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• W1760646092 abstract "ABSTRACTIPOs underperform on average. Nevertheless, the skewed distribution of returns offers the chance to gain extremely high rewards (e.g., identifying the “next Microsoft“, as discussed by Loughran and Ritter, 1995). Hinging on this argument, this paper proposes a new method to help investors screen IPOs for the high-performing tail of the returns distribution. Using a straightforward definition of “winner IPOs” based on buy-and-hold abnormal returns, this study employs logistic regression to forecast whether a firm is still a top performer 1, 2 or 3 years after listing, relying only on publicly available information. Investors using our forecasting model would always have an adjusted rate of successful predictions higher compared to a naively classification that consider all IPOs as “winners”." @default.
• W1760646092 created "2016-06-24" @default.
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• W1760646092 date "2014-01-01" @default.
• W1760646092 modified "2023-10-16" @default.
• W1760646092 title "Forecasting winner IPOs" @default.
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• W1760646092 doi "https://doi.org/10.1080/10293523.2014.11082567" @default.
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