Spring 2027

Financial Microeconomics

Financial Microeconomics covers contemporary theory of financial markets, portfolio choice, and asset pricing.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 7

Past student reviews · Marek Weretka

The course required completing at least eight problem sets involving Python and calculations, maintaining a relatively fast pace throughout the term.

What you’ll be able to do

  • Formulate the classic consumption-based framework and financial equilibrium

  • Derive the predictions for portfolio choice, asset pricing, and social efficiency

  • Recover Fisher prices for YTM of treasury bonds and use these prices to valuate fixed income assets (bonds, mortgages)

  • Recover Stochastic Discount Factor (SDF) from option prices and valuate risky assets (stocks, options)

  • Understand the implication of SDF for a risk premium

  • Model financial markets with default and collateral and systemic risk

  • Model thin markets with institutional investors and insider’s trading

Source: UW Guide

Grade history

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average GPA
letter grades
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AB
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All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
ECON 721

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Graduate/professional standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

MAREK WERETKA is recorded teaching in Fall 2022, Fall 2023, Fall 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

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Student experience

the class

Past student reviews · Marek Weretka

Marek Weretka taught a fast-paced course that linked macro topics to financial micro, offering a manageable Python project alternative to the final exam.

Recent recorded grades — Fall 2023: 3.50 GPA, 68.0% A/AB (n=25 letter grades); Fall 2024: 3.70 GPA, 83.8% A/AB (n=74 letter grades); Fall 2025: 3.62 GPA, 76.5% A/AB (n=17 letter grades).

difficulty & workload

Past student reviews · Marek Weretka

The course required completing at least eight problem sets involving Python and calculations, maintaining a relatively fast pace throughout the term.

Past student reviews · Marek Weretka

One student noted that the option to substitute the final exam with a Python project was relatively easy to manage.

Topics

  • Contemporary theory of financial markets, portfolio choice, and asset pricing.
  • Consumption-based frameworks, financial equilibrium, and social efficiency.
  • Fixed income valuation using Fisher prices and risky asset valuation using SDFs.
  • Market models addressing default, collateral, systemic risk, and insider trading.

Skills

  • Formulating consumption-based frameworks and deriving predictions for portfolio choice and asset pricing.
  • Valuating fixed income and risky assets using Fisher prices and Stochastic Discount Factors.
  • Modeling financial markets involving default, collateral, systemic risk, and insider trading.

Grades

Latest available · Fall 2025— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Fall 2025

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

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Selected offering source records

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Student reviews

Original comments behind the course and instructor summaries.

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Grade history

Recorded grade distributions by term, section, and instructor.

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Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Model & dataset provenance
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  "output_id": "d5b5b115a9c57b434b66ca960629044df25e86e7bf3d29e5cc0904fe09157cb3",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}