【Application】The Gospel for Dividend Investors? Backtesting Performance of High Dividend ETF
Highlight
- Article Difficulty：★☆☆☆☆
- Exploring the Relationship Between High Dividend ETF, Weighted Returns, and Calculating the Total Dividend Yield.
- Using the TQuant Lab Backtesting Platform to Develop a Buy-and-Hold Strategy for High Dividend ETF and Backtest the Risk and Performance.
Preface
What is High Dividend Yield?
High dividend yield, as the name suggests, refers to companies distributing higher profits to investors in cash dividends without a specific definition of what constitutes a high dividend. While high dividend yield may sound appealing at first glance, it’s essential to recognize that every investment has pros and cons. Before entering into high-dividend ETFs, it’s crucial to consider the following two points to evaluate the most suitable investment approach :
- Pay attention to a company’s profitability: High dividends do not equal high returns. If a company is not making a profit or is incurring losses, even high dividend payouts are meaningless.
- Future investment development: When a company is profitable, it can use the funds for more investments, enhancing its value. However, if it provides high dividends to investors, the amount available for company investment will remain the same, potentially limiting its growth.
High Dividend ETF
This article will select the top 5 popular high-dividend ETFs in the market as investment targets:
- 0056’s full name is “Yuanta Taiwan Dividend Plus ETF” which tracks the “FTSE TWSE Taiwan Dividend+ Index”
- 00713’s full name is “Yuanta FTSE4Good TIP Taiwan ESG ETF” which tracks the “FTSE4Good TIP Taiwan ESG Index”
- 00878’s full name is “Cathay MSCI Taiwan ESG Sustainability High Dividend Yield ETF” which tracks the “MSCI Taiwan Select ESG Sustainability High Yield Top 30 Index” MSCI stands for Morgan Stanley Capital International, one of the world’s three major index companies.
- 00919’s full name is “CAPITAL TIP CUSTOMIZED TAIWAN SELECT HIGH DIVIDEND EXCHANGE TRADED FUND” which tracks the “Customized Taiwan Select High Dividend Index”
- 00929’s full name is “Fuh Hwa Taiwan Technology Dividend Highlight ETF” which tracks the “Taiwan Technology Dividend Highlight Index”
Editing Environment and Module Requirements
This article uses MacOS and VS code as the editor.
Data Import and Create a Logbook to Record Dividend Information
import os
import pandas as pd
tej_key = 'your key'
api_base = 'https://api.tej.com.tw'
os.environ['TEJAPI_KEY'] = tej_key
os.environ['TEJAPI_BASE'] = api_base
start = '2018-01-01'
end = '2023-12-31'
stock_list = ['0056', '00713', '00878', '00919', '00929', 'IR0001']
calendar_name = 'TEJ'
os.environ['mdate'] = start + ' ' + end
os.environ['ticker'] = ' '.join(stock_list)
!zipline ingest -b tquant
log_handler = FileHandler('log.txt',
format_string='[{record.time:%Y-%m-%d %H:%M:%S.%f}]: ' +
'{record.level_name}: {record.func_name}: {record.message}',
level=INFO)
log_handler.push_application()
log = Logger('Algorithm')Create backtest environment
Select High Dividend ETF Data
Next, we are going to build the backtest environment. First, we could collect multiple targets’ quantitative indicators and price and volume data. In this case, we used it to obtain data on five High Dividend ETFs, which can avoid processing stocks that have yet to be listed.
Build a daily trading environment
- Slippage cost
- Commission fees
- Returns Index (IR0001) as the benchmark index
- Import the above-calculated data result into the transaction process
Build the Trading Strategy
The function below is essential for building a trading strategy. It will be called every day after the backtest starts. The main task of this article is to set an order equal to 25% of the current value of the investment portfolio.
def handle_data(context, data):
out_dir = pipeline_output('mystrats')
for asset in out_dir.index:
stock_position = context.portfolio.positions[asset].amount
if stock_position == 0:
order_percent(asset, 0.25)Execute the Trading Strategy
Next, we can start carrying out our trading strategy. Based on the set above, we execute the buy-and-hold of the High Dividend ETF. Set the trading period from start_date (2018–01–01) to end_date (2023–12–31) and use the data we collected earlier. The initial capital for the trading strategy is $1,000,000. The output results are a detailed list of daily performance and transactions.
