Project 1 - Tech Stock Analysis

This is an analysis of 3 tech stocks in order to make a purchase recommendation to client

DISCLOSURE:

No guarantee can be offered that projections or estimates will actually occur. Actual results may be materially different from projections or estimates. Additionally, past performance should not be relied upon as a forecast of future performance.

PROJECT DESCRIPTION

Equity Trading

While working for The Quants, a large equity-trading company, your team is tasked with researching a client’s portfolio. Your client wants to invest in a short list of popular tech stocks and needs expert analysis to make the right decision. Client understands that stocks will go up in the long term but the client does not want to see themselves under water in next 90 days.

Using NASDAQ historical data, we need to pull five year’s worth of trading data for the major US technology companies: Apple, Microsoft, and Google and compare their historical performance to each other and to the S&P 500 and NASDAQ indexes.

Our goals:

1. Which stocks are trending upward?
2. Which stocks are trending downward?
3. Based on the data, what would you recommend to your client?
4. How does the tech stock performance compare to the S&P and NASDAQ indexes?

INSTALLATION

1. Ensure you have Python 3.10 or higher installed.
2. Clone this repository: git clone https://github.com/Pete1001/Tech_stock_analysis.git
3. Ensure to run '!pip install prophet' at beginning of code

USAGE

1. Run the project: Tech_stock_analysis.ipynb

DATA PROCESSING:

1. Data Collection: 5 years of daily close data was downloaded from https://www.nasdaq.com/ for each of the stocks (Apple, Google, Microsoft) and both of the indexes (NASDAQ and S&P 500) resulting in 5 .csv files.

2. Data Exploration: The 5 .csv files were read in as DataFrames and each file was explored through the info() method. This method revealed that there were no null values and the files for the indexes had 1 less row. Additionally, the closing price was read in as an object in the three stock files since there was a dollar sign ($) included in the price.

3. Data Cleanup:

   • Closing price columns were renamed to reference the stock or index
   • Merge files with only needed columns (closing price columns)
   • Replace() method was used to replace $ with single quotes ’ ’
   • Change data type from object to float by using the astype() method

Historical Trends

Apple:
-Industry – Computer Manufacturing
-5 year performance  - 250% increase
-Share volume – 19,342,616
-Current yield – 0.44%

Summary Statistics:
Mean -  $146.07
Standard Deviation - $41.33


Microsoft:
-Industry – Computer Software
-5 year performance  - 245% increase
-Share volume – 9,090,960
-Current yield – 0.72%

Summary Statistics:
Mean - $281.25
Standard Deviation - $80.22


Google (Class A):
-Industry – Computer Software
-5 year performance  - 174% increase
-Share volume – 21,606,095
-Current yield – 0.49%

Summary Statistics: 
Mean - $113.88
Standard Deviation - $31.75

Correlation and 2024 Trends
The correlation matrix shows how the daily returns of Microsoft, Apple, and Google relate to each other in 2024.

• Microsoft and Apple (0.46): There’s a moderate positive relationship, meaning they sometimes move in the same direction but not strongly aligned.
• Microsoft and Google (0.61): A stronger positive relationship, indicating that their returns tend to move together more often.
• Apple and Google (0.39): A weak positive relationship, showing that their returns are relatively independent.

Although historically all three stocks have an upward trend, more recently (October) the trends are downward.

Cumulative Returns - 2024

• A special function of Pandas called .cumprod() calculates a cumulative returns of the stock.
• From the analysis of the graph we would expect Apple to have the best growth rate.
• Additionally, the Google and Apple stocks show more price volatility.

Forecast and Expected Growth

Apple:
    -The time series forecast for Apple shares shows an upward trend for the next 90 days. 
    -The forecast predicts that the Apple share price will increase 5.4% over the next 90 days.

Microsoft:  
    -The time series forecast for Microsoft shares shows an upward trend for the next 90 days.  
    -The forecast predicts that the Google share price will increase 10.4% over the next 90 days.  

Google (Class A):
    -The time series forecast for Google (class A) shares shows an upward trend for the next 90 days. 
    -The forecast predicts that the Google share price will increase 5.4% over the next 90 days.

APPROACH IN ACHIEVING PROJECT GOALS:

Stocks

1. Look at historical trends for the 3 stocks and determine if we were seeing an upward or downward trend.
2. We wanted to understand if there was a correlation between any of the 3 stocks.
3. We wanted to get a closer look at the trends for 2024 only.
4. The team reviewed cumulative returns.
5. Prophet was used to forecast expected stock price over the next 90 days and a growth rate calculated from the forecast along with plotting components.

Comparison to Indexes
To understand how the stock performance compared to index performance, we also forecasted expected performance using Prophet and calculated the expected growth rate.

• S&P Index
  • The time series forecast for S&P index shows an upward trend for the next 90 days
  • The forecast predicts that the S&P index price will increase 9.1% over the next 90 days

• NASDAQ Index
  • The time series forecast for NASDAQ index shows an upward trend for the next 90 days
  • The forecast predicts that the NASDAQ index price will increase 9.5% over the next 90 days

RECOMMENDATIONS:

1. The Quants Team recommends to buy Microsoft as a growth stock with fantastic short term forecasted gain.

2. By using Prophet we’re able to forecast forward 90 days and measure the expected growth rate of each stock and index.

   • Microsoft had the highest expected growth rate at 10.4%
   • Apple and Google had lower expected growth at 5.4%
   • NASDAQ and S&P indexes had 9.2% and 9.1%, respectively

3. Historically, October had the lowest expected stock price for Microsoft as shown through .plot_components() method.

   • This indicates a potentially good time to purchase Microsoft stock

4. The highest projected growth stock is Microsoft.

PROBLEMS ENCOUNTERED:

Collaborating with Jupyter notebook and GitHub was clunky and troublesome and lead to a number of conflicts identified when trying to merge to main branch.

• To resolve the team divided coding into sections assigned to specific team members
• The team also coordinated changes to the code and upload to GitHub during meetings
• This is a known issue as identified and corrected by NBDEV

During review of the output of .plot_components() the team recognized that the weekly graph looked the same for all stocks

• The team identified that a forecasting with Monday through Friday data lead to the same graphs
• The resolution was to turn off weekly seasonality in the Prophet model and adjust the prediction DataFrame to remove weekend days using the following code:
  • model = Prophet(weekly_seasonality=False)
  • future_trends = future_trends[future_trends[‘ds’].dt.weekday <5]

FUTURE CONSIDERATIONS:

• Compare to non-technology stock performance.
• Consider the risk of a single stock versus index stocks or a more diverse portfolio
• Sentiment analysis
• Other factors impacting stock price growth (Ex. R&D spending)

REFERENCES

1. https://www.nasdaq.com/market-activity/quotes/historical

2. GOOGLE COLAB & GENERATIVE AI

3. https://github.com/you915/Sentiment-Analysis-of-Twitter-Data-for-predicting-Apple-stock-price.git

4. https://bootcampspot.instructure.com/

5. https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax

TEAM MEMBERS

1. Mark Johnson
2. Pete Link
3. Marianne Mittelstadt
4. Steve Vierling
5. Ethan Wyman

LICENSE

This project is licensed under The Quants.