Divididend analysis of Telenor ASA using Jupyter Notebook

Cross posted from Divididend analysis of Telenor ASA using Jupyter Notebook

I just published the notebook dividends.ipynb on my GitHub repository jorisp/tradingnotebooks which shows how dividends contribute to the total return. This notebook uses the yfinance API to retrieve the data.  I used Telenor ASA (a Norwegian telecom operator) as an example.

If you are considering to invest in foreign dividend stocks as a Belgian investor, you need to keep in mind the double taxation of dividends. Even with a withholding tax applied abroad, the Belgian government will tax your dividend again at a flat rate of 30%.

Disclaimer: The information on this blog is intended solely for informational and educational purposes. I am not a certified financial advisor, and the content provided here does not constitute professional financial advice. (Full disclaimer)

Getting financial data in Python using the OpenBB SDK

The OpenBB SDK (also known as OpenBB Platform) is developed as open-source (the code is available on https://github.com/OpenBB-finance/OpenBB) by the company Open BB. The OpenBB SDK provides programmatic access to a wide range of financial data sources from one place in a standard way.

The OpenBB SDK was developed to drive the OpenBB Workspace (See Introducing the new OpenBB Terminal ) which provides a customizable platform for financial analysts, investors and researchers that rivals traditional financial terminals without the steep costs.

By default, the OpenBB SDKwill attempt to download data from free sources such as Yahoo Finance but OpenBB SDK integrates with multiple other data sources as well such as , Alpha Vantage, FRED,FMP,SEC,etc .... In most OpenBB API platform calls, you can indicate a different data source - some of them free others requiring a separate subscription - allowing you to pull equities, options, crypto, forex and macroeconomic data using a single SDK. 

Since you can access both historical and real-time market data, OpenBB is ideal for backtesting and live trading strategies. The SDK is compatible with Jupyter Notebooks, Python scripts, and automated trading systems. I recently tested the OpenBB SDK as an alternative to Pandas_DataReader in Jupyter Notebooks, and it worked flawlessly.

I shared this Jupyter notebook on my Github repo:

https://github.com/jorisp/tradingnotebooks/blob/master/openbbdemo.ipynb

Please note that many of the code samples found in various articles and posts are no longer functional due to significant changes in the codebase. The shared Jupyter notebook has been tested with OpenBB 4.3.5 and Python 3.12.8.

The Rational Reminder podcast - favorite episodes from 2024

I started listening to the Rational Reminder Podcast somewhere around the beginning of this year and I can highly recommend it to anyone interested in passive evidence based investing and financial decision making. 

The Rational Reminder podcast is weekly podcast about personal finance and investing from a couple of Canadians, Benjamin Felix and Cameron Passmore - both work at PWL Capital. The hosts are experienced portfolio managers who share their knowledge about a broad range of financial topics and the show often features interviews with industry experts, offering diverse perspectives and deep dives into specific areas of finance.

Benjamin Felix also creates educational content on YouTube https://www.youtube.com/@BenFelixCSI where he shares insights on investing and financial planning

Both advocate for evidence-based investing, which often includes passive investment strategies such as index funds and they emphasize the benefits of low-cost, diversified, and long-term investment approaches, which align well with passive investing principles.

Episodes which I really liked:

• Episode 321 - Evidence in Practice with Håkon Kavli (CIO of Reitan Kapital)
• Episode 320 - Kyla Scanlon: In This Economy?!
• Episode 316 - Andrew Chen: "Is everything I was taught about cross-sectional asset pricing wrong?!" about Open Source Asset Pricing
• Episode 313 - When Should You Hire a Financial Advisor?
• Episode 310 - Professor Antoinette Schoar: Consumer Finance, Crypto, and Private Equity
• Episode 303 - Scott Galloway: The Algebra of Wealth
• Episode 302 - Michael Green: Market Efficiency Is Not The Question
• Episode 292 - Rob Copeland: Ray Dalio and Bridgewater
• Episode 291 - The Quant Winter, and is Canada Pension Plan a Scam?
• Episode 289 - Retiring Retirement Income Myths with the Retirement Income Dream Team
• Episode 286: Errol Morris: Tuning out the Noise

Looking at historical returns of stocks and bonds with Power BI and Python

You might already have seen below graph taken from a study by JP Morgan Asset Management, but what if you would like to look at historical returns without going  through the hassle of having to collect all the data yourself?

There is an interesting Excel sheet shared by Aswath Damodaran (@AswathDamodaran)  that you can download from Historical Returns on Stocks, Bonds and Bills: 1928-2022 which looks at returns of different asset classes (stocks, bonds, bills, real estate and gold) over a longer time period.

In this post I will share some tips on how you can use this data in Power BI, Python and Jupyter notebooks. 

This Historical Returns on Stocks, Bonds and Bills: 1928-2023 - Excel file  file is updated in the first two weeks of every year and it is being maintained by Aswath Damodaran, who is a professor of Finance at the Stern School of Business at NYU, he is also known as the "Dean of Valuation" due to his experience in this area.

