Financial Analytics: Forecasting, Modeling, and Optimization

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Course Dates

STARTS ON

October 28, 2021

Course Duration

DURATION

6 weeks, online
4 to 6 hours per week

Course Duration

PROGRAM FEE

US$2,600 or get US$260 off with a referral

Course Information Flexible payment available
Course Information Special group enrollment pricing

Welcome

To claim your US$260 program fee coupon for Financial Analytics: Forecasting, Modeling, and Optimization, please complete the information form.

Overview

Research, modeling, forecasting — every aspect of the financial industry is driven by data and analytics to a greater degree than ever. This program will help you build the foundational knowledge you need to grasp the core concepts of data analytics, as well as how to apply them to create a framework for finance strategies that fit your organization's needs. From fine-tuning customer sales to assessing corporate credit risks, learn to use the analytics principles that drive informed decision making in this growing industry.

The financial analytics market was valued at $6.32 billion in 2020 and is expected to reach $11.02 billion by 2026.

Source: Mordor Intelligence

Data-centered organizations are 58% more likely to exceed their revenue goals.

Source: Forrester, The Business Impact of Data Intelligent Management

Seventy percent of all financial services firms are using machine learning to predict cash flow events, fine-tune credit scores and detect fraud.

Source: Deloitte Insights
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Key Takeaways

Wharton Financial Analytics emphasizes real-world applications of data science across the finance sector. At the end of this six-week program, you will be able to:

  • Recognize the value data analytics provides to the financial decision-making process
  • Manage and engage effectively with data analytics teams and tools
  • Analyze the opportunities and limits of data and analytics when they are used in finance for causality and forecasting
  • Apply data analytic strategies and tools to real-world financial challenges
  • Create value for the organization based on financial data analytics

Program Modules

In this program, our world-class faculty will equip you to ask the right questions about your organization's finances — questions that generate informed decisions supported by data.

Module 1:

The Value of Data Analytics for Decision Making

Begin your journey by learning the value of data science and how to apply analytics to everyday decision making. You will:

  • Understand the value of data analytics when making financial decisions
  • Create an outline of the data science workflow
  • Learn potential pitfalls of data analytics

Module 2:

Analyzing Data — The Basics

Build a foundation for actionable financial data analysis by defining variables in targeted markets that lead to insights. You will:

  • Determine goals for financial data analysis
  • Perform exploratory data analysis
  • Formulate and refine relevant questions based on available data
  • Gain insights into data patterns

Module 3:

Forecasting Strategies

Learn to use modeling and other proven techniques to set realistic expectations and generate well-informed business decisions. You will:

  • Gain insights into the role of modeling in shaping market expectations
  • Apply forecasting techniques to business decisions
  • Choose an appropriate modeling technique for the desired outcome
  • Interpret machine-learning output

Module 4:

Quantifying Financial Risk

Learn the fundamentals of determining financial risk through data science. You will:

  • Define credit risk
  • Analyze financial statements for credit risk
  • Identify credit key performance indicators (KPIs)
  • Interpret credit risk models

Module 5:

Improving Investment Decisions

Explore different investment portfolios, asset classes, and the investment process to optimize financial decisions. You will:

  • Define investment portfolios
  • Identify various asset classes
  • Construct optimal portfolios, using a variety of methods, strategies, and tools

Module 6:

Integrating and Applying Data Analytics to Challenges in Your Organization

Use what you have learned to create an action plan for implementing a justifiable data analytics solution to a real-world problem at your organization. You will:

  • Specify the challenge you wish to solve at your organization by using data analytics
  • Propose a data analytics solution
  • Identify potential pitfalls and problems
  • Select appropriate methods to visualize and present data
  • Justify the choice of modeling techniques for real-world problem solving
  • Analyze internal and external resource needs for team building
  • Produce an action plan that prioritizes results and identifies key deliverables

Module 1:

The Value of Data Analytics for Decision Making

Begin your journey by learning the value of data science and how to apply analytics to everyday decision making. You will:

  • Understand the value of data analytics when making financial decisions
  • Create an outline of the data science workflow
  • Learn potential pitfalls of data analytics

Module 4:

Quantifying Financial Risk

Learn the fundamentals of determining financial risk through data science. You will:

  • Define credit risk
  • Analyze financial statements for credit risk
  • Identify credit key performance indicators (KPIs)
  • Interpret credit risk models

Module 2:

Analyzing Data — The Basics

Build a foundation for actionable financial data analysis by defining variables in targeted markets that lead to insights. You will:

  • Determine goals for financial data analysis
  • Perform exploratory data analysis
  • Formulate and refine relevant questions based on available data
  • Gain insights into data patterns

Module 5:

Improving Investment Decisions

Explore different investment portfolios, asset classes, and the investment process to optimize financial decisions. You will:

  • Define investment portfolios
  • Identify various asset classes
  • Construct optimal portfolios, using a variety of methods, strategies, and tools

Module 3:

Forecasting Strategies

Learn to use modeling and other proven techniques to set realistic expectations and generate well-informed business decisions. You will:

  • Gain insights into the role of modeling in shaping market expectations
  • Apply forecasting techniques to business decisions
  • Choose an appropriate modeling technique for the desired outcome
  • Interpret machine-learning output

Module 6:

Integrating and Applying Data Analytics to Challenges in Your Organization

Use what you have learned to create an action plan for implementing a justifiable data analytics solution to a real-world problem at your organization. You will:

