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    <title>EconStor Collection: Journal of Applied Finance &amp; Banking</title>
    <link>http://hdl.handle.net/10419/49019</link>
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      <title>Does government linked companies (GLCs) perform better than non-GLCs? Evidence from Malaysian listed companies</title>
      <link>http://hdl.handle.net/10419/49040</link>
      <description>Title: Does government linked companies (GLCs) perform better than non-GLCs? Evidence from Malaysian listed companies
&lt;br/&gt;
&lt;br/&gt;Authors: Razak, Nazrul Hisyam Ab; Ahmad, Rubi; Joher, Huson Aliahmed
&lt;br/&gt;
&lt;br/&gt;Abstract: The purpose of this paper is to examine the impact of an alternative ownership/control structure of corporate governance on firm performance. Specifically, we investigated the governance system of government linked companied (GLCs) in Malaysia. In this paper, we examine governance mechanism and firm performance of Malaysian GLCs and non-GLCs over a 11 year period from 1995 to 2005. We only select a sample of companies which are listed in Main Board. We chose a sample of 210 firms. We used Tobin's Q which is an indicator of market performance is used as a proxy for company's performances; meanwhile ROA is used to determine accounting performance. . This paper is to determines whether after controlling firm specific characteristics such as corporate governance, agency cost, growth, risk and profitability, GLCs perform better than non-GLCs. Findings highlight that non-GLCs performance is better GLCs in term of corporate governance, and other firm specific characteristics. The relationship between ownership structure and firm performance has been issue of interest among academics, investors and policy makers as one of key issues in understanding the effectiveness of alternative governance systems where government ownership serves as a control mechanism.</description>
      <pubDate>Fri, 29 Oct 2010 22:58:59 GMT</pubDate>
    </item>
    <item>
      <title>The status quo bias of bond market analysts</title>
      <link>http://hdl.handle.net/10419/49039</link>
      <description>Title: The status quo bias of bond market analysts
&lt;br/&gt;
&lt;br/&gt;Authors: Gubaydullina, Zulia; Hein, Oliver; Spiwoks, Markus
&lt;br/&gt;
&lt;br/&gt;Abstract: Status quo bias is a systematic cognitive error which makes it difficult for individuals to make decisions independently of the currently dominant situation. This study pursues the question of whether bond market analysts are affected by status quo bias. We evaluated interest rate forecast series from twelve industrial nations. This revealed that, on average, forecasts were much too close to the status quo - the current interest rate at the time when the forecast was made. With the aid of various analytical procedures it can be shown that the actual extent of interest rate changes is systematically and significantly underestimated.</description>
      <pubDate>Fri, 29 Oct 2010 22:58:59 GMT</pubDate>
    </item>
    <item>
      <title>Determining the probability of default of agricultural loans in a French bank</title>
      <link>http://hdl.handle.net/10419/49038</link>
      <description>Title: Determining the probability of default of agricultural loans in a French bank
&lt;br/&gt;
&lt;br/&gt;Authors: Jouault, Amelie; Featherstone, Allen M.
&lt;br/&gt;
&lt;br/&gt;Abstract: Recently, financial institutions have developed improved internal risk rating systems and emphasized the probability of default and loss given default. The default characteristics are studied for 756 loans from a French bank: CIC- Banque SNVB. A binomial logit regression is used to estimate several models of the probability of default of agribusiness loans based on information available at loan origination. The results show that leverage, profitability and liquidity at loan origination are statistically significant indicators of the probability of default. As leverage increases, profitability decreases, or liquidity decreases, the probability of default increases. As the length of loan increases, the probability of default also increases. Finally, it is more accurate to develop a model for each type of collateral (activity). By developing more quantitative credit scoring models, banks may benefit from lower capital requirements while borrowers may see better rates where the risk of loans is appropriately priced.</description>
      <pubDate>Fri, 29 Oct 2010 22:58:59 GMT</pubDate>
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    <item>
      <title>A recommended financial model for the selection of safest portfolio by using simulation and optimization techniques</title>
      <link>http://hdl.handle.net/10419/49037</link>
      <description>Title: A recommended financial model for the selection of safest portfolio by using simulation and optimization techniques
&lt;br/&gt;
&lt;br/&gt;Authors: Arekar, Kirti; Kumar, Sanjeevani
&lt;br/&gt;
&lt;br/&gt;Abstract: Investment of portfolio known that there is an important level of uncertainty about the future worth of a portfolio. The concept of value at risk (VAR) has been used to help describe a portfolio's uncertainty. The current trend of investment in India is to invest in stock market which categorized as a high-risk level of investment. There are various methods to calculate the variance. Monte Carlo simulation method is one of the methods to calculate the VAR of the portfolio. Monte-Carlo simulation method is considered to be the optimization technique in which objective is to minimize/maximize the risk/profit before making any type of investment with portfolio. The Monte Carlo simulation method calculation for VAR of a portfolio can briefly be summarized in two steps. In the first step, a stochastic process is specified for financial variables. In the second step, financial variable of interest are simulated to get fictitious price path. The aim of the research is to develop the financial model for the safest portfolio selection based on VAR and Markowitz classical models. In the financial model, at first we measures the value at risk of Indian equity markets over short horizon of time (less than one year) by creating multiple scenarios by using Monte Carlo simulation. With the help of financial model, we ranks measured values at risk by using statistical tools. Finally, financial model will suggest an optimal portfolio over the same horizon of time using a developed optimization model. A real case study was selected and introduced to find the safest allocation of a portfolio; eight of the most active share volume was selected to perform a analysis. The results obtained by financial model indicates that the reliability description of the portfolio's uncertainty and then gave highly reliable recommendation on portfolio optimization.</description>
      <pubDate>Fri, 29 Oct 2010 22:58:59 GMT</pubDate>
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