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Investor Attention and Insider Trading

Journal of Financial and Quantitative Analysis 2025 60(5), 2293-2333 open access
Abstract We identify a new mechanism of opportunistic insider trading linked to attention-driven mispricing. Insiders are more likely to sell their company’s stock during periods of heightened retail attention and more inclined to buy when attention diminishes. The results are particularly pronounced for lottery-type stocks and firms with substantial retail ownership. We demonstrate that our findings—which relate to indicators of mispricing, retail order imbalances, and Robinhood herding episodes—extend to seasoned equity issuances and cannot be solely explained by firm fundamentals. Attention-based insider trading is less likely to result in SEC enforcement actions and persists across different regulatory regimes.

Effects of the volatility smile on exchange settlement practices: The Hong Kong case

Journal of Banking & Finance 2009 33(1), 98-112
The well-documented volatility smile phenomenon in the US options market has affected the option settlement practices of other markets. To settle Hang Seng Index (HSI) options, the Hong Kong Stock Exchange artificially builds in a piecewise linear “smile” or “sneer” volatility function, which is determined daily by market makers rather than directly by market forces. In this study, we investigate the time-varying settlement function and find the following economic determinants of the volatility function: lag parameters, current-day HSI returns, the distribution of HSI returns, transaction costs as proxied by the bid-ask spread, and the “Monday effect”. For evaluation purposes, we use as a benchmark the estimated piecewise linear volatility function as directly driven by market forces. The comparison analyses show that base volatilities set by market makers run somewhat high, while downside slopes are not steep enough. This results in the overpricing of the lion’s share of traded options. An economic determinants analysis of market-force-driven parameters reveals that market makers can better align artificial volatility parameters both by reducing reliance on the function parameters of prior days and by more precisely accounting for current-day HSI returns, option time-to-maturity, bid-ask spreads and buying pressure.