Stock Exchange Visualizations
in Python
August 25, 2022
Philippe Lambot
When choosing eight financial institutions, special attention has been paid to international diversity. These eight financial institutions originate from China, France, Germany, Japan, the Netherlands, the United Kingdom, or the United States. They have been picked up using Yahoo! Finance and LexisNexis. The eight financial institutions are listed below with their names and ticker symbols.
Stock exchange information used in this project consists of stock quote prices: open, high, low, and close. They have been retrieved from Yahoo! Finance. They cover the period from 2000 until August 25, 2022. Three institutions have partial data: MUFG's data start on April 2, 2001, BNPQY's on June 5, 2003, and BACHY's on December 29, 2009. Stock quotes originate from the NYSE except for BACHY and BNPQY (OTC). All stock quotes are denominated in US$.
Let's draw a closing price line chart for the eight financial institutions together.
The y-axis scale is determined by the sharp drop of Citigroup's stock price. Consequently, let's split the group of eight financial institutions into three subgroups in accordance with the range of closing price variation over the whole period since 2000. In the first subgroup, we'll keep just Citigroup, in the second subgroup, BCS, BNPQY, DB, ING, JPM, and in the third subgroup, BACHY and MUFG.
Let's start with Citigroup on its own.
Let's draw line charts for these five financial institutions. Each institution will be presented separately but all will have the same y-axis scale with a view to fostering comparability.
And now, let's draw closing price line charts for both financial institutions with the smallest range of closing price fluctuation in absolute value: BACHY and MUFG.
The line charts above have displayed all closing prices. Let's pinpoint here the extreme closing prices throughout the whole period for the eight financial institutions.
What is the highest closing price for each financial institution's stock over the whole period?
BACHY is displayed on the same period as JPM.
In this section, we'll use data about closing prices, "high" prices, and "low" prices, on a daily basis. These data will be used to build up Bollinger Bands®.
- a simple moving average, which is the middle band,
- an upper band,
- and a lower band.Let's draw Bollinger Bands® for BNPQY's stock since June 2022. BNP Paribas SA is the biggest bank in Europe on the basis of assets as of the end of 2020, as shown by InsiderIntelligence. On July 29, 2022, Reuters reported that "BNP Paribas thrived in Q2, shares jump [...] French bank BNP Paribas reported better than expected profit in the second quarter [...]"
As stated by Bloomberg, "ING Groep N.V. (ING) is a global financial institution. The Company provides retail and wholesale banking services to private clients, small businesses, large corporations, financial institutions, and governments. ING Groep operates worldwide." On August 4, 2022, it was acknowledged that "ING Beats Estimates After Unwinding Russia Loan Provisions"
The worst closing price drop was on January 20, 2019 for four financial institutions out of the eight that have been picked up for this working paper; JPM is among them. January 20, 2019 was Inauguration Day. Let's quantify the drop for these four institutions.
JPM's closing price had lost almost 21% on January 20, 2009. But its strongest percentual increase happened one day later, on January 21, 2009, and it almost completely erased the drop on January 20. This can be clearly visualized on Macrotrends.
With a view to retrieving some visual evidence from relationships between the eight return time series of the eight financial institutions, let's draw a scatter plot matrix, which is comprised of 56 bilateral scatter plots. For readability purposes, the period has been limited to July and August 2022. Also for readability purposes, the graphs on the diagonal, which could have been histograms, have been removed.
The picture is completely different if we broaden the period by starting in 2010.
When comparing the correlation matrix for the period 2010-2022 and the correlation matrix for July-August 2022, many statements can be made. Let's express a few ones: