A Fast Literature Search Engine based on top-quality journals, by Dr. Mingze Gao.
- Topic classification is ongoing.
- Please kindly let me know [mingze.gao@mq.edu.au] in case of any errors.
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Results 379 resources
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Diversified conglomerates are valued less than matched portfolios of pure‐play firms. Recent studies find that this diversification discount results from conglomerates' inefficient allocation of capital expenditures across divisions. Much of this work uses Tobin's q as a proxy for investment opportunities, therefore hypothesizing that q is a good proxy. This paper treats measurement error in q. Using a measurement‐error consistent estimator on the sorts regressions in the literature, I find no evidence of inefficient allocation of investment. The results in the literature appear to be artifacts of measurement error and of the correlation between investment opportunities and liquidity.
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Volatility in equity markets is asymmetric: contemporaneous return and conditional return volatility are negatively correlated. In this article I develop an asymmetric volatility model where dividend growth and dividend volatility are the two state variables of the economy. The model allows both the leverage effect and the volatility feedback effect, the two popular explanations of asymmetry. The model is estimated by the simulated method of moments. I find that both the leverage effect and volatility feedback are important determinants of asymmetric volatility, and volatility feedback is significant both statistically and economically.
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This paper examines the effects of uncertainty about the stock return predictability on optimal dynamic portfolio choice in a continuous time setting for a longhorizon investor. Uncertainty about the predictive relation affects the optimal portfolio choice through dynamic learning, and leads to a state‐dependent relation between the optimal portfolio choice and the investment horizon. There is substantial market timing in the optimal hedge demands, which is caused by stochastic covariance between stock return and dynamic learning. The opportunity cost of ignoring predictability or learning is found to be quite substantial.
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Evaluating default correlations or the probabilities of default by more than one firm is an important task in credit analysis, derivatives pricing, and risk management. However, default correlations cannot be measured directly, multiple-default modeling is technically difficult, and most existing credit models cannot be applied to analyze multiple defaults. This article develops a first-passage-time model, providing an analytical formula for calculating default correlations that is easily implemented and conveniently used for a variety of financial applications. The model also provides a theoretical justification for several empirical regularities in the credit risk literature.
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- Bond (10)
- Mergers and Acquisitions (7)
- CEO (2)
- Capital Structure (1)
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- Journal Article (379)