Risk and Financial Management https://j.ideasspread.org/index.php/rfm <p><em>Risk and Financial Management</em>&nbsp;is an international, double-blind peer-reviewed, open-access journal published by the&nbsp;IDEAS SPREAD INC.&nbsp;<br> The<em> Risk and Financial Management&nbsp;</em>adheres to rigorous peer-review as well as editorial processes, and publishes leading research on financial management and risk management. The goal of&nbsp;<em>RFM</em>&nbsp;is to enable rapid dissemination of high impact research to the scientific community.</p> IDEAS SPREAD en-US Risk and Financial Management <p>Copyright for this article is retained by the author(s), with first publication rights granted to the journal.<br>This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).</p> Determinants of the Performance of Insurance Companies in Tunisia https://j.ideasspread.org/index.php/rfm/article/view/348 <p>This article aims to study the determinants of performance of Tunisian insurance companies for the period from 2002 to 2018 using the panel data method. The variables used are microeconomic and macroeconomic. Our results conclude that the determinants of the performance of Tunisian insurance companies are the capital structure, solvency, risk capital management, premium growth, volume of capital, age of the firm and financial investments.</p> Abdelkader Derbali Lamia Jamel ##submission.copyrightStatement## http://creativecommons.org/licenses/by/4.0 2019-10-21 2019-10-21 1 1 p1 p1 10.30560/rfm.v1n1p1 Advanced Stochastic Optimization Algorithm for Deep Learning Artificial Neural Networks in Banking and Finance Industries https://j.ideasspread.org/index.php/rfm/article/view/387 <p>One of the objectives of this paper is to incorporate fat-tail effects into, for instance, Sigmoid in order to introduce Transparency and Stability into the existing stochastic Activation Functions. Secondly, according to the available literature reviewed, the existing set of Activation Functions were introduced into the Deep learning Artificial Neural Network through the “Window” not properly through the “Legitimate Door” since they are “Trial and Error “and “Arbitrary Assumptions”, thus, the Author proposed a “Scientific Facts”, “Definite Rules: Jameel’s Stochastic ANNAF Criterion”, and a “Lemma” to substitute not necessarily replace the existing set of stochastic Activation Functions, for instance, the Sigmoid among others. This research is expected to open the “Black-Box” of Deep Learning Artificial Neural networks. The author proposed a new set of advanced optimized fat-tailed Stochastic Activation Functions EMANATED from the AI-ML-Purified Stocks Data&nbsp; namely; the Log – Logistic (3P) Probability Distribution (1st), Cauchy Probability Distribution (2nd), Pearson 5 (3P) Probability Distribution (3rd), Burr (4P) Probability Distribution (4th), Fatigue Life (3P) Probability Distribution (5th), Inv. Gaussian (3P) Probability Distribution (6th), Dagum (4P) Probability Distribution (7th), and Lognormal (3P) Probability Distribution (8th) for the successful conduct of both Forward and Backward Propagations of Deep Learning Artificial Neural Network. However, this paper did not check the Monotone Differentiability of the proposed distributions. Appendix A, B, and C presented and tested the performances of the stressed Sigmoid and the Optimized Activation Functions using Stocks Data (2014-1991) of Microsoft Corporation (MSFT), Exxon Mobil (XOM), Chevron Corporation (CVX), Honda Motor Corporation (HMC), General Electric (GE), and U.S. Fundamental Macroeconomic Parameters, the results were found fascinating. Thus, guarantee, the first three distributions are excellent Activation Functions to successfully conduct any Stock Deep Learning Artificial Neural Network. Distributions Number 4 to 8 are also good Advanced Optimized Activation Functions. Generally, this research revealed that the Advanced Optimized Activation Functions satisfied Jameel’s ANNAF Stochastic Criterion depends on the Referenced Purified AI Data Set, Time Change and Area of Application which is against the existing “Trial and Error “and “Arbitrary Assumptions” of Sigmoid, Tanh, Softmax, ReLu, and Leaky ReLu.</p> Jamilu Auwalu Adamu ##submission.copyrightStatement## http://creativecommons.org/licenses/by/4.0 2019-11-26 2019-11-26 1 1 p8 p8 10.30560/rfm.v1n1p8 A Case Study Approach for Managing Risks & Challenges When Expanding to Emerging Markets https://j.ideasspread.org/index.php/rfm/article/view/420 <p>Globalization created new opportunities and many companies decide to expand to take advantage of these opportunities to improve their competitiveness. The present study, using a case study methodology, examines the expansion of three large companies (IKEA, Coca Cola and Kellogg's) in emerging markets. Through a critical literature review and review of corporate reports, the study analyzes companies’ adopted strategies and practices, influential factors and risks when expanding abroad, providing the rationale behind their strategic choices. The study findings, applying theory into practice, indicate the factors and practices that are important to be considered by companies operating in a foreign environment in order to address business risks, and concludes that in order to be successful they have to incorporate into their strategy effective risk management policies to mitigate risks and turn challenges into opportunities. The study bridges risk management and strategy development.</p> Evangelia Fragouli Zoi Nikolaidou ##submission.copyrightStatement## http://creativecommons.org/licenses/by/4.0 2019-12-27 2019-12-27 1 1 p44 p44 10.30560/rfm.v1n1p44 Safety & Environmental Risk Management: Borrowing from the Past to Enhance Knowledge for The Future https://j.ideasspread.org/index.php/rfm/article/view/429 <p>The oil and gas industry is facing more and more challenges in the latest decades. Indeed, as oil and gas became more difficult to be found, new areas are targeted, as deep water offshore or more hostile environments like Alaska. These involve high technology industry and imply deep uncertainty. Thus, the application of classical approaches of risk management is limited, as shown by major safety and environmental oil disasters like the Deep Water Horizon Accident. This paper analyses the later as a case study, to discuss the complexity of safety and environmental risk management in deep-water drilling. Given this complexity, the paper will also analyse how feedback from previous accidents can improve knowledge, and change the perception and thus the way safety and environmental risks are managed in deep-water drilling. The findings of the paper contribute to development of risk management policy and risk decision making.</p> Evangelia Fragouli Faye Nzioka Selma Manar ##submission.copyrightStatement## http://creativecommons.org/licenses/by/4.0 2019-12-30 2019-12-30 1 1 p64 p64 10.30560/rfm.v1n1p64