Impact of Data Analytics in Stock Market Analysis
Probably, most people got their first taste of Data Analytics is the Hollywood blockbuster, ‘Moneyball’, wherein the coach Brad Pitt selected players for his baseball team by using data analytics to identify undervalued players. Oakland Athletics lost its star players after losing to the New York Yankees; the team needed to rebuild its roster with a limited budget. Brad Pitt was recruited as the General Manager of Oakland Athletics to rebuild the team. Brad Pitt used data analytics to recruit players and selected players on the basis of their on-base percentage. Using data analytics, Brad Pitt formed a completely new team by signing players at extremely low contracts prices, and this team went to win the Western American League. Imagine the return on investment Oakland Athletics made scouting players using Data Analytics.
Now, let’s apply the same principles of Data Analysis in the Stock Market Analysis and imagine the return on investments an investor can earn by making investment decisions through data analysis. Identifying underperforming stocks with the potential of delivering exceptional performance in the near future, and thereby allowing the investors to purchase the stock at rock bottom rates and sell the stock when it reaches its peak. Data analysis can help investors determine their buying, selling and holding the decision to assure maximum capital gains and return on their investment.
What is Data Analysis and Big Data?
Data analysis can be defined as the methodology utilised for analysis and process of random data to enable making sense out of it. There is a treasure of quantitative and qualitative data which businesses accumulate. This data can be highly valuable if analyse and interpreted in the right manner towards deriving useful insights and results.
Big Data can be defined as large sized or highly complex data sets which are generally difficult to analyse and process using traditional methods of data processing and analysis. These difficulties may include analysis of data, capturing of data, metadata, data storage, search, data transfer and sharing, visualisation and data privacy.
Applications of Data Analysis and Big Data
Data analysis can investigate and discover trends and influences which determine consumer behaviour and buying decisions which can help businesses make important decisions pertaining their product design, delivery mechanism, pricing strategy, marketing and promotion strategy etc. in order to obtain a position of competitive advantage.
For instance, e-commerce businesses are employing some of the best minds in data analysis and Big Data towards deciding almost every aspect of its business from changes in pricing trends, logistics, fast moving products etc. Professionals Sports Teams are using data analysis to determine which player to draft or recruit, team formation, in-game strategy etc. The movie industry is using data analytics to understand consumer preference and designing its movies according to the consumer taste, desires and preferences to ensure their production is a box-office success.
Similarly, data analytics can be used in the stock market towards identifying stocks and shares with growth potential, buying them at rock bottom prices and selling them when the share prices are at its peak.
In the age of digitisation, all share market transactions take place online using Demat Accounts. One of the advantages Demat accounts offer is the easy access to historical financial data regarding the transaction history of the user and the historical performance of a stock or a share.
What is a Demat Account?
Demat Account is a form of an account which enables investors of stock and shares to hold their shares in electronic or dematerialised form. Post 5th Dec 2018, SEBI has restricted transfer of shares in physical form and transactions in share using a Demat Account enables electronic settlement of all transaction.
A Demat account is operated similar to a bank account wherein the Demat account holds stocks, shares and other forms of financial instruments instead of cash and similar to a bank account wherein the Demat account is credited and debited when shares are bought and sold. Investors can open Demat account with zero balance of shares and also maintain a zero balance of shares in their Demat account.
Applications of Data Analysis and Big Data in the Stock Market
Data Analysis and Big Data are on the cusp of completely revolutionising how the stock markets in India will function and how investors will make their buying, selling and investment decisions. The technology of data analysis and Big Data is growing rapidly across industries, and the financial sector is not far behind in the development of data analysis and Big Data technologies.
Trading on the stock market required accurate and timely inputs. The magnitude of data that is generated within the stock market on t a daily basis is impossible to be managed analysed and made sense of by human beings due to the sheer volume of data generated and speed at which this financial data is being generated from various sources.
Find some applications of data analysis and Big Data in the Indian Stock Market.
Leverage Data Analytics and Big Data Analytics into Financial Modelling
In the current day and age, financial analysis alone is no longer adequate for examining share prices and share price behaviour. These financial analyses need to be integrated with external factors such as social and economic trends within the economy, political environment, consumer behaviour and preferences etc. which have the potential of impacting the share prices of a particular stock or stocks prices within a particular industry.
Data analysis and Big Data Analytic can utilise predictive models for estimation of probable outcomes and returns on investment. With the increase in access of these results and increase in the level of accuracy of data analysis and Big Data predictions, investors can leverage this information and prediction towards mitigating their risk associated in trading on the stock market.
The latest buzzword within the world of stock market trading is ‘Algorithmic Trading’. Machine Learning technologies and algorithms are allowing computers towards making investment decisions, execute trades just like human beings, but at a rapid pace and high frequencies which is not possible to be carried by people. These algorithms incorporate best buying prices to be traded at specific times and reduction of manual errors which can occur due to behavioural influences.
Real-time data analytics also has the potential of improving the investing capabilities of individual retail traders and high-frequency traders and firms with the algorithmic analysis providing insights which provide access to valuable information, with this enabling making accurate and timely investment decisions towards maximising returns on investment.
The strength of algorithmic trading is within its limitless capabilities of analysing data, making real-time investment decisions and executing trades at a fast pace and high frequency using a wide array of structured and unstructured data obtain from various sources such as stock market information, social media, analysis of recent news etc. towards making intuitive judgements. This analysis of situational sentiments can be highly valuable in stock market trading.
Machine Learning Technologies are still in its very nascent stages with the technology yet to realise its full potential. Theoretically, the applications of this technology are wide and far-reaching and have its applications in trading within the stock market as well. Machine Learning technology can enable computers to learn to make financial decisions and learn from its past mistakes, employ logic in investment decisions etc.
Machine Learning Technologies have the potential of delivering accurate perceptions and executing profitable trades. Though this technology is in its nascent stages, the growth potential of this technology and the endless possibilities in the stock market trading application can enable implementation a combination of Data Analytics, Big Data and Machine Learning without human involvement in decision making.