Fauzias Mat Nor

Factors Affecting Speed of Adjustment to Target Leverage: Malaysia Evidence

This study focuses on the dynamic aspect of capital structure which is a relatively new area in the finance literature. By employing panel data, 790 of non-financial listed firms in Malaysia are examined for the period 2000–2009. Conducted using the dynamic Partial Adjustment Model and estimated based on the Generalized Method of Moment, this study investigates the existence of target capital structure, speed of adjustment and factors affecting speed of adjustment. There are evidences of target capital structure and firms do adjust to be at their target from time to time with a considerably rapid speed of adjustment, in line with dynamic trade-off theory. Firms in Malaysia are found to be under-adjusted, being below the required adjustment to be at the target within a year. Study on factors affecting speed of adjustment is a new contribution to the literature as no work has been done regarding this issue in Malaysia. This study also analyzes firm-specific factors affecting speed of adjustment. There is strong evidence on the negative relationship between the speed of adjustment and distance from target leverage suggesting fast adjustment if not being far from target. Firm size and profitability significantly influence speed of adjustment for firms in Malaysia.

Mean-drawdown risk behavior: drawdown risk and capital asset pricing

We develop an alternative approach based on mean-drawdown risk behavior versus the mean-variance behavior. We develop two risk measures as the maximum draw down risk and average drawdown risk to estimate two new betas and then propose two CAPM -like models. The data includes a comprehensive universe of more than 11,000 US equity-based mutual funds from first month of 2000 to third month of 2011.The evidence clearly shows superiority of the maximum and average drawdown betas and their pricing models, the maximum drawdown CAPM and the average drawdown CAPM , over the traditional beta and CAPM , respectively.

Conceptual graph formalism for financial text representation

We present an approach to automatically transform a financial text into conceptual graph formalism. The approach exploits the constituent structure of sentences and general English grammar rules to perform the transformation. We suggest face validation and traces as the evaluation method to be performed on the resulting formalism to validate its accuracy. We also discuss the potential manipulation and application of the constructed conceptual graph database.

Automatic extraction of performance indicators from financial statements

We present a method to automatically analyze financial statements for the purpose of recognizing and extracting relevant financial indicators together with its values and its related narratives. We employ a rule-based approach to solve the problem of syntactical and morphological variations contained within the financial text. The information was extracted using a multi-pass scan to process the text in a series of pre-programmed functions. Experiments were carried out to demonstrate the feasibility of the system by deriving the precision and recall scores. The extracted results can be used to construct and augment knowledge bases for a more complex text mining systems.

Dissimilarity algorithm on conceptual graphs to mine text outliers

The graphical text representation method such as Conceptual Graphs (CGs) attempts to capture the structure and semantics of documents. As such, they are the preferred text representation approach for a wide range of problems namely in natural language processing, information retrieval and text mining. In a number of these applications, it is necessary to measure the dissimilarity (or similarity) between knowledge represented in the CGs. In this paper, we would like to present a dissimilarity algorithm to detect outliers from a collection of text represented with Conceptual Graph Interchange Format (CGIF). In order to avoid the NP-complete problem of graph matching algorithm, we introduce the use of a standard CG in the dissimilarity computation. We evaluate our method in the context of analyzing real world financial statements for identifying outlying performance indicators. For evaluation purposes, we compare the proposed dissimilarity function with a dice-coefficient similarity function used in a related previous work. Experimental results indicate that our method outperforms the existing method and correlates better to human judgements. In Comparison to other text outlier detection method, this approach managed to capture the semantics of documents through the use of CGs and is convenient to detect outliers through a simple dissimilarity function. Furthermore, our proposed algorithm retains a linear complexity with the increasing number of CGs.

Conceptual Graph Interchange Format for Mining Financial Statements

This paper addresses the automatic transformation of financial statements into conceptual graph interchange format (CGIF). The method mainly involves extracting relevant financial performance indicators, parsing it to obtain syntactic sentence structure and to generate the CGIF for the extracted text. The required components for the transformation are detailed out with an illustrative example. The paper also discusses the potential manipulation of the resulting CGIF for knowledge discovery and more precisely for deviation detection.

