Financial Forecasting using AI

Transform Financial Forecasting with AI: Accurate, Real-Time Predictions

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MODE
Online/ e-LMS

TYPE
Self Paced

LEVEL
Moderate

DURATION
3 Weeks

About

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.

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.

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.

Program Structure

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
12. Final Project
  - Develop an AI-based financial forecasting model using real-world financial data.
  - Example: Build a stock price prediction system, a volatility forecasting tool, or an AI-driven portfolio optimizer.

Participant’s Eligibility

Finance professionals, data scientists, AI engineers, and financial analysts interested in applying AI to financial forecasting.

Program Outcomes

Proficiency in building and deploying AI-driven financial forecasting models.
Expertise in time series analysis for financial data prediction.
Ability to optimize investment strategies using AI.
Practical experience in risk management through predictive analytics.

Fee Structure

Standard Fee: INR ~~4,998~~ USD 78

Discounted Fee: INR 2499 USD 39

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!

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Key Takeaways

Program Assessment

Certification to this program will be based on the evaluation of following assignment (s)/ examinations:

Exam	Weightage
Mid Term Assignments	50 %
Project Report Submission (Includes Mandatory Paper Publication)	50 %

To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.