r/valueinvesting
I compiled a list of structural market inefficiencies and why they persist
Author compiled a list of structural market inefficiencies and explains why these factor anomalies persist despite being widely known.
Hey, I’ve been reading a lot of the debate around structural market inefficiencies.
The discussion usually gets stuck between “markets are efficient” and “just buy value/momentum/low beta and win,” which feels too simplistic.
So I compiled a list of the main anomalies I keep seeing: value, momentum, low beta, small caps, quality, accruals, shareholder yield, etc.
The part I find more interesting is not just that they have worked historically, but why they can keep existing even after people know about them.
Some are behavioral. Some are institutional. Some come from benchmarks, incentives, career risk, liquidity, or plain human overreaction. And some only really make sense when combined with other factors.
I also added interactive charts to explore the historical data and compare how different factors have behaved over time.
wrote it up here if anyone’s interested: https://www.jeravalue.com/en/blog/market-inefficiencies
The main issue for retail investors trying to capture factor anomalies like momentum is the implementation drag, especially turnover-induced tax drag and fund fees. For example, if a momentum strategy yields a gross 12% return but has 80% turnover in a taxable account, and you pay a 24% tax rate on realized gains, the tax drag eats about 1.9% of that return. A simple buy-and-hold index fund yielding 10% with 3% turnover has a tax drag of less than 0.3% from dividends. The factor alpha has to exceed 1.6% every year just to break even with the passive index. How do you account for this tax drag in your backtests when comparing momentum to buy-and-hold?
Yep, implementation drag matters. But you don’t need to run a high-turnover strategy to benefit from factors.
Just knowing the base rates and using them to avoid obvious junk is already useful, and basically free. And beyond that, retail investors can still use lower-turnover factor tilts directly or through ETFs.
AI slop. Factor analysis is not useful anyways.
Why not?
Alpha can't break commision costs. All the good stuff is taken by PhD's in prop firms. What's left is not useful for retail.
Agree for high-turnover mechanical alpha. A lot gets eaten by costs or arbitraged away. But that’s not how I’d use factor analysis as retail.
The value is in base rates: knowing which signals are tailwinds/headwinds, filtering junk, and focusing research time better. Prop firms can arbitrage clean scalable alpha; they don’t make valuation, dilution, earnings quality, momentum, or liquidity irrelevant.
Thanks for the write up — it was interesting reading them, but also confusing at the same time. I was expecting structural inefficiencies coming in, like big funds can’t buy enough small cap so they don’t.