#Global #ETF Weekly & Month-end (thread): 29 Jan 2021

- 2020 worst perf #sector making comeback in 2021 $IXC

- This is helping #oil producing countries with #Nigeria & #UAE in top5 #Country ETF YTD performers in USD $NGE $UAE

- #SouthAfrica $EZA moved into top15 YTD performers

2/12

#EmergingMarkets #ETF still storming ahead against #DevelopedMarkets ETF, still not relatively "overbought" yet, but getting close.

#SouthAfrica might need a lot of catch up, but $EZA YTD performance in USD (+0.2%) continued 2nd half on 2020 recovery against $URTH (-0.8%)

3/12

#Global #Value #stocks still making short-term recovery, with $IWVL #ETF still looking strong relatively over the short-term. Over 3yr period it is however still lagging quite substantially.

4/12

This one is worrying, but then again, it's been for some time. #Global #Tech #stocks relative to #MSCI All Country World Index is definitely creating some concern. Be very careful.

$IXN vs $ACWI

5/12

#Internet companies still big driver on performance, with US Internet Co's #ETF growing 1.8% in January (YTD) in USD versus #MSCI World $URTH losing 0.8% over same period

#Chinese Internet Co's $KWEB however grew 13% in Jan. Wow! That helped #Naspers/#Prosus & also SA $EZA

Things I've learned trading for the past 4 years in no particular order while reflecting this Sunday on it all:

* It always goes higher than you expect it will.

* It always comes back down.

* It is very easy to make money in these markets. It is much harder to keep it.

* You will FOMO no matter how much you say you won't. Make sure you don't FOMO with everything and blow your account.

* You don't need some fancy indicator to be profitable. Get solid with a few (personally for me, Ichimoku and OBV are my favorites)

* Horizontal levels are more reliable than diagonal.

* Trade with friends. They'll tell you when you've fucked up and shame you for it.

* You can follow someone else's TA, but you better be able to tell if they're full of shit or not.

* Risk Management will ultimately lead to your success over time. Luck runs out.

* "As soon as you stop trying to get rich quick, you'll get rich quick."

* Beware of getting sucked into coin communities. They're full of hopium, bag hodlers, and outrageous price predictions. Echo chambers are dangerous.

* Always leave behind a small moon bag. Makes you feel better when it does another 10x.

A lot has been written today about the $GME pump on /r/WallStreetBets from a financial angle.

But I think there's another angle - this it also works as a *multiplayer game* and one with an interesting design.

Don't believe me? Let's look at it structurally!

1/

WSB pump of $GME exhibits a number of gamelike elements:
1. resource mechanics
2. multiplayer social mechanics
3. progression mechanics
4. multi-system interactions
5. prediction complexity, and
6. a powerful player fantasy to tie them together

2/

1. Just by itself, the stock market is an engrossing game (for those who can afford the time and money). It's got a variety of simple resource mechanics (buy / sell stocks), more complex mechanics (buy / sell options), super complex mechanics (would you like some futures?) ... 3/

... and a very high analytic complexity (which mechanic should I apply to maximize return, short term or long term?).

Like in chess, this analysis is very hard but therefore interesting.

And the system gives somewhat clear feedback about how well you're playing ($$$). 4/

2. Now WSB specifically adds social mechanics. Being a lone wolf timing the market is one thing - but banding together to see what you can do together, now that's something else!

Specifically WSB allows not just socializing/meta, but also for coordination. 5/

here’s a slightly wonky thread that people who use post-nominal letters will not like: let’s talk about real-time learning in quant-driven strategies and how to do it in practice, because a lot us do this as a result of real-world constraints. its basically TA with a dress-code.

so lets say you found a nee factor, but data is limited becuse it is new-ish? how the hell do you implement it without waiting eons in market-time? first: in small size because distributions are unknown. second: you update the calibration parameters dynamically as you go.

sometimes i am asked why i favor matching assets with volatility estimators instead of betas:
1) volatilities are not stationary but they undergo relatively synchronized regime changes
2) exponentially weighted vols are more responsive to changes in current conditions

3) betas are not stable in shorter terms and identifying changes takes longer and in the short term you get measurement errors if your sample is driving your benchmark
4) honestly the numerical methods are just simpler and i’m a dumdum so it makes it easier for me

5) for long-only baskets of different assets it avoids painful assumptions which may be wrong about correlation, which is good when you know your sample is low quality
6) for long-short baskets of like assets it avoids painful assumptions about intercepts (and therefore betas)

I've been trying to understand for some time the impact of $GBTC (Grayscale Bitcoin Trust) on the price of $BTC. Here's a good article to start, and here's a thread on my current thoughts...

