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| financial analyst to data analyst: Financial Data Analytics Sinem Derindere Köseoğlu, 2022-04-25 This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics. |
| financial analyst to data analyst: Financial Analytics with R Mark J. Bennett, Dirk L. Hugen, 2016-10-06 Financial Analytics with R sharpens readers' skills in time-series, forecasting, portfolio selection, covariance clustering, prediction, and derivative securities. |
| financial analyst to data analyst: Python for Finance Yves J. Hilpisch, 2018-12-05 The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks. |
| financial analyst to data analyst: Principles of Financial Engineering Robert Kosowski, Salih N. Neftci, 2014-11-26 Principles of Financial Engineering, Third Edition, is a highly acclaimed text on the fast-paced and complex subject of financial engineering. This updated edition describes the engineering elements of financial engineering instead of the mathematics underlying it. It shows how to use financial tools to accomplish a goal rather than describing the tools themselves. It lays emphasis on the engineering aspects of derivatives (how to create them) rather than their pricing (how they act) in relation to other instruments, the financial markets, and financial market practices. This volume explains ways to create financial tools and how the tools work together to achieve specific goals. Applications are illustrated using real-world examples. It presents three new chapters on financial engineering in topics ranging from commodity markets to financial engineering applications in hedge fund strategies, correlation swaps, structural models of default, capital structure arbitrage, contingent convertibles, and how to incorporate counterparty risk into derivatives pricing. Poised midway between intuition, actual events, and financial mathematics, this book can be used to solve problems in risk management, taxation, regulation, and above all, pricing. A solutions manual enhances the text by presenting additional cases and solutions to exercises. This latest edition of Principles of Financial Engineering is ideal for financial engineers, quantitative analysts in banks and investment houses, and other financial industry professionals. It is also highly recommended to graduate students in financial engineering and financial mathematics programs. - The Third Edition presents three new chapters on financial engineering in commodity markets, financial engineering applications in hedge fund strategies, correlation swaps, structural models of default, capital structure arbitrage, contingent convertibles and how to incorporate counterparty risk into derivatives pricing, among other topics - Additions, clarifications, and illustrations throughout the volume show these instruments at work instead of explaining how they should act - The solutions manual enhances the text by presenting additional cases and solutions to exercises |
| financial analyst to data analyst: Financial Statistics and Data Analytics Shuangzhe Li, Milind Sathye, 2021-03-02 Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three. |
| financial analyst to data analyst: Data Analytics for Accounting Vernon J. Richardson, Ryan Teeter, Katie L. Terrell, 2018-05-23 |
| financial analyst to data analyst: Statistics and Data Analysis for Financial Engineering David Ruppert, David S. Matteson, 2015-04-21 The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest. |
| financial analyst to data analyst: Data Mining For Dummies Meta S. Brown, 2014-09-04 Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business's entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn't take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their business's needs. In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. The book explains the details of the knowledge discovery process including: Model creation, validity testing, and interpretation Effective communication of findings Available tools, both paid and open-source Data selection, transformation, and evaluation Data Mining for Dummies takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You'll gain the confidence you need to start making data mining practices a routine part of your successful business. If you're serious about doing everything you can to push your company to the top, Data Mining for Dummies is your ticket to effective data mining. |
| financial analyst to data analyst: 2022 CFA Program Curriculum Level I Box Set CFA Institute, 2021-05-04 Prepare for success on the 2022 CFA Level I exam with the latest official CFA® Program Curriculum. The 2022 CFA Program Curriculum Level I Box Set contains all the material you need to succeed on the Level I CFA exam in 2022. This set includes the full official curriculum for Level I and is part of the larger CFA Candidate Body of Knowledge (CBOK). Highly visual and intuitively organized, this box set allows you to: Learn from financial thought leaders. Access market-relevant instruction. Gain critical knowledge and skills. The set also includes practice questions to assist with your recall of key terms, concepts, and formulas. Perfect for anyone preparing for the 2022 Level I CFA exam, the 2022 CFA Program Curriculum Level I Box Set is a must-have resource for those seeking the foundational skills required to become a Chartered Financial Analyst®. |
