#INFOSYS

Time to be alert on this one on long side

Cmp 1875, expecting wave C move down towards 1600 in coming days

View negates above 1954 previous top

Okay look - I didn’t grow up with comic books. But I read stacks of science fiction digests and I kit-bashed model cars into fantasy rods, and just tried to maintain a low profile until I could get out of the house. So you could say I’m a distant cousin of the comic book fan. /1

And sometimes one’s childhood passion follows them into adulthood, sometimes not, sometimes as an adult one finds a passion they never had as a child and embraces it. For me, that must include getting involved with a puppet show at the age of thirty. /2

So who’s to say whose passion’s legitimate and whose is bogus? Nobody. Someone might indict your passion but they have no ground to stand on and should be dismissed out of hand. /3

Maybe they’re envious that you have a passion, maybe they think that their passion is cooler than yours, or maybe they’re just insecure, who knows. Doesn’t matter. /4

What matters is that if something gives you joy, and in the process doesn’t hurt others, then by all means follow it. It’s how we explore our secret hopes, our shadow sides, our inner character. /5

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.

Low-risk, High-return stocks are what we look for relentlessly

But many of them we know but ignore
some of them don't grab financial headlines

Here are 10 low-risk, high return stocks (that we ignore):

What I mean by “high-return”?

These stocks have OUTPERFORMED the market:

💹Chart with upwards trend Since IPO
💹Chart with upwards trend Over the last 5 Years or since IPO

These are all long-term winners that will keep on winning

What do We Mean by “low-risk”?

A combination of:

❣️Great management
❣️Broad moat
❣️Financially strong
❣️High ROCE
❣️Low Disruption/Dilution/Volatility
❣️Consistent Organic Growth
❣️Assorted Customers/Suppliers
❣️Low debt

1-#CDSL

A depository is a big business and will grow continuously, Its a sustainable business
cause "Market hai toh CDSL bhi hai or rahega"

2- #Dixon

High Management Efficiency with a high ROE of 19.30%
Healthy long-term growth as Net Sales has grown by an annual rate of more than 30%
High Institutional Holdings