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We demonstrate that a broad set of asset pricing factors/anomalies are significantly exposed to “noise trader risk,” and the noise trader risk is priced in factor premia. We first confirm that mutual funds’ flow-induced trading of factors are uninformed, as they generate a large price impact on factor returns, followed by a complete reversal. We then show that asset pricing factors are subject to flow-driven noise trader risk in that expected variation (covariation) of flow-induced noise trading strongly forecasts variance (covariance) of factor returns. Importantly, factor premia are higher when flow-driven noise trader risk is expected to be more salient.

We analyze a single-factor setting in which there is private information regarding cash flows as well as their betas. Private information about betas, together with market makers’ risk aversion and mean betas’ nonnegativity, implies a nonlinear price schedule whose stochastic slope covaries positively with order flow when the expected factor payoff is positive and vice versa. We predict a negative relation between the covariance and expected returns and an attenuation of the beta anomaly in asset returns after accounting for this relation. Empirical tests confirm these predictions.

Mutual funds investing in illiquid corporate bonds actively manage Treasury positions to buffer redemption shocks. This liquidity management practice can transmit non-fundamental fund flow shocks onto Treasuries, generating excess return volatility. Consistent with this hypothesis, we find that Treasury excess return volatility is positively associated with bond fund ownership, and this pattern is more pronounced among funds conducting intensive liquidity management. Causal evidence is provided by exploiting the U.S. Securities and Exchange Commission’s 2017 Liquidity Risk Management Rule. Evidence also suggests that the COVID-19 Treasury market turmoil was attributed to intensified liquidity management, an unintended consequence of the 2017 Liquidity Risk Management Rule.

We document and explain the sharp performance deterioration of smart beta indexes after the corresponding smart beta ETFs are launched for investment. While smart beta is purported to deliver excess returns through factor exposures, the market-adjusted return of smart beta indexes drops from about 3% “on paper” before ETF listings to about −0.50% to −1% after ETF listings. This performance decline cannot be explained by variation in factor premia, strategic timing, or diminishing returns to scale. Instead, we find strong evidence of data mining in the construction of smart beta indexes, which helps ETFs attract flows, as investors respond positively to backtests.

Low-beta stocks deliver high average returns and low risk relative to high-beta stocks, an opportunity for professional investors to “arbitrage” away. We argue that beta-arbitrage activity generates booms and busts in the strategy’s abnormal trading profits. In times of low arbitrage activity, the beta-arbitrage strategy exhibits delayed correction, taking up to three years for abnormal returns to be realized. In contrast, when arbitrage activity is high, prices overshoot and then revert in the long run. We document a novel positive-feedback channel operating through firm leverage that facilitates these boom-and-bust cycles.

We study how derivatives (with nonlinear payoffs) affect the underlying asset’s liquidity. In a rational expectations equilibrium, informed investors expect low conditional volatility and sell derivatives to the others. These derivative trades affect different investors’ utility differently, possibly amplifying liquidity risk. As investors delta hedge their derivative positions, price impact in the underlying drops, suggesting improved liquidity, because informed trading is diluted. In contrast, effects on price reversal are ambiguous, depending on investors’ relative delta hedging sensitivity (i.e., the gamma of the derivatives). The model cautions of potential disconnections between illiquidity measures and liquidity risk premium due to derivatives trading.

We show that managerial learning from stock prices can lead to feedback loop vulnerability: corrective actions based on perceived negative market signals reduce the sensitivity of asset payoffs to stock market information. Less sensitivity discourages liquidity provision and increases the price impact of liquidity shocks. Interestingly, overconfident managers who disregard stock price information may be less vulnerable to the adverse price impact of nonfundamental liquidity shocks. Our empirical evidence strongly supports the model’s underlying premises and predictions: First, investment decisions of overconfident CEOs are significantly less responsive to stock price fluctuations. Second, the price impact of liquidity shocks, for example, mutual fund fire sales, is substantially smaller for firms with overconfident CEOs.

We re-examine the puzzling pattern of lead-lag returns among economically-linked firms. Our results show that investors consistently underreact to information from lead firms that arrives continuously, while information with the same cumulative returns arriving in discrete amounts is quickly absorbed into price. This finding holds across many different types of economic linkages, including shared-analyst-coverage. We conclude that the ǣfrog in the panǥ (FIP) momentum effect is pervasive in co-momentum settings, suggesting that information discreteness (ID) serves as a cognitive trigger that reduces investor inattention and improves inter-firm news transmission.

We develop a data-sales model to study the implications of alternative data for financial markets. Investors acquire skills to process the purchased raw data, and developing such skills is costly and involves considerable uncertainty. The data vendor controls the size of the data sample to influence the precision of the information investors can extract from the purchased data. Price informativeness is hump-shaped in skill-acquisition costs although the cost of capital and return volatility are U-shaped in skill-acquisition costs. Similar patterns can arise for skill mean and volatility. Our analysis suggests that the funds and data industries foster each other.