| financial analyst to data analyst: Series 7 Study Guide Series 7 Exam Prep Review Team, 2017-11-07 Series 7 Study Guide: Test Prep Manual & Practice Exam Questions for the FINRA Series 7 Licence Exam Developed for test takers trying to achieve a passing score on the Series 7 exam, this comprehensive study guide includes: -Quick Overview -Test-Taking Strategies -Introduction to the Series 7 Exam -Regulatory Requirements -Knowledge of Investor Profile -Opening and Maintaining Customer Accounts -Business Conduct Knowledge & Suitable Recommendations -Orders and Transactions in Customer Accounts -Professional Conduct and Ethical Considerations -Primary Marketplace -Secondary Marketplace -Principal Factors Affecting Securities, Markets, and Prices -Analysis of Securities and Markets -Equity Securities -Debt Securities -Packaged Securities and Managed Investments -Options -Retirement Plans -Custodial, Edcation, and Health Savings -Practice Questions -Detailed Answer Explanations Each section of the test has a comprehensive review that goes into detail to cover all of the content likely to appear on the Series 7 exam. The practice test questions are each followed by detailed answer explanations. If you miss a question, it's important that you are able to understand the nature of your mistake and how to avoid making it again in the future. The answer explanations will help you to learn from your mistakes and overcome them. Understanding the latest test-taking strategies is essential to preparing you for what you will expect on the exam. A test taker has to not only understand the material that is being covered on the test, but also must be familiar with the strategies that are necessary to properly utilize the time provided and get through the test without making any avoidable errors. Anyone planning to take the Series 7 exam should take advantage of the review material, practice test questions, and test-taking strategies contained in this study guide. |
| financial analyst to data analyst: An Introduction to Analysis of Financial Data with R Ruey S. Tsay, 2014-08-21 A complete set of statistical tools for beginning financial analysts from a leading authority Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including: Linear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparison Different approaches to calculating asset volatility and various volatility models High-frequency financial data and simple models for price changes, trading intensity, and realized volatility Quantitative methods for risk management, including value at risk and conditional value at risk Econometric and statistical methods for risk assessment based on extreme value theory and quantile regression Throughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques. An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets. |
| financial analyst to data analyst: The Quants Scott Patterson, 2011-01-25 With the immediacy of today’s NASDAQ close and the timeless power of a Greek tragedy, The Quants is at once a masterpiece of explanatory journalism, a gripping tale of ambition and hubris, and an ominous warning about Wall Street’s future. In March of 2006, four of the world’s richest men sipped champagne in an opulent New York hotel. They were preparing to compete in a poker tournament with million-dollar stakes, but those numbers meant nothing to them. They were accustomed to risking billions. On that night, these four men and their cohorts were the new kings of Wall Street. Muller, Griffin, Asness, and Weinstein were among the best and brightest of a new breed, the quants. Over the prior twenty years, this species of math whiz--technocrats who make billions not with gut calls or fundamental analysis but with formulas and high-speed computers--had usurped the testosterone-fueled, kill-or-be-killed risk-takers who’d long been the alpha males the world’s largest casino. The quants helped create a digitized money-trading machine that could shift billions around the globe with the click of a mouse. Few realized, though, that in creating this unprecedented machine, men like Muller, Griffin, Asness and Weinstein had sowed the seeds for history’s greatest financial disaster. Drawing on unprecedented access to these four number-crunching titans, The Quants tells the inside story of what they thought and felt in the days and weeks when they helplessly watched much of their net worth vaporize--and wondered just how their mind-bending formulas and genius-level IQ’s had led them so wrong, so fast. |
| financial analyst to data analyst: Become a Python Data Analyst Alvaro Fuentes, 2018-08-31 Enhance your data analysis and predictive modeling skills using popular Python tools Key Features Cover all fundamental libraries for operation and manipulation of Python for data analysis Implement real-world datasets to perform predictive analytics with Python Access modern data analysis techniques and detailed code with scikit-learn and SciPy Book Description Python is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations. Become a Python Data Analyst introduces Python’s most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations. In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques. By the end of this book, you will have hands-on experience performing data analysis with Python. What you will learn Explore important Python libraries and learn to install Anaconda distribution Understand the basics of NumPy Produce informative and useful visualizations for analyzing data Perform common statistical calculations Build predictive models and understand the principles of predictive analytics Who this book is for Become a Python Data Analyst is for entry-level data analysts, data engineers, and BI professionals who want to make complete use of Python tools for performing efficient data analysis. Prior knowledge of Python programming is necessary to understand the concepts covered in this book |
