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- Fachbereich Wirtschaftswissenschaften (1100) (remove)
This paper investigates the extent to which corporate governance affects the cost of debt and equity capital of German exchange-listed companies. I examine corporate governance along three dimensions: financial information quality, ownership structure and board structure. The results suggest that firms with high levels of financial transparency and bonus compensations face lower cost of equity. In addition, block ownership is negatively related to firms' cost of equity when the blockholders are other firms, managers or founding-family members. Consistent with the conjecture that agency costs increase with firm size, I find significant cost of debt effects only in the largest German companies. Here, the creditors demand lower cost of debt from firms with block ownerships held by corporations or banks. My findings demonstrate that a uniform set of governance attributes is unlikely to satisfy suppliers of debt and equity capital equally.
Names of individuals
(2017)
Next Generation Access Networks: Why is there a higher risk of investment and how to deal with it?
(2009)
Offene und geschlossene WLAN: Rechtliche Hürden bei Betrieb eines öffentlichen Internetzugangs
(2015)
Optimal Adjustment Policies
(1990)
Optimale Maschinenjustierung
(1991)
Organisation
(2010)
Organisation
(2014)
Organisationslehre
(2001)
Outlier Robust Estimation of an Euler Equation Investment Model with German Firm Level Panel Data
(2002)
Bitcoin is a cryptocurrency and is considered a high-risk asset
class whose price changes are difficult to predict. Current research focusses
on daily price movements with a limited number of predictors. The paper at
hand aims at identifying measurable indicators for Bitcoin price movement s
and the development of a suitable forecasting model for hourly changes. The
paper provides three research contributions. First, a set of significant
indicators for predicting the Bitcoin price is identified. Second, the results of
a trained Long Short-term Memory (LSTM) neural network that predicts
price changes on an hourly basis is presented and compared with other
algorithms. Third, the results foster discussions of the applicability of neural
nets for stock price predictions. In total, 47 input features for a period of
over 10 months could be retrieved to train a neural net that predicts the
Bitcoin price movements with an error rate of 3.52 %.