Growth investors invest in companies that exhibit signs of above-average growth, even if the share price appears expensive. Value investing involves buying securities that appear underpriced by some form of fundamental analysis.
Speculation and some data on why growth investing has beaten value investing for a while now in the US:
… it makes sense that computers are winning in arbitrage-style hedge fund strategies, where an asset worth 95 cents is bought in one market and a quasi-identical one is sold in another market for $1.00. Most of those traditional hedge fund strategies are rules-based, and if anything, data ubiquity means computers have a huge advantage in parsing ever-increasing amounts of data not only to find that often temporary discount, but also to step in front of slower investors for a smaller spread if necessary, essentially beating them to the punch… even if the specific metric used for implementation of value (say, a price-to-book or price-to-earnings ratio) changes because of market conditions, competitive dynamics, or the financial reporting tendencies of corporate executives, computers are far better positioned to identify the shift faster than we are.
On the other hand, growth is perhaps harder to spot. It’s not just that doing so involves making simple forecasts about the future — computers generally beat humans at that, too, by the way — but it requires something more than that. In my experience, successful growth investors have an ability to synthesize unstructured data across multiple, often seemingly unrelated, domains.
Since inception in 2010, the AI index has absolutely crushed the average hedge fund, with the machines annualizing at 12.8 percent per annum versus just 5.0 percent for the humans. If efficiency is the enemy of alpha, then computers are its worst nightmare, hulked out on steroids.
Given this, there should still be a window (months? years?) to create high alpha value based funds that use technology in India. There has never been more compute power available for cheaper. Machine learning’s also a relatively new field and hasn’t been applied widely to Indian data sets. This means you can make failures (cost) free and successes real. The race is then for the best data.