Book summary: George Soros the unauthorized biography

Profile

  • Founder of quantum fund

Key take aways

  • There is a gap between perception and reality
    • Its the distortion that shapes events
    • Market participants operate with biases and these biases influence the course of events
    • Market prices are always going to be wrong because its offers a biased view of the future
    • biases works both ways
    • biases are self reinforcing, overreaction happens
    • boom and bust are attributed to the flux and uncertainty due to this gap
    • since the market is always wrong if you follow everyone else, you will perform poorly
  • On risk taking
    • Its alright to take risk
    • When taking risk don’t bet the ranch
    • Once you know what the market is thinking, bet on the unexpected
    • look for a sudden change in the market not yet identified by anyone else
    • develop a thesis and test it on the market
    • take a position where you have time on your side
    • learn how to survive
    • Attain superior long term returns through preservation of capital and home runs
    • Its not whether you are right or wrong but how much you make when you are right and how much you lose when you are wrong
  • On positions
    • be willing to endure the pain of following your logic when everyone else is going the other way
    • pick the best and worst performers in an industry
    • if your investment is going well, follow your instincts and go with all you’ve got
  • To avoid
    • lopsided trend following is necessary to produce a violent market crash
    • investors trying to influence prices by acquiring a large position in a currency will face disastrous results when position is sold
  • At times gut feelings will need to override logical analysis
  • gain access to world leaders for better insights

Related readings

The alchemy of finance by Georgo Soros

  • A gap exists between perception and reality
  • The theory of reflexivity
    • herd mentality dictates that the market driven by momentum will eventually over extend itself.
    • the herd will eventually realize its over extension and revert to mean
    • however momentum will once again do its work and result in over extension in the opposite direction
  • at times you will need to be somewhat schizophrenic by keeping in mind two equally plausible but conflicting mental models and predictions while doing your trade.
  • What matters most is your gain when you are right and your losses when you are wrong
    • When you know you are right, go for the jugular
    • it is the correct but non-obvious trades that generate the most outsized returns
  • A country with poor fundamentals, high level debts and limited foreign reserves will have limited ability in propping up its foreigns reserves much as it attempts to do so
  • maintaining a wide network spread across multiple territories will help in forming a better informed mental picture on the state of things

Chats with Ilya on hedge funds

  • Management fees are too high to be sustainable for funds sized below 300 million
  • Funds sized above 300 million tend to have a shelf life of 10 years
  • staying motivated and dedicated to the business is hard when you already have a lot of cash
  • funds that last for a long time have founders who are absolutely focused on building up an institution that will sustain beyond the founder
  • it is much more lucrative to build and own the system than to be working in the system
  • Sharpe ratio above 1.5 is acceptable
  • SnP Sharpe ratio tends to be really low
  • important to figure out how to still generate returns in a down market
  • qualitative assessment of the business model is what drives outsized investment returns

Evening out watching Rambo, last blood

From Sujit on trading

  • not necessary to get numbers further back than six months
  • stock market subjected to fractal distribution
  • it is possible to generate returns of up to 140% per year by trading on stocks that are moving within a range
  • going all in on each position each time leads to a very low Sharpe ratio
  • Sharpe ratio should be calculated separately for method and for SnP benchmarked against US treasury interest rates. The difference is the actual returns

On Rambo Last blood

A movie is a reflection of the culture and attitude of an age. Rambo was a very popular cultural icon during the eighties and the early nineties when memories of the Second World War and the Cold War against the communist were still very fresh in the minds of the people in America.

If you looked at the world today through the eyes of someone like Rambo, you would have been able to easily draw facts to back the narrative painted by Trump prior to being elected president.

When operating in an environment of uncertainty, a decision maker formulates multiple often competing narratives in the head that best explains majority of the facts presented. He calibrates the weightage assigned to the probability of each narrative as new pieces of data become available. He simultaneously utilizes multiple ones that are assigned high plausibility in his decision making to strive for the best possible expected outcome . It is a cognitively demanding iterative activity that goes on indefinitely.

  • common themes between movie and Trump’s narrative
    • Mexico drug cartels
    • Mexico prostitution rings
    • The world is a dark place
    • illegal border crossing
    • poor border fence
    • white male
    • Rust belt
    • Protagonist is in his 70s
    • Freedom fighter who fought the communist in Vietnam and Russia
    • guns and lots of blood
    • man of steel
    • manifest destiny
  • Cognitive biases
    • Narrative fallacy
    • Framing bias
    • selective bias

Related readings

  • Expert political judgement, Philip Tetlock

Afternoon with Tomasso on the limitations of Artificial intelligence’s application

For us to be able to successfully apply artificial intelligence on any domain, the following needs to be true

  • The behavior the system to be modeled must not be stochastic
  • The state of the system must be decipherable by the data scientist
    • it should be possible to understand the state in which the system is at through interpretation of data gathered
  • The domain can be modeled
    • the parameters for modeling the domain must be well defined

Only when all three premise are true can we determine where the adjustment should be made when a model fails to predict an outcome

The financial markets is stochastic  in the short run.

