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DATA-DRIVEN BUSINESS AND BEHAVIOUR ANALYTICS
创建日期：2024-04-23 18:08:40
标签： DATA
DATA-DRIVEN BUSINESS AND BEHAVIOUR ANALYTICS

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COURSEWORK 2

DATA-DRIVEN BUSINESS AND BEHAVIOUR ANALYTICS 
Submitting instructions
For this coursework, you will be asked to develop an agent-based model and report and analyse the
results you get from it. The ABM must be coded in Python. You can either use a notebook or an IDE
of your choice, but you must submit all your code in a single zip file (even if it is just one python
file, this must be zipped). You also must submit a pdf in which you report the results and discuss
them. Submission will be available on Learn (and not DICE).
Introduction
For this coursework, you will have to develop a short yet complete agent-based model (ABM) of the
early 2021 GameStop (GME) short squeeze (https://en.wikipedia.org/wiki/GameStop_short_
squeeze). You can use any library you want, and any resource you want, but everything you use
must be appropriately referenced. The main goal of this coursework is to make sure you know how
to develop an ABM for a real-world financial scenario. In fact, after the GME short squeeze, several
investment funds started to recruit people who where i) familiar with financial subreddits, and ii)
capable to model events based on Reddit activity.
If you are not familiar with the GME short squeeze event, I suggest you read about it before starting
the coursework. Its Wikipedia page is an excellent starting point (and probably enough for what you
need to do).
Details of the model and assumptions you can make:
1. Your ABM should aim to model the period between 01-December-2020 and 04-February-2021.
2. You can use any research result (Google Scholar is your friend!) to model the agents and the
environment, and to validate your model.
3. Make good use of the Lucchini et al. paper uploaded on Learn. No need to reinvent the wheel.
Task 1 - 15 marks
Build the hedge fund class. Part of the reason the market behaved as it did, is that some large
institutional investors where betting on GameStop to go fail, and GME (its stock) to go to $0. To
account for this, build an agent class that models this type of agent. Specifically, this agent-type should
be a fundamentalist with a target fundamental price of $0. Also, this class of agents should be able
to short sell. Short selling means that it is possible to sell shares that are not owned, by borrowing
them from the market. In practice, this means that the agent can open a ’sell’ position and close it
1
anytime, but if the current price is higher than the price at the position opening, the agent will incur a
loss. Because of this, potential losses are infinite. Assume this class of agents can switch to a different
behaviour. Model the value function of this agent-type by using prospect theory.
Describe this agent by using the four steps we have seen in the lectures. You can add to the agent
behaviour outlined above, but clearly motivate any further assumption you use to model this agent
class.
Task 2 - 30 marks
Build Reddit traders. Develop agent classes that reflect the findings in Lucchini et. al. Describe
each agent class you build by using the usual four steps. You can use any additional resource you like,
but you must reference it properly and justify its use.
Task 3 - 15 marks
Setup the ABM experiment. Model the market environment in which agents interact by buy-
ing/selling. Model any additional interaction if you specified any in the previous steps.
Task 4 - 20 marks
Validate your model. Validate your model using the results by Lucchini et al. and any other
resource you like. Motivate your choice of parameters for the calibration and discuss the results.
Task 5 - 10 marks
Sensitivity analysis. In the interest of time, you are not required to run a sensitivity analysis.
However, explain how you would do it for this ABM, including a discussion of which parameters you
think are the most important to analyse.
Task 6 - 10 marks
Discussion of your results. Discuss your results and how good is your ABM to explain the
GameStop short squeeze event. Discussion should include any limitations of the model (anything
you think affected your results).
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MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 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