In this episode of ODSC’s Ai X Podcast, Dr. Yves J. Hilpisch, founder and CEO of The Python Quants (http://tpq.io), and founder and CEO of The AI Machine (http://aimachine.io), joins us to discuss reinforcement learning for finance.
Yves is also the author of the book "Reinforcement Learning for Finance” and has a diploma in Business Administration and a Ph.D. in Mathematical Finance. Yves is also an adjunct professor for Computational Finance at the Miami Herbert Business School.
Show Topics:
Overview of The Python Quants
The speaker's new book, “Reinforcement Learning for Finance” and why the focus on reinforcement learning
Dynamic time problems
Markov decision processes
Key types of reinforcement learning models
Deep Q-Learning (DQL) and how it relates to Q-Learning
How deep Q-Learning be applied to financial contexts, such as trading strategies or portfolio management
Issues associated with using static historical time series data for training DQL agents in finance
End-of-day data vs tick data
Adding white noise to historical time series data to improve the training of DQL agents
Key differences between the noisy time series data and the simulated time series data approaches
Generative Adversarial Networks (GANs) utility for generating synthetic financial time series data
GANs’ advantages over traditional Monte Carlo simulations in generating financial data
How to check the quality of synthetic data
The role of Kolmogorov-Smirnov (KS) test in evaluating the synthetic data generated by GANs
How the chapter compare the effectiveness of GAN-generated data to real financial data
The primary goal of the trading agent
The role of buy bots
The role of agentic AI
Topic analysis and sentiment analysis
Overview of the “Researchers Find AI Model Outperforms Human Stock Forecasters ‘Financial Statement Analysis with Large Language Models’” paper
Yves’ session at ODSC Europe
SHOW NOTES
Monte Carlo Simulation in Finance: https://www.investopedia.com/articles/investing/112514/monte-carlo-simulation-basics.asp
Python Quants: https://home.tpq.io/
Certificate in Python for Finance: https://home.tpq.io/certificate/
Markov decision process: https://en.wikipedia.org/wiki/Markov_decision_process
Black Scholes model: https://www.investopedia.com/terms/b/blackscholes.asp
Deep Q Learning: https://www.tensorflow.org/agents/tutorials/0_intro_rl
Backtesting: https://www.investopedia.com/terms/b/backtesting.asp
Model collapse: https://en.wikipedia.org/wiki/Model_collapse
GANS: https://en.wikipedia.org/wiki/Generative_adversarial_network
Black Swan Events: https://www.investopedia.com/terms/b/blackswan.asp
Kamograve Smirnov test: https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test
Delta Hedging: https://www.investopedia.com/terms/d/deltahedging.asp
Hedging strategies: https://www.investopedia.com/trading/hedging-beginners-guide/
Option Replication: https://www.cfainstitute.org/en/membership/professional-development/refresher-readings/option-replication-put-call-parity
Geometric Brownian motion: https://en.wikipedia.org/wiki/Geometric_Brownian_motion
Jump Diffusion: https://en.wikipedia.org/wiki/Jump_diffusion
Heston model: https://en.wikipedia.org/wiki/Heston_model
Bates Mode: https://en.wikipedia.org/wiki/Stochastic_volatility_jump
Gain Fallacy (A loss of 70% requires a 300% gain to break even): https://www.rgbcapitalgroup.com/preserving-capital
Prime Brokers: https://www.investopedia.com/terms/p/primebrokerage.asp
Algorithmic trading: https://www.investopedia.com/articles/active-trading/101014/basics-algorithmic-trading-concepts-and-examples.asp
Financial statement analysis, with large language models: https://arxiv.org/pdf/2407.17866
Information
- Show
- FrequencyUpdated Weekly
- PublishedSeptember 17, 2024 at 9:00 PM UTC
- Season1
- Episode38