Program Objectives:

• Learn AI techniques for financial data analysis and prediction.
• Master time series forecasting for financial markets using AI.
• Build predictive models for risk management and asset pricing.
• Understand how AI-driven strategies optimize portfolio management.
• Gain hands-on experience in deploying financial forecasting models.

What you will learn?

1. Modules for Financial Forecasting using AI:

   1. Introduction to Financial Forecasting and AI
      • Overview of Financial Forecasting
      • Role of AI in Finance: Benefits and Challenges
      • Applications of AI in Financial Markets (Stock Prices, Currency Exchange, Portfolio Optimization)
   2. Financial Time Series Data
      • Characteristics of Financial Data: Volatility, Trends, and Noise
      • Data Sources: Stock Market Data, Economic Indicators, Sentiment Data
      • Data Preprocessing: Normalization, Handling Missing Data, and Feature Engineering
   3. Classical Financial Forecasting Models
      • Moving Averages, Autoregressive (AR), ARIMA, and GARCH Models
      • Exponential Smoothing and Trend Analysis
      • Limitations of Traditional Models in Financial Forecasting
   4. Machine Learning for Financial Forecasting
      • Regression Models for Forecasting (Linear, Ridge, Lasso)
      • Decision Trees, Random Forests, and Gradient Boosting
      • Feature Selection and Engineering for Financial Data
   5. Deep Learning for Financial Time Series
      • Introduction to Neural Networks for Financial Forecasting
      • Recurrent Neural Networks (RNNs), LSTMs, and GRUs
      • Convolutional Neural Networks (CNNs) for Time Series Forecasting
   6. AI for Volatility and Risk Forecasting
      • Predicting Financial Volatility with AI
      • AI for Risk Management in Trading Strategies
      • AI-Driven Value-at-Risk (VaR) Models
   7. Sentiment Analysis and Alternative Data Sources
      • Using Sentiment Data from Social Media and News for Forecasting
      • Natural Language Processing (NLP) for Sentiment Analysis
      • Integrating Alternative Data Sources (Google Trends, Social Sentiment) with AI Models
   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
   9. AI for High-Frequency Trading (HFT)
      • Introduction to High-Frequency Trading
      • AI Techniques for HFT Strategies
      • Predictive Modeling in Millisecond Data: Opportunities and Challenges
   10. Probabilistic Forecasting and Uncertainty
       • Bayesian Methods for Financial Forecasting
       • AI for Uncertainty Estimation in Predictions
       • Applications in Financial Risk and Derivatives Pricing
   11. Ethics and Regulations in AI-Driven Finance
       • Ethical Considerations in AI-Based Financial Models
       • Regulatory Challenges for AI in Finance (e.g., GDPR, Market Manipulation)
       • Transparency and Explainability of AI Models in Financial Decision-Making