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This book addresses the implications of technology, entrepreneurship, and business ‎‎development gadgets ‎for applications in societies. In this book proceedings, we attempt to address the importance and impact of digitalization and ‎on business development in the context ‎of economic ‎diversity, that is through various propositions of ‎modern technology and entrepreneurial ‎actions, and through the lens of case studies, experiments, empirical ‎assessments, just to name a ‎few research methodological stances and approaches.‎ This book highlights a range of topics in the fields of technology, ‎entrepreneurship, business administration, accounting, and economics that can contribute to business ‎development in developing countries, such as learning machines, artificial intelligence, big data, ‎deep ‎‎learning, game-based learning, management information system, ‎accounting information ‎system, knowledge management, entrepreneurship and social enterprise, corporate social responsibility and sustainability, business policy and strategic management, international management and organizations, organizational behavior and HRM, operations management and logistics research, controversial issues in management and organizations, turnaround, corporate entrepreneurship, and innovation, legal issues, business ethics, and firm governance, and firm financial affairs, non-traditional research, and creative methodologies. This book is ideal for academicians, activists, curriculum ‎developers, researchers, professionals, administrators, and policymakers. The readers of this book could gain an up-to-date know-how on state-of-the-modern technology, entrepreneurship, and business development and achievements in this regard from the research standpoint of view.

Engineering mathematics. --- Engineering—Data processing. --- Business mathematics. --- Mathematical and Computational Engineering Applications. --- Business Mathematics. --- Data Engineering. --- Arithmetic, Commercial --- Business --- Business arithmetic --- Business math --- Commercial arithmetic --- Finance --- Mathematics --- Engineering --- Engineering analysis --- Mathematical analysis --- Management information systems --- Data processing

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This textbook offers an easily understandable introduction to the fundamental concepts of financial mathematics and financial engineering. The author presents and discusses the basic concepts of financial engineering and illustrates how to trade and to analyze financial products with numerous examples. Special attention is given to the valuation of basic financial derivatives. In the final section of the book, the author introduces the Wiener Stock Price Model and the basic principles of Black-Scholes theory. The book’s aim is to introduce readers to the basic techniques of modern financial mathematics in a way that is intuitive and easy to follow, and to provide financial mathematicians with insights into practical requirements when applying financial mathematical techniques in the real world. .

Financial engineering. --- Capital market. --- Social sciences—Mathematics. --- Financial risk management. --- Financial Engineering. --- Capital Markets. --- Mathematics in Business, Economics and Finance. --- Risk Management. --- Risk management --- Capital markets --- Market, Capital --- Finance --- Financial institutions --- Loans --- Money market --- Securities --- Crowding out (Economics) --- Efficient market theory --- Computational finance --- Engineering, Financial --- Business mathematics. --- Arithmetic, Commercial --- Business --- Business arithmetic --- Business math --- Commercial arithmetic --- Mathematics

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This advanced textbook for business statistics teaches statistical analyses and research methods utilizing business case studies and financial data with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry. This second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the first volume for dedicated content on financial statistics, and portfolio analysis.

Statistics. --- Mathematical statistics—Data processing. --- Programming languages (Electronic computers). --- Business mathematics. --- Quantitative research. --- Statistics in Business, Management, Economics, Finance, Insurance. --- Statistics and Computing. --- Programming Language. --- Business Mathematics. --- Data Analysis and Big Data. --- Data analysis (Quantitative research) --- Exploratory data analysis (Quantitative research) --- Quantitative analysis (Research) --- Quantitative methods (Research) --- Research --- Arithmetic, Commercial --- Business --- Business arithmetic --- Business math --- Commercial arithmetic --- Finance --- Mathematics --- Computer languages --- Computer program languages --- Computer programming languages --- Machine language --- Electronic data processing --- Languages, Artificial --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Electronic spreadsheets --- Computer programs. --- Data processing. --- Statistical methods. --- Finances --- Estadística matemàtica --- Processament de dades --- Python (Llenguatge de programació) --- R (Llenguatge de programació) --- Visual Basic (Llenguatge de programació)