Financial analysis and decisions using AI tools 2400-ZEWW978

Key differences between major LLMs, trade-offs between cost, performance, and capabilities.
-LLM specifications and suitability in specific use cases.
-Structure of effective financial prompts, advanced prompting techniques for complex financial analysis.
-Prompt engineering for financial queries, data integration in financial prompts.
-Python packages for financial applications; connecting to financial APIs, using AI to generate market data summaries.
-AI-powered financial statement analysis; income statement, balance sheet, and cash flow analysis.
-Automated ratio calculations, trend and industry analysis, EDGAR database integration, cross-company analyses.
-AI driven data visualization techniques in financial analysis.
-Comprehensive AI workflows for investment research.
-Explore and visualize investment strategies performance and risk using AI techniques.
-Time series preprocessing, normalization, handling missing data in financial datasets.
-Transformer and foundation models for financial time series, implementing transformer architectures for multi-horizon return forecasting.
-Portfolio optimization using AI enhancements for Markowitz optimization and Black-Litterman implementation.
-Practical considerations in designing quantitative investment strategies.
-Designing, implementing and backtesting technical trading strategies with AI, financial metrics for strategy assessment.
-Designing and analyzing investment strategies through specialized prompting.

Szacunkowy nakład pracy studenta:
Typ aktywności K (kontaktowe) S (samodzielne)
wykład (zajęcia) 30 0
ćwiczenia (zajęcia) 0 0
egzamin 0 0
konsultacje 10 0
przygotowanie do ćwiczeń 0 0
przygotowanie do wykładów 0 0
praca z materiałami dodatkowymi umieszczanymi na platformie Moodle 0 15
przygotowanie do kolokwium 0 0
przygotowanie do egzaminu(prace zaliczeniowe) 0 20
Razem 40 35 = 75