DATA606

MARKET OUTLOOK: Areas of Opportunity in the Post Coronavirus World

Presentation 1 YouTube Link
Presentation 2 YouTube Link
Presentation 3 YouTube Link

Project Aim:

A time of Global Pandemic, protests, and presidential changes, but mainly a time of uncertainty. No one knows how long it will take the world to develop a vaccine, and until then, our financial markets are going to be very unstable. As I write this, I realize that the past two weeks showed on of the fastest stock rebounds in history. Then today (6/11/2020), the Dow experienced the 27th largest 1-day decline in history after experiencing one the fastest stock rebounds in history. My goal is to find lucrative investment opportunities during this black swan event.

Research Questions:

1. What are the current market conditions?

2. How has the Coronavirus impacted stock prices and why is investing now such a troubling time?

3. There are many companies researching Coronavirus vaccines, could investing in these companies be the most prudent option?

4. We are in the middle of a textbook definition of a recession (two consecutive quarters of GDP decline).

5. Do certain stocks perform better following a recession?

6. Can machine learning be used to classify the outperforming stocks from the underpeforming stocks? If so, what are the characteristics that separate winners from losers?

Datasets

1. Daily New U.S. Covid-19 Confirmed Cases: Provided by the CDC [Used in figure 1].

2. Daily S&P 500 Daily Closing Prices (12/01/2019 - 6/26/2020): Provided by Yahoo Finance [Used in figure 1 & 2].

3. Daily S&P 500 Daily Closing Prices (12/31/1967 - 12/31/1967): inspired by Bloomberg article [Used in figure 2].

4. Mosiac Dataset of 2008 - 2018 Russell 3000 Index Constituents : Provided by Bloomberg Terminal subscription, but purely for analysis. None of the underlying data is made accessible in this project. The Russell 3000 Index tracks the performance of approximately 98% of all U.S. incorporated equity securities (https://www.investopedia.com/terms/r/russell_3000.asp ). [Used in model construction].

5. Efficient Frontier Portfolio: Daily closing prices of stock data from yahoo [Used in Deliverable 3].

Graphs

If time allots, visuals will be created through Tableau. However, most will start out as a python visual and may stay as a python visual if that seems to be best option.

References

1. Bloomberg Terminal (for research purposes, any underlying data is not available on this website. Manipulated and de-identified data is available on github).

2. Chen, James. “Learn What a Piotroski Score Is.” Investopedia, Investopedia, 5 Feb. 2020, www.investopedia.com/terms/p/piotroski-score.asp.

3. Donges, Niklas. “A Complete Guide to the Random Forest Algorithm.” Built In, 2020, builtin.com/data-science/random-forest-algorithm.

4. Kim, Ricky. “Efficient Frontier Portfolio Optimisation in Python.” Medium, Towards Data Science, 11 Jan. 2019, towardsdatascience.com/efficient-frontier-portfolio-optimisation-in-python-e7844051e7f.

5. Koehrsen, Will. “How to Visualize a Decision Tree from a Random Forest in Python Using Scikit-Learn.” Medium, Towards Data Science, 19 Aug. 2018, towardsdatascience.com/how-to-visualize-a-decision-tree-from-a-random-forest-in-python-using-scikit-learn-38ad2d75f21c.

6. Pathak, Manish. “TPOT in Python.” DataCamp Community, 2018, www.datacamp.com/community/tutorials/tpot-machine-learning-python.

7. Piotroski, Joseph D. “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers.” Journal of Accounting Research, vol. 38, 2000, p. 1., doi:10.2307/2672906.

8. Slidehack. (2019). The X Note [PowerPoint slides]. Retrieved July 1, 2020, from https://elements.envato.com/all-items/slidehack+x+note

9. Wixom, Dakota. “Introduction to Portfolio Risk Management in Python.” DataCamp, 2020, www.datacamp.com/courses/intro-to-portfolio-risk-management-in-python.

10. “Yahoo Finance - Stock Market Live, Quotes, Business & Finance News.” Yahoo! Finance, Yahoo!, finance.yahoo.com/.