One of the biggest changes in the past decade is the rapid adoption of machine learning, AI, and big data in investment decision making. This course introduces learners with knowledge of the investment industry to foundational statistical concepts underpinning machine learning as well as advanced AI techniques. This course demonstrates core modeling frameworks along with carefully selected real-world investment practice examples. The course seeks to familiarize learners with two important programming languages — Python and R (no prior knowledge of Python or R necessary). The motivation is to demonstrate the elegance — and speed — simple programming brings to the investment decision-making process. The reading material in this course offers in-practice insights curated from the blogs of CFA Institute as well as other leading publications. After taking this course you will be able to: - Describe the importance of identifying information patterns for building models - Explain probability concepts for solving investing problems - Explain the use of linear regression and interpret related Python and R code - Describe gradient descent, explain logistic regression, and interpret Python and R code - Describe the characteristics and uses of time-series models This course is part of the Data Science for Investment Professionals Specialization offered by CFA Institute.

This course provides a brief introduction to the fundamentals of finance, emphasizing their application to a wide variety of real-world situations spanning personal finance, corporate decision-making, and financial intermediation. Key concepts and applications include: time value of money, risk-return tradeoff, cost of capital, interest rates, retirement savings, mortgage financing, auto leasing, capital budgeting, asset valuation, discounted cash flow (DCF) analysis, net present value, internal rate of return, hurdle rate, payback period.

Put your investment and portfolio management knowledge to the test through five weeks of hands-on investment experiences: • Developing and managing your own simulated investment portfolio, resulting in a peer-graded report covering portfolio strategy, analysis, and performance • Advising case study clients on a variety of investment topics, essentially acting as an investment advisor in a simulated environment recommending strategies for and changes in portfolios based on challenges and issues faced by your clients • Using the sophisticated web-based analytical tools of Silicon Cloud Technologies LLC’s Portfolio Visualizer including portfolio mean variance optimization, historical and forecasted efficient frontiers, Fama-French factor models, and many more Your capstone experiences are directly applicable to managing real world investment portfolios and the final report can be shared with family, friends, and potential or current employers. By the end of the capstone project, you will have incorporated concepts from all four courses, including: • Analyzing multiple asset classes • Asset allocation and risk management • Current market trends • Behavioral finance • Investment styles and strategies • Financial market innovation • Investment performance evaluation

In this course you will learn how companies decide on how much debt to take, and whether to raise capital from markets or from banks. You will also learn how to measure and manage credit risk and how to deal with financial distress. You will discuss the mechanics of dividends and share repurchases, and how to choose the best way to return cash to investors. You will also learn how to use derivatives and liquidity management to offset specific sources of financial risk, including currency risks. Finally, You will learn how companies finance merger and acquisition decisions, including leveraged buyouts, and how to incorporate large changes in leverage in standard valuation models. Upon successful completion of this course, you will be able to: • Understand how companies make financing, payout and risk management decisions that create value • Measure the effects of leverage on profitability, risk, and valuation • Manage credit risk and financial distress using appropriate financial tools • Understand the links between payout policies and company performance • Use derivatives and liquidity management to offset financial risks • Pick an appropriate financing package for an M&A or leveraged buyout deal This course is part of the iMBA offered by the University of Illinois, a flexible, fully-accredited online MBA at an incredibly competitive price. For more information, please see the Resource page in this course and onlinemba.illinois.edu.

By the end of this project, you will be able to complete a financial statement with Microsoft Excel, composed of a transactions page, profit and loss statement, and balance sheet. You will learn how to enter your business transactions and interpret the data presented in the profit and loss statement. You will also learn about the components of a balance sheet and will have a better understanding of how a financial statement can be used to help track and present financial information for your company.

This course teaches how to value and price M&A deals and to choose the optimal financing mix for an M&A deal. The course focuses on all the major types of M&A deals including strategic M&A, private equity leveraged buyouts (LBOs), and restructuring deals such as spinoffs and asset transfers.

