Course 2025-2026 a.y.

Course Content

The course is structured around three interlinked building blocks: understanding the working of financial markets, developing theoretical models, and analyzing empirical evidence.

A thorough knowledge of how modern trading platforms function is a prerequisite for building rigorous economic models and for interpreting empirical data correctly.

The course progresses logically from the functioning of trading mechanisms to the modeling of market behavior and causal relationships, integrating empirical testing at every stage. Theory and empirics are blended throughout: each theoretical model is naturally followed by empirical analysis using real-world, high-frequency financial data to test its predictions and assess its practical relevance.

This integrated approach ensures students gain a coherent and practically relevant understanding of financial market functioning, design, and regulation.

I. Working of Financial Markets

This section focuses on understanding how modern financial markets operate, the impact of new trading technologies, and the evolving regulatory landscape.

Market Structure and Trading Mechanics

- Trading sessions, execution systems, and order types: including how a limit order book operates as the core mechanism behind most modern electronic trading platforms.

- Market participants and their strategic incentives.

- Auction versus dealer markets.

Modern Trading Mechanisms

- Algorithmic trading and high-frequency trading (HFT).

- Price monitoring and price discovery: a comparison between LSE and U.S. markets.

Key Regulatory Debates

- Dark markets and market transparency.

- Tick size regulation and the U.S. Tick Size Pilot Program.

- Trading fees and the role of big data.

- Closing auction volume dynamics.

- Recent SEC and MiFID II regulatory updates.

II. Theory and Policy Implications

Theoretical models are developed separately for dealership and auction market structures, linking them to empirical predictions and policy design.

A. Dealership Markets

Transaction Costs and Asymmetric Information

- Adverse selection and market-making.

High-Frequency Trading and Spoofing

- Market manipulation in high-frequency environments.

Algorithmic Pricing and Machine Learning

- Adaptive pricing and the role of machine learning in dealership markets, with a view toward its increasing importance in order-driven markets such as limit order books.

B. Auction Markets

Asymmetric Information, Transparency, and Price Discovery

- Price efficiency and information aggregation: exploring both one-period and multi-period models to understand how information is reflected in prices over time

Behavioral Biases and Bounded Rationality

- Learning, biases, and departures from full rationality: examining how behavioral distortions affect equilibrium prices across standard market settings

Batch Clearing and Price Formation

- Strategic trading and manipulation in batch auction mechanisms

C. Limit Order Book (LOB) Markets

Order Choice and Trading Strategies

- Endogenous decision between limit and market orders.

- Role of asymmetric information in order-driven markets.

Market Design in LOBs

- Effects of tick size and execution quality.

Continuous vs. Batch Auction Models

- Impacts on liquidity, volatility, and market stability.

Market Manipulation and Design

- Challenges in designing resilient and manipulation-resistant market structures.

III. Empirical Evidence and Market Design Evaluation

This section examines how theoretical models are tested against real-world market data and experimental evidence. Students will engage with high-frequency financial datasets to evaluate how well theoretical predictions align with observed market behavior.

Measuring Transaction Costs and Information Asymmetry

- Techniques for estimating bid-ask spreads.

- Methods for assessing informed trading.

Market Design Features

- Dynamics of closing auctions in different market structures.

- Liquidity effects of dark pools and hidden liquidity mechanisms.

.

Retail Trading and Market Makers

- Role of wholesalers in retail order execution.

- Competition and quality of retail execution services.

Machine Learning Approaches to Price Discovery

- Use of machine learning techniques to analyze and predict price formation using high-frequency market data.

Closing Auction Volume

- Examination of auction volume trends and their implications for price discovery and market liquidity across major exchanges in the U.S. and Europe.

Tick Size, Trading Fees, and Pricing Structure

- Evaluation of regulatory initiatives affecting trading costs and market liquidity.

By the end of the course, students will have a solid understanding of how modern financial markets function, including trading mechanisms, market structure, and regulatory frameworks. They will be familiar with key theoretical models used to analyze market behavior under asymmetric information, as well as empirical techniques to evaluate transaction costs, liquidity, and the effects of changes in market design. Students will also understand how to work with high-frequency financial data and recognize the role of behavioral biases and algorithmic strategies in shaping market outcomes.

Students will be expected to apply the concepts, models, and empirical tools learned in the course to assess real-world trading environments, interpret high-frequency data, and evaluate the effects of changes in market design and regulation. They will be able to formulate and test theoretical predictions, support decision-making in trading or regulatory contexts, and communicate results clearly in both written and oral form. The course also fosters active engagement through class interaction and group discussions, encouraging the development of critical thinking, quantitative reasoning, and communication skills essential for professional practice in finance, research, and policy.

• Lectures
• Guest speaker's talks (in class or in distance)

The course will be delivered through a combination of:

• lectures
• practical exercises.

Active interaction - both among students and between students and the instructor - will be strongly encouraged and facilitated throughout the course. Discussions, questions, and collaborative analysis will form an integral part of the learning experience, supporting critical thinking and peer learning.

Attending students will be assessed through an individual written exam designed to evaluate their understanding of selected core topics from the course.

The exam may include both closed and open-ended questions. Closed questions will consist of practical exercises based on the first part of the course (focused on the functioning of financial markets), while open-ended questions will assess students’ ability to reason through and explain key theoretical concepts.

Please note that the exam does not cover the entire course. 

Since empirical content is integrated into both the market functioning and theory sections, students may choose to focus their preparation on:

• the first part of the course (working of financial markets),
• the second part (theoretical models and policy implications), or
• a combination of both.

A detailed list of the topics relevant for the exam will be provided by the end of the course to support effective preparation.

Non-attending students have the option to choose between taking the written exam or submitting an individual essay. The essay topic must be agreed upon in advance with the instructor and should reflect the student’s ability to apply the theoretical and empirical tools covered in the course to a relevant question in financial market design or regulation.

The core of the course will be based on:

·       Lecture notes - posted on BBoard before each lecture

Additional material for background reading (not compulsory):

·       Selected journal articles

·       de Jong, F. and Rindi, B. (2009), The Microstructure of Financial Markets, Cambridge University Press

·       Foucault, T., M. Pagano and A. Roell (2013), Market Liquidity: Theory, Evidence, and Policy, Oxford University Press.

·       Harris, L. (2003), Trading & Exchanges, Oxford University Press – Chapters 4, 5, 6

·       Johnson, B. (2010), Algorithmic Trading & DMA, Myeloma Press

·       Hasbrouck, J. (2007), Empirical Market Microstructure, Oxford University Press

·       London Stock Exchange (2023), MIT201 – Guide to the Trading System, Issue15.4 (effective from 20 February 2023)