| financial analyst to data analyst: HBR Guide to Data Analytics Basics for Managers (HBR Guide Series) Harvard Business Review, 2018-03-13 Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started? Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure Run experiments and A/B tests Ask the right questions of your data experts Understand statistical terms and concepts Create effective charts and visualizations Avoid common mistakes |
| financial analyst to data analyst: Big Data Analytics Frank J. Ohlhorst, 2012-11-15 Unique insights to implement big data analytics and reap big returns to your bottom line Focusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities. Reveals big data analytics as the next wave for businesses looking for competitive advantage Takes an in-depth look at the financial value of big data analytics Offers tools and best practices for working with big data Once the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries. From how to mine the data your company collects, to the data that is available on the outside, Big Data Analytics shows how you can leverage big data into a key component in your business's growth strategy. |
| financial analyst to data analyst: The Portable Financial Analyst Mark P. Kritzman, 2004-03-31 Financial professionals are faced with increasingly technical topics that are theoretically complicated but practically necessary in determining the trade-off between risk and return. The Portable Financial Analyst, Second Edition is a unique collection of essays that address the heart of every analyst's and investor's dilemma: how to make decisions in the face of unknown forces and how to assert some control over the outcome |
| financial analyst to data analyst: Keeping Up with the Quants Thomas H. Davenport, Jinho Kim, 2013-05-21 Why Everyone Needs Analytical Skills Welcome to the age of data. No matter your interests (sports, movies, politics), your industry (finance, marketing, technology, manufacturing), or the type of organization you work for (big company, nonprofit, small start-up)—your world is awash with data. As a successful manager today, you must be able to make sense of all this information. You need to be conversant with analytical terminology and methods and able to work with quantitative information. This book promises to become your “quantitative literacy guide—helping you develop the analytical skills you need right now in order to summarize data, find the meaning in it, and extract its value. In Keeping Up with the Quants, authors, professors, and analytics experts Thomas Davenport and Jinho Kim offer practical tools to improve your understanding of data analytics and enhance your thinking and decision making. You’ll gain crucial skills, including: How to formulate a hypothesis How to gather and analyze relevant data How to interpret and communicate analytical results How to develop habits of quantitative thinking How to deal effectively with the “quants” in your organization Big data and the analytics based on it promise to change virtually every industry and business function over the next decade. If you don’t have a business degree or if you aren’t comfortable with statistics and quantitative methods, this book is for you. Keeping Up with the Quants will give you the skills you need to master this new challenge—and gain a significant competitive edge. |
| financial analyst to data analyst: Data Analysis Using SQL and Excel Gordon S. Linoff, 2010-09-16 Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like. |
| financial analyst to data analyst: Fixed Income Analysis for the Chartered Financial Analyst Program Frank J. Fabozzi, CFA Institute, 2005 |
| financial analyst to data analyst: How to Be a Financial Analyst: A Comprehensive Guide Simon Meadows, How to Be a Financial Analyst: A Comprehensive Guide is your essential roadmap to mastering the intricate and dynamic field of financial analysis. This in-depth book and course guide provides a complete curriculum for aspiring financial analysts, covering everything from foundational concepts and advanced techniques to real-world applications and industry insights. With twenty detailed chapters, each featuring practical exercises, case studies, and expert advice, this guide equips you with the knowledge and skills needed to excel in financial analysis. Whether you're a student preparing for a career in finance or a professional seeking to enhance your expertise, this comprehensive resource will help you navigate your journey with confidence and clarity. |
