Which ETF gives best exposure to nasdaq

A long but must read thread. Udaybhan Rathore and myth making in Maratha history: An analysis. This is the thread on Udaybhan Rathore, who he was and how over the time period, his life was portrayed in maratha chronicles and pseudo history. (1/n)

Udaybhan rathore was the head of Bhinai thikana (84 villages) in Ajmer. He was the direct descendant of the famous Rao Chandrasen, Raja of Marwar (4th generation) who resisted Akbar all his life and never made any alliance with mughals like Maharana Pratap. (2/n)

Later in 1783, Maharaja of Jodhpur gave title of Raja to Bhinai family for their military service. Bhinai family and their direct descendant Raja Sh Mayuraj Singh still lives in Ajmer. Anyway coming to Udaybhan Rathore and story of Tanaji. (3/n)

Jai Singh's march on Shivaji and treaty of Purandar (1665) is a well known episode to repeat here. After getting the hill forts from Shivaji under the treaty, Jai Singh didn't have enough man-power to man every one of the small forts because of his next (4/n)

campaign against Bijapur. Jai Singh personally took over a couple of important forts like Kondhana (Sinhgarh), Purandar etc. Others were given to some mughals or their fortification walls destroyed. Jai Singh died in 1667 near Burhanpur while enroute to the north. (5/n)

1 of 16: WE ARE ALL JEWS HERE: THE STORY OF RODDIE EDMONDS

One of the most moving and relevant stories of the Battle of the Bulge, or any American Soldier in any war, is that of Master Sergeant Roddie Edmonds, a Knoxville, Tennessee native, who served with the 106th Infantry.

2 of 16:

Roddie was captured early on in the Battle of the Bulge, on December 19th, when Panzer forces plowed through his unit.

He, along with almost his entire regiment, was forced to surrender.

3 of 16:

The men were transported to the Stalag IX-A POW camp in Ziegenhain, Germany.

Roddie was the senior enlisted American Soldier at the site. As such, he was the conduit between all American Soldiers and their German captors.

4 of 16:

In late January, the camp’s commandant, Major Siegmann [pictured here], ordered Roddie, a Christian, to identify all Jewish Soldiers and order them to stand in formation by themselves the next day.

[Jewish Soldiers were a minority within American units]

5 of 16:

Throughout WWII, captured Jewish Soldiers were often tortured and then killed by their German captors.

In fact, Jewish Soldiers had been told to bury their dog tags before capture. The dog tag identified Jews with the letter “H” for Hebrew.

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

why do I hear boss music

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.