Accelerated 4+1 MS in Financial Engineering Program

Maximize your potential with Lehigh’s Accelerated 4+1 MS in Financial Engineering (MFE) Program.

This 4+1 program provides Lehigh undergraduate students with a clear path to fulfilling master’s degree requirements in a one-year post-baccalaureate program.

Contact

Professor Patrick Zoro
MS in Financial Engineering Program Manager
Email: pjz218@lehigh.edu
Phone: 610-758-2789

or

Graduate Programs Office
business@lehigh.edu
610-758-4450

Flexibility

Lehigh undergraduate students can enter the Accelerated 4+1 MFE program at any time during their undergraduate degree pursuit. However, early adoption of the accelerated program is more likely to guarantee completion of the master’s degree in Financial Engineering in a one-year timeframe.

Acceleration

Undergraduate students with room in their schedules may have the opportunity to take and reserve graduate-level courses during their undergraduate study. A maximum of 6 graduate-level credits is permitted to be reserved toward the 31 credits required for the MS in Financial Engineering degree.

Tuition Savings

Undergraduate students who reserve 6 graduate credits towards the degree enjoy graduate tuition savings by reducing the number of credits that need to be taken as graduate students.  Undergraduate students who are not able to reserve graduate-level courses will be granted a 20% tuition award on the graduate tuition needed to complete 31 graduate-level credits toward this degree.

4 + 1 Advisor

A faculty advisor from your undergraduate college is assigned to you to assist you in navigating the pathway to the fulfillment of all requirements.

Guaranteed Admission

Any Lehigh undergraduate student graduating with a GPA of 3.0 or higher and who has taken the minimum prerequisite courses would automatically be admitted into the MFE Program for the start of the next semester after completing the requirements for an undergraduate Lehigh degree. Undergraduate students must file an abbreviated graduate application, confirming their intention to continue into graduate study. 

Required Courses

Program open to all Lehigh undergraduate majors and prospective undergraduate students that incorporate the following coursework into their undergraduate degree.

• Calculus I
• Calculus II
• Probability and Statistics
• Investments

Other highly recommended, but not required, coursework includes Calculus III, Linear Algebra, Financial Accounting, Corporate Finance, and Optimization. Note that students with equivalent courses from an undergraduate degree program that is part of the required MFE coursework will be given credit for fulfilling the field requirement and will be permitted to replace the credits from the list of approved electives. The program director(s) must approve courses for each student’s choice of electives. The MFE Program will still require 31 credit hours.

Typically, students studying in the following BS/BA disciplines would qualify:

• Economics
• Engineering (all types, but especially Industrial and Systems Engineering)
• Finance
• Mathematics (all types, but especially Applied Mathematics or Statistics)

This list of BS/BA degrees is not meant to be exhaustive.  Other disciplines would be considered on a case-by-case basis.

Pathways for Qualified Majors

Finance Major (College of Business)

Follow the Finance Major Requirements:

• Business Core Substitutions
  • ECO 045 Statistics Methods (Recommend: MATH 231)
  • MATH 081/021 (Recommend: MATH 021/031, 022/032, 023/033)

• Foundation Coursework
  • FIN323 Investments
  • FIN328 Corporate Financial Policy
• 5-Course Elective Requirement (Recommended, but not required)
  • FIN 324: Security Analysis and Portfolio Management
  • FIN 330: Financial Markets and Institutions
  • FIN 333: Global Finance
  • FIN 334: Derivatives and Management of Risk
  • FIN 335: Advanced Topics—Financial Management (various topics) Note: Course offered with a different topic can be repeated.
  • FIN 336: Real Estate Finance
  • FIN 377: Advanced Topics—Investments (various topics) Note: Course offered with a different topic can be repeated.
  • ACCT XXX: (Any 300-level Accounting course of at least 3 credits [except ACCT 371 and 372].)
  • ECO XXX: Any 200 level (or higher) Economics course of at least 3 credits [except ECO 201, 259, 273, 274, 300, 362, 371 and 389].)
  • REAL XXX: (Any 300 Real Estate course of at least 3 credits [Can only count 1 REAL course if also taking FIN 336].)
  • IE 316: Optimization Models and Applications
  • IE 339: Stochastic Models and Applications
  • MATH 205: Linear Methods
  • MATH 231: Probability and Statistics
  • MATH 241: Applied Linear Algebra
  • MATH 263: Introduction to the Theory of Probability
  • MATH 310: Random Processes and Applications
     

MATH Major BA/BS (College of Arts & Sciences)

Follow the Math Major Requirements:

• Calculus requirements are automatically included.
• Linear Algebra requirement is automatically included.
• MATH Electives must include:
  • MATH 263 – Intro to the Theory of Probability
  • MATH 264 – Intro to Statistics
• We recommend that Math electives include:
  • ISE 316 – Optimization Models & Applications
• Any of the following courses would satisfy a requirement of MFE and will be allowed to be replaced by another course:
  • MATH 310 – Random Processes
  • MATH 312 – Statistical Computing
  • MATH 365 – Statistical Machine Learning
• Elective courses must include:
  • ACCT 151 – Intro to Financial Accounting
  • FIN 125 – Intro to Finance
  • FIN 323 – Investments
• We recommend that Elective courses include:
  • FIN 328 – Corporate Financial Policy
     

STAT Major BA/BS (College of Arts & Sciences)

Follow the Math Major Requirements:

• Calculus requirements are automatically included.
• Linear Algebra requirement is automatically included.
• Probability & Statistics requirement is automatically included.
• The following Statistics core courses will satisfy a requirement of MFE and will be allowed to be replaced by another course:
  • MATH 310 – Random Processes
  • MATH 312 – Statistical Computing
  • MATH 365 – Statistical Machine Learning
• We recommend that Major electives include:
  • ISE 316 – Optimization Models & Applications
• Elective courses must include:
  • ACCT 151 – Intro to Financial Accounting
  • FIN 125 – Intro to Finance
  • FIN 323 – Investments
• We recommend that Elective courses include:
  • FIN 328 – Corporate Financial Policy
     

ISE Major BS (P.C. Rossin College of Engineering and Applied Science)

Follow the ISE Major Requirements:

• Calculus requirements are automatically included:
  • MATH 21 – Calculus I
  • MATH 22 – Calculus II
  • MATH 23 – Calculus III
• Linear Algebra requirement is automatically included:
  • MATH 205 – Linear Methods
  • ISE 240 – Introduction to Deterministic Optimization Models in Operations Research (in which additional Linear Algebra concepts are covered)
• Optimization requirement is automatically included:
  • ISE 240 – Introduction to Deterministic Optimization Models in Operations Research
• Probability and Statistics requirements are automatically included:
  • ISE 111 – Engineering Probability
  • ISE 121 – Applied Engineering Statistics
• Accounting requirements are automatically included:
  • ACCT 108 – Fundamentals of Accounting
• Recommended Stochastic Processes knowledge is automatically included:
  • ISE 230 – Introduction to Stochastic Models in Operations Research

Additionally, follow the next requirements and consider the next recommendations:

• Elective courses must include (using six (6) out of the ISE nine (9) free elective credits):
  • FIN 125 – Intro to Finance
  • FIN 323 – Investments
• We recommend that Elective courses include (using three (3) out of ISE nine (9) free elective credits):
  • FIN 328 – Corporate Financial Policy
• We recommend that one (1) of your ISE Technical Electives be in the area of Machine Learning or Computational Statistics (using one (1) of your required two (2) ISE Technical Electives). For example:
  • ISE 364 – Introduction to Machine Learning
  • ISE 365 – Applied Data Mining
  • ISE 367 – Mining of Large Datasets