Government regulations on housing flippers target their high capital gains but ignore their risk-sharing function: rational arbitrageurs reduce market return volatility borne by other participants while undertaking high idiosyncratic risk. Regulations that tighten the limits of arbitrage in housing markets can adversely affect market efficiency by blocking this risk-sharing function. Using comprehensive housing transaction records in Hong Kong from 1993 to 2021, we find although flippers obtain higher annual capital gain returns than long-term buyers by 8.76 percentage points, they undertake substantially higher idiosyncratic risk due to its unique downward-sloping term structure. Only experienced flippers, who have at least two prior purchase experiences and constitute less than 20% of the flippers, outperform long-term buyers in risk-adjusted returns. Following the enactment of an anti-speculation policy that decreases the share of flippers by 14.2 percentage points in one year, the market return volatility of the entire housing market increases by 21.9

We show that demand pressure from retail investors makes options on low-price stocks relatively expensive—delta-hedged options on low-price stocks underperform those on high-price stocks by 0.63% per week for calls and 0.36% for puts. Natural experiments corroborate this finding: options become more expensive following stock splits, options on mini indices are more expensive than those on main indices, and mini contract options are more expensive than standard options. We attribute our findings to retail investors’ preference for skewness and divergence of opinion. Limits to arbitrage and strategic quote setting by market makers contribute to, but do not fully explain, this effect

This paper introduces the ``limited arbitrage'' asset pricing mechanism into a pure exchange general equilibrium economy. The ``limited arbitrage'' model insists on the role of financial intermediaries in conducting fire sales. In our model, financial markets are incomplete, and households face uninsured idiosyncratic endowment risks. Given such market incompleteness, financial intermediaries can gain arbitrage profits by issuing risk-free debts and investing in risky assets. However, the margin requirement ratio limits the amount of debt. Shocks to intermediaries' balance sheets force them to repay debt and sell shares, causing stock prices to deviate from fundamental levels. We investigate how the risk of a fire sale affects the desirable financial regulation. Lowering the margin requirement ratio has the following trade-offs for welfare. On the one hand, it exaggerates a decline in stock prices due to fire sales, which deteriorates welfare. On the other hand, it improves welfare by providing households with sufficient self-insurance measures against their idiosyncratic endowment risks. Our numerical examples show that a natural debt limit under a laissez-faire economy is undesirable. Governments can improve welfare by introducing financial regulations (raising the margin requirement ratio

By decomposing close-to-close mid-quote returns of ETFs into their overnight and intraday components, we find that the overnight return is significantly positive, whereas the intraday return is negative. This overnight–intraday return differential is ubiquitous across ETFs tracking different asset classes or assets located in different time zones. This phenomenon cannot be explained by differences in overnight and intraday risks, macroeconomic announcements, or information asymmetry. Instead, our analysis reveals that the return pattern is primarily driven by demand shocks from retail investors and limited supply from arbitrageurs. These results indicate that the convenience of buying ETFs during intraday trading hours carries a hidden cost to investors

We assess the information content of order flow for the cross-section of cryptocurrency returns. Our analysis is based on a set of international order flows denominated in 11 major currencies that reflect world order flow. We find that world order flow has strong explanatory and predictive power for cryptocurrency returns. Order flow tends to dominate economic fundamentals for out-of-sample prediction, especially in the context of non-linear machine learning models, and its performance cannot be explained by limits to arbitrage. Overall, our findings indicate that order flow has a permanent effect on cryptocurrency returns

In contrast to the “lazy prices” phenomenon in the stock market, more 10-K textual changes lead to larger increases in volatility smirks—consistent with options traders buying more out-of-the-money put options based on negative information disclosed in textual changes. Moreover, the lazy-prices effect is mainly driven by stocks with tradable options, suggesting that limits to arbitrage lead to a delayed response of stock prices. Finally, the return predictability of textual changes is stronger for stocks with larger option volatility smirk changes. Sophisticated options traders, therefore, demonstrate superior skills at extracting relevant information from public filings

Companies’ strategic alternatives announcements lead to negative future stock returns. First, we investigate whether this anomaly exists. We demonstrate that it is significant and pervasive across years, industries, firm size, and information environments and that it is not driven by confounding variables nor risk. We then investigate why the market misprices the announcements and find that investors appear overly optimistic about a potential merger or acquisition and do not fully incorporate the negative fundamental news conveyed by the announcement. Meanwhile, short sellers exploit the mispricing. We also evaluate market frictions as limits to arbitrage. This study’s contributions are (i) evaluating behavioral and risk explanations for an event that causes extreme stock returns, (ii) challenging investors’ widely held belief that such announcements reflect good news, and (iii) warning investors and analysts about a behavioral bias they might unknowingly adopt

We propose a statistical model of heterogeneous beliefs wherein investors are represented as different machine learning model specifications. Investors form return forecasts from their individual models using common data inputs. We measure disagreement as forecast dispersion across investor-models (MFD). Our measure aligns with analyst forecast disagreement but more powerfully predicts returns. We document a large and robust association between belief disagreement and future returns. A decile spread portfolio that sells stocks with high disagreement and buys stocks with low disagreement earns a value-weighted return of 13% per year. Further analyses suggest MFD-alpha is mispricing induced by short-sale costs and limits-to-arbitrage

Deviations from covered interest rate parity (CIP) are often linked to limits to arbitrage, yet trading volumes surge during periods of apparent no-arbitrage violations. We show that these distortions stem from constraints on non-U.S. agents’ access to wholesale U.S. dollar markets and reflect a premium for unencumbered synthetic dollar funding: non-U.S. banks substitute secured USD borrowing with FX swaps to meet regulatory requirements. A shadow cost-augmented CIP condition holds, implying no riskless arbitrage. U.S. dealers extract rents on dollar provision while non-U.S. customers bear $10.4 billion in additional annual hedging costs. Our results illustrate how intermediary constraints segment global dollar funding

This paper investigates the relation between crowded trades, those in which many investors hold the same stocks possibly exhausting their liquidity provision, and future stock returns on a set of well-known stock market anomalies. We find that anomaly risk-adjusted returns are primarily generated by the most (least) crowded stocks for the long-leg (short-leg) portfolio. Moreover, we find that our results remain significant after publication dates. We hypothesize that crowded equity positions in anomaly stocks increase institutional investors’ exposure to crash risk. Our findings are consistent with this hypothesis and suggest that crowding adds a new consideration to the limits of arbitrage