| financial analyst to data analyst: Derivatives Analytics with Python Yves Hilpisch, 2015-08-03 Supercharge options analytics and hedging using the power of Python Derivatives Analytics with Python shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language. This unique guide offers detailed explanations of all theory, methods, and processes, giving you the background and tools necessary to value stock index options from a sound foundation. You'll find and use self-contained Python scripts and modules and learn how to apply Python to advanced data and derivatives analytics as you benefit from the 5,000+ lines of code that are provided to help you reproduce the results and graphics presented. Coverage includes market data analysis, risk-neutral valuation, Monte Carlo simulation, model calibration, valuation, and dynamic hedging, with models that exhibit stochastic volatility, jump components, stochastic short rates, and more. The companion website features all code and IPython Notebooks for immediate execution and automation. Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver portfolio, trading, and risk management results. This book is the finance professional's guide to exploiting Python's capabilities for efficient and performing derivatives analytics. Reproduce major stylized facts of equity and options markets yourself Apply Fourier transform techniques and advanced Monte Carlo pricing Calibrate advanced option pricing models to market data Integrate advanced models and numeric methods to dynamically hedge options Recent developments in the Python ecosystem enable analysts to implement analytics tasks as performing as with C or C++, but using only about one-tenth of the code or even less. Derivatives Analytics with Python — Data Analysis, Models, Simulation, Calibration and Hedging shows you what you need to know to supercharge your derivatives and risk analytics efforts. |
| financial analyst to data analyst: Sams Teach Yourself SQL in 10 Minutes Ben Forta, 2004 With this updated text, readers can learn the fundamentals of SQL quickly through the use of numerous examples depicting all the major components of SQL. |
| financial analyst to data analyst: Quantitative Financial Analytics: The Path To Investment Profits Edward E Williams, John A Dobelman, 2017-07-20 This book provides a comprehensive treatment of the important aspects of investment theory, security analysis, and portfolio selection, with a quantitative emphasis not to be found in most other investment texts.The statistical analysis framework of markets and institutions in the book meets the need for advanced undergraduates and graduate students in quantitative disciplines, who wish to apply their craft to the world of investments. In addition, entrepreneurs will find the volume to be especially useful. It also contains a clearly detailed explanation of many recent developments in portfolio and capital market theory as well as a thorough procedural discussion of security analysis. Professionals preparing for the CPA, CFA, and or CFP examinations will also benefit from a close scrutiny of the many problems following each chapter.The level of difficulty progresses through the textbook with more advanced treatment appearing in the latter sections of each chapter, and the last chapters of the volume. |
| financial analyst to data analyst: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| financial analyst to data analyst: A Wealth of Common Sense Ben Carlson, 2015-06-22 A simple guide to a smarter strategy for the individual investor A Wealth of Common Sense sheds a refreshing light on investing, and shows you how a simplicity-based framework can lead to better investment decisions. The financial market is a complex system, but that doesn't mean it requires a complex strategy; in fact, this false premise is the driving force behind many investors' market mistakes. Information is important, but understanding and perspective are the keys to better decision-making. This book describes the proper way to view the markets and your portfolio, and show you the simple strategies that make investing more profitable, less confusing, and less time-consuming. Without the burden of short-term performance benchmarks, individual investors have the advantage of focusing on the long view, and the freedom to construct the kind of portfolio that will serve their investment goals best. This book proves how complex strategies essentially waste these advantages, and provides an alternative game plan for those ready to simplify. Complexity is often used as a mechanism for talking investors into unnecessary purchases, when all most need is a deeper understanding of conventional options. This book explains which issues you actually should pay attention to, and which ones are simply used for an illusion of intelligence and control. Keep up with—or beat—professional money managers Exploit stock market volatility to your utmost advantage Learn where advisors and consultants fit into smart strategy Build a portfolio that makes sense for your particular situation You don't have to outsmart the market if you can simply outperform it. Cut through the confusion and noise and focus on what actually matters. A Wealth of Common Sense clears the air, and gives you the insight you need to become a smarter, more successful investor. |
| financial analyst to data analyst: Financial Data Analytics with Machine Learning, Optimization and Statistics Sam Chen, Ka Chun Cheung, Phillip Yam, 2024-10-18 An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions To apply effective data dimension reduction tools to enhance supervised learning To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking. |
