Quantitative Analysis in Accounting
Professor Doug Schroeder
Honors Accounting and Linear Algebra
Instructor Information
Instructor: Professor D. Schroeder
Office: 424 Fisher Hall
Email: schroeder.9@osu.edu
Phone: 614-292-6427
Office Hours: 1:00-2:00 TR
Course Information
Spring 2024
Classroom/Times: 2:20-3:40 TR in 210 Schoenbaum Hall
Recitation: 2:20-3:40 F in 305 Gerlach Hall
AMIS 3100H Spring 2024 syllabus
TAs: Rachel Cavote cavote.5@buckeyemail.osu.edu and Marissa Turner turner.1878@buckeyemail.osu.edu
Office hours: will vary, check in class or with Rachel and Marissa
Textual materials:
- Notes and example problems are posted below.
- Recommended but optional texts include
- Christensen and Demski (CD), Accounting Theory: An Information Content Perspective, McGraw-Hill Irwin, 2003.
- Demski (D), Managerial Uses of Accounting Information, Springer, 2008.
- Strang, Introduction to Linear Algebra, Wellesley-Cambridge Press, 2009.
- Pearl, Glymour, and Jewell, Causal Inference in Statistics: A Primer, Wiley, 2016.
- Nielsen and Chuang, Quantum Information and Quantum Computation, Cambridge University Press, 2000.
Other helpful items:
- R Statistical Computing Package
- R is a freely available, open-source version of S/S-plus.
- It’s extremely well versed at handling vector computation.
- Follow this link: R statistical computing package
- R sample programs
- Tutorials: R tutorial
- http://en.wikibooks.org/wiki/R_Programming/
- some sample programs are posted below to complement assignment material (but you’re encouraged to try writing your own programs)
Tentative Outline:
| Session | Topic | Examples |
| 1-2 | Introduction – A matrix, network graph, aggregate accounts | Ralph’s structure
complete appendix |
| 3 | Linear systems of equations –fundamental theorem of linear algebra; matrix operations (addition, multiplication, vector inner & outer products, transposition) | Ralph’s subspaces |
| 4-5 | Identities & inverse operations | Ralph’s inverse
appendix A.3 |
| 6-7 | Triangularization – LU factorization | Ralph’s decomposition
appendix A.3, A.4 |
| 8-9 | Diagonalization – eigenvalues & eigenvectors | Ralph’s equilibrium
appendix A.4 |
| 10-11 | Diagonalization – Cholesky & spectral decomposition | Ralph’s symmetry
appendix A.4 |
| 12-13 | Singular value decomposition & pseudo-inverse and QR decomposition | Ralph’s row component
appendix A.4, A.5, notes on row component notes on pseudoinverse |
| 14 | Optimization – fundamental theorem of linear programming, duality theorems, framing, theorem of the separating hyperplane
Lagrangian, Karush-Kuhn-Tucker conditions |
D ch. 8, appendix A.1, |
| 15-16 | Introduction – uncertainty and optimization | Ralph’s probability assignment
ch 4 Maximum entropy distributions, appendix H.2 |
| 17-18 | Uncertainty and optimization – linear regression & projections | Ralph’s estimate; |
| 19 | Uncertainty and optimization – linear regression, projections & conditional expectations, GLS & Cholesky decomposition | Ralph’s double residual regression
ch. 2.7, appendix D.2 |
| 20 | Bayes theorem (sum & product rules, law of total probability, iterated expectations, variance decomposition) | Ralph’s Bayesian Accruals |
| 21 | Bayes theorem (sum & product rules, law of total probability, iterated expectations, variance decomposition) | Bayesian Ralph
CD ch. 5; appendix B, appendix C |
| Session | Topic | Examples |
| 22 |
Classical information analysis – Bayes normal & inferring transactions |
Ralph’s accounting information appendix C, notes, numerical example |
| 23-25
26-28 |
The future of science:
Structural causal modeling
Quantum information; everything is information, Bell’s Inequality, pure and mixed state systems |
Ralph’s Technology (A and C) |
| Final exam and project Wed 4/24/24 2:00-3:45
|
Ralph’s financial statement analysis |
Archived materials:
Programs
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