Abstract: This paper highlights the empirical results of analyzing the correlation between accounting information and systematic risk. This association is analyzed among financial ratios and systematic risk by considering the financial statement of 39 companies listed on the Tehran Stock Exchange (TSE) for five years (2014-2018). Financial ratios have been categorized into four groups and to describe the special features, as representative of accounting information we selected: Return on Asset (ROA), Debt Ratio (Total Debt to Total Asset), Current Ratio (current assets to current debt), Asset Turnover (Net sales to Total assets), and Total Assets. The hypotheses were tested through simple and multiple linear regression and T-student test. The findings illustrate that there is no significant relationship between accounting information and market risk. This indicates that in the selected sample, historical accounting information does not fully reflect the price of stocks.
Abstract: Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper.
Abstract: Innovations not only contribute to competitiveness of
the company but have also positive effects on revenues. On average,
product innovations account to 14 percent of companies’ sales.
Innovation management has substantially changed during the last
decade, because of growing reliance on external partners. As a
consequence, a new task for purchasing arises, as firms need to
understand which suppliers actually do have high potential
contributing to the innovativeness of the firm and which do not.
Proper organization of the purchasing function is important since
for the majority of manufacturing companies deal with substantial
material costs which pass through the purchasing function. In the past
the purchasing function was largely seen as a transaction-oriented,
clerical function but today purchasing is the intermediate with supply
chain partners contributing to innovations, be it product or process
innovations. Therefore, purchasing function has to be organized
differently to enable firm innovation potential.
However, innovations are inherently risky. There are behavioral
risk (that some partner will take advantage of the other party),
technological risk in terms of complexity of products and processes
of manufacturing and incoming materials and finally market risks,
which in fact judge the value of the innovation. These risks are
investigated in this work. Specifically, technological risks which deal
with complexity of the products, and processes will be investigated
more thoroughly. Buying components or such high edge technologies
necessities careful investigation of technical features and therefore is
usually conducted by a team of experts. Therefore it is hypothesized
that higher the technological risk, higher will be the centralization of
the purchasing function as an interface with other supply chain
members.
Main contribution of this research lies is in the fact that analysis
was performed on a large data set of 1493 companies, from 25
countries collected in the GMRG 4 survey. Most analyses of
purchasing function are done by case study analysis of innovative
firms. Therefore this study contributes with empirical evaluations that
can be generalized.
Abstract: There are three main ways of categorizing capital in banking operations: accounting, regulatory and economic capital. However, the 2008-2009 global crisis has shown that none of these categories adequately reflects the real risks of bank operations, especially in light of the failures Bear Stearns, Lehman Brothers or Northern Rock. This paper deals with the economic capital allocation of global banks. In theory, economic capital should reflect the real risks of a bank and should be publicly available. Yet, as discovered during the global financial crisis, even when economic capital information was publicly disclosed, the underlying assumptions rendered the information useless. Specifically, some global banks that reported relatively high levels of economic capital before the crisis went bankrupt or had to be bailed-out by their government. And, only 15 out of 50 global banks reported their economic capital during the 2007-2010 period. In this paper, we analyze the changes in reported bank economic capital disclosure during this period. We conclude that relative shares of credit and business risks increased in 2010 compared to 2007, while both operational and market risks decreased their shares on the total economic capital of top-rated global banks. Generally speaking, higher levels of disclosure and transparency of bank operations are required to obtain more confidence from stakeholders. Moreover, additional risks such as liquidity risks should be included in these disclosures.
Abstract: This study employs a bivariate asymmetric GARCH
model to reveal the hidden dynamics price changes and volatility
among the emerging markets of Thailand and Malaysian after the
Asian financial crisis from January 2001 to December 2008. Our
results indicated that the equity markets are sharing the common
information (shock) that transmitted among each others. These
empirical findings are used to demonstrate the importance of shock
and volatility dynamic transmissions in the cross-market hedging and
market risk.
Abstract: This paper examines the relationship between financial
risks and profitability of the conventional and Islamic banks in
Malaysia for the period between 1996 and 2005. The measures of
profitability that have been used in the study are the return on equity
(ROE) and return on assets (ROA) while the financial risks are credit
risk, interest rate risk and liquidity risks. This study employs panel
data regression analysis of Generalised Least Squares of fixed effects
and random effects models. It was found that credit risk has a
significant impact on ROA and ROE for the conventional as well as
the Islamic banks. The relationship between interest rate risk and ROE
were found to be weakly significant for the conventional banks and
insignificant for the Islamic banks. The effect of interest rate risk on
ROA is significant for the conventional banks. Liquidity risk was
found to have an insignificant impact on both profitability measures.