| financial analyst to data analyst: The Accidental Analyst Eileen Mcdaniel, Stephen McDaniel, 2012-10-01 The Accidental Analyst: Show Your Data Who's Boss Are you drowning in a sea of data? Would you like to take control of your data and analysis to quickly answer your business questions and make critical decisions? Do you want to confidently present results and solutions to your managers, colleagues and clients? If so, The Accidental Analyst is for you! Although you didn't plan for a career as a data analyst, you're now in a position where you have to analyze data to be successful. Whether you've been working with data for a few years or are just getting started, you can learn how to analyze your data to find answers to real-world questions. Using illustrated examples, we'll walk you through a clear, step-by-step framework that we call The Seven C's of Data Analysis. Read this book for inspiration, ideas and confidence to begin tackling the problems you face at work. Keep it by your desk as a reference on how to organize, analyze and display your data. Don't worry, you can continue to use your favorite spreadsheet or data analysis software—this information is not tied to any particular application. Throughout the book, we also include expert tips, tricks, and shortcuts that took years of analyzing data to discover and understand! Please visit us at www.AccidentalAnalyst.com for articles, our free newsletter and upcoming training events. Quotes This is a wonderful book, filled with practical advice. Business people who are struggling to make sense of their data will find it accessible and directly applicable to their work— a great resource for building analytical prowess. Stephen Few, best-selling author of Show Me the Numbers and Now You See It Finally, a book that clearly explains the fundamentals of business analytics! I wish that I had this book at the start of my career as a data analyst. Tim Latendress, Financial Analyst This book is an amazing resource for regular business people who want to make sense of their data and take charge of their business! It provides simple yet comprehensive coverage of business analytics. Diego Saenz, President, Petplace and former CIO of Pepsi Latin America Authors Eileen McDaniel, PhD, is Co-Founder and Managing Partner of Freakalytics, LLC, specializing in analytical training and short-term projects that empower people to get the most out of their data and take decisive action to solve problems in their daily work. She is co-author of Rapid Graphs with Tableau Software 7 and the Rapid Dashboards Reference Card, also available as a mobile app, and leads the development of course training materials. Working in both scientific research and business, Eileen realized that business analysts needed a formal, step-by-step method similar to the one scientists use to collect and analyze their data. This inspired her to develop the seven-step framework for data analysis found in The Accidental Analyst. Stephen McDaniel is passionate about helping people understand, present and take action with their data. He is co-author of multiple books and courses including SAS® for Dummies and Rapid Graphs with Tableau Software 7. Stephen has been on the Faculty of The American Marketing Association and The Data Warehouse Institute and is currently Director of Analytic Product Management at Tableau Software and Principal Analyst at Freakalytics, LLC. |
| financial analyst to data analyst: How to Become a Data Analyst Annie Nelson, 2023-11-23 Start a brand-new career in data analytics with no-nonsense advice from a self-taught data analytics consultant In How to Become a Data Analyst: My Low-Cost, No Code Roadmap for Breaking into Tech, data analyst and analytics consultant Annie Nelson walks you through how she took the reins and made a dramatic career change to unlock new levels of career fulfilment and enjoyment. In the book, she talks about the adaptability, curiosity, and persistence you’ll need to break free from the 9-5 grind and how data analytics—with its wide variety of skills, roles, and options—is the perfect field for people looking to refresh their careers. Annie offers practical and approachable data portfolio-building advice to help you create one that’s manageable for an entry-level professional but will still catch the eye of employers and clients. You’ll also find: Deep dives into the learning journey required to step into a data analytics role Ways to avoid getting lost in the maze of online courses and certifications you can find online—while still obtaining the skills you need to be competitive Explorations of the highs and lows of Annie’s career-change journey and job search—including what was hard, what was easy, what worked well, and what didn’t Strategies for using ChatGPT to help you in your job search A must-read roadmap to a brand-new and exciting career in data analytics, How to Become a Data Analyst is the hands-on tutorial that shows you exactly how to succeed. |
| financial analyst to data analyst: Blockchain in Data Analytics Mohiuddin Ahmed, 2020-01-16 Blockchain technology facilitates a decentralized database where business is rendered transparent without the involvement of middlemen. The first use of this technology was its application in digital currency (bitcoin). However, other potential uses of blockchain are yet to be explored. It is expected to have a major impact on cyber security, the internet of things, supply chain management, market prediction, governance, information management, and financial transactions, among others. Blockchain has redesigned the way in which people deal with their money due to its effectiveness, especially in terms of security. Therefore, from the data analytics point of view, investigation of the application of blockchain technology in a wide range of domains is crucial. In this context, this book provides a broad picture of the concepts, techniques, applications, and open research directions in this area, and will serve as a single source of reference for acquiring knowledge on this emerging technology. |
