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SAS for Finance
SAS is the ground-breaking tool for advanced, predictive, and statistical analytics. Right from refining your data using power of SAS analytics, you will be able to exploit the capabilities of high-powered package to create accurate financial models. You can easily assess the pros and cons of models to suit unique business needs.
eBook
Financial modeling : an introductory guide to Excel and VBA applications in finance
This book provides a comprehensive introduction to modern financial modeling using Excel, VBA, standards of financial modeling and model review. It offers guidance on essential modeling concepts around the four core financial activities in the modern financial industry today: financial management; corporate finance; portfolio management and.
eBook
Hands-On Financial Modeling with Microsoft Excel 2019
This book aims to provide a gateway to financial modeling through easy to follow examples in Excel 2019. It will explore the process of modeling starting from a thorough understanding of the project to gathering historical information and arriving at assumptions. Good modeling practices are emphasized and demonstrated throughout the book.
eBook
Incentive mechanism to prevent moral hazard in online supply chain finance
With e-commerce developing rapidly, banks have begun to cooperate with online platform operators to finance small and medium-sized enterprises (SMEs). However, this process engenders its own unique financial risks. This study highlights and investigates the risks in a four-party supply chain that include a third-party logistics provider, a bank, a B2B platform operator, and SMEs. In an asymmetric information setting, the collusion mechanisms in this four-party online supply chain are also explored. Subsequently, a two-part incentive contract is designed that can reduce the moral hazard faced by the banks while addressing the trade-off between the payments to the platform operator for better credit rating information and the payments to the third-party logistics provider for supervising collateral storage. For further confirmation, a numerical analysis is presented. The results indicate that based on a suitable capital coefficient, the two-part incentive contract may prevent moral hazard in online supply chains. Furthermore, when the line of credit is high, the bank must increase the incentives for the B2B platform operator to avoid default risk and decrease the incentives for 3PL.
Journal Article
An analysis of enterprise resource planning systems and key determinants using the Delphi method and an analytic hierarchy process
Enterprise resource planning (ERP) systems are software module packages which can be customized up to a certain limit to suit the specific needs of each organization. Many ERP projects have not been effective at achieving all of the intended results due to high cost and high failure risks in ERP implementation. This study integrates the prior theories and knowledge gained from several textile industry practitioners for ERP projects. A two-stage method involving the Delphi and analytic hierarchy process decision support methodology was conducted. Based on the case of the textile industry in Taiwan, the findings illustrate the top 10 key factors: a clear project plan by defined ERP capacity requirement; a limited scope and focused flowchart; goal congruence between ERP project implementation and corporate strategy; top management support and commitment; the extent of standard operating procedures and institutional processes; a user-friendly interface; systems integrated with aggressive schedules and timelines; provision of technical assistance for rapid, effective transfer of best practice interventions; good interdepartmental communication and coordination on a focused issue solution; and enablement of business process reengineering and solid management for project team building. The findings of this research will be beneficial to those apparel companies that adopted the ERP.
Journal Article
Do SMEs Consider Open Data as a Vital Intellectual Asset? a Systematic Literature Review
This systematic literature review evaluates the impact of global open data policies on small and medium-sized enterprises (SMEs) in different economic levels. Six case studies were analyzed to provide insights into the utilization of open data in the private sector. The review followed the PRISMA 2020 checklist and selected studies based on specific criteria, including high quality, strong methodology, and published by a valid publisher. The findings suggest that open data promotion can bring significant benefits to SMEs in terms of innovation, efficiency, and competitiveness. However, SMEs also face significant challenges in accessing and utilizing open data due to technical, legal, and cultural barriers. Therefore, practical aspects should be taken into account when implementing open data initiatives for SMEs. A framework is needed to measure the impact of open data policies on SMEs, and governments and policymakers should support open data initiatives in their countries, especially for SMEs whose valuable data can contribute to society’s development. Using the GRADE approach, the certainty of evidence was rated as moderate according to limitations in study design and inconsistency across studies. Overall, this systematic literature review highlights the potential for open data policies to drive growth and development in small businesses while acknowledging the challenges that must be addressed for these policies to be effective. The review provides a guide for SMEs on measures to take prior to releasing their data and whether to release their data from an economic aspect. Moreover, this paper emphasizes the importance of practical aspects when implementing open data initiatives for SMEs and proposes a framework for measuring their impact. Finally, it highlights the need for government policies and support to facilitate SME adoption of open data initiatives.
Journal Article
Private climate change reporting: an emerging discourse of risk and opportunity?
Purpose – This paper aims to explore the nature of the emerging discourse of private climate change reporting, which takes place in one‐on‐one meetings between institutional investors and their investee companies.Design/methodology/approach – Semi‐structured interviews were conducted with representatives from 20 UK investment institutions to derive data which was then coded and analysed, in order to derive a picture of the emerging discourse of private climate change reporting, using an interpretive methodological approach, in addition to explorative analysis using NVivo software.Findings – The authors find that private climate change reporting is dominated by a discourse of risk and risk management. This emerging risk discourse derives from institutional investors' belief that climate change represents a material risk, that it is the most salient sustainability issue, and that their clients require them to manage climate change‐related risk within their portfolio investment. It is found that institutional investors are using the private reporting process to compensate for the acknowledged inadequacies of public climate change reporting. Contrary to evidence indicating corporate capture of public sustainability reporting, these findings suggest that the emerging private climate change reporting discourse is being captured by the institutional investment community. There is also evidence of an emerging discourse of opportunity in private climate change reporting as the institutional investors are increasingly aware of a range of ways in which climate change presents material opportunities for their investee companies to exploit. Lastly, the authors find an absence of any ethical discourse, such that private climate change reporting reinforces rather than challenges the “business case” status quo.Originality/value – Although there is a wealth of sustainability reporting research, there is no academic research on private climate change reporting. This paper attempts to fill this gap by providing rich interview evidence regarding the nature of the emerging private climate change reporting discourse.
Journal Article
Data production and the coevolving AI trajectories: an attempted evolutionary model
This paper contributes to the understanding of the relationship between the nature of data and the artificial intelligence (AI) technological trajectories, on the one hand, and on the dynamic processes triggered by demand during the evolution of an industry, on the other hand. We develop an agent-based model in which firms are data producers that compete on the markets for data and AI. The model is enriched by a public sector that fuels the purchase of data and trains the scientists that will populate firms as workforce. Through several simulation experiments, we analyze the determinants of each market structure, the corresponding relationships with innovation attainments, the pattern followed by labor and data productivity, the quality of data traded in the economy, and in which forms demand does affect innovation and the dynamics of industries. We question the established view in the literature of industrial organization according to which technological imperatives are enough to experience divergent industrial dynamics on both the markets for data and AI blueprints. Although technical change behooves if any industry pattern is to emerge, the actual unfolding is not the outcome of a specific technological trajectory, but the result of the interplay between technology-related factors and the availability of data-complementary inputs such as labor and AI capital, the market size, preferences, and public policies.
Journal Article