This course will provide back test results for all the strategies in developed and emerging markets. The learner will also be taught scientific ways of back testing without succumbing to either look ahead (or) survival bias. You will learn various methods of building a robust back testing system for the strategies discussed in the previous course. You will be taught how to differentiate between mere data mining and results based on solid empirical or theoretical foundation. Next, you will learn the ways and means of back testing the results and subjecting the back test results to stress tests. After which, you will learn the various ways in which transaction costs and other frictions could be incorporated in the back testing algorithm. Finally, you will learn techniques for measuring a strategies' performance and the concept of risk adjusted return. You will use some of the famous measures for risk adjusted returns such as Sharpe ratio, Treynor's Ratio and Jenson's Alpha. You will see how to pick an appropriate benchmark for a proposed fund.

This course provides an intensive conceptual and applied introduction to auditing in society. It focuses on concepts and applications related to financial-statement auditors’ professional responsibilities as well as major facets of the audit process including risk assessment and audit reporting. In the U.S. financial-statement audits and related services generally are provided by Certified Public Accountants (CPAs). To succeed in this course, you should anticipate engaging in critical thinking and thoughtful communication about audit professionals' decision environments, decision processes, and deliverables. Additionally, you should understand the macro-level learning objectives in each of the course's weekly modules.

The MOOC Global Financing Solutions is your online gateway to better understanding of the dynamics of Finance, and its role at the very heart of promoting the “real economy” and global growth. Concretely, you will learn how companies finance themselves using banks and capital markets and how Environmental, Social and Corporate Governance criteria are now deeply integrated in all financing processes. We will look at the role of syndication, and how it links issuers looking to raise capital to grow their businesses with investors looking to manage their assets and possibly liabilities. The role of banks in wider society will also be explored, from helping airlines to lease aircraft to transport people, to financing roads and bridges that help promote transport and trade, to funding renewable sources energy such as wind or solar farms, right through to explaining the role of export finance, and the pivotal role exporting countries governments’ play, in promoting the movement of goods worldwide. We will showcase the lifecycle of commodities, from exploration, to processing and refining, to how banks facilitate the global trade of products ranging from oil and gas, to agriculture products, to everyday items such as fruits or beans. We will also look at the exciting world of acquisition finance and leverage buy-outs, enabling strategic moves for industry players, as well as securitization, the repackaging of debt, and hedging, especially important as a mean to protect corporate companies against rate or price fluctuations. Having a positive impact on society and the planet is clearly key for financial institutions. Throughout the whole course, we will illustrate with real cases the recent evolutions finance has gone through to incorporate ESG matters into its business decision making. The collaboration between Societe Generale and EDHEC Business School builds on a long-standing partnership based on one common objective: to provide future talents with access to information and expertise that enables them to grow. Through this MOOC, we are taking an innovative approach to learning by providing you with the theoretical basics of finance, thanks to the expertise of EDHEC as one of the world’s foremost business schools in Finance, and combining it with practical insights of business experts from Societe Generale, widely regarded as a global leader in Structured Finance, to show how the theory is put into practice.

In this course, you will discover models and approaches that are designed to deal with challenges raised by the empirical econometric modelling and particular types of data. You will: – Explore the motivations of each approach by means of graphs, preliminary statistics and presentation of economic theories – Discuss the problem of identification of the parameters, and how to address this problem by modelling simultaneous equations and causality in economics. – Examine the key features of panel data, and highlight the advantages and disadvantages of working with panel data rather than other structures of data. – Learn how to choose what econometric specification to adopt by introducing the test for poolability and the Hausman tests. – Discuss models for probability that are used where the variable under investigation is qualitative, and needs to be treated with a different approach. – Learn how to apply this approach to building an Early Warning system to forecast systemic banking crises using data from the World Bank. It is recommended that you have completed and understood the previous two courses in this Specialisation: The Classical Linear Regression Model and Hypothesis Testing in Econometrics. By the end of this course, you will be able to: – Respond appropriately to issues raised by some feature of the data – Resolve address problems raised by identification and causality – Resolve problems raised by simultaneous equation and instrumental variables models – Resolve problems raised by longitudinal data – Resolve problems raised by probability models – Manipulate and plot the different types of data.