2016
DOI: 10.1016/j.jbusres.2016.05.016
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Corporate financing decisions under ambiguity: Pecking order and liquidity policy implications

Abstract: This paper addresses the following unresolved questions from the perspective of ambiguity theory: Why do some firms issue equity instead of debt? Why did most firms retain their cash holdings instead of distributing them as dividends in recent times? How do firms change their financing policies during a period of severe financial constraints and ambiguity, or when facing the threat of an unpredictable financial crisis? We analyze how the values of the firm's equity and debt are affected by ambiguity. We also s… Show more

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Cited by 34 publications
(29 citation statements)
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References 67 publications
(50 reference statements)
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“…= 2 − 1 and = √4 (1 − ) Ambiguity score and capacity variable c captures investors' ambiguity, with 0 < c < 0.5 interpreted in the CU literature as ambiguity aversion, pessimism (negative m) and under-(over-) weighting of good (bad) outcome probabilities, 0.5 < c < 1 implying ambiguity-seeking, optimism (positive m) and over-(under-) weighting of good (bad) outcome probabilities (see Chateauneuf et al, 2001;Kast and Lapied, 2010;Agliardi, 2017), and equal weight c = 0.5 (similar weighting) corresponding to the traditional ambiguity-neutral and Bayesian framework (see e.g., Kast and Lapied, 2010;Abdellaoui et al, 2011;Agliardi et al, 2016;and Driouchi et al, 2018). Score c affects the discount rate, dividend yield and volatility used in the behavioral valuation.…”
Section: Choquet Ambiguitymentioning
confidence: 99%
See 1 more Smart Citation
“…= 2 − 1 and = √4 (1 − ) Ambiguity score and capacity variable c captures investors' ambiguity, with 0 < c < 0.5 interpreted in the CU literature as ambiguity aversion, pessimism (negative m) and under-(over-) weighting of good (bad) outcome probabilities, 0.5 < c < 1 implying ambiguity-seeking, optimism (positive m) and over-(under-) weighting of good (bad) outcome probabilities (see Chateauneuf et al, 2001;Kast and Lapied, 2010;Agliardi, 2017), and equal weight c = 0.5 (similar weighting) corresponding to the traditional ambiguity-neutral and Bayesian framework (see e.g., Kast and Lapied, 2010;Abdellaoui et al, 2011;Agliardi et al, 2016;and Driouchi et al, 2018). Score c affects the discount rate, dividend yield and volatility used in the behavioral valuation.…”
Section: Choquet Ambiguitymentioning
confidence: 99%
“…The price of the underlying stock S follows the ambiguity-based Choquet (set of) Brownian motion(s), or so-called symmetric Choquet random walk, of the form (see e.g., Kast and Lapied, 2010;Agliardi et al, 2016):…”
Section: Choquet Ambiguitymentioning
confidence: 99%
“…Uncertainty is introduced as a distorted stochastic process over the effects of the stock of carbon pollutant accumulating over time, where the distortion depends on the ambiguity of DM. We follow a capacity approach, where a capacity simultaneously represents the ambiguity experienced by the DM and his/her attitude towards this ambiguity (see also Chateauneuf, Eichberger and Grant, 2007;Agliardi, Agliardi and Spanjers, 2016). We refer to the combined effect of the perceived ambiguity and the DM's ambiguity aversion as his/her ambiguity aversion bias.…”
Section: Ambiguity and The Damage Dynamicsmentioning
confidence: 99%
“…The trade-offs between the future value of in-house operations and that of outsourcing for the client -and their influence on outsourcing pricing and quantity decisions -are also studied in our paper using our ambiguity-based real options approach with trust. This is the first research to examine the partial outsourcing real option problem and the issue of the hidden costs of outsourcing from the perspective of behavioral theory (see e.g., Agliardi et al, 2016;Leiblein et al, 2017 and their behavioral valuation models) while considering ambiguity and trust jointly. Our behavioral real options model of partial outsourcing captures a number of features neglected in the existing literature: vendor's ambiguity, client (dis)trust, and volume-based learning from in-house production and outsourced operations.…”
Section: Introductionmentioning
confidence: 99%