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On Modeling IPO Failure Risk
This paper offers a novel framework, combining firm operational risk, IPO pricing risk, and market risk, to model IPO failure risk. By analyzing nearly a thousand variables, we observe that prior IPO failure risk models have suffered from a major missing-variable problem. Evidence reveals several key new firm-level determinants, e.g., the volatility operating performance, the size of its accounts payable, pretax income to common equity, total short-term debt, and a few macroeconomic variables such as treasury bill rate, and book-to-market of the DJIA index. These findings have major economic implications. The total value loss from not predicting the imminent failure of an IPO is significantly lower with this proposed model compared to other established models. The IPO investors could have saved around $18billion over the period between 1994 and 2016 by using this model.
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The Effect of Language on Investing: Evidence from Searches in Chinese Versus English
Pacific-Basin Finance Journal,
This study examines the language effect on investing behavior in local stock markets for local- and foreign-language investors using Google search records. First, we find that attention to a local language stimulates attention to a foreign language, increases abnormal news coverage, and has better predictability on stock returns. Second, investors who do Google searches in the local language react faster to a news event's shock than those who search in the foreign language. Third, only attention to the local language can reduce the price drift of an earnings surprise. Last, firm-level information asymmetry is a channel for local advantage. Therefore, we suggest that investors who use a stock market's local language have a local advantage when seeking more profitable investment opportunities in that stock market.
Tail-risk Protection Trading Strategies
Starting from well-known empirical stylized facts of financial time series, we develop dynamic portfolio protection trading strategies based on econometric methods. As a criterion for riskiness, we consider the evolution of the value-at-risk spread from a GARCH model with normal innovations relative to a GARCH model with generalized innovations. These generalized innovations may for example follow a Student t, a generalized hyperbolic, an alpha-stable or a Generalized Pareto distribution (GPD). Our results indicate that the GPD distribution provides the strongest signals for avoiding tail risks. This is not surprising as the GPD distribution arises as a limit of tail behaviour in extreme value theory and therefore is especially suited to deal with tail risks. Out-of-sample backtests on 11 years of DAX futures data, indicate that the dynamic tail-risk protection strategy effectively reduces the tail risk while outperforming traditional portfolio protection strategies. The results are further validated by calculating the statistical significance of the results obtained using bootstrap methods. A number of robustness tests including application to other assets further underline the effectiveness of the strategy. Finally, by empirically testing for second-order stochastic dominance, we find that risk averse investors would be willing to pay a positive premium to move from a static buy-and-hold investment in the DAX future to the tail-risk protection strategy.
12.04.2017 • 20/2017
Auch der Osten ist im moderaten Aufschwung – Implikationen der Gemeinschaftsdiagnose Frühjahr 2017 für Ostdeutschland
Für das Jahr 2017 prognostiziert das Leibniz-Institut für Wirtschaftsforschung Halle (IWH) einen Anstieg des ostdeutschen Bruttoinlandsprodukts mit Berlin um 1,7% (Gemeinschaftsdiagnose für Deutschland insgesamt: 1,5%). Maßgeblicher Treiber ist wie in Deutschland insgesamt die Binnennachfrage. Insbesondere profitiert die Wirtschaft von der hohen Dynamik in Berlin und Sachsen. Die Produktion pro Einwohner bzw. Einwohnerin dürfte in diesem Jahr im Osten wohl erneut etwas schneller als im Westen steigen; somit setzt sich die Tendenz kleiner Fortschritte bei der ökonomischen Konvergenz fort.
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