Global Macro Trading Journal

"* NONE of my best deals (Shyp, Shopify, Uber, Twitter, Facebook, Evernote, Alibaba, etc.) came from cold intros from acquaintances." - Tim Ferriss
https://tim.blog/2015/10/29/startup-vacation-2/

That harder someone pushes someone else a 'convexity' bet (startup pitch, speculative equity investment, new technology/patent), the less of a convexity investment it really is. I guess that's why I have been reluctant to jump in ICOs at this point. It just doesn't feel right, most of them right now. And the ones that seem to have potential appear to be quite hot in terms of demand so there is less margin of safety
 
https://tim.blog/2010/06/28/mba/

I might do something similar, set aside a few percentage of my capital to do some startup investing in Brazil and see if I get lucky. Worst case I'm down 2-3% and learned a few things. The tricky thing is the need for connections/contacts. Without the 4 Hour Workweek book its possible that Ferriss would not have been pitched things like Uber, Twitter, FB and others. He would have been stuck holding the bag on crappy deals and scams
 
Finally read this dude's first post as it pops up on the front page.
He thought equities were overvalued in 2011. The market then went up multiple times since then.
And he was bullish on commodities in that post, right before a major down trend.
 
Cyrix said:
Finally read this dude's first post as it pops up on the front page.
He thought equities were overvalued in 2011. The market then went up multiple times since then.
And he was bullish on commodities in that post, right before a major down trend.
More...
Yes I made mistakes but I learned from them. Back in 2011 I was still too deeply influenced by the New York finance literature and culture (which includes even decent books like Market Wizards) and this lead me to have a contrarian mindset that was inapropriate for the stock market. As far as commodities go, I was riding the trend, when that changed I bailed.
 
I started to trade and invest back in late 2006. It took me one bear cycle and one bull cycle to learn the lessons I needed and build a philosophy that is more robust and makes sense. The real world, trial and error and mistakes taught me what I needed to improve. I'm happy with that, most people will spend their whole life without figuring things out
 
Cyrix said:
He thought equities were overvalued in 2011. The market then went up multiple times since then.
More...
To me, this was a HUGE lesson and I developed my systems/methods from this lesson (just look how much I have been rambling about convexity and the negative NY influence in the last 6 months). Yet I see gurus/clowns who comment on the market all the time, who have been involved with investing/markets for 30-40 years and they NEVER get it. Its like they have a blind spot on the fact that negativity in the stock market is fragile (and thats true not only in the US but in most countries with very few exceptions), so they keep making the same worthless comments forever. Either that or they say one thing but do another (Jeremy Grantham)
 
I think a lot of that is related to the fact that part of the negative NY influence (coming from books, media, NY gurus) is the reliance a lot on statistics and analysis. And statistics/analysis can be very persuasive to some people.

You show to someone the stats of Shiller PEs and stock returns and they become fans, they think they 'understand' the market. That they can predict it. Next thing you know they are the next John Hussman, betting their entire career/financial future on some statistical relationship off stock market data

I'm actually doing an online course now that I should have done years ago. Its called "Understanding Clinical Research", its about empirical tests, statistics, etc. What you learn from Clinical Research is how stock market research is bad compared to it. On Clinical trials the gold standard for empiricism are double blind randomized trials with controls. And a lot of drugs need several trials of these (Phase 1 to 3) before someone can say 'hey, this works'. In finance its different, its very rare that you can run experiments (trials), conclusions are made off observational studies using historical data. Observational studies are of worse quality as compared to experiments. On experiments you can make conclusions about causation, on observational studies you simply get an association (correlation). And it gets worse by the fact that financial prices are not normally distributed. So you get little correlations off extremely unstable datasets. This is pretty much the least empirical form of empiricism that can still be called empiricism (sometimes not even that). Yet people get suckered in this stuff and believe it deeply. Taleb nailed when he said: "Understanding is a poor substitute for convexity".

Someone who thinks he's got all figured out will go against convexity using statistics, facts, data, etc and then he gets burned badly (like I did in 2011). The nice thing about convexity is that you don't have to be right often to justify that stance, the payoff matrix is in your favor
 
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Daal said:
I think a lot of that is related to the fact that part of the negative NY influence (coming from books, media, NY gurus) is the reliance a lot on statistics and analysis. And statistics/analysis can be very persuasive to some people.