| financial analyst to data analyst: Microsoft SQL Server 2012 High-Performance T-SQL Using Window Functions Itzik Ben-Gan, 2012-07-15 Gain a solid understanding of T-SQL—and write better queries Master the fundamentals of Transact-SQL—and develop your own code for querying and modifying data in Microsoft SQL Server 2012. Led by a SQL Server expert, you’ll learn the concepts behind T-SQL querying and programming, and then apply your knowledge with exercises in each chapter. Once you understand the logic behind T-SQL, you’ll quickly learn how to write effective code—whether you’re a programmer or database administrator. Discover how to: Work with programming practices unique to T-SQL Create database tables and define data integrity Query multiple tables using joins and subqueries Simplify code and improve maintainability with table expressions Implement insert, update, delete, and merge data modification strategies Tackle advanced techniques such as window functions, pivoting and grouping sets Control data consistency using isolation levels, and mitigate deadlocks and blocking Take T-SQL to the next level with programmable objects |
| financial analyst to data analyst: Key Business Analytics Bernard Marr, 2016-02-10 Key Business Analytics will help managers apply tools to turn data into insights that help them better understand their customers, optimize their internal processes and identify cost savings and growth opportunities. It includes analysis techniques within the following categories: Financial analytics – cashflow, profitability, sales forecasts Market analytics – market size, market trends, marketing channels Customer analytics – customer lifetime values, social media, customer needs Employee analytics – capacity, performance, leadership Operational analytics – supply chains, competencies, environmental impact Bare business analytics – sentiments, text, correlations Each tool will follow the bestselling Key format of being 5-6 pages long, broken into short sharp advice on the essentials: What is it? When should I use it? How do I use it? Tips and pitfalls Further reading This essential toolkit also provides an invaluable section on how to gather original data yourself through surveys, interviews, focus groups, etc. |
| financial analyst to data analyst: SQL in 10 Minutes, Sams Teach Yourself Ben Forta, 2012-10-25 Sams Teach Yourself SQL in 10 Minutes, Fourth Edition New full-color code examples help you see how SQL statements are structured Whether you're an application developer, database administrator, web application designer, mobile app developer, or Microsoft Office users, a good working knowledge of SQL is an important part of interacting with databases. And Sams Teach Yourself SQL in 10 Minutes offers the straightforward, practical answers you need to help you do your job. Expert trainer and popular author Ben Forta teaches you just the parts of SQL you need to know–starting with simple data retrieval and quickly going on to more complex topics including the use of joins, subqueries, stored procedures, cursors, triggers, and table constraints. You'll learn methodically, systematically, and simply–in 22 short, quick lessons that will each take only 10 minutes or less to complete. With the Fourth Edition of this worldwide bestseller, the book has been thoroughly updated, expanded, and improved. Lessons now cover the latest versions of IBM DB2, Microsoft Access, Microsoft SQL Server, MySQL, Oracle, PostgreSQL, SQLite, MariaDB, and Apache Open Office Base. And new full-color SQL code listings help the beginner clearly see the elements and structure of the language. 10 minutes is all you need to learn how to... Use the major SQL statements Construct complex SQL statements using multiple clauses and operators Retrieve, sort, and format database contents Pinpoint the data you need using a variety of filtering techniques Use aggregate functions to summarize data Join two or more related tables Insert, update, and delete data Create and alter database tables Work with views, stored procedures, and more Table of Contents 1 Understanding SQL 2 Retrieving Data 3 Sorting Retrieved Data 4 Filtering Data 5 Advanced Data Filtering 6 Using Wildcard Filtering 7 Creating Calculated Fields 8 Using Data Manipulation Functions 9 Summarizing Data 10 Grouping Data 11 Working with Subqueries 12 Joining Tables 13 Creating Advanced Joins 14 Combining Queries 15 Inserting Data 16 Updating and Deleting Data 17 Creating and Manipulating Tables 18 Using Views 19 Working with Stored Procedures 20 Managing Transaction Processing 21 Using Cursors 22 Understanding Advanced SQL Features Appendix A: Sample Table Scripts Appendix B: Working in Popular Applications Appendix C : SQL Statement Syntax Appendix D: Using SQL Datatypes Appendix E: SQL Reserved Words |
| financial analyst to data analyst: Financial Analysis with Microsoft Excel Timothy R. Mayes, Todd M. Shank, 1996 Start mastering the tool that finance professionals depend upon every day. FINANCIAL ANALYSIS WITH MICROSOFT EXCEL covers all the topics you'll see in a corporate finance course: financial statements, budgets, the Market Security Line, pro forma statements, cost of capital, equities, and debt. Plus, it's easy-to-read and full of study tools that will help you succeed in class. |
