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Applied Finance

Informacje ogólne

Kod przedmiotu: 2400-DS2AF Kod Erasmus / ISCED: 14.3 / (0311) Ekonomia
Nazwa przedmiotu: Applied Finance
Jednostka: Wydział Nauk Ekonomicznych
Grupy: Anglojęzyczna oferta zajęć WNE UW
przedmioty obowiązkowe dla II roku Data Science
Punkty ECTS i inne: (brak)
zobacz reguły punktacji
Język prowadzenia: angielski
Rodzaj przedmiotu:

obowiązkowe

Skrócony opis:

Applied Finance course consists of a 30 hour lecture and 15 hour lab. It’s a patchwork course conducted by several lecturers and is covering different current topics in the area such as machine learning and statistical tools in algorithmic trading, path dependent option pricing, linear factor models, risk modelling in financial institutions, financial management and capital structure. The details may vary year to year depending on the professors invited to give lectures

Pełny opis:

For the 2018/2019 version of the course:

1. Machine Learning/Statistical tools in algorithmic trading

The aim of this part of the Applied Finance course is to give basic theoretical background for high-frequency algorithmic trading. Students will learn characteristics of high-frequency data and steps needed to prepare the data, aggregate it to desired frequency. It will be shown how to build and verify profitability of simple own trading strategies (backtesting/validation). The specifics of validation of machine learning algorithms applied on time series data will be also discussed. R environment will be used for practical examples – its previous knowledge is expected.

2. Path dependent option pricing with Monte Carlo simulations and Rcpp package

During this module we will learn how to perform simple Monte Carlo simulations to perform valuation of path dependent exotic derivatives. To improve efficiency of simulations, we will see how to bring C++ code into R using Rcpp package. Also, we will discuss antithetic sampling as one of the ways to reduce variance of option price approximations. We will also learn how to present result of analysis with plots created with ggplot2 package.

3. An Introduction to Linear Factor Models

This module comprises of two components, introducing students to linear factor models motivated by the multifactor Arbitrage Pricing Theory (APT) framework.

The first component sets out the theoretical basis of linear factor models and their development.

The second component is of a practical nature and introduces students to the construction, estimation and interpretation of time-series linear factor models. Students are expected to have a basic knowledge of time-series econometrics and analysis.

4. Risk modelling in financial institutions

Credit Risk - Credit Scorecards (Logistic regression)

Market Risk - Value-at-Risk and Expected Shortfall (Historical Simulation, VC method, GARCH models)

Operational Risk - LDA approach (Poisson + EVT models)

Fraud Risk - Binary Choice Models (Econometrics

& ML methods)

5. Financial Management

Investment appraisal: identifying and calculating relevant cash flows for investment projects; calculating and discussing the relative merits of NPV and IRR; adjusting for risk and uncertainty in investment appraisal;

Specific investment decisions: lease or buy; asset replacement; capital rationing

Management of inventories, accounts receivable, accounts payable and cash

6. Capital Structure

Part I

Capital structure – meaning, practices and formal definitions. Impact on firm’s valuation.

Two perspectives of companies’ valuation in the presence of uncertainty

Concepts of arbitrage – two periods. Farkas’ Lemma, Stiemke’s theorem and “iron law” of no-arbitrage. The first Modigliani-Miller’s theorem in the case of economic paradise

Interpretation and derivation of Arrows’ coefficients – impact of investors’ utility and time preferences. Extension of M-M theory- the case of n>2 periods.

Part II

Law of iterated expectations and the third Modigliani-Miller’s theorem. Practical consequences for dividend policy.

M-M theory in the world of asymmetric information and taxes

Pecking order alternatives to M-M world

Capital structure theories – empirical evidence and verification. Approaches and ambiguous results.

Determinants of capital structure, optimality and other practical issues

Literatura:

Adedeji, A. (2002). "A cross-sectional test of pecking order hypothesis against static trade-off theory on UK data." available at SSRN: http://ssrn. com/abstract-302827.

Aldridge, I. (2013), High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems

Almazan, A. and C. A. Molina (2005). "Intra-Industry Capital Structure Dispersion." Journal of Economics \& Management Strategy 14(2): 263-297.

Beattie, V., et al. (2006). "Corporate financing decisions: UK survey evidence." Journal of Business Finance and Accounting 33(9-10).

Berry, M. A., Burmeister, E., & McElroy, M. B. (1988). Sorting out risks using known APT factors. Financial Analysts Journal, 44(2), 29-42.

Cao, C. and D. Mauer (2010). "CEO Turnover and Debt Policy Change."

Chan, E. (2008), Quantitative Trading: How to Build Your Own Algorithmic Trading Business

Chan, E. (2013), Algorithmic Trading: Winning strategies and their rationale

D. Eddelbuettel, Seamless R and C++ Integration with Rcpp

DeAngelo, H. and R. W. Masulis (1980). "Optimal capital structure under corporate and personal taxation." Journal of Financial Economics 8(1): 3-29.

E. G.Haug, The Complete Guide to Option Pricing Formulas

Erhart, S., et al. (2007). "Monetary Policy Committee Size and Inflation Volatility." Kiel Institute for the World Economy Working Paper 1377.

Fabozzi, F.J., Focardi, S.M. and Kolm, P.N. (2010), Quantitative Equity Investing: Techniques and Strategies

Fazzari, S. M., et al. (1988). "Financing constraints and corporate investment." Brooking Papers on Economic Activity 1.

Frank, M. and V. Goyal (2007). "Trade-off and pecking order theories of debt."

Frank, M. Z. and V. K. Goyal (2009). "Capital structure decisions: Which factors are reliably important?" Financial Management 38(1): 1-37.

H. Wickham, ggplot2: Elegant Graphics for Data Analysis, Springer

H. Wickham, R for Data Science, http://r4ds.had.co.nz/

M. S. Joshi, C++ Design Patterns and Derivatives Pricing, 2nd Edition

Miller, M. H. and K. Rock (1985). "Dividend Policy under Asymmetric Information." The Journal ofFinance 40(4).

Northield Information Services (2015). U.S. Macroeconomic Equity Risk Model. Available at http://www.northinfo.com/documents/7.pdf

S. Prata, C++ Primer Plus 6th Edition

Sadorsky, P., & Henriques, I. (2001). Multifactor risk and the stock returns of Canadian paper and forest products companies. Forest Policy and Economics, 3(3-4), 199-208.

Sogorb-Mira, F. and J. Lopez-Gracia (2003). "Pecking order versus trade-off: an empirical approach to the small and medium enterprise capital structure."

Strebulaev, I. and B. Yang (2006). "The mystery of zero-leverage firms."

Szczygielski, J. J., & Chipeta, C. (2015). Risk factors in returns of the South African stock market. Studies

Efekty uczenia się:

On finishing the course students possess versatile knowledge encompassing theoretical and practical aspects of selected topics in applied finance. They will be able to analyze and aggregate different types of data. They will know how to prepare and backtest investment strategies including different instruments (e.g. options), calculate appropriate evaluation statistics and select best performing strategies. Students will be familiar with risk modelling techniques and several aspects of corporate finance

K_W01, K_U03

Metody i kryteria oceniania:

Lab class:

During each lab class there will be a chance to obtain 10 points and it’s up to the teacher to provide the rules of getting them. The sum of the points will then result in a course grade.

Lecture:

A written exam for which the lecturers will provide one question for each course module, e.g. 7 modules > 7 exam questions graded by the teacher of the corresponding module. The final mark depends on the percentage of the points obtained.

Przedmiot nie jest oferowany w żadnym z aktualnych cykli dydaktycznych.
Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet Warszawski.