities — posed by companies’ performance in critical areas such as climate change, executive remuneration, and diversity and inclusion. But a definite lack of transparency and standardization in such reporting presents major challenges for investors.
This article attempts to lay a framework to allow any investor/agent to collect, analyse and gather insight into countries’ ESG metrics at granular and macro-levels. It reflects the DataStream Sustainable Development Goals Country Scores Excel capability.
A lot of company valuation speculation has come about since the C0rona-VIrus-Disease-2019 (COVID-19 or COVID for short) started to impact the stock market (estimated on the 20thth of February 2020, 2020–02–20). Many investors tried to estimate the impact of the outbreak on businesses and trade accordingly as fast as possible. In this haste, it is possible that they miss-priced the effect of COVID on certain stocks.
This article lays out a framework to investigate whether the Announcement of Financial Statements after COVID (id est (i.e.): after 2020–02–20) impacted the price of stocks in any specific industry sector. It will proceed simply…