Inspiration

Many Americans are not prepared for retirement. Many Americans have not saved enough, or have saved but are not wisely managing their money. There are so many data points and economic indicators out there that we believe have significant relationships to equity performance. By using data analysis, modeling, and data visualization, we hope to create a tool that will better educate investors as to how equity performance, and thus the performance of their savings will be impacted by economic factors. In addition, we hope that by finding some convincing relationships, we can convince Americans who were previously unwilling to invest that they can manage their savings in an effective way.

What it does

We created a user friendly website that will demonstrate the impacts of different economic data metrics on equity performance. This website will show investors different models for different stocks representative of various sectors, as well as of the overall economy. We hope that investors will learn and explore the relationship between certain economic indicators and equity performance and will use that knowledge to hypothesize on future scenarios.

How we built it

We collected the nine economic indicators from FRED, which includes non-farm payroll, jobless rate, retail sales, trade balance, CPI, housing, wholesales, PPI, and industrial production. Some of the data was collected monthly and some was collected daily, so we set the monthly collected data same for all days in that month and merged the daily collected data into it. We expanded the monthly data using python, and merged them with daily collected data in R. We also downloaded all equity data, including open value, close value and volume. We added one column of calculated percentage of change based on open and close value, into that data set. We then join the equity data with the predictors’ data, and used R to find the best prediction model for SPY, and randomly selected ten stocks. We put the equation of model into Javascript, so that when users input their estimate of economic predictors of this month, they can get an output of the predicted close market value. Tableau was also used for generating a graph of stock value of 45 stock markets over 25 years (from 1992 to 2017).

Challenges we ran into

At first it was difficult to reconcile monthly economic data with equity prices that come out daily. It was difficult to find a statistically supportable method but after trial and error and debate, we decided to stretch out the monthly data to fit the daily data. It was also difficult working between R, Python, Tableau, and Javascript. Another issue was trying to understand more about each economic indicator and learn about its components and how it is calculated.

Accomplishments that we're proud of

I think that this project is something that will have along term impact. Going forward, all of us hope to be able to invest to grow our savings, and we all want to be educated investors. The applicability of this project to real world scenarios, and the usefulness of a tool like this for ordinary Americans, is something that we are very proud of.

What we learned

The models that we found made us think about the relationships between Economic indicators and prices. Some relationships made sense, but others were less intuitive until we researched more. In addition, because we were working at the intersection of statistics, data science, and front end development, we all got to learn about each. When we started out, each of us probably specialized more in one field, but by interacting and learning from each other, we were able to get a good understanding of all of them.

What's next for Economic Indicators' relationship with Equity prices

We hope to be able to integrate R and Python in order to automate the model generation process. Instead of loading each model in R and evaluating it by hand, we want Python to be able to generate models and evaluate them more objectively by relying on the statistics provided. We hope to be able to expand the modeling to other financial instruments such as fixed income investments.

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