Our project focuses on optimizing portfolio performance by analyzing six industries' performance the past ten years, over the last five midterm - elections.
We aim to validate trends in our data based on the political and economic climate during this period.
This analysis will provide investors with insights on portfolio positioning to safeguard their investments.
Additionally, we will emphasize geographic data to visualize regions with political party affiliation and cross-reference them with relevant industries.
- The Pre and Post-midterm Election Stock Market Performance is written in Python.
- Visualizations are generated using the hvPlot and Matplotlib libraries.
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Transfer the repository contents to the designated folder on the user's computer, ensuring the preservation of the directory structure.
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The application was created using Python version 3.11.7.
To run the application on your local machine, ensure the installation of the following Python packages: -
Pandas
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Matplotlib
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Yahoo_fin
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HvPlot
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Jupyterlab
These packages can be installed individually into the preferred environment.
pip install pandas
pip install matplotlib
pip install hvplot
pip install jupyterlab
pip install yfinance
- Through a market research, we chose the top 6 industries (Technology, Healthcare, Financial, Consumer Discretionary, Energy, and Weapons Manufacturing) and 3 stocks of each.
- Defined stock ticker and date range, and dowloaded the data from Yahoo Finance.
- Gather data on the outcomes of the Senate and House of Representatives elections for the last 5 elections.
- Created a DataFrame of the closing prices and dates of each stock per industry
- Combined all stocks from the 6 industries into a single DataFrame:
custom_portfolio_df - Assigned the close column to a new DataFrame :
small_df - Used the
.columnsattribute to assign new column names to the DataFramesmall_df - Created a new DataFrame that includes only columns correspondent ton the stock of each industry :
tech_df = small_df[['AAPL', 'MSFT', 'GOOG']]health_df=small_df[['JNJ','PFE','UNH']]finance_df=small_df[['JPM','BAC','V']]consumer_df=small_df[['AMZN','NKE','DIS']]energy_df=small_df[['XOM','FSLR','NEE']]weapons_df=small_df[['LMT','RTX','NOC']]
- Calculated Daily Returns of each industry in order to do a performance comparison
tech_dailyhealth_dailyfinance_dailyconsumer_dailyenergy_dailyweapons_daily - Dropped Nulls
- Generated a combined Daily Returns DataFrame
- Renamed each column with the ticker' name
- Set weights
- Created an empty dictonary to store the returns of each sector
- Cumulative returns of Each Industry
- Collect cumulative returns for each industry for the last 10 years.
Overall performance of the Tech industry has been positive over the last ten years on market value:
Consistently reporting strong earnings and revenue growth, it can boost investor confidence and lead to higher stock prices.
Continued innovation and the development of new technologies can attract investors, driving up the value of tech stocks.
Overall market trends and investor sentiment can influence the performance of the tech industry. Positive sentiment, along with favorable economic conditions, can contribute to higher cumulative returns.
Economic factors such as low interest rates, economic growth, and global stability can impact the performance of the tech industry.
- Annualized Standard Deviation
When we say that a consumer has a higher annualized standard deviation, we are referring to the variability or dispersion of the consumer's returns over a specific period, typically expressed on an annualized basis. The annualized standard deviation is a measure of risk or volatilityin the context of investments or financial returns.
It suggests that their financial situation or returns on investments are more variable or volatile. This could be due to factors such as unpredictable income, changing spending patterns, or investments with fluctuating returns.
- Annualized Sharpe Ratios
- Compute the Sharpe ratio for each industry using the cumulative returns
- The Sharpe ratio measures the risk-adjusted return of an investment. It's calculated as the average return minus the risk-free rate, divided by the standard deviation of returns
- Tech sector consistently had the highest average Sharpe ratio before and after each midterm, indicating strong risk-adjusted returns.
- Finance sector saw a significant increase in average Sharpe ratio after each midterm, suggesting improved risk-adjusted returns.
- Health sector experienced a notable decline in average Sharpe ratio after each midterm, indicating decreased risk-adjusted returns.
- Energy sector had the lowest average Sharpe ratio both before and after each midterm, indicating poor risk-adjusted returns compared to other sectors.
- Analysed the performance of each industry during time frame - 1 year before midterm, 1 year after midterm -- do this for 5 different terms.
- Calculate the average growth of each industry.
- Top performing industries get put into a portfolio weighted accordingly.
- Why does the market underperform in the 12 months leading up to midterm elections and over-perform the 12 months after midterm elections?
- What variables lead to the underperformance and over-performance in the midterm elections?
- Analyze Performance based on Election Outcomes.
- Group the election outcomes by party (e.g., Republican or Democrat).
- Calculate the average cumulative returns for each industry for periods following elections when each party won control of the Senate and the House of Representatives.
- Compare the average cumulative returns across industries for each party's control.
Midterm 1: 12 Months Before 2013-11-01 to 2014-10-31 12 Months After 2014-11-01 to 2015-10-31
Midterm 2: 12 Months Before 2015-11-01 to 2016-10-31 12 Months After 2016-11-01 to 2017-10-31
Midterm 3: 12 Months Before 2017-11-01 to 2018-10-31 12 Month After 2018-11-01 to 2019-10-31
Midterm 4: 12 Months Before 2019-11-01 to 2020-10-31 12 Months After COVID SO THIS IS AN ANOMALY: 2020-11-01 to 2021-10-31
Midterm 5: 12 months Before 2021-11-01 to 2022-10-31 12 Months After 2022-11-01 to 2023-10-31
Upcoming Election that hasn't occurred yet: 12 Months Before
- Predictions on how well our portfolio will do before the next midterm election. 2023-11-01 to 2024-10-31
- Forecasting: We would want to predict which industry would potentially do the best in the next primary election based on what party gets elected.
- Determine which industries performed best and were safest under different election outcomes.
- Analyzed which party was in power after each major election.
- Consider any trends or patterns that emerge from your analysis
. - Identify any industries that consistently perform well or poorly regardless of election outcomes
. - Created our own ideal portfolio that only include the industries that performed the best for the last 5 elections
. - Averaged the cumulative returns (include points where the party in power changes) of each industry and find the highest
. - Included the top 3 industries.
**Monte Carlo Simulation: Now until 2024-11-01
Statistical Analysis on the portfolio: Volatility (interact hvplot), Cumulative Returns plot Data Visualization: Results of all the past 5 midterm elections
Heloísa Bonetti
Kayla Hofmann
Krish Nair
Prospective developments or improvements to this project encompass:
- Integrate the Russia-Ukraine conflict into the analysis
- How would the Consumer Price Index (CPI) be affected before and after the last 5 midterm elections in the US ?
- Incorporate functionalities that enable users to fine-tune their portfolios
- Enhance the quality of visual representations
- Clone the repository and execute it from the notebook in chronological order of the midterm dates
- The 'image' folder must be established in the environment; otherwise, an error will occur.