AI for Financial Analysis
Build Predictive Trading Models with Machine Learning and Financial DataBy Renata Holloway
Length8h 4m
About this audiobook
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.
Audiobook details
GenreTechnology
Length8 hrs 4 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
LanguageEnglish
Table of contents
1Introduction
2Preface
3Chapter 1 — Setting Up Your AI Trading Lab
4Chapter 2 — Acquiring and Cleaning Financial Data
5Chapter 3 — Feature Engineering: Technical Indicators and Price Patterns
Show all chaptersShow less
6Chapter 4 — Backtesting 101: A Simple Moving Average Crossover Strategy
7Chapter 5 — Machine Learning Foundations for Financial Data
8Chapter 6 — Predicting Price Direction with Logistic Regression and Random Forests
9Chapter 7 — Gradient Boosting and XGBoost for Trading Signals
10Chapter 8 — Deep Learning for Time Series: LSTMs and Sequence Models
11Chapter 9 — Transformer Models for Financial Forecasting
12Chapter 10 — Sentiment Analysis: Harvesting News and Social Media Alpha
13Chapter 11 — Building a Fear & Greed Sentiment Index
14Chapter 12 — Risk Management and Position Sizing
15Chapter 13 — Portfolio Optimization with AI
16Chapter 14 — Reinforcement Learning for Trading
17Chapter 15 — Alternative Data: Earnings Calls, SEC Filings, and High-Frequency Data
18Chapter 16 — From Backtest to Paper Trading: Building Your Bot’s Execution Logic
19Chapter 17 — MLOps for Traders: Automating Retraining and Monitoring
20Chapter 18 — Ethics, Regulations, and Avoiding Catastrophic Loss
21About the Author
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