Deviation detection in text using conceptual graph interchange format and error tolerance dissimilarity function

The rapid increase in the amount of textual data has brought forward a growing research interest towards mining text to detect deviations. Specialized methods for specific domains have emerged to satisfy various needs in discovering rare patterns in text. This paper focuses on a graph-based approach for text representation and presents a novel error tolerance dissimilarity algorithm for deviation detection. We resolve two non-trivial problems, i.e. semantic representation of text and the complexity of graph matching. We employ conceptual graphs interchange format CGIF --a knowledge representation formalism to capture the structure and semantics of sentences. We propose a novel error tolerance dissimilarity algorithm to detect deviations in the CGIFs. We evaluate our method in the context of analyzing real world financial statements for identifying deviating performance indicators. We show that our method performs better when compared with two related text based graph similarity measuring methods. Our proposed method has managed to identify deviating sentences and it strongly correlates with expert judgments. Furthermore, it offers error tolerance matching of CGIFs and retains a linear complexity with the increasing number of CGIFs.

Board Structure, Capital Structure and Dividend Per Share: Is there Interaction Effect?

This study examines the direct effects of firm�s characteristics such as board structure and capital structure on divided per share as a proxy of firm�s performance and interaction between board structure and capital structure on dividend. The fixed effect regression uses a sample of 361 non-financial Malaysian listed firms over the period of 2002 to 2007. The decision made by the board of directors with duality role of Chairman cum Chief Executive Officer and larger board size to pay dividend demonstrates that duality role of chairman cum chief executive officer have negative effect on dividend payment but not outside independent director(s). The interaction between board structure namely duality, independent directors, board size and capital structure namely debt ratio reveals that duality weakens the negative effect of debt ratio on dividend while independent directors strengthens the negative effect of debt ratio on dividend payment. Overall, the results of this study may be summarized to suggest that distributable income to shareholders increases through a balanced financing decision between capital structure choice and dividend payment made by the board of directors that possessed duality role.

Asset divestitures and corporate operational returns: an agency theory perspective on Malaysian public-listed companies

Divestitures of property, plant and equipment (PPE) assets are a common form of corporate restructuring. However, divesting companies do not necessarily attain improved post-divestiture shareholder wealth. Studies show company characteristics and use of divestiture proceeds may influence divestiture outcomes. This paper attempts to determine these divesting company characteristics and use of proceeds associated with improved shareholder wealth based on the Agency Theory. A sample of Malaysian public-listed companies that divested assets within 2002--2005 is used. Logistic regression segregates these companies based on their industry-adjusted operational returns. Companies that improve post-divestiture operational performance require urgent pay out motives to divest. Companies with deteriorated post-divestiture performance divest without urgent pay out motive and tend to retain proceeds. This suggests agency problem of managerial discretion in asset divestitures. In asset selection, divestitures of larger and more inefficient assets were found associated with improved company performance. Malaysian companies prefer divesting assets related to their core businesses indicating a diversified company structure is more beneficial than a focused structure in developing countries.

A text mining system for deviation detection in financial documents

Attempts to mine text documents to discover deviations or anomalies have increased in recent years due to the ele- vated amount of textual data in todays data repositories. Text mining assists in uncovering hidden information contents across multiple documents. Although various text mining tools are available, their focus is mainly to assist in data summarization or document classification. These tasks proved to be helpful, however; they do not provide semantic analysis and rigorous textual comparison to detect abnormal sentences that exist in the documents. In this paper, we describe a text mining system that is able to detect sentence deviations from a collection of financial documents. The system implements a dissimilarity function to compare sentences represented as graphs. Our evaluation on the proposed system revolves around experiments using finan- cial statements of a bank. The findings provide valid evidence that the proposed system is able to identify deviating sentences occurring in the documents. The detected deviations can be beneficial for the authorities in order to improve their business decisions.