Financial Forecasting using AI

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Mentor Based

Program Syllabus

Module 1

About This Course

Participants will learn how AI enhances traditional financial forecasting by leveraging machine learning, time series models, and neural networks. The course covers AI techniques for predictive analytics in finance, risk management, and real-time forecasting of financial trends.

Module 2

Aim

This program focuses on equipping professionals with AI techniques for accurate financial forecasting, enabling them to predict trends, manage risks, and optimize financial performance.

Module 3

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.

Module 4

Modules for Financial Forecasting using AI:

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)
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
Classical Financial Forecasting Models
- Moving Averages, Autoregressive (AR), ARIMA, and GARCH Models
- Exponential Smoothing and Trend Analysis
- Limitations of Traditional Models in Financial Forecasting
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
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
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
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
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
AI for High-Frequency Trading (HFT)
- Introduction to High-Frequency Trading
- AI Techniques for HFT Strategies
- Predictive Modeling in Millisecond Data: Opportunities and Challenges
Probabilistic Forecasting and Uncertainty
- Bayesian Methods for Financial Forecasting
- AI for Uncertainty Estimation in Predictions
- Applications in Financial Risk and Derivatives Pricing
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

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.