There's no doubt that $GBTC has been a driving force in allowing investors to gain exposure to $BTC. For many investors, it provides the easiest way to get crypto exposure. As a result, $GBTC often trades at a large premium (20%-30%) over spot $BTC 2/9

Recently in January, $BTC prices have declined from a high of >$40k to <$30k (now back near $35k). $GBTC's premium to NAV has also dropped to <5% 3/9

It's worth understanding the mechanics of $GBTC. $GBTC allows accredited investors to buy private shares for 0% premium to NAV, and in return the accredited investors are locked up for 6 months. If you are a non-accredited investor, i.e. retail, you don't have this option 4/9

Many sophisticated investors have taken advantage of this arbitrage opportunity. One way an accredited investor can buy private shares of $GBTC is by depositing BTC (often times with leverage). In doing so, they can collect the $GBTC premium 5/9

Mkt view :
Only 290 companies trading above dec 2017 prices which was top of overall broader market. If one looks at nifty which is concentrated in 5 stocks, then it may scare them. Whereas individual stocks have yet lot of room to grow.

In history of markets, when interest rates can not grow and remain so low for another 1 year minimum then stock prices are bound to soar.

How to play in current markets :
1. Debt free and low working capital companies ( as% of sales) with less receivable days and small cash conversion cycles . This ensures company always has money to operate n stay afloat.

2. High growth companies with above 1st condition. Top line of 10% plus and bottom line of 25% plus consistently with increasing or stable operating margins make the company do better in robustness of share price.

In such pandemic corona conditions or earlier demonetization, nbfc liquidity crunch, ilfs, small & midcap dwnfall from 2018: if all of these situations can not stop these companies then What will?

How I Trade

Easy. I trade with the trend. Buy low sell high for up trends. Sell high, buy low during down trends. I don’t care about fundamentals. Often I don’t even know what sector the stock is in. Half my trades, don’t even know the name of the company.

I look at the setup. I look for skew. If a few other variables line up. I stalk it for an entry.

Then I use options to maximize my capital. I mitigate the risk with diversification.

How to spot the trend?

Let’s take a look at this chart. Now. Let’s trade it.

I have no idea how. There are no landmarks. It could be going up, or down, maybe sideways. If I have no clue where it’s going. How do I make a plan? Where do I enter? Where is the exit?

Elliott Waves Principle

Now this one. A simple fact. If you see a 1-2 setup. 3 is next. 3 runs fast and hard. Usually between 100% to 161.8% of 1. However if price break down below 2. This trade is invalid. We have to consider an alternative.

Can you manage a trade like this?

How long does it take to learn Elliott Waves?

Months probably years to be an expert in it.

Not that is not going to do you any good eh? Years to master something before making any money.

Dealing with Losses.

Perfectionists are often motivated by avoidance. The need to avoid unpleasant emotional experiences. Traders who seek the perfect system are also likely to be motivated by the need to avoid unpleasant experiences.

With trading losses, the only pain that you will suffer is the emotional reaction to a loss. If you concentrate on avoiding losses rather than on maximizing your overall gain, you are unlikely to succeed at trading.

Traders may all incur the same loss but suffer vastly different emotional responses. A lot depends on their past experiences not only while trading but in the broader arena of life.

More competitive u are, More uncomfortable u are about taking loss. From an early age, we programmed to compete, to win, to accomplish, to be top of the class, to always be right & to succeed at whatever we set out to do. Losses are equated with losing to be avoided at all costs.

Newcomers to trading often start off with a string of positive experiences. Unfamiliar with the basics of money management, they are often over-confident and prepared to bet large amounts of their capital in the expectation of further gains.

Good evening and welcome to this week’s edition of 11 Wall Street.

This is a weekly thread where i look at happenings in the US equity space in the outgone week.

Please retweet from the very first tweet, so those on your timeline do not miss out.

If you have any questions or comments, please feel free to share.

It was a red week across board. The Dow Jones Industrial Average (commonly known as the Dow) fell by 0.91%.

The S&P 500 index fell by 1.48% and the NASDAQ composite index dipped by 1.54%.

Last week marked the commencement of the earnings season, with a number of banks releasing their earnings.

JP Morgan released its Q4 2020 numbers. It made $31.6 billion in revenue and an earnings per share of $3.79

Citigroup made $16.5 billion in revenue, and an earnings per share of $2.08.

Wells Fargo made$17.9 billion in revenue and an earnings per share of $0.64

THE ONE WHO STAYED BEHIND

Dimitar Berbatov & Christiano Ronaldo played for Manchester United in the 2008/2009 football session.
That was the only season both players were on the books of Manchester United at the same time...

Berbatov said of Ronaldo; “I was lucky to play alongside Ronaldo for a season. He was 100% professional, on the training pitch & off it.
I don't remember him ever being late for training...

He was always preparing in the gym, after training he would stay & do extra work like working on his shooting, then he would go swimming & go to the gym again…”
The club had strict training sessions. The players had to fit into the training schedules to stay fit & competitive...

They all wanted to excel.
They stuck religiously to these training schedules.
Their lifestyles- their training, eating, drinking, sleeping- everything was under scrutiny.
They were professional athletes, their physical fitness was very key to their success...

So they aligned themselves to these rigid training schedules.
when the training sessions were over, the players would leave for home.
BUT RONALDO WOULD STAY BEHIND!
He would train more.
He would practice more.
He would go swimming to tone his muscles more...