Buying and Holding Tech Stocks: $500k in 10 Years : Chris
by: Chris
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Disclaimer: Just a heads up: this isn’t financial advice! Always do your own research and talk to a professional before making any investment decisions.
Here are the top technology companies by market cap in 2014 (source):
Let’s assume a simple buy-and-hold strategy where you simply buy the top six tech stocks (by market cap) in the year 2014, assuming you don’t have any particular insight into the tech industry (e.g., seeing NVidia or Tesla early):
- Apple
- Microsoft
- IBM
- Oracle
- Meta
tech_stocks = ['AAPL', 'GOOGL', 'MSFT', 'IBM', 'ORCL', 'META']In the US, the average income of a coder was roughly $76k per year in 2014 (source).
If you save only 10% of that, i.e., $7,200 per year or $600 monthly and you distribute that equally across the six chosen tech stocks, here’s the annual ownership of your stock portfolio:
Table: The table displays the year-end values of equal monthly $100-investments in our six tech stocks from 2014 to 2024, alongside their Compound Annual Growth Rates (CAGR).
Each row corresponds to a different stock, with columns indicating the investment’s worth at the close of each year.
The CAGR column reflects the average annual growth rate of the investment over the ten-year period.
For instance, an investment in Apple (AAPL) grew from $1,734 in 2014 to $134,446 by 2024, with a CAGR of 27.38% and annual contributions of $1,200 into AAPL.
Your total portfolio value of this simple buy-and-hold portfolio would be north of $500,000 after 10 short years! In this scenario, a 50-year-old could retire today, having started with $0 in 2014!
Assuming the compound annual growth rate remains ~15%, you’re technically retired because you don’t need your $76k paycheck anymore, given your $75k+ return on investment from year 11 onward!
Code
You can recreate this using the following Python script.
First install these libraries:
pip install yfinance pandas numpy matplotlib
Then copy and paste the following code into your Python script (my_file.py) and run it:
import yfinance as yf
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
# Define the time period for the data
end_date = datetime.datetime.now()
start_date = end_date - datetime.timedelta(days=10*365)
# List of top tech stocks based on market capitalization from about 10 years ago
tech_stocks = ['AAPL', 'GOOGL', 'MSFT'] # , 'IBM', 'ORCL', 'META'] # , 'TCEHY', 'QCOM', 'INTC', 'CSCO']
# Function to perform dollar-cost averaging investment strategy
def dollar_cost_averaging(initial_investment, monthly_investment, data):
investment_values = {}
total_asset_values = pd.Series(dtype=float)
for stock in tech_stocks:
# Initial investment
initial_stock_investment = initial_investment / len(tech_stocks)
# Monthly investment dates
investment_dates = pd.date_range(start=data[stock].index[0], end=data[stock].index[-1], freq='M')
stock_investment = np.zeros(len(data[stock].index))
# Apply initial investment
stock_investment[0] += initial_stock_investment
for date in investment_dates:
# Find the closest trading date
closest_date = data[stock].index[data[stock].index.get_loc(date, method='nearest')]
investment = monthly_investment / len(tech_stocks)
stock_investment[data[stock].index.get_loc(closest_date)] += investment
# Calculate cumulative investment
cumulative_investment = np.cumsum(stock_investment)
# Adjusted close price for the stock
adj_close = data[stock]['Adj Close']
# Calculate asset value over time
asset_value = cumulative_investment / adj_close.iloc[0] * adj_close
investment_values[stock] = asset_value
total_asset_values = total_asset_values.add(asset_value, fill_value=0)
# Aggregate yearly values
yearly_values = {}
for stock, asset_value in investment_values.items():
# Resample the asset value data to get the value at the end of each year
yearly_values[stock] = asset_value.resample('Y').last()
return yearly_values, total_asset_values
# Function to calculate CAGR
def calculate_cagr(start_value, end_value, num_years):
return (end_value / start_value) ** (1 / num_years) - 1
# Main function to execute the strategy
def main(initial_investment, monthly_investment):
# Download historical data for the stocks
stock_data = yf.download(tech_stocks, start=start_date, end=end_date, group_by='ticker')
yearly_values, total_asset_values = dollar_cost_averaging(initial_investment, monthly_investment, stock_data)
# Creating a DataFrame for yearly values
yearly_table = pd.DataFrame(yearly_values).T
yearly_table.columns = yearly_table.columns.year # Use year as column names
# Format the table for better readability
formatted_table = yearly_table.round().astype(int).applymap("${:,}".format)
# Calculate CAGR
cagr_values = {}
for stock in tech_stocks:
start_value = stock_data[stock]['Adj Close'].iloc[0]
end_value = stock_data[stock]['Adj Close'].iloc[-1]
num_years = (stock_data[stock].index[-1] - stock_data[stock].index[0]).days / 365.25
cagr_values[stock] = calculate_cagr(start_value, end_value, num_years)
cagr_df = pd.DataFrame(cagr_values, index=['CAGR'])
cagr_df = cagr_df.T
cagr_df['CAGR'] = cagr_df['CAGR'].apply(lambda x: f'{x:.2%}')
# Combine yearly values and CAGR in a single DataFrame for display
combined_df = pd.concat([formatted_table, cagr_df], axis=1)
print("Yearly Investment Values and CAGR for Each Stock:")
print(combined_df.to_string())
# Plotting the total asset value over time
ax = total_asset_values.plot(title='Total Asset Value Over Time')
ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, p: f'${x/1000:,.0f}K'))
plt.xlabel('Date')
plt.ylabel('Total Asset Value')
plt.show()
if __name__ == "__main__":
initial_investment = 0 # Modify this value as needed
monthly_investment = 600 # Modify this value as needed
main(initial_investment, monthly_investment)
You can modify the initial and monthly investments in the variables at the bottom of this script. If you don’t know how to run a script, consider this Python guide:
January 22, 2024 at 08:37PM
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