Stop making investment decisions using this metric!

The Pitfalls of Relying on the Central Limit Theorem in Portfolio Return Analysis.
In the world of finance, both individuals and investing professionals alike strive for making sound investment decisions with consideration of risk. Usually this process involves understanding and analysis of portfolio returns. Central to this methodology is the Central Limit Theorem (CLT), a statistical concept widely employed to assess the behavior of financial data. However, beneath its apparent utility lies a series of assumptions that can mislead rather than illuminate. This article delves into why a critical examination of the CLT’s applicability in portfolio return analysis is imperative, shedding light on its limitations and the alternatives that offer a more accurate view of financial reality.
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[@FXPhysics]

Just about to hit 20 years in trading and portfolio management. I can say that the Sharpe Ratio is more of a staple risk-adjusted metric that is expected to "belong" in a prospectus. However, everybody in the industry overlooks it as reductive and impractical. What I have been using with much more practical value is the MAR or Calmar ratios; and to a lesser extent, a CAGR-to-average drawdown ratio. The former two also happen to take care of real-world fat tail distributions, especially to the downside.

[@axe863]

I prefer Generalized Rachev Ratio. Why not use a weighted string of metrics { tail co-moments; path-dependent; path-independent} and create a spider plot. "Heavy tailedness" is a function of the level of temporal aggregation. In the real world, different class of assets have varying degrees of speed towards "Gaussification" even when accounting for non-stationarities. Some "crazy" assets/asset based strategies are so heavy-tailed that they dont even have finite mean

[@trevorthrash2160]

Glad you’re back! Love your videos

[@goodlack9093]

fantastic! thank you for this info, a lot to consider..!

[@Rex-Daemon]

Your videos really good, please do them regularly 🙌

[@kimsourtat601]

When I study time series data, I always doubt that how come they use only date and value of stock to predict the future outcome while they can just observe the social economic and political policy such as inflation rate, unemployed rate,..

[@alessiob.3632]

Wow I really missed your videos

[@ABKW119]

Hey love the videos, a question I’ve always had was why did you choose to focus on CME’s products in your weather derivatives series rather than looking at other OTC products?

[@QuantPy]

Plan was to use the only available (undisputed) market prices for weather derivatives and imply the market risk premium.

[@janslusarek5587]

Will be there videos of using discrete models for modelling price asset volatility (non derivatives), for example DCC-GARCH?

[@conversationsoncharacter3069]

Have you considered building a market structure tool to watch the short volume (supply) and a sentiment analysis score (demand) for each stock? If you follow the US market it would very useful to get your input on what's happening recently, for example how options move the market in opex, how Vix affects opex, or ways of protecting against quad witch, etc. Especially if you can explain some of these concepts in your videos, and then how to build them into algos, or another thing is "if you built your own algo for personal use, what kind of lf strategies would you consider to use, another one with your knowledge, what do you watch in the market? Like Reply

[@jacquesfournier4616]

Hi, I have a problem using seaborn, what version are you using for seaborn and pandas? Thinks

[@timmolendijk]

So… around #16:37 … the MLE-estimated mean and variance of a normally distributed set of returns R equal… ("someone has done the hard job for you, they've gone through the math")… the mean and variance of samples r_i…?!? 🤨 Guess I'm missing the point of the entire MLE-concept in this context.

[@AronLichteFilm]

I heard the omega ratio is a better metric than the sharpe ratio couse it doesn't punish positive volatility

[@ertbalalbert1312]

What do you think of the Serenity Ratio as introduced by KeyQuant? Ulcer Index Root Mean Square Measure of the average Risk of Drawdowns of Drawdowns (the lower the better) UPI (Ulcer Return “Sharpe Ratio”-like Indicator Performance Index) UPI = Ulcer Index (Return over Average Risk of Drawdowns) (the higher the better) CDaR(95%) Average of the 5% Measure of the Extreme Risk of Drawdowns “biggest” drawdowns (the lower the better) Pitfall Ind. CDaR(95%) Penalty Factor - Measure of the Extreme Vol Risk of Drawdowns in number of volatilities (the lower the better) Penalized Risk Ulcer x Pitfall Measure of the Global Risk of Drawdowns (lower is better) Serenity Return “Sharpe Ratio”-like Indicator Pen. Risk (Return over Global Risk of Drawdowns) (the higher the better)

[@zachadkisson9533]

I have been searching everywhere for the formula for theta on a call option and I can’t find it (for excel). Could you help me out? Thanks!

[@FXPhysics]

Look up the partial derivation of Black-Scholes around the parameter T - t

[@QuantPy]

It’s in the table at the bottom for European Options. en.m.wikipedia.org/wiki/Greeks_(finance)

[@SzTz100]

I don't understand why you have a giant microphone yet you are using your laptop's microphone.

[@QuantPy]

Neither do I, thought I was recording with correct microphone - in the end not. Honest mistake.

[@SzTz100]

@@QuantPy no problem, love your channel.

[@dominickballard2698]

😪 Promo'SM