and the stock market has gone hand in hand since its very existence, and are going to till the end.
The only thing that has changed in the relationship is the usage of data & medium of connection. Initially, the stock was bought & sold one-on-one like an auction.
Soon people started to realize the upsides of buying stocks. Therefore, with the rise of awareness, the scale of the stock market became such that the brokers/traders business became a job to make big bucks. All of the data for buying/selling was handwritten on papers until the computers arrived.
With computers, data started getting stored on PCs. Today, with the evolution of cloud & big data, everything is online. All of us, investors or not, can see indexes of companies trading in the stock market.
One of the evolutions in parallel to the shift of data to big data & cloud has been the use of data science in all these. Today, usage of data science is universal. Universities, as well as e-universities such as JCU online, have often emphasized the impact of data science in the stock market.
Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
With data science, one can:
1. Visualize current data:
Data Visualization is a big part of Data Science. It represents the visuals(graphs, charts, maps, etc.) of given data sets.
Using Data Visualization & real-time analysis, investors can see the detail of every stock at any moment. The graph representations show the change happening from time to time. Instant selling or buying, i.e., trading, can be done based on the visualizations.
For e.g., The above is the Data visualization of Google’s stock. Here, real-time data of the trading value in the market can be seen & compared.
2. Check past data:
Visualizations of stocks throughout history, from the company going public to the current time, can be examined. Major decisions can be made based on past data.
E.g. Again, this is the graphical representation of Google’s stock price over the last year. Here investors can see the downfalls as well as the rise of trading value over time.
3. Predict future prices:
In the stock market, history repeats itself. Every company faces the same consequences based on a similar decision made. If Nokia declines to use a massively evolving & growing Google’s product, Android, the fall is inevitable.
One can Manually predict future of stocks based on historical data. For e.g., In the above screenshot, there are two significant rises in Google’s stock. One can check the events during that time, and invest for a similar event coming in the future.
However, manually checking is a burden, that too more than 5000 stocks in the market. Here’s where the real application of data science is. With data science using Machine learning & predictive analysis, investors can predict the future.
Based on the past data, buzz it is making over the internet, news of the person directly attached to the company, Innovation/Products/Service a company is coming with, etc. ML & Data Science algorithms can give probable future prediction.
These predictions have an immense impact on the investors trading decisions, and hence on the stock market directly.
4. Get notified when to sell:
Selling at a good time is as vital as buying at a good time.
Selling before anything goes haywire is necessary to succeed in the stock market. Every inch of data collected from sources and ran through data science algorithms help in achieving that.
E.g., When Instagram introduced the story feature, investors knew that it was time to sell Snap. stock before it falls with the evolving Instagram.
Data analysis lets you do that. Proper data of what’s going on in the competitors’ barracks, what’s going in the world, how’s the economy of an individual country, in how much debt the country is, etc. when gathered with historic data can help make predictions.
The data science algorithms can pre-smell the economic bubbles such as the 2008 economic crisis, and assist investors in selling beforehand.
5. Make Pre-known investments:
This is not exactly related to the stock market; however, it is related to investors. Investors can invest in companies at an early stage with the help of data science.
Data such as the number of app downloads, reviews on the app/service, the revenue of the startup, the market need of the product/service, etc. can be analyzed using a Machine learning algorithm to predict a probable value of the stock when & if the company goes public.
In short, Data science assists investors with underpriced assets to make a long-term profit.
6. Get ROI details:
Data science can also give Return on Investment details.
If one enters data of:
The number of stocks one holds in individual companies,
And the time when it was bought,
data science algorithm can give an estimated Return on Investment report in the context of upcoming years if nothing goes haywire.
Data science, as a discipline, is booming. In stock markets, it can analyze past data, events occurring that data, and much more to predict a probable future. This helps investors a big deal.
- Data Visualization,
- Predictive Analysis,
- Machine learning,
- Data Mining,
Etc. of data science when aggregated can make an impact in the stock market.
And therefore, the demand for these roles is also high.
Machine learning and AI are still in a stagnant stage, so can’t be trusted within something where the money is involved. However, data science, when combined with the human brain, can give extraordinary results in the stock market.
Though many of these technologies can’t give an accurate stock prediction, one prediction is for sure: The demand for ML, Data science expert will grow. So, if a student is reading this who hasn’t decided a career option, entry-level Data scientists make an average of AU$ 91,000. This is possible by getting a degree from any college, and if one’s boat has sailed, online universities like JCU online can help.