1. Teaching futures markets with the “ZIP Code” trading game
Durham University Inaugural Learning and Teaching Conference
St. Aidan’s College 13th September 2016
Ekaterina P. Damianova* and Damian S. Damianov
Durham University Business School

2. Introduction and Motivation
Experiments, classroom games, and trading simulations are alternative, experiential approaches to teaching.
The purpose of this paper is to demonstrate one such experiment suitable for classes on derivatives or investment
We present an instructor-friendly version of the experiment and discuss the outcomes when this experiment was performed with students from the MSc Finance program at Durham University

3. Benefits of experiments
Classroom games:
Increase student interest and engagement
Promote student confidence
Enhance creativity and problem solving abilities
Provide a connection between classroom and professional environments
Help illustrate complex concepts in a way that is more accessible to students
Help remove barriers between the instructor and students leading to better student-teacher relationship
Breaks the ice among students and helps them form bonds and get to know each other

4. Related literature Bell’s 1993 “Asset Trading Game”
A stock is traded that provides dividends. Dividends are a realization of a random variable.
Root and Lien’s 2005 “GPA Futures Trading Game.”
Students trade futures contract based on the average grade received by the class at the end of the semester.
Smolira and Travis 2011 “Calls and Puts on Grades” Students trade one of their most precious assets: points on their final exam.

5. Related literature (cont’d)
Husander et al 2011 “Futures Market Game” Uncertainty is modelled by the throw of a dice and a flip of a coin.
Hull’s 2012 “Telephone Number Game”: initial idea for the current experiment.

6. Purpose of the futures market experiment
To introduce key concepts in futures trading in an engaging manner that breaks away from the traditional lecture format.

7. Key concepts taught Functioning of electronic futures exchanges
Trading volume
Open interest
Settlement price
Marking to market process
Gains and losses of traders
Optimal strategies of traders
Arbitrage spot-price relationship based on the cost of carry model
Informed trading and the efficient market hypothesis

8. The “ZIP code” game
The delivery price in a futures contract is determined by the sum of the digits of a secret (5 digit) ZIP code
Each digit is determined as a random draw where the digits 0,1…9 are equally likely
One digit is revealed after each round of trading
If needed depending on the size of the class trading teams can be created.
Trader identifiers (A, B, C,…etc.) are assigned for convenience

9. Trading rules Only whole numbers are accepted as bids and offers
Traders can trade one contract at a time
The delivery price is determined by the sum of the digits of a secret zip code
One digit is revealed after each round of trading

10. The “ZIP code” game (continued)
Five rounds of trading
Traders can freely submit bids and offers
Quotes are recorded and displayed to all traders
Traders can accept pre-existing quotes
Trading teams can enter both into long and short positions in the futures contracts

11. Instructor Excel Sheet Example

12. Payoffs Long Team Bid Offer Short A 25 30 B Long Team Bid Offer ShortC
Example: Delivery price=$28
Loss: $28-$30=$-2
Gain: $30-$28=$2

13. Exercises
Determine the trading volume and open interest after each round of trading
Determine the profit or loss of each trader
Assume that the trader who entered into the first long/short position holds the position until the delivery date. Create a table that reflects the daily settlement process of his margin account.

14. Optimal trading strategy
Current sum of digits + # rounds remaining x 4.5 Extensions to discuss market efficiency and the price discovery process (i.e. issues regarding the transmission of information into prices, dominant and satellite markets, etc.) can be incorporated

15. In class implementation
We performed the experiment in one of the first few sessions of a derivatives class in the MSc Finance at Durham University
Total in-class time devoted was 45 minutes
13 sessions of the experiment were run independently
Within each sessions students were divided in trading teams referred to as a trader consisting of 2-3 students
We had 6-9 traders per session

16. Preliminary Findings: Overall Trading
Number of traders
Number of transactions
Session R1 R2 R3 R4 R5 1 8 4 2 5 9 3 7 6 10 11 12 13
Total
100 14 33 32 41 36

17. Preliminary Findings: Trades vs. Fundamental Value

18. Preliminary Findings: Expected values and confidence intervals

19. Preliminary Findings: Distribution of Profits

20. Thank you!
Questions?