Allocation. The ultimate toolkit for long term investing success. TA/Indicators. Why Log Returns | Quantivity. A reader recently asked an important question, one which often puzzles those new to...
https://quantivity.wordpress.com/2011/02/21/why-log-returns/#comment-11721
I think the SSRN paper linked belabours the obvious i.e. Jensen's inequality. The formula they found with Taylor series algebra is just mean of log-normal distribution. No-one should, a priori, e...
https://quantivity.wordpress.com/2011/02/21/why-log-returns/#comment-11682
However, it may be debated that we should instead use log-returns. The advantage of this is that we no longer need to normalise values when we compare with other currencies. Further information ...
https://quantivity.wordpress.com/2011/02/21/why-log-returns/#comment-8950
and analyze the result. Let us use the Apple adjusted close price as our dataset. We use log-returns, standardized by subtracting their mean and dividing by their standard deviation, as our targ...
https://quantivity.wordpress.com/2011/02/21/why-log-returns/#comment-8924
The benefit of using returns, versus prices, is normalization: measuring all variables in a comparable metric, thus enabling evaluation of analytic relationships amongst two or more variables de...
https://quantivity.wordpress.com/2011/02/21/why-log-returns/#comment-8737
Now I know that this idea of applied (algebraic) topology crops up from time to time also in finance, see e.g. this blog post from Quantivity: Manifold learning.
been a researching minimum variance portfolios (from this link) and find that by building MVPs adding constraints on portfolio weights and a few other tweaks to
https://quantivity.wordpress.com/2011/04/17/minimum-variance-portfolios/#comment-8694
Why Log Returns
https://quantivity.wordpress.com/2011/02/21/why-log-returns/#comment-8511
Remember that that we can add up log returns to calculate the final return. For details I refer to this.
https://quantivity.wordpress.com/2011/02/21/why-log-returns/#comment-8487
In NLL, minimizing the loss function assists us get a better output. The negative log likelihood is retrieved from approximating the maximum likelihood estimation (MLE). This means that we try t...
https://quantivity.wordpress.com/2011/05/23/why-minimize-negative-log-likelihood/#comment-8485