Why Data Science becomes inevitable for Investors & Speculators?

“Data is a better choice than Belief & Conjecture”

In this article, I would like to share my knowledge on how Data Science becomes precious technology for Investors and Traders to increase the profit margin and reducing risks on Investment Strategies. I strongly believe that Data plays significant role in all financial market participants to construct or model their Portfolio and Hedging Techniques rather being optimistic on future economic circumstances.

Alright! Before getting into Data Science, I just want to give an overview on Buy Side firms and its roles. The main purpose of an investor goes to an advisor or a Buy Side Institution, depends on the investment strength, is mentioned in the  image. Investment Managers will always try to make their clients to be in profit zone.

Buy Side 

Most of the Asset Management, Fund Management and Investment Management Institutions are coming under Buy Side firms which commit of assets and funds with a long-term time framework to bring additional income to regular receipts and growth to the investor. Investments are generally optimistic and involves waiting for a future reward in terms of income through regular interest, dividends, premiums, or appreciation in the value of the principal capital.

FACT: Fidelity as an Asset Management Company, manages $755 billion in U.S. Equity Assets Under Management

There are many investment avenues are available where Asset Management Companies can make investments available in Investment Programs which can be constructed by the Investors who are familiar with direct investments or alternative investments. This option is available because some investments are appropriate for one type of investor and another may be suitable to another person. Left side image is considered as major clarification on Investment Avenue where Buy Side firms are working on.

Data Science

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. It is predominantly used to make decisions and predictions making use of Predictive Analytics and Machine Learning. Many of us are still unable to find the difference between Business Intelligence (BI) & Data Science.

Data Sources for BI are usually Structured Data and those are often SQL & Data Warehouse but in Data Science, sources are both Structured & Unstructured and Data are from Logs, Media, SQL and NoSQL. BI mainly deals with statistics and it focuses on past and present. In case of Data Science, alone with statistics approach, it includes Machine Learning & Neuro-Linguistic Programming (NLP) to focus on future.

FACT: By 2020, about 1.7 megabytes a second of new information will be created for every human being on the planet – McKinsey 

Use Cases

Automating Risk Management: Risks for Investment Management Institutions can come, such as competitors, investors, regulators, or company’s customers. For protecting the investors, Buy Side Institutions can use Data Science for identifying risk information from pool of data, prioritizing and monitoring risks, which are the perfect tasks for machine learning. With training on the huge amount of investor data, financial information, social media data and insurance results, algorithms can not only increase the risk scoring models but also enhance cost efficiency and sustainability.

Algorithmic Trading: Fund Managers can avoid their loss from impulsive market fluctuations by using Programmable Trading with Data Science. Based on the most recent information from analyzing both traditional and non-traditional data, fund managers can make real-time beneficial decisions. And because this data is often only valuable for a short time, being competitive in this sector means having the fastest methods of analyzing it.

Recommendations: Data Science can provide personalized financial advice for Investors on how to invest money from current market condition and how to detect unusual transactions in Company Profiles. A Robo-Advisor can suggest a financial portfolio manager to match the goals and risk tolerance of a particular client.

FACT: Apparently $2.2 Trillion in Assets will be managed by Robo-Advisors by 2020 versus $0.3 Trillion in 2016. - A.T. Kearney

Implementations

According to McKinsey, Asset Management Companies wildly apply Data Science and Machine Learning techniques across the full value chain and they can create a robust client data repository includes the best of internal and external data sources. Moreover, Asset Management Companies are focusing on new sources of investment research such as Consensus Platform, Investor Relationship Platform, Sentiment Analysis and Marketplace Researches by using Data Science techniques like Linear Regression, Non-linear Modeling and Shrinkage. (I will discuss about Data Science in a separate article in detail)

Here, I bring my conceptual architecture of Data Science Platform for Investor or Speculator. This platform may not fit in all investors or fund managers but it can be considered as core components of any Data Analytic Platform using Data Science techniques. You can place appropriate software tool on each component based on your Investment Strategy.

FACT: Machine Learning techniques allow us the flexibility to create dynamic models that adapt to the data. Quantitative techniques in the past relied on more simplistic rules for ranking companies based on certain pre-determined metrics - Osman Ali, Portfolio Manager, GSAM

Great! I hope you had good reading on Data Science and I would like to complete this article with few fun facts about Data Science.

• Data is never clean
• Effort requires for cleaning and preparing data is considerably high
• None of the Data Model is approved as 100% Correct
• End To End Automation is not possible

Thank You! Have A Wonderful Day!

Share: