Put Your Money Where Your Mouth Is

July 29, 2024 (MLN): In a literal sense it is supporting activities you believe are right, however, metaphorically the phrase carries unique meaning for everyone whether he/she is a professor, preacher, or professional. So to for stock investors. To them, it is investing in stocks where the money is believed to siphon.

The “Belief” once regarded as a divine blessing, is now considered an outcome of information analytics.

The explosion of financial data, continuous streaming of it in bits & bytes, and use of application software together made predicting the money flow to a stock so accurate that the predictions approximated to belief, prompting stock investors to invest in tandem with findings. The widespread use of technical analysis is one example in this respect, others are Heat Maps and Scoreboards.

Data analytics, the accumulating, transforming, and modeling of raw data into actionable insights, once considered tedious is now as simple as pressing a button. You press a button and the Heat Maps pops up (Visual-1), reflecting the buying interest of investors in sectors and stocks on a particular day.

Similarly, Visual-2, which shows the cumulative net interest of FIPI over a specified period also a matter of striking a button. And, so holds for others infographics loaded with decisive information.

Data Analytics leaped with the advent of Artificial intelligence, bringing all aspects of data analysis, from accumulating data to presenting findings, within the folds of AI.

Should it be taken as a predomination of no more human engagement in Data Analytics? So far not as ingenuity in redefining exotic relationships among data sets are still within the very confluence of human intellects.

For example, Heat Map, Visual-1, despite offering insights about the buying interest of investors in sectors and stocks on a particular day, does not suggest anything about investors’ preferences for sectors or stocks with ranks and order. Similarly, Visual-2, reflexive of cumulative net interest of FIPI over a specified period, does not shed light on the accumulation strategy of Foreign Investors.

However, when Visual-1 and Visual-2, are complemented with information in Visual-3 and Visual-4, together, they unequivocally display who are major buyers/sellers in sectors and stocks and the investing strategy of Foreign Investors over the period.

Similarly, when visual 5 is seen in isolation it simply suggests foreign investors’ buying/selling preference w.r.t sectors, such as Banking, Power Generation, and Cement by order.

However, when the same is coupled with daily net FIPI (Visual 6), it exposes the underlying shift in buying/selling interest of foreign investors from one to another sector during the period under consideration.

So is the case with net LIPI, when daily net LIPI, Visual 6, is examined against daily net FIPI, it appears as a mirror image of the latter. However, when the same is extrapolated vis-à-vis to classes of local investors, such as individuals, Banks/DFI, Mutual Funds, Brokers, and Insurance (Visual-7),

it tells whether local investors complemented or contravened FIPI buying/selling in a sector or a stock on that particular day. Information is important enough to assess the directional strength of the market and stock price.

Leaving the hard part of Data Analytics to Data Scientists, the rest depends on one’s imagination & skills to extract actionable investment insights from the plethora of data. Data Analytics is like peeling off information layer by layer. With a layer off, the analyst comes closer to the mouth where the money is siphoning.

Data Analytics is no longer unchartered water. Lots of investors are already using it to identify investable stock. However, their reliance is mostly on freely available application software and without any formal education and training in Data Analytics. Taking Data Analysis to the zenith, a formal learning of Data Science is imperative.

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