| financial analyst to data analyst: Statistical Analysis of Financial Data in R René Carmona, 2013-12-13 Although there are many books on mathematical finance, few deal with the statistical aspects of modern data analysis as applied to financial problems. This textbook fills this gap by addressing some of the most challenging issues facing financial engineers. It shows how sophisticated mathematics and modern statistical techniques can be used in the solutions of concrete financial problems. Concerns of risk management are addressed by the study of extreme values, the fitting of distributions with heavy tails, the computation of values at risk (VaR), and other measures of risk. Principal component analysis (PCA), smoothing, and regression techniques are applied to the construction of yield and forward curves. Time series analysis is applied to the study of temperature options and nonparametric estimation. Nonlinear filtering is applied to Monte Carlo simulations, option pricing and earnings prediction. This textbook is intended for undergraduate students majoring in financial engineering, or graduate students in a Master in finance or MBA program. It is sprinkled with practical examples using market data, and each chapter ends with exercises. Practical examples are solved in the R computing environment. They illustrate problems occurring in the commodity, energy and weather markets, as well as the fixed income, equity and credit markets. The examples, experiments and problem sets are based on the library Rsafd developed for the purpose of the text. The book should help quantitative analysts learn and implement advanced statistical concepts. Also, it will be valuable for researchers wishing to gain experience with financial data, implement and test mathematical theories, and address practical issues that are often ignored or underestimated in academic curricula. This is the new, fully-revised edition to the book Statistical Analysis of Financial Data in S-Plus. René Carmona is the Paul M. Wythes '55 Professor of Engineering and Finance at Princeton University in the department of Operations Research and Financial Engineering, and Director of Graduate Studies of the Bendheim Center for Finance. His publications include over one hundred articles and eight books in probability and statistics. He was elected Fellow of the Institute of Mathematical Statistics in 1984, and of the Society for Industrial and Applied Mathematics in 2010. He is on the editorial board of several peer-reviewed journals and book series. Professor Carmona has developed computer programs for teaching statistics and research in signal analysis and financial engineering. He has worked for many years on energy, the commodity markets and more recently in environmental economics, and he is recognized as a leading researcher and expert in these areas. |
| financial analyst to data analyst: Investment Banking Joshua Rosenbaum, Joshua Pearl, 2020-03-20 A timely update to the global bestselling book on investment banking and valuation – this new edition reflects valuable contributions from Nasdaq and the global law firm Latham & Watkins LLP plus access to the online valuation models and course. In the constantly evolving world of finance, a solid technical foundation is an essential tool for success. Due to the fast-paced nature of this world, however, no one was able to take the time to properly codify its lifeblood--namely, valuation and dealmaking. Rosenbaum and Pearl originally responded to this need in 2009 by writing the first edition of the book that they wish had existed when they were trying to break into Wall Street. Investment Banking: Valuation, LBOs, M&A, and IPOs, 3rd Edition is a highly accessible and authoritative book written by investment bankers that explains how to perform the valuation work and financial analysis at the core of Wall Street – comparable companies, precedent transactions, DCF, LBO, M&A analysis...and now IPO analytics and valuation. Using a step-by-step, how-to approach for each methodology, the authors build a chronological knowledge base and define key terms, financial concepts, and processes throughout the book. The genesis for the original book stemmed from the authors' personal experiences as students interviewing for investment banking positions. As they both independently went through the rigorous process, they realized that their classroom experiences were a step removed from how valuation and financial analysis were performed in real-world situations. Consequently, they created this book to provide a leg up to those individuals seeking or beginning careers on Wall Street – from students at undergraduate universities and graduate schools to career changers looking to break into finance. Now, over 10 years after the release of the first edition, the book is more relevant and topical than ever. It is used in over 200 universities globally and has become a go-to resource for investment banks, private equity, investment firms, and corporations undertaking M&A transactions, LBOs, IPOs, restructurings, and investment decisions. While the fundamentals haven't changed, the environment must adapt to changing market developments and conditions. As