Review Dividend Information During the Backtesting Period
log.txt records the information for each dividend received :
- Equity： ETF Ticker Symbol
- cash_dividend amount： Cash Dividend Per Share
- pay_date： Payment Day
- div_owed：Dividend Received (Cash Dividend Per Share * Number of Shares Held)
Performance Evaluation
To further understand the performance of this strategy, we compared the cumulative returns of this strategy with the cumulative return of the benchmark index and visualized the results as follows:
The chart shows that the overall market has delivered a return of 2 times over the past five years. However, the return from buying and holding high-dividend ETFs slightly lags but still accumulates nearly a 2-fold return. Additionally, we notice that the volatility of the high-dividend ETF portfolio closely mirrors that of the overall market but with relatively more minor fluctuations. This suggests lower investment risk, as evidenced by the annualized volatility of 9.114% and the maximum drawdown of -16.382%. Furthermore, the Beta value of 0.43 indicates that the portfolio is less sensitive to changes in the overall market.
Comparing the dividend performance of High Dividend ETF and Market Capitalization ETF
The chart shows that High Dividend ETFs yield more dividends under the same conditions during the backtesting period than Market Capitalization ETFs. It’s worth noting that four Market Capitalization ETFs are held for over 1000 days, while High Dividend ETFs have only three. Despite this, High Dividend ETFs still outperform Market Capitalization ETFs in dividends, indicating that High Dividend ETFs indeed deliver on their touted characteristic. For investors, High Dividend ETFs can provide more generous dividend returns during the holding period. This not only enhances the cash flow of the investment portfolio but also provides a certain level of stability during market volatility. This reflects that High Dividend ETFs typically include companies with robust financials that can regularly pay dividends. These companies often have strong profitability and cash flow, enabling High Dividend ETFs to maintain relatively stable performance during market downturns. This makes them suitable investment targets for long-term investors or those who prefer passive investing without closely monitoring the market.
Conclusion
In this exercise, we selected five High Dividend ETFs as investment targets and conducted a backtesting performance analysis using TQuant Lab. The backtesting results for these five High Dividend ETFs show favorable cumulative and annualized returns, reaching 94.373% and 12.129%, respectively. The annualized volatility and maximum drawdown are 9.114% and -16.382%, respectively, indicating that High Dividend ETFs have achieved steady returns while controlling risk. The Beta value suggests that they have exhibited relatively stable performance compared to the overall market and are less affected by market fluctuations.
However, the Sharpe Ratio and Alpha indicate that ESG ETFs have not generated excess returns. On the other hand, High Dividend ETFs offer a significant advantage in providing stable cash flow and higher dividend yields, especially under similar holding conditions. This reassures investors that they can rely on a consistent income stream, reflecting the fact that companies paying high dividends are often mature and may lack significant growth potential in the long term.
Overall, High Dividend ETFs are suitable for more conservative investors unwilling to take on high risks and not seeking short-term high profits. They might be a good choice if you aim to steadily receive dividends over the long term and accumulate passive income gradually.
Please note that the strategy and target discussed in this article are for reference only and do not constitute any recommendation for specific commodities or investments. In the future, we will also introduce using the TEJ database to construct various indicators and backtest their performance. Therefore, we welcome readers interested in various trading strategies to consider purchasing relevant solutions from TQuant Lab. You can construct a trading strategy that suits your needs with our high-quality databases.
Source Code
Extended Reading
- Should ESG ETF Be a Buy? Backtest Performance of ESG ETF
- Is TQuant Lab Measuring Stock a Buy? Backtest performance of measuring stocks
