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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