Learning Objectives
5 objectives- Understand fundamental concepts and computational techniques used in financial analysis and decision making.
- Apply mathematical models and algorithms for valuation, portfolio optimization, and risk management.
- Analyze and implement option pricing models and quantitative trading strategies.
- Explore machine learning applications and algorithmic trading in modern financial markets.
- Evaluate various risk management methods and their practical applications in finance.
Content Outline
PreviewUnit 1208: Computational Finance
1. Introduction to Computational Finance
- Definition and scope of computational finance
- Role of computer algorithms and mathematical models in finance
- Applications: financial data analysis, investment decision-making, risk management
- Overview of financial markets and instruments
2. Time Value of Money and Discounted Cash Flows
- Concept of time value of money (TVM)
- Present value (PV) and future value (FV) calculations
- Discounted cash flow (DCF) analysis
- Importance of TVM and DCF in asset valuation and investment appraisal
- Examples of DCF in project and security valuation
3. Portfolio Optimization Techniques
- Introduction to Modern Portfolio Theory (MPT)
- Risk and return trade-offs
- Asset allocation strategies
- Efficient frontier and the Capital Market Line (CML)
- Optimization techniques: mean-variance optimization
- Diversification benefits and portfolio construction
4. Option Pricing Models
- Introduction to derivative securities and options
- Black-Scholes model
- Assumptions
- Formula derivation and interpretation
- Binomial option pricing model
- Multi-period binomial trees
- Applications in option valuation and risk management
- Implementing option trading strategies
5. Risk Management in Finance
- Types of financial risks: market risk, credit risk, operational risk, liquidity risk
- Risk measurement techniques:
- Value at Risk (VaR)
- Conditional VaR (CVaR)
- Stress testing and scenario analysis
- Risk management strategies:
- Hedging
- Diversification
- Use of derivatives for risk mitigation
- Regulatory frameworks and risk compliance
6. Machine Learning in Finance
- Overview of machine learning concepts
- Supervised learning techniques:
- Regression
- Classification
- Unsupervised learning techniques:
- Clustering
- Neural networks and deep learning basics
- Applications in financial data analysis:
- Market trend prediction
- Credit scoring
- Fraud detection
- Algorithmic trading
7. High-Frequency Trading and Algorithmic Trading
- Definition and characteristics of high-frequency trading (HFT)
- Algorithmic trading strategies and their design
- Market microstructure and order book dynamics
- Role of technology and infrastructure in HFT
- Regulatory and ethical considerations
8. Quantitative Trading Strategies
- Introduction to quantitative trading
- Statistical arbitrage:
- Concept and implementation
- Pairs trading
- Trend-following strategies
- Mean-reversion strategies
- Developing and backtesting quantitative models
- Risk and performance evaluation of trading algorithms
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