AI for Financial Analysis

Build Predictive Trading Models with Machine Learning and Financial Data

Renata Holloway

• Publisher: Chiify
• Year: 2026
• eBook ISBN:
  9798259604636
• Subtopic: Artificial Intelligence (AI) & Semantics

Machine learning for finance and quantitative analysis are transforming how analysts work. In AI for Financial Analysis, Renata Holloway delivers a hands-on guide to building practical tools for valuation, risk modeling, and time-series prediction. From beginner to pro, you'll learn to forecast markets, automate workflows, and gain a competitive edge. This book stands apart from generic texts by focusing on real-world implementation. Competing with authors like [placeholder], Holloway offers clear code examples and case studies that bridge theory and practice. What You'll Build Automated valuation models using regression and neural networks Risk assessment tools with classification and anomaly detection Time-series forecasting for stocks, bonds, and macroeconomic data Portfolio optimization via reinforcement learning Who This Is For Financial analysts, data scientists, and developers seeking to apply AI/ML to finance. No prior ML experience required—just basic Python skills. Holloway's step-by-step approach ensures you can deploy models immediately. Unlike [placeholder]'s theoretical works, this book emphasizes practical tools and actionable insights. Master financial modeling with machine learning. From data preprocessing to deployment, AI for Financial Analysis equips you with the skills to automate analysis, predict trends, and make data-driven decisions. Start your journey today.

Table of contents

• AI for Financial Analysis
• AI for Financial Analysis
• Preface
• Chapter 1 — Setting Up Your AI Trading Lab
• Chapter 2 — Acquiring and Cleaning Financial Data
• Chapter 3 — Feature Engineering: Technical Indicators and Price Patterns
• Chapter 4 — Backtesting 101: A Simple Moving Average Crossover Strategy
• Chapter 5 — Machine Learning Foundations for Financial Data
• Chapter 6 — Predicting Price Direction with Logistic Regression and Random Forests
• Chapter 7 — Gradient Boosting and XGBoost for Trading Signals
• Chapter 8 — Deep Learning for Time Series: LSTMs and Sequence Models
• Chapter 9 — Transformer Models for Financial Forecasting
• Chapter 10 — Sentiment Analysis: Harvesting News and Social Media Alpha
• Chapter 11 — Building a Fear & Greed Sentiment Index
• Chapter 12 — Risk Management and Position Sizing
• Chapter 13 — Portfolio Optimization with AI
• Chapter 14 — Reinforcement Learning for Trading
• Chapter 15 — Alternative Data: Earnings Calls, SEC Filings, and High-Frequency Data
• Chapter 16 — From Backtest to Paper Trading: Building Your Bot’s Execution Logic
• Chapter 17 — MLOps for Traders: Automating Retraining and Monitoring
• Chapter 18 — Ethics, Regulations, and Avoiding Catastrophic Loss
• About the Author