When I was searching for something I enjoyed working on and had a level of passion when trying to complete the work, I fell across Investments. Mainly dealing with securities and their performance fundamentally. I found myself sifting through companies financial reports in order to understand the data and come up with a conclusion as for their performance. This meant finding data and analyzing that data with a specific goal in mind. This sounds very familiar doesn’t it?
That wasn’t the most interesting part of investments for me obviously. It was the fact that you can learn about any company in any industry. If the goal was to look at a specific company in the Energy Sector, I would have to take time to do research on the industry and research that company beyond just their finances. This constant ability to learn something new was refreshing mentally and felt like a new challenge. This interest was something I was exploring on my own time so I decided I should enter the job market to make a career out of it. The only problem was, I didn’t know how?
After doing research on the job market I realized that the best way I can begin having an impact was by using Data Science. Investment and Finance companies are one of the first industries to begin implementing data science. They also use the most data in comparison to any other industry. They deal with all types of data:
- trading (with historical)
- company financial data
- analysis
Some of this data gets released by the seconds, some quarterly and annually. Billions of trades being executed every week for large sums of money. Investment companies across the board perform financial analysis to make predictions. Publicly traded companies released tons of data to keep their investors updated. All of this is just Data Data Data…and this was just one aspect of the investment world.
How does Data Science really cross over to the investment and finance world?
The goal is to Derive Useful Insights in different areas of the field.
It can help the company to Understand Consumer Behaviour. The better a company is able to understand its consumers, the more it will know which group of consumers are willing to come back in the future. This targeted approach will lead to higher sales.
Fraud Detection is crucial for a company to keep their consumers information safe. Data science combats fraud by point out any anomalies in the consumers transactions. Identifying outliers involves collecting information on how a consumer behaves and whether or not that behaviour has suddenly changed.
In integral part of investing is Managing Risk. Risk analytics supports a company in mitigating its risk before having to make a decision. In finance and Investment, this is practically the most important job. Risk is a part of investing and making a profit so analyzing a project or investment opportunity for any potential drawbacks requires data science/analytics.
Data Driven Investment Process. The investment process has been a model that been around for decades. Idea generation, research management, portfolio construction, risk management and performance attribution. Each of these areas can generate better results by using data science. They require data, constant and possibly automated review and insight generation. We’ve been using models on excel to understand the performance of these investments, but technological advancements have allowed for the emergence of automation. The use of data science in this process was inevitable. Data science optimizes this process through the use of Machine Learning models.
Algorithmic Trading also an area where real time analytics help traders perform better. Generating unique strategies on the go is priceless for an algo-trader.
These are just some of the ways one can have an impact on the investment/finance world. My particular interest lies in the data driven investment process. So during our covid lock down, I began studying Javascript and Python. I learned how to code by studying on Udemy and was able to build a few projects by the end of the lock down. I began networking in order to land an entry level job, but I was given the same response by almost everyone: “You need to have your education be backed by a strong institution or have some experience in an entry level job already”. I accepted this advice so this led to my search for schools that provide data science bootcamps.
Flatiron School based out of New York was exactly what I needed. They provided a data science bootcamp that fit my schedule, finances and was backed by testimony from my own friends. I applied and thus my journey into data science formally began.
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