CRICOS Provid er Cod e 0009 8G Assessment 1, Activity 1

Statistics & Microstructure Analysis

Weight: 10%

Submission Deadline: End of Week 3, i.e. Friday 9:00 pm in Moodle

1. Overview

This individual assessment integrates statistical methods and market microstructure analysis.

In this activity, you will:

1. Analyse financial data

2. Apply statistical techniques

3. Conduct market microstructure analysis.

2. Instructions for Students

2.1. Preparation (Before Class):

• Review Key Concepts: Revisit statistical techniques such as regression analysis,

correlation analysis, and key microstructure metrics.

• Dataset Access: Download the dataset shared on Moodle, which contains historical

intraday data on orders and trades for stocks and ETFs.

2.2. In-Class Activity: Statistics & Microstructure Analysis

Step 1: Data Processing

• Identify and handle missing values, outliers, or anomalies (e.g., missing trade

timestamps, incorrect price formatting, or extreme bid-ask spreads).

• Justify preprocessing choices, linking them to improved model accuracy and data

integrity.

• Use a tool of your choice (e.g., Excel, Python, R) to clean the data.

Page 2 Step 2: Market Microstructure Analysis

• Calculate and interpret key market microstructure measures, including:

o Trade Feed metrics, e.g. trade volume, trade count, trade value, VWAP

o Order Book metrics, e.g.bid ask spread, market depth, order imbalance

o Mid Price calculations, e.g. simple, volume weighted, spread crossing volume

weighted, minimum depth volume weighted.

• Update and assess microstructure measures as per a real-time environment.

Step 3: Statistical Analysis

• Select appropriate statistical measures to examine relationships between Traded Prices

and Mid Price calculations.

• Evaluate and justify model performance using statistical indicators.

2.3. Post-Class Submission (Due End of Week 3)

Submit the following via Moodle:

1. Cleaned Dataset: Provide the cleaned dataset used in the analysis

2. Analysis Report (max 500 words): Address the assessment questions below concisely

3. Code or Spreadsheet: Submit the code (Python, R) or spreadsheet (Excel) used for

your analysis.

3. Assessment Questions

3.1. Data Processing:

• What preprocessing steps did you perform, and why were they necessary?

• Provide examples from your dataset to justify your decisions.

3.2. Market Microstructure Analysis:

• Summarise your microstructure analysis results.

3.3. Statistical Analysis:

• Summarise your findings

• What, if any insights does your analysis provide about the relationship between Traded

Prices and Mid Prices ?

3.4. Critical Reflection:

• Strengths & limitations: What are the strengths and limitations of your analysis ?

• Real World Applications: How could your analysis be improved for real world

applications ?

CRICOS Provid er Cod e 0009 8G

Criteria HD (100-85) D (84-75) C (74-65) P (64-50) F (49-0) Improvement Tips

Data

Preprocessing

(20%)

Comprehensive,

well-justified

cleaning; all

anomalies

addressed.

Clear, minor

omissions in

preprocessing or

justification.

Basic steps

included, but

some data issues

remain.

Minimal

processing with

major gaps.

Data cleaning is

missing or

poorly executed.

Provide specific examples

of missing data or outliers.

Justify preprocessing steps

with importance of

accuracy.

Market

Microstructure

Analysis (30%)

Highly accurate,

insightful

connections to

price dynamics.

Well-developed

analysis, but minor

gaps exist.

Basic analysis

with some

feasibility issues.

Incomplete or

unclear

justifications.

No analysis or

unrealistic

conclusions.

Explain the link between

order book & trade feed

metrics to price

movements.

Statistical

Analysis (30%)

Relevant

measures

selected with

strong

justification.

Well-applied

measures but minor

inaccuracies in

interpretation.

Basic but correct

measures with

limited depth.

Incomplete or

weak statistical

understanding.

Incorrect or

missing

analysis.

Use different statistical

measures and compare

performance. Explain your

findings.

Critical Reflection

(20%)

Deep, insightful

evaluation with

strong

improvement

suggestions.

Thoughtful analysis

but missing some

key points.

Basic reflection

with limited real- world

connections.

Minimal or vague

reflection.

No critical

reflection

provided.

Critically assess limitations

and suggest real world

improvements. Use

examples to strengthen

your evaluation.

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