Data Science and Machine Learning ACCFIN5246
UNIVERSITY OF GLASGOW
ADAM SMITH BUSINESS SCHOOL
Data Science & Machine Learning in Finance (ACCFIN5246)
Assignment 2 – Spring 2024
Hormoz Ramian
Instruction
— This is an individual assessment. Answer all questions listed in Section 4: Q.1, Q.2, Q.3, Q.4.
— Submission to be made electronically via the course Moodle page.
— This submission includes a report including brief accounts on the logical steps taken, nu-
merical answers, diagrams, and comments (each part in Section 4 specifies how answers are
required to be presented).
— Submit only the main report (no additional spreadsheet, nor software routine, etc.).
— Clearly number each part in your report and structure the answers to follow the same order
in Section 4: Q.1, Q.2, Q.3, Q.4. The project outline is presented across four sections to
describe the implementations, estimation and arrangement of the results.
— Each part, within the assignment’s overall grade, carries a weight described below:
Part Weighthttps://weibo.com/u/7916053997
Q.1 10%
Q.2 20%
Q.3 30%
Q.4 40%
— Results should be reported in a clear format. Avoid reporting numbers in the ‘scientific
format’ e.g. 7.2031e-06. All reported numbers should be rounded to two decimal points.
For example, report 0.00 in place of 7.2031e-06.
— The project includes an Appendix (X1) which provides additional data and their descrip-
tions.
Section 1: Models
Consider the standard and two extended capital asset pricing models characterised below in equa-
tions (1)-(3), denoted as Model (A), Model (B) and Model (C), rspectively:
A: rt − rf,t = αA
w + βA
w .(rM,t − rf,t) + εA
t
yX (1)
B: rt − rf,t = αB
w + βB
w .(rM,t − rf,t) +
KX
i=1
ψi,w.xi + εB
t (2)
C: rt − rf,t = αC
w + βC
w .(rM,t − rf,t) + X
j∈F (w)
θj,w.xj + εC
t (3)
Each model aims at interrelating the real excess equity log-return as the outcome variable, on a
given asset rt − rf,t where rf,t is the risk-free log-return, to the right-hand-side explanatory vari-
ables, where:
ADAM SMITH BUSINESS SCHOOL
Data Science & Machine Learning in Finance (ACCFIN5246)
Assignment 2 – Spring 2024
Hormoz Ramian
Instruction
— This is an individual assessment. Answer all questions listed in Section 4: Q.1, Q.2, Q.3, Q.4.
— Submission to be made electronically via the course Moodle page.
— This submission includes a report including brief accounts on the logical steps taken, nu-
merical answers, diagrams, and comments (each part in Section 4 specifies how answers are
required to be presented).
— Submit only the main report (no additional spreadsheet, nor software routine, etc.).
— Clearly number each part in your report and structure the answers to follow the same order
in Section 4: Q.1, Q.2, Q.3, Q.4. The project outline is presented across four sections to
describe the implementations, estimation and arrangement of the results.
— Each part, within the assignment’s overall grade, carries a weight described below:
Part Weighthttps://weibo.com/u/7916053997
Q.1 10%
Q.2 20%
Q.3 30%
Q.4 40%
— Results should be reported in a clear format. Avoid reporting numbers in the ‘scientific
format’ e.g. 7.2031e-06. All reported numbers should be rounded to two decimal points.
For example, report 0.00 in place of 7.2031e-06.
— The project includes an Appendix (X1) which provides additional data and their descrip-
tions.
Section 1: Models
Consider the standard and two extended capital asset pricing models characterised below in equa-
tions (1)-(3), denoted as Model (A), Model (B) and Model (C), rspectively:
A: rt − rf,t = αA
w + βA
w .(rM,t − rf,t) + εA
t
yX (1)
B: rt − rf,t = αB
w + βB
w .(rM,t − rf,t) +
KX
i=1
ψi,w.xi + εB
t (2)
C: rt − rf,t = αC
w + βC
w .(rM,t − rf,t) + X
j∈F (w)
θj,w.xj + εC
t (3)
Each model aims at interrelating the real excess equity log-return as the outcome variable, on a
given asset rt − rf,t where rf,t is the risk-free log-return, to the right-hand-side explanatory vari-
ables, where:
