Key insights
- The post questions the persistence of value investing in an era dominated by algorithmic trading and readily available information. It suggests that algorithms should theoretically eliminate market inefficiencies and mispricings that value investors exploit. The implication is that either value investing strategies have adapted to exploit different inefficiencies, or that behavioral factors and other non-quantifiable elements still create opportunities for human investors.
I've been trying to wrap my head around this and would love some perspectives from people who understand markets better than I do.
The classic premise of value investing is that the market misprices stocks — that you can find companies trading below their intrinsic value and profit when the market eventually "corrects" itself. That made a lot of sense in Graham's era, when information was scarce and humans were slow.
But today? The market is largely run by algorithms and quant funds operating at millisecond speeds with access to infinite info, credit card transaction data, sentiment analysis, and basically every public signal imaginable. If a stock is undervalued, wouldn't a bot have already detected and corrected that inefficiency before any human investor could act on it?