Program Structure

Module 1: Introduction to Financial Forecasting and AI

• Overview of financial forecasting and its role in financial decision-making.
• The benefits and challenges of applying AI to finance.
• Key applications of AI in financial markets (stock prices, currency exchange, portfolio optimization).

Module 2: Financial Time Series Data

• Characteristics of financial data: volatility, trends, and noise.
• Sources of financial data: stock market data, economic indicators, sentiment data.
• Data preprocessing: normalization, handling missing data, and feature engineering.

Module 3: Classical Financial Forecasting Models

• Overview of traditional models: moving averages, AR, ARIMA, GARCH.
• Exponential smoothing and trend analysis techniques.
• Limitations of classical models in predicting complex financial markets.

Module 4: Machine Learning for Financial Forecasting

• Regression models: Linear, Ridge, Lasso for financial data prediction.
• Advanced models: Decision Trees, Random Forests, Gradient Boosting.
• Feature selection and engineering for financial data.

Module 5: Deep Learning for Financial Time Series

• Introduction to neural networks for financial forecasting.
• Recurrent Neural Networks (RNNs), LSTMs, and GRUs for time series prediction.
• Using Convolutional Neural Networks (CNNs) for time series analysis.

Module 6: AI for Volatility and Risk Forecasting

• Predicting financial volatility using AI models.
• AI-driven strategies for risk management in trading.
• Developing AI-based Value-at-Risk (VaR) models.

Module 7: Sentiment Analysis and Alternative Data Sources

• Leveraging sentiment data from social media and news for financial forecasting.
• Natural Language Processing (NLP) techniques for sentiment analysis.
• Integrating alternative data sources like Google Trends and social sentiment into AI models.

Module 8: Reinforcement Learning in Finance

• Basics of Reinforcement Learning (RL) and its applications in trading.
• AI-driven portfolio optimization and trading strategies.
• Deep reinforcement learning for autonomous financial agents.

Module 9: AI for High-Frequency Trading (HFT)

• Introduction to high-frequency trading (HFT) strategies.
• AI techniques used in HFT.
• Challenges and opportunities in predictive modeling for millisecond-level data.

Module 10: Probabilistic Forecasting and Uncertainty

• Bayesian methods for financial forecasting.
• AI techniques for uncertainty estimation in predictions.
• Applications in financial risk management and derivatives pricing.

Module 11: Ethics and Regulations in AI-Driven Finance

• Ethical considerations in building AI-based financial models.
• Regulatory challenges (GDPR, market manipulation) in AI-driven finance.
• Ensuring transparency and explainability of AI models in financial decision-making.

Final Project

• Develop an AI-based financial forecasting model using real-world data.
• Example projects: Stock price prediction system, volatility forecasting tool, or AI-driven portfolio optimizer.