  • Specify the challenge you wish to solve at your organization by using data analytics
  • Propose a data analytics solution
  • Identify potential pitfalls and problems
  • Select appropriate methods to visualize and present data
  • Justify the choice of modeling techniques for real-world problem solving
  • Analyze internal and external resource needs for team building
  • Produce an action plan that prioritizes results and identifies key deliverables
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Program Experience

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Polls

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Try-it Activities

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Knowledge Checks

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Live Office Hours

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Assignments

Wharton Financial Analytics in Action

Understanding financial analytics in theory is only the start. The Wharton Financial Analytics program goes a step further by illustrating how to apply financial analytics in the real world, with a particular focus on:

  • Customer Sales Learn to set realistic sales goals with informed hypotheses developed through incisive questions. You can then identify the key variables that yield actionable insights — all determined through financial data analysis.
  • Firms' Earnings Gain insights into how modeling helps the market form expectations — and use those insights to develop modeling techniques to achieve desired outcomes. You will also learn how to interpret machine-learning output and how to use forecasting techniques when making business decisions.
  • Corporate Credit Risk Leverage data science to define credit risk more precisely. Learn how by conducting financial statement analyses, identifying credit KPIs, and using machine-learning classification outputs.
  • Asset Management Use financial analytics to create better-performing portfolios by identifying various asset classes more precisely, examining key elements of the investment process, and conceptualizing optimal outcomes.

Program Faculty

Faculty Member Jules H. van Binsbergen, PhD

Jules H. van Binsbergen, PhD

The Nippon Life Professor in Finance; Professor of Finance, The Wharton School

Professor van Binsbergen specializes in theoretical and empirical research in finance, with a focus on asset pricing. His current research is mostly in asset pricing and focuses on the relationship between financial markets and the macro economy and the organization, skill, and performance of financial intermediaries... More info

Faculty Member Michael Roberts, PhD

Michael Roberts, PhD

William H. Lawrence Professor; Professor of Finance, The Wharton School

Professor Roberts' primary research is in the area of corporate finance, specifically in capital structure, investment policy, financial contracting, and payout policy. His recent work has examined the design of debt securities and the role of control rights in influencing financial and investment policy... More info

Faculty Member Jules H. van Binsbergen, PhD

Jules H. van Binsbergen, PhD

The Nippon Life Professor in Finance; Professor of Finance, The Wharton School

Professor van Binsbergen specializes in theoretical and empirical research in finance, with a focus on asset pricing. His current research is mostly in asset pricing and focuses on the relationship between financial markets and the macro economy and the organization, skill, and performance of financial intermediaries. Recent papers shed light on the real implications of stock market mispricing, the effectiveness of monetary policy, and measuring the skill of investment managers.

Professor van Binsbergen's research has appeared in leading academic journals, such as the American Economic Review, the Journal of Finance, the Journal of Financial Economics, and the Journal of Monetary Economics. Before joining the Wharton School in 2014, he taught at the Stanford Graduate School of Business and the Kellogg School of Management of Northwestern University. Professor van Binsbergen is a research associate at the NBER and the CEPR, a former editor and current advisory editor of the Review of Finance, and an associate editor at the Journal of Finance and the Journal of Financial Economics. His work has won numerous research and teaching awards. He received his PhD from the Fuqua School of Business at Duke University.
Faculty Member Michael Roberts, PhD

Michael Roberts, PhD

William H. Lawrence Professor; Professor of Finance, The Wharton School

Professor Roberts' primary research is in the area of corporate finance, specifically in capital structure, investment policy, financial contracting, and payout policy. His recent work has examined the design of debt securities and the role of control rights in influencing financial and investment policy. Professor Roberts' research has received several awards, including the Brattle Prize for Distinguished Paper published in the Journal of Finance, where he is co-editor, and Best Paper awards at the Financial Management Association and Southwestern Finance Association annual conferences.

He serves on the editorial boards at the Review of Financial Studies, the Journal of Financial and Quantitative Analysis, the Review of Corporate Finance Studies, the Financial Research Letters, and the International Review of Finance. In addition to his experience at the Wharton School, where he has been since 2004, Professor Roberts has taught at Duke University's Fuqua School of Business. He earned his BA in economics from the University of California at San Diego along with his MA in statistics and PhD in economics from the University of California at Berkeley.

Guest Speaker

Faculty Member Anamaria Pieschacon

Anamaria Pieschacon

Guest Speaker

Anamaria Pieschacon, director in the Predictive Analytics unit at Moody's Analytics, shares her expertise in corporate credit risk and environmental, social, and governance models. She was part of the team that won the model validation solution category in the Chartis RiskTech100.

Before joining Moody’s in 2015, Anamaria was a vice president in the Global Investment Research division at Goldman Sachs. She holds a PhD in economics from Duke University and has served as a visiting assistant professor of finance at the Kellogg School of Management, Northwestern University; an external research associate at OxCarre, University of Oxford; and a global fellow at the Center for Global Business and the Economy, Stanford University Graduate School of Business.

Certificate

Example image of certificate that will be awarded after successful completion of this program

Certificate

Upon successful completion of the program, you will earn a digital certificate of completion from the Wharton School.

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Note: After successful completion of the online program, your verified digital certificate will be emailed to you in the name you used when registering for the program. All certificate images are for illustrative purposes only and may be subject to change at the discretion of the Wharton School.

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