The underlying parameters are constantly changing and thus hard to model due to the emergent nature of impacts caused by human activities. The data is qualitative and thus hard to convert into clean quantitative datasets.

While the price movements are obvious it is hard, it is hard to attribute impact to the various parameters.

As such, it requires human neural networks that consumed all these qualitative data to perform the prediction/decision making.

Macro economic extension to loss aversion reversion to mean trading methodology – Government versus Government scenario

Trading Heuristics

Chain of events and decision making

The decision making processes from June 2019 after the federal reserve signal likelihood of cutting interest rate leading up to the 2019 August downward mean reversion of the SnP.

19th June 2019, Federal Reserve signals for first time likely decrease of interest rates

Federal Chairman expresses concerns about potential cross winds caused by US trade policies and its impact on their dual mandate.

10th July 2019, news paper reports SnP reaches all time high

SnP on the cusp of crossing 3000 threshold for the first time to an all time high. Market is euphoric. Trade issue between US/China yet resolved but discussions are underway.

Short positions SQQQ and SRTY were utilized as hedges against downward macro environment reversion risk when the SnP extended beyond 3000 to reach an all time high

1st August 2019, US announcement of 10% tariffs on US300 billion imports from China starting September 2019

on 1st August, 24 hours after actual interest rates cut, Trump signaled 10% tariffs on USD300 billion Chinese import. SnP dropped.

Exited SQQQ and SRTY for 10% capital gain after reaching 30SMA and 50SMA range. Net combined loss to portfolio was 0.5%.  Left remaining long positions open.

4th August 2019, Chinese response

On 4th Aug 2019, China’s exchange rate dropped for the first time below 7RMB/1USD.

5th Aug 2019 trading day

On 5th Aug 2019, China announced they will halt all imports of agricultural goods from US.

Portfolio continued declining an additional 2% on the trading day of 5th August 2019.

Closed all long positions with the exception of REITs

Decline overview

  • SnP
  • REITs
  • Value shares
  • Growth shares

6th Aug 2019 trading day

On the evening of 6th Aug 2019, China announced decision to control fluctuation of RMB exchange rates to USD

Bought into TQQQ and URTY at below 30SMA and 50SMA

7th Aug 2019 trading day

TQQQ and URTY automatically exited at the mid point between the two following highs and lows:

  • highest point  before Trump’s tariff announcement came into effect
  • lowest point after China’s halt on US agricultural imports and RMB/USD breaking 7 came into effect

Related readings

Book summary: Everybody lies by Seth Stephens-Davidowitz

Signals from Search

  • What people search for is in itself a signal
  • The order of keywords in which they search is also a signal
  • Quality of google search data is better than in Facebook because
    • You are alone with no fear of being judged
    • You have an incentive to be honest

On Big data

  • the needle is still the same size but the haystack has been getting bigger
  • Be judicious by cutting down the sample size of the data to be used

Data science

  • Trust your intuition as the initial signal but verify quantitatively to avoid narrative bias
  • Correlation is most often sufficient for utilization purposes – often the explanation of why the model works comes after the fact
  • critically assess the actual data underlying the narrative. At times it might tell a very different story than narrative presented
  • Clustering of groups of people helps predicts behavior – Netflix and baseball
  • AB testing to discover causations

Social Impact

  • great business are found on:
    • secrets about nature
    • secrets about people
  • Modeling
    • Physics – utilize neat equation
    • Human behavior – probabilistically via Naive Bayes classification

Managing angry people

  • Lecturing them will provoke their anger
  • Provoking their curiousity will cause their attention to be diverted causing anger to subside

Related readings

  • Zero to one, Peter Thiel

Book summary – The Signal and the noise

Risk versus uncertainty

  • Risk can be mathematically modeled to yield a probability
  • uncertainty cannot be mathematically modeled

Conditions for quality data

Why google’s Search data is better than Facebook profile data

  • subject feels she has privacy privacy
  • subject feels she is not judged
  • subject sees tangible benefit from being honest

The hedgehog versus the fox

  • The hedgehog approaches reality through a narrative/ideology while the fox thinks in terms of probabilities
  • The hedgehog goes very deep in an area while the fox employs multiple different models
  • The fox is a better forecaster than the hedgehog
  • The fox is more tolerant of uncertainty

Big data

  • More data does not yield better results and predictions
  • Deciding the right kind of data from the abundance available
  • To do prediction it is important to start from intuition and to keep model simple
  • qualitative data should be weighted and considered
  • Be self aware of your own biases

Prediction

  • Similarity scores – clustering in Netflix and baseball
  • Be wary of confirmation biases
  • Be wary of overfitting using small sample size – Tokyo earthquakes and global warming
  • Correlation does not equal causation
  • short hand heuristics to reduce the computational space – for example chess

Related references

  • Irrational exuberance, Robert Shiller
  • Expert political judgement, Philip E. Tetlock
  • Future shock, Alvin and Heidi Toffler
  • Principles of forecasting, J Scott Armstrong
  • Predicting the unpredictable, Hough