O'Shaughnessy founded his first company as a quantitative consultant building normal portfolios for pension plans in the late 1970s / early 1980s, and found extensive closet indexing (managers claiming to be active while mimicking the index).

Quantitative strategies can serve as a 'canary in the coal mine' for market dislocations because mispricings, if real, will show up first in factor-based portfolios before they manifest in obvious ways; O'Shaughnessy observed tiny steel companies appearing in growth portfolios circa 2004-2005, presaging the China commodity boom months before it became public.

Leverage is a powerful tool for diversified portfolios but should never be used on concentrated bets or illiquid assets because the combination of concentrated risk and illiquidity creates uncontrollable downside when liquidity dries up, a lesson taught repeatedly by history (LTCM, private equity drawdowns, 2008).

In mid-to-late 1990s academic research, many papers studying stock market anomalies used small-cap stocks, some of which had minimal to no trading volume; when Asness challenged academics on this, they responded that you could theoretically capture the premium if you had enough capital to move markets or infinite time, but these responses avoided addressing the real constraint: practical tradability.

The four main psychological forces that drive poor investment decisions are fear, greed, hope, and ignorance, with ignorance being the only one that can theoretically be addressed through education.

The Martingale strategy (doubling bets after losses) is being resold to retail traders as a novel discovery despite existing for 400 years, and advocates omit the requirement for infinite bankroll and infinite willingness, making it equivalent to 'pennies in front of a steamroller.'

A machine learning specialist who previously worked at a major tech firm told O'Shaughnessy that humans cannot examine millions of data points and find patterns, but ML can find patterns; however, ML can tell you 'what and when' but never 'why,' creating a fundamental interpretability limitation.

Black swans defined as unpredictable tail events cannot be predicted, but the question is whether a black swan can be *confirmed* or detected in real time; using oil's crash to negative prices as an example, could ML have detected the black swan approaching before it fully occurred.

Currency quoting conventions (some as foreign per dollar, some as dollar per foreign) create unnecessary cognitive friction that persists despite being 'dumb'; O'Shaughnessy still has to think about which direction he wants even after 35 years, and firms won't change because they'd eventually make errors due to muscle memory.

Putting private equity into ETFs would increase trading and price discovery, forcing real-time marking that would destroy the 'low volatility' story and likely kill the illiquidity premium by revealing true price movements.

O'Shaughnessy ran a microcap strategy that adjusted for liquidity and was successful, distinguishing it from the index-level small cap effect because the strategy filtered for tradeable names and quality adjustments.

Brokers responding to quant traders seeking better execution explicitly modeled the optimal amount to overcharge clients (slippage) while keeping them satisfied enough to maintain the relationship, treating it as a deliberate revenue extraction problem rather than cost reduction.

When O'Shaughnessy pointed out to a large pension plan that a specific active manager was essentially the S&P 500 index, the manager was doing something very expensive and active, but market conditions happened to make their active bets invisible due to correlation with the index.

O'Shaughnessy was shut down by academics on his ability to pitch to their endowment when he admitted he only had a BA, illustrating how credentialism (PhD requirement) can override intellectual merit in institutional settings.

Retail investors consistently lose money long-term according to the published literature; high-frequency individual stock trading and options trading on apps like Robinhood transfer wealth from retail to professional traders and market makers, not because professionals are smarter but because retail has structural disadvantages (speed, information, transaction costs).

Approximately 68% of quant managers overrode their quantitative models during the Global Financial Crisis, meaning they stopped following their systematic process in response to panic, whereas AQR and O'Shaughnessy Asset Management did not override their models despite significant losses.

O'Shaughnessy witnessed a Cisco message board discussion in the late 1990s where he asked investors at what price they would sell, receiving 30 mostly irrelevant answers and only one person answering with a price—but that answer was momentum-based ('if it goes down 50%') rather than valuation-based, showing how manic investors lose the ability to think in terms of fundamental value.

O'Shaughnessy attempted to remove stock names from trading systems, replacing them with ticker numbers to reduce emotional attachment, but faced rebellion from traders citing logistical communication costs, showing the conflict between theoretical dispassionateness and practical information flow.

Backtesting is essential to quantitative investing because it provides 45 years of data showing base rates, worst-case drawdowns, and probability distributions; while backtests can be overengineered and data-mined, comparing a backtest to making investment decisions based on gut feeling and anecdotes ('I talked to my buddy') is not a fair tradeoff.

Private equity investors in Connecticut increasingly admit that they are paying much closer to public market multiples than they used to, which is consistent with the theory that private equity is becoming more crowded and multiples are compressing.