ØABED2000 Investering og finansiering Emneplan

Students taking the course should be familiar with elementary calculus, including the concepts of complex numbers and numerical methods, and with basic linear algebra. Moreover, the students should be in command of a programming language/computing environment such as, e.g., Python, MATLAB or C(++).

In this regard, it is worth mentioning that some relevant mathematical and numerical concepts will be revised during the the first lectures.

• A brief re-acquaintance with vectors, matrices and complex numbers
• Computational methods of quantum physics
• Introduction to quantum phenomena
• Introduction to classical information theory
• Qubits and quantum gates
• Entanglement and interference as key components of quantum computing
• Fundamental quantum algorithms
• Quantum annealing as a way of addressing optimization problems
• Quantum cryptography
• The challenge of de-coherence and openness in quantum systems

A student who has completed this course should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

On successful completion of the course the student

• is familiar with fundamental key concepts within information theory such as Shannon Entropy, noiseless and noisy-channel coding theorems, and optimal coding algorithms.
• knows what a qubit is and how the information content grows when qubits are connected.
• is familiar with the elementary operations, or gates, of quantum computing - including gates such as the Hadamard gate and CNOT.
• knows the present state of the art when it comes to existing quantum computers.
• can implement simple quantum algorithms and run them on actual quantum computers.
• knows basic quantum communication protocols such as key distributions and secret sharing and understands the ideas behind them
• is familiar with several methods, such as Shor’s algorithm and quantum annealing, which enables quantum computers to solve problems considerably faster than classical computers.
• is familiar with how quantum technology affects traditional encryption schemes, and provides novel ones.

Skills

On successful completion of the course the student

• is able to model and simulate numerically simple quantum systems and processes - both on classical and quantum computers.can independently devise, implement and run calculations and simulations of simple quantum systems.
• can design her/his own quantum algorithms.

General competence

On successful completion of the course the student

• is familiar with several phenomena specific to quantum physics - such as quantization, particle interference, collapse of the wave function, particle spin, entanglement and decoherence - and how they may manifest themselves within quantum computing.
• is familiar with how information may be described by quantitative means - both within a classical and a quantum context.
• knows how to revise and improve on implementations of quantum programs.
• can address some of the practical challenges related to building quantum computers.
• knows the importance of quantum computing within information technology and the open challenges yet to be solved in this scope.

Studenten skal etter å ha fullført emnet ha følgende totale læringsutbytte definert i kunnskap, ferdigheter og generell kompetanse:

Kunnskap

Studenten

• kan gjøre rede for grunnleggende teorier relevante for å forstå finansmarkedenes oppgaver i en moderne markedsøkonomi
• kan gjøre rede for sentrale økonomiske begreper relevante for å forstå og beskrive investeringsprosjekter og finansieringsprosjekter som økonomisk aktivitet
• kan beskrive det teoretiske grunnlaget bak metoder som brukes for å lønnsomhetsberegne investeringsprosjekter
• kan gjøre rede for sammenhengen mellom metoder for å analysere lønnsomhetsberegning og eiernes økonomiske interesser
• kan beskrive ulike finansieringskilder og hvordan finansieringsstruktur påvirker avkastning og risiko
• kan gjøre rede for sentrale teorier og kontroverser innen finansiell økonomi
• har kjennskap til aktuell empiri innen finansmarkedene i Norge

Ferdigheter

Studenten kan

• budsjettere beslutningsrelevant kontantstrøm til totalkapitalen og egenkapitalen i investeringsprosjekter
• bruke finansmatematikk for å diskontere kontantstrømmer
• beregne risikojustert avkastningskrav ved hjelp av kapitalverdimodellen
• gjennomføre lønnsomhetsanalyser av investeringsprosjekter basert på nåverdi, internrente, modifisert internrente og tilbakebetalingsmetoden
• utføre sensitivitetsanalyser for å belyse risiko i investeringsprosjekter
• beregne økonomisk levetid for investeringsprosjekter
• beregne effektiv rente og kan anvende den i ulike finansieringsbeslutninger
• ta hensyn til skatt og inflasjon ved investerings- og finansieringsbeslutninger
• beregne forventet avkastning og avkastningens standardavvik for risikoutsatte prosjekter
• beregne forventet avkastning og avkastningens standardavvik for porteføljer, og kan finne minimum-varians porteføljen
• beregne Value-at-Risk for risikoutsatte prosjekter og porteføljer
• bruke regresjonsanalyse for å avdekke kredittrisiko
• bruke IKT-verktøy for å analysere lønnsomhet og risiko i investerings- og finansieringsprosjekter

Generell kompetanse

Studenten kan

• reflektere kritisk rundt etiske problemstillinger knyttet til investerings- og finansieringsprosjekter

Forelesninger og øvinger

The assessment will be based on a portfolio of the following:

• One individual project delivery consisting of a report (2000 - 4000 words)
• An individual oral examination (30 minutes)

The portfolio will be assessed as a whole and cannot be appealed.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

In the event of a postponed examination in this course the exam may be held as an oral exam. Oral exams cannot be appealed.

All aids are permitted, provided the rules for plagiarism and source referencing are complied with.

For the oral exam, students will only have access to the project report.

Grade scale A-F.

Two internal examiners. External examiner is used periodically.

Sergiy Denisov

Knowledge of standard classical optimization algorithms would be beneficial. Familiarity with undergraduate physics is also an advantage, but by no means any prerequisite.