Quantitative Finance
Comprehensive exploration of financial modeling, risk analysis, and investment strategies, equipping learners with advanced tools for data-driven decision-making in the financial sector.
Deep Learning for Sequences in Quantitative Finance
Overview of deep learning sequence methods in quantitative finance, covering feature extraction, return forecasting, portfolio allocation, and trading execution. Explores applications and challenges in the investment process.
From Smart Betas to Smart Alphas - Quantum Mental in the Age of Machine Learning
Explore smart beta and alpha strategies in finance with Milind Sharma, covering machine learning, deep learning, and portfolio construction techniques for modern quantitative investing.
A Reinforcement Learning Mechanism for Trading Wind Power Futures
Explore reinforcement learning for trading wind power futures, covering energy security, market setup, and machine learning models in this NYU seminar by Bruno Kamdem.
Option Pricing Bottom Up and Top Down
Explore option pricing methodologies, from classic bottom-up to innovative top-down approaches, analyzing their strengths and applications in financial markets.
When to Sell an Asset? A Distribution Builder Approach
Explore optimal asset selling strategies using distribution builder approach, analyzing utility functions, investor behavior, and market dynamics in this mathematical finance lecture.
Oil Futures Volatility Smiles in 2020: Why the Bachelier Smile is Flatter
Explore oil futures volatility smiles, comparing Bachelier and Black models. Gain insights into market behavior during 2020's unprecedented events through data analysis and theoretical frameworks.
Optimal Execution with Quadratic Variation Inventories - BQE Lecture Series
Explore optimal execution strategies in financial markets, focusing on quadratic variation inventories and automated trading, with insights from Princeton researcher Laura Leal.
A Factor Model of Company Valuation - BQE Lecture Series
Explore a factor model for company valuation with Prof. Liuren Wu, covering topics like liquidity, evaluation factors, and value investing in this insightful finance lecture.
Optionality as a Binary Operation - BQE Lecture Series
Explore optionality as a binary operation with Peter Carr, delving into time relativity, market building, and advanced concepts like conjugate power and skewness in financial engineering.
Turbulence in Finance - BQE Lecture Series
Explore financial turbulence with Mike Lipkin, covering topics like order drop, rough paths, and hedging strategies in this insightful lecture on advanced quantitative finance concepts.
Brooklyn Quant Experience Lecture Series - Financial Derivatives and Collateral Liquidity
Explore financial derivatives, risk management, and regulatory impacts with expert Samim Ghamami in this insightful lecture on quantitative finance and economic modeling.
A Smarter Model Risk Management Discipline - Making Smarter Models
Explore smart model risk management strategies, including identity tokens and usage tracking, to enhance financial modeling practices and regulatory compliance.
Quantifying Model Performance in Financial Risk Engineering
Explore quantitative model performance with Alexandre Antonov, covering replication, financial portfolios, and hedging strategies in this insightful lecture on advanced financial analysis techniques.
Super-Hedging Prices in Financial Markets with Transaction Costs
Poznaj strategie super-hedgingu na rynkach finansowych z kosztami transakcyjnymi. Zgłęb matematyczne podejście do wyceny i zabezpieczania inwestycji.
Trading and Hedging Bitcoin Volatility
Explore bitcoin volatility trading, hedging strategies, and crypto market dynamics. Gain insights into implied volatility, variance risk premium, and perpetual futures in unregulated exchanges.
Multivariate Portfolio Choice via Quantiles
Explore multivariate portfolio optimization using quantiles, extending univariate approaches to solve complex financial problems and develop new numerical solutions for multivariate preferences.
They Might Be Robots - Using Data for Investment Management and Trading
Explore data-driven investment strategies, from industry trends to machine learning techniques, for portfolio management and active trading in this insightful talk by Dan Von Kohorn.
Breadth Thrusts - Zweig, Whaley, and Breakaway Momentum
Learn to code the Breadth Thrust Indicator in Python and explore its application in identifying tradable market bottoms, with insights on various thrust indicators and breakaway momentum.
Investment Portfolio Management with Python - Maximizing Return and Minimizing Risk
Learn to maximize returns and minimize risk using Markowitz Portfolio Optimization and Sharpe Ratio. Master efficient frontier, volatility analysis, and advanced data science techniques for investment portfolio management.
Low Risk Investments - Python for Finance 4
Learn Python techniques for analyzing low-risk investments, including portfolio creation, risk assessment, ROI analysis, and stock sector diversification using data science libraries.