Quantitative Financial Data Analysis in Matlab

1.3.2027 - 2.4.2027

5 cr

Registration open

Face-to-faceOnline

Derivation of the Ordinary Least Squares (OLS) point estimator and the covariance matrix given standard OLS assumptions
Standard linear regression model
Estimating the Capital Asset Pricing Model (CAPM) by means of OLS
Multivariate linear regression model (Fama and French 3-factor model, Fama and French 5-factor model, Fama and French 6-factor model)
Long/short-strategies based on the CAPM
Relaxation of OLS assumptions and the associated problems for the applied researcher
Volatility clustering as stylized fact of financial markets
Statistical hypothesis tests: (Testing factor model exposures, testing for conditional heteroskedasticity (ARCH-effects), testing for causality in volatility)
Multiple equation models: Estimating Seemingly-Unrelated-Regression (SUR) models
Implementing and evaluation momentum-based trading strategies

Contains

Check the institution’s website for the required studies.

No active implementations at present.

Additional information and registration

Price and registration period: Check on the university's website

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Additional information:

Fields

Business, administration and law

Scope

5 cr

Code

LASK3063

The students are expected to be able to implement standard trading strategies and to independently perform empirical research in asset pricing. In this course, the U.S. equity market is explored as a showcase for pricing assets. By the end of this course students should be able to:

Test linear asset pricing models (factor models)
Implement zero-cost trading strategies
Perform independent research of financial data
The course will also support the development of students' competences in the following areas: - competence in analytical/mathematical thinking
competence in data analysis (programming skills)
creativity and innovation
self-organisation
time management

By the end of the course students will achieve the following intended learning outcomes:

Students will demonstrate knowledge related to analysis of securities, testing statistical hypotheses and implementation of various econometric methodologies
Students will apply critical thinking skills, problem solving skills and knowledge of matrix algebra and econometrics to solve different managerial and economic problems
Students will demonstrate skills in data analytics while conducting a termpaper using empirical real life stock market data from U.S. S&P 500 index

How does the course relate to the UN’s sustainable development goals?
The course supports the UN Sustainable Development Goals quality education and decent work and economic growth.

Green, W.H., 2008, Econometric Analysis, chapter 1-4 (until
Shen, D., Urquart, A., Wang, P., 2019. A three-factor pricing cryptocurrencies, Finance Research Letters (forthcoming).
Bodie, Z., Kane, A., and Marucs, A.J., 2008, Investments, chapter 8.
Green, W.H., 2008, Econometric Analysis, chapter 8 (part 8.1-8.4).
Altman, E., 1968. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance 23, 189–209.
Altman, E. I., Haldeman, R.G. and Narayanan, P. (1977) ‘ZETA Analysis: A New Model to Identify Bankruptcy Risk of Corporations’. Journal of Banking and Finance 129-54.
Grobys, K., and N. Sapkota, 2019. Predicting Cryptocurrency Defaults, working paper.
Jegadeesh, N., and Titman, S., 1993, Returns to Buying Winners and Selling Losers: Implications for stock market efficiency. Journal of Finance 48, 65-91.
Grobys, K., Sapkota, N., 2019. Cryptocurrencies and Momentum, Economics Letters 180, 6-10.

Organiser

University of Vaasa

Contact details

avoinyo@uwasa.fi

029 449 8004

Belongs to themes:

Change management and analytics

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