a result, Rosenbaum and Pearl have updated their widely adopted book accordingly, turning the latest edition of Investment Banking: Valuation, LBOs, M&A, and IPOs into a unique and comprehensive training package, which includes: Two new chapters covering IPOs plus insightful contributions from Nasdaq, the leading U.S. exchange and technology provider for IPOs and new listings, and global law firm Latham & Watkins LLP Access to six downloadable valuation model templates, including Comparable Companies Analysis, Precedent Transactions Analysis, Discounted Cash Flow Analysis, Leveraged Buyout Analysis, M&A Analysis, and IPO Valuation Six-month access to online Wiley Investment Banking Valuation Course featuring bite-sized lessons, over five hours of video lectures, 100+ practice questions, and other investment banking study tools Launch your career on Wall Street and hone your financial expertise with Rosenbaum and Pearl’s real-world knowledge and forward-looking guidance in the latest edition of Investment Banking: Valuation, LBOs, M&A, and IPOs. |
| financial analyst to data analyst: Analytics for Insurance Tony Boobier, 2016-10-10 The business guide to Big Data in insurance, with practical application insight Big Data and Analytics for Insurers is the industry-specific guide to creating operational effectiveness, managing risk, improving financials, and retaining customers. Written from a non-IT perspective, this book focusses less on the architecture and technical details, instead providing practical guidance on translating analytics into target delivery. The discussion examines implementation, interpretation, and application to show you what Big Data can do for your business, with insights and examples targeted specifically to the insurance industry. From fraud analytics in claims management, to customer analytics, to risk analytics in Solvency 2, comprehensive coverage presented in accessible language makes this guide an invaluable resource for any insurance professional. The insurance industry is heavily dependent on data, and the advent of Big Data and analytics represents a major advance with tremendous potential – yet clear, practical advice on the business side of analytics is lacking. This book fills the void with concrete information on using Big Data in the context of day-to-day insurance operations and strategy. Understand what Big Data is and what it can do Delve into Big Data's specific impact on the insurance industry Learn how advanced analytics can revolutionise the industry Bring Big Data out of IT and into strategy, management, marketing, and more Big Data and analytics is changing business – but how? The majority of Big Data guides discuss data collection, database administration, advanced analytics, and the power of Big Data – but what do you actually do with it? Big Data and Analytics for Insurers answers your questions in real, everyday business terms, tailored specifically to the insurance industry's unique needs, challenges, and targets. |
| financial analyst to data analyst: Python for Finance Yuxing Yan, 2017-06-30 Learn and implement various Quantitative Finance concepts using the popular Python libraries About This Book Understand the fundamentals of Python data structures and work with time-series data Implement key concepts in quantitative finance using popular Python libraries such as NumPy, SciPy, and matplotlib A step-by-step tutorial packed with many Python programs that will help you learn how to apply Python to finance Who This Book Is For This book assumes that the readers have some basic knowledge related to Python. However, he/she has no knowledge of quantitative finance. In addition, he/she has no knowledge about financial data. What You Will Learn Become acquainted with Python in the first two chapters Run CAPM, Fama-French 3-factor, and Fama-French-Carhart 4-factor models Learn how to price a call, put, and several exotic options Understand Monte Carlo simulation, how to write a Python program to replicate the Black-Scholes-Merton options model, and how to price a few exotic options Understand the concept of volatility and how to test the hypothesis that volatility changes over the years Understand the ARCH and GARCH processes and how to write related Python programs In Detail This book uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance. The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM's market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option. Style and approach This book takes a step-by-step approach in explaining the libraries and modules in Python, and how they can be used to implement various aspects of quantitative finance. Each concept is explained in depth and supplemented with code examples for better understanding. |
| financial analyst to data analyst: Machine Learning and Data Science Blueprints for Finance Hariom Tatsat, Sahil Puri, Brad Lookabaugh, 2020-10-01 Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You'll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations |
| financial analyst to data analyst: Post Office Jobs Dennis V. Damp, 2010 Describes salaries, job descriptions, and skill requirements for a variety of Post Office jobs. |
| financial analyst to data analyst: Case Interview Secrets Victor Cheng, 2012 Cheng, a former McKinsey management consultant, reveals his proven, insider'smethod for acing the case interview. |
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