Omega ratio explained: risk-adjusted performance evaluation (Excel)
How to take into account the interaction between the upside and the downside of the portfolio as well as the whole shape of the portfolio return distribution? The answer is often the Omega ratio - an intuitive and simple tool for risk-adjusted performance evaluation developed by Keating and Shadwick in 2002 and popular with investment practitioners. Today we will learn how to apply and interpret the Omega ratio in Excel using a portfolio of five stocks. Don't forget to subscribe to NEDL and give this video a thumbs up for more videos in Investment! Please consider supporting NEDL on Patreon: / nedleducation
▶︎
Post-modern portfolio theory explained: Sortino ratio and volatility skewness (Excel)
▶︎
Adjusting for downside risk: Calmar, Sterling, and Sortino (Excel)
▶︎
Excel for Finance and Accounting Full Course Tutorial (3+ Hours)
▶︎
Python vs Excel vs SQL vs Power BI | Which Tool Solves It Fastest?
▶︎
Portfolio Optimisation with Higher Moments (Excel)
▶︎
Cointegration in time series and pair trading strategies (Excel)
▶︎
How Clement Ang Achieved 150%+ Returns in the US Investing Championship
▶︎
Calculate performance ratios in Excel! Sharpe, Sortino, Treynor, and Information Ratio!
▶︎
The Real Reason Gold Sold Off This Week | Frank Giustra
▶︎
Billionaire's WARNING: I'm SELLING. The Crash Is Already Here!
▶︎
Microsoft Excel 2021/365 Tutorial: 3.5+ Hours of Advanced Excel Training Course
▶︎
Capital Market Line (CML) vs Security Market Line (SML)
▶︎
Risk-adjusted performance evaluation: Sharpe, Treynor, and Jensen's Alpha (Excel) (SUB)
▶︎
Risk parity portfolio explained: risk contributions of asset classes (Excel)
▶︎
The Only Fundamental Analysis Video You Will Ever Need... (Full Course: Beginner to Advanced)
▶︎
The Ultimate LOOKUP Guide (XLOOKUP, VLOOKUP, HLOOKUP and more)
▶︎
The Sharpe Ratio Explained (by a quant trader)
▶︎
Optimal rebalancing: tolerance bands approach (Excel)
▶︎
Sharpe Ratio vs. Sortino Ratio Explained | Risk-Adjusted Performance Metrics ⚖️📊
▶︎