Millions change hands in the stock markets in a second, rewarding some and impoverishing the others. What distinguishes the winners from the losers? The winners have the ability to accurately predict stock behavior on a given day or in a given market condition. This can either be pure genius or knowledge developed through years of experience of dealing in stocks. The key point is that they know how to analyze the data and decipher relevant patterns.
Now, what if we told you that anyone could make accurate stock predictions? Moreover, the icing on the cake being that you don’t have to have an IQ of 9 and above or be experienced in the trade, as you can make predictions with data analytics. In fact, Big Data and Analytics today help institutions and individuals make better investment decisions with consistent returns by giving them insight into the data.
Big Data and Cloud are a necessity
Today, an increasing number of people are investing in bonds and mutual funds to save taxes. Good returns on investment translate into financial well-being of families. To be able to do this without burning a hole in the pocket, an investor needs to have knowledge about the market to make sound decisions. However, the biggest constraint being that they don’t have the financial wherewithal of institutions or big corporates to hire data scientists. In such a scenario, this technology is indispensable.
Analytics is the new buzzword
According to the Harvard Business Review, being a Data Scientist is the sexiest job of the 21st Century. Apart from the glamour, at a macro level, the adoption of Analytics and Big Data would also help in employment generation, an urgent need in the Indian context. Global revenue in the analytics market is forecast to reach $16.9 billion in 2016, according to the latest forecast from Gartner, the international research firm.
How does it work?
Big Data is mined and analyzed through algorithms that utilize huge amounts of historical data and insert them into complex mathematical models to provide accurate predictions to maximize portfolio returns. With the ever-increasing capabilities of computers to churn and handle data, automated computer programs perform stock trading at the best possible prices at speeds that a human broker cannot. It also removes the possibility of human error, thus creating less risky investments.
Algorithms such as Apriori, FPGrowth (Frequent Pattern Growth) together with analytical methods such as Lift, Kulc, IR, Chi-square help users identify useful data and discard data that is of little value using highly sophisticated statistical techniques, factoring in real-time news, updates from social media and frequently changing stock data.
Similarly, robo-advisors manage wealth and investments online through generating automated, algorithm-based portfolio management advice without the use of human planners by bringing huge amounts of data on a digital platform.
While predictive analysis is out of reach of most small-time investors, there are affordable applications that abstract complex data through statistical approaches and market sentiment.
Applications such as InTenMin and Investtech run high-speed analytics on historical and real-time stock data to provide in-depth statistics. This information is then correlated with sentiment analysis from social media in order to provide a 360-degree analysis on stocks. Data include billions of records of stock ticker information and live twitter feeds.
However, the use of Analytics and Big Data in the Indian scenario remains restricted to a few.
Data: Structured or Unstructured
The New York Stock Exchange generates 1 terabyte of data every day, according to one estimate, which is undecipherable by a normal human being. Only a small part of such data is structured and which can be managed through relational databases and spreadsheets. Unstructured data, on the other hand, cannot be fit into a predetermined model. Investment banks and asset management firms need to analyze such voluminous data to make good investment decisions.
Therefore, today, stock markets, financial institutions such as banks and capital markets, and insurance and retirement institutions increasingly use Analytics and algorithmic techniques for active risk management.
