Time series analysis and its application to statistical arbitrage in financial markets; statistical properties of financial data including trends, seasonality, and volatility; application of econometric and quantitative techniques for modeling and forecasting asset prices; design and evaluation of data-driven trading strategies based on mean reversion, cointegration, relative value; practical analysis of real financial time series.
Machine learning techniques and their applications in financial analysis and decision making; Financial Data Sources, Data Cleaning, Feature Engineering; supervised learning; unsupervised learning; Classification Methods; Credit Scoring; Bankruptcy Prediction; Fraud Detection; Ensemble Methods, Bagging, Boosting, XGBoost; Dimensionality Reduction, Clustering, PCA; Predictive Analytics; Credit Risk Modeling, Loan Default Prediction.
Security analysis and portfolio optimization; valuation of equities, bonds, other financial assets using fundamental and quantitative approaches; application of modern portfolio theory to risk-return trade-offs; use of financial data, performance measures, optimization techniques; construction of efficient investment portfolios and support of practical investment decision-making.
Modeling and analysis of interest rates and credit risk; behavior of interest rates, term structure models, and yield curves; quantitative approaches to measuring and managing credit risk; application of mathematical and statistical techniques to real financial data; pricing of bonds and credit-sensitive instruments; assessment of default risk and support of risk management, investment decisions in financial institutions.
Application of investment theory and technical analysis through real-world case studies; ethical decision-making in investment management; advising individual and institutional investors; long-term wealth management strategies; short-term tactical trading with emphasis on technical analysis; portfolio construction and evaluation; disciplined investment decision-making aligned with professional standards.
Quantitative methods for measuring, modeling, and managing financial risk; analysis of market, credit, and operational risks; application of statistical and mathematical techniques to real financial data; use of risk metrics, simulation methods, and stress testing; support of risk management practices and regulatory compliance in financial institutions.
The internship training provides the student with the opportunity to gain valuable practical business experience and insights in an organizational environment for a continuous period of 16 weeks to explore career interests and prepare student for the demands of today’s job market. The course is designed to expose students to the reality of his professional practice which contributes to their academic and career development. The student is required to write one progress report, a brief final report and make a presentation on their training experience and knowledge gained.
The summer training provides the student with the opportunity to gain valuable practical business experience and insights in an organizational environment for a continuous period of 8 weeks to explore career interests and prepare student for the demands of today’s job market. The course is designed to expose students to the reality of his professional practice which contributes to their academic and career development. The student is required to write one progress report, a brief final report and make a presentation on their summer training experience and knowledge gained.
This is the first part of a two-semester senior-year project in Accounting & Corporate Finance. Students will start the research project proposal process under the supervision of a faculty member.
This is the second part of a two-semester senior-year project in Accounting & Corporate Finance. Students continue working on the research project proposed under the supervision of a faculty member. The course involves implementing mathematical methods, analyzing results, and documenting findings in a formal written report and an oral presentation.
Market microstructure and trading mechanisms; order-driven markets, price formation, and liquidity; design and evaluation of algorithmic trading strategies; analysis of transaction costs and execution risk; impact of trading algorithms on market quality; practical analysis of high-frequency and intraday market data.