You show to someone the stats of Shiller PEs and stock returns and they become fans, they think they 'understand' the market. That they can predict it. Next thing you know they are the next John Hussman, betting their entire career/financial future on some statistical relationship off stock market data

I'm actually doing an online course now that I should have done years ago. Its called "Understanding Clinical Research", its about empirical tests, statistics, etc. What you learn from Clinical Research is how stock market research is bad compared to it. On Clinical trials the gold standard for empiricism are double blind randomized trials with controls. And a lot of drugs need several trials of these (Phase 1 to 3) before someone can say 'hey, this works'. In finance its different, its very rare that you can run experiments (trials), conclusions are made off observational studies using historical data. Observational studies are of worse quality as compared to experiments. On experiments you can make conclusions about causation, on observational studies you simply get an association (correlation). And it gets worse by the fact that financial prices are not normally distributed. So you get little correlations off extremely unstable datasets. This is pretty much the least empirical form of empiricism that can still be called empiricism (sometimes not even that). Yet people get suckered in this stuff and believe it deeply. Taleb nailed when he said: "Understanding is a poor substitute for convexity".

Someone who thinks he's got all figured out will go against convexity using statistics, facts, data, etc and then he gets burned badly (like I did in 2011). The nice thing about convexity is that you don't have to be right often to justify that stance, the payoff matrix is in your favor
More...

Hussman does have a background in Economics, considering.

His professors have given him a background in Kenyesian policy, and that is probably what guides him most.
To this day, my long term holdings are a cross between Kenysian policy and my hands on experience of credit creation.
Now, I'm a numbers guy. I analyze till death do us part, but ironically the most simplistic answer (occam) gave me something no one else could.

I'd say just try and look for things not taught mainstream and you just might find what you're looking for.
 
"The tragedy is as follows. Suppose that you are deriving probabilities of future occurrences from the data, assuming (generously) that the past is representative of the future. Now, say that you estimate that an event happens every 1,000 days. You will need a lot more data than 1,000 days to ascertain its frequency, say 3,000 days. Now, what if the event happens once every 5,000 days? The estimation of this probability requires some larger number, 15,000 or more. The smaller the probability, the more observations you need, and the greater the estimation error for a set number of observations. Therefore, to estimate a rare event you need a sample that is larger and larger in inverse proportion to the occurrence of the event.

If small probability events carry large impacts, and (at the same time) these small probability events are more difficult to compute from past data itself, then: our empirical knowledge about the potential contribution—or role—of rare events (probability × consequence) is inversely proportional to their impact. This is why we should worry in the fourth quadrant!"

https://www.edge.org/conversation/n...th-quadrant-a-map-of-the-limits-of-statistics

I had read the Black Swan many times in the past but I only truly got this once I started to do 'empirical' work myself through Asset Allocation backtests. Then I would add more data to my sample or make little mistakes in my spreadsheet, the consquences to final results (and conclusions/lessons) would be huge. Little changes would lead to large consequences. After that I became a lot more skeptical of backtests
I do think they have a role to play but its not as big as most people make them out to be. I like it as a part of a multi-method approach for confirmation, so perhaps one element out of 4 or 5, just for additional confirmation.
 
Speaking of the Shiller PEs and Asset Allocation, I recall that in my AA tests, if I started my test from 1879 (instead of the classic year of 1926 that is used in the academic literature), the ideal portfolio (one that makes sense given the historical data) would be vastly different than from the 1926 one . Gold had very little value in a portfolio for almost 50 years (in fact, it was probably a lot more than 50 as bonds were a pretty good hedge in the great depression, so one didn't need gold only until the 1970's).

That was mostly because of the gold standard and the small government price stability (I think). That had consquences to the ideal bond allocation as well. So the starting year had a huge impact on what kind of portfolio made sense and which one didn't. But someone might say 'it was a gold standard period, it makes no sense to use a backtest that goes that far, we live in a different world now' but then these same people will look at Shiller PE backtests that DO use the 1870 start period!! (that is the Shiller sample, 1871-2017)

In fact, that's a good research/backtest to run, to redo the Shiller PE 'empirical' tests but to remove the gold standard period. That will be like what, 1/4 or 1/3 of the sample size that was used? I would be very shocked if that doesn't affect the reliability of the indicator (along with the suspect measures of 'statistical significance') or at least, affects the avg or median PE, which a lot of people use as indicators for mean reversion.

These observational studies based off extremely unstable data series need to be used with a lot of caution. Its more art (or alchmedy as Soros says) than science
 
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