nep-cfn New Economics Papers
on Corporate Finance
Issue of 2012‒02‒20
four papers chosen by
Zelia Serrasqueiro
University of the Beira Interior

  1. The Relationship Between Uncertainty and the Market Reaction to Information: How is it Influenced by Market and Stock-Specific Characteristics? By Ron Bird; Krishna Reddy; Danny Yeung
  2. Market Uncertainty and Sentiment, and the Post-Earnings Announcement Drift By Ron Bird; Daniel Choi; Danny Yeung
  3. Why is Price Discovery in Credit Default Swap Markets News-Specific? By Marsch, I.; Wagner, W.B.
  4. Pitfalls in modeling loss given default of bank loans By Hibbeln, Martin; Gürtler, Marc

  1. By: Ron Bird (School of Finance and Economics, University of Technology, Sydney); Krishna Reddy (Waikato Management School, University of Waikato); Danny Yeung (School of Finance and Economics, University of Technology, Sydney)
    Abstract: Numerous empirical studies dating back to Ball and Brown (1968) have investigated how markets react to the receipt of new information. However, it is only recently that authors have focussed on differentiating between, and learning from, how investors react to good and bad news. In this paper we find that investors swing between being optimistic and being pessimistic in their interpretation of the new information driven by not only the prevailing market uncertainty and sentiment but also by a significant number of firm-specific characteristics. Pessimism prevails when uncertainty is high, sentiment is weak and the information is being disseminated by companies that are lowly-valued, have high risk, are thinly traded and/or are small cap stocks. However, investors swing to being optimistic when one reverses some or all of these factors. The conclusion that we draw is that risk, uncertainty and the attitude of investors combine to determine how the markets react to new information and this flows through to asset valuations.
    JEL: G11 G12 G14 D81
    Date: 2011–09–01
  2. By: Ron Bird (School of Finance and Economics, University of Technology, Sydney); Daniel Choi (Waikato Management School, University of Waikato); Danny Yeung (School of Finance and Economics, University of Technology, Sydney)
    Abstract: The post-earnings announcement drift (PEAD) was first identified over 40 years ago and seems to be as much alive today as it ever was. There have been numerous attempts to explain its continued existence. In this paper we provide evidence to support a new explanation: the PEAD is very much a reflection of the level of market uncertainty and sentiment that prevails during the post-announcement period. The finding that uncertainty plays a role in explaining how investors respond to information suggests that it should be included as a factor in our pricing models while the fact that market sentiment also has a role is another instance of the importance of human behaviour in establishing prices.
    JEL: G12 G14 D81
    Date: 2011–09–01
  3. By: Marsch, I.; Wagner, W.B. (Tilburg University, Center for Economic Research)
    Abstract: Abstract: We analyse daily lead-lag patterns in US equity and credit default swap (CDS) returns. We first document that equity returns robustly lead CDS returns. However, we find that the CDSlag is due to common (and not firm-specific) news and arises predominantly in response to positive (instead of negative) equity market news. We provide an explanation for this newsspecific price discovery based on dealers in the CDS market exploiting their informational advantage vis-à-vis institutional investors with hedging demands. In support of this explanation we find that the CDS-lag and its newsspecificity are related to various firm-level proxies for hedging demand in the cross-section as well measures for economy-wide informational asymmetries over time.
    Keywords: price discovery;CDS;hedging demand;informational asymmetries.
    JEL: G12 G15 G21
    Date: 2012
  4. By: Hibbeln, Martin; Gürtler, Marc
    Abstract: The parameter loss given default (LGD) of loans plays a crucial role for risk-based decision making of banks including risk-adjusted pricing. Depending on the quality of the estimation of LGDs, banks can gain significant competitive advantage. For bank loans, the estimation is usually based on discounted recovery cash flows, leading to workout LGDs. In this paper, we reveal several problems that may occur when modeling workout LGDs, leading to LGD estimates which are biased or have low explanatory power. Based on a data set of 71,463 defaulted bank loans, we analyze these issues and derive recommendations for action in order to avoid these problems. Due to the restricted observation period of recovery cash flows the problem of length-biased sampling occurs, where long workout processes are underrepresented in the sample, leading to an underestimation of LGDs. Write-offs and recoveries are often driven by different influencing factors, which is ignored by the empirical literature on LGD modeling. We propose a two-step approach for modeling LGDs of non-defaulted loans which accounts for these differences leading to an improved explanatory power. For LGDs of defaulted loans, the type of default and the length of the default period have high explanatory power, but estimates relying on these variables can lead to a significant underestimation of LGDs. We propose a model for defaulted loans which makes use of these influence factors and leads to consistent LGD estimates. --
    Keywords: Credit risk,Bank loans,Loss given default,Forecasting
    JEL: G21 G28
    Date: 2011

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