Visualizing S&P 500 and US Treasury bond returns using Power BI

I first converted the Excel from  xls to xlsx format and afterwards it is quite easy to  import the data from an Excel workbook files in Power BI . It is quite easy to visualize the returns of  both stocks and US treasury bonds using a clustered column chart - I also added a minimum line for both stock and bond returns.

Expected risk and expected return should go hand in hand: the higher the expected return, the higher the expected risk. Risk means means that the future actual return may vary from the expected return (and the ultimate risk is loosing all of your assets). The first visual showed a 20-year annualized return between 1999 and 2018 for the S&P 500 of 5.8%.  Average returns hide however the big swings in yearly returns - e.g. in 2008 (the Great Financial Crisis), the S&P 500 had a -36.5% yearly return. Bonds on average have a lower return but also have a lower risk profile. 

The basic rule of thumb is to keep your “safe money” (i.e., money you don’t want to risk in stocks) in high-quality bonds. While this doesn’t give you 100% protection against losses at all times, it does provide you some peace of mind. I really like this quote: "If you can't sleep at night because of your stock market position, then you have gone too far. If this is the case, then sell your positions down to the sleeping level. (Jesse Livermoore)"

As you can see in the visualization below, in most years with a negative return for the S&P 500, the return for bonds is positive - with two notable exceptions 1969 and 2022. A common saying is to have your age in bonds. Using that general rule, a 45-year-old might have 45% of the total portfolio in bonds. If you want to more aggressive, you would have less than your age in bonds. The last decade with interest rates very low (or even negative) this probably wasn't a very profitable asset allocation but 

things might have shifted.

The US Treasury Bond used in the Excel file is the 10-year US treasury bond for which you can download the data from FRED . The yearly return has been calculated by taking the yield and the price change for a par bond with that specific yield.

You can download the Power BI file histreturns.pbix from my Power BI Github repo

In the long run (see example below for different rolling windows from a 1-year to a  20 year period)  stocks will outperform bonds but this again works with averages and it ignores the tail risk which might wreak havoc in your portfolio.

Reading data from Excel using Python

Now let's take a look at how you can read and manipulate the data in this Excel sheet using Python. To read an excel file as a DataFrame, I will use the pandas read_excel() method. Internally, Pandas. .read_excel uses a library called xlrd which you also need to install but I used the  openpyxl library as an alternative which also works. So before you can read an excel file in pandas, you will need to install 

the openpyxl library. 

The above code reads only the table with data from the Excel file (which I downloaded in a subdirectory data from the Jupyter notebook) - see  pandas.read_excel in the Pandas referencel documentation for full details:

• sheet_name: can be an integer (for the index of a worksheet in an Excel file, default to 0, the first sheet) or the name of the worksheet you want to load
• nrows: number of rows to read
• skiprows: number of rows to skip
• usecols: by default all columns will be read but also possible to pass in a list of columns to read into the dataframe like in the example

I just started exploring some data around stock-bond correlations and will be updating the Juypyter notebook on Github - https://github.com/jorisp/tradingnotebooks/blob/master/HistoricalReturns.ipynb

A couple of weeks ago I noticed this interesting tweet on rolling one-year-stock-bond correlations for six regimes from @WifeyAlpha - I think it would make an interesting exercise to see how to rebuild this using Python.

References:

• Pandas – read_excel() – How to read Excel file in python
• Musings on markets: a return to teaching - the Spring 2023 Edition
• Guide to the Markets | JP Morgan Asset Management

Related posts:

• Interactive chart visualizations using Python and bqplot: visualizing S&P500 returns
• Using the yFinance Python package to download financial data from Yahoo Finance - part 2
• Using the yFinance Python package to download financial data from Yahoo Finance
• Using Python and Pandas Datareader to retrieve financial data - part 2: Fama & French data library
• Using Python and Pandas Datareader to retrieve financial data - part 1: Federal Reserve Data (FRED)

Using the yFinance Python package to download financial data from Yahoo Finance - part 2

In a previous post I showed how you can download ticker data from Yahoo Finance using the yFinance Python package.  I now updated the  Jupyter notebook code sample using YFinance to also show how you can retrieve additional information (sector, industry, trailing and forward earnings per share, etc...). The Ticker class in the yFinance library contains the info property which returns a dictionary object ( a collection of key-value pairs where each key is associated with a value) which allows you to access specific information about an asset.

Since I wanted to know how fast data retrieval would be I also include the %%time magic command . Wall clock time measures how much time has passed. CPU time is how many (milli)seconds the CPU was busy.

Yahoo Finance contains data about stocks, Exchange Traded Funds  (ETF), mutual funds and stock market indices - the information that you can retrieve for each of these differs, so it is safe to check in your code for the quoteType. Below example retrieves information about Apple stock, the iShares MSCI AWCI UCITS ETF (Acc) and a thematic mutual fund from KBC.

I also included a code snippet which shows how to retrieve this information for multiple assets and convert this into a Pandas dataframe.