Summary of readings and conversations for the week

Trends observed

  • Worsening income inequality
    • driven by increased globalization and automation with failure in re-education as the primary cause
    • Continued low worldwide interest rates as central banks the world over struggles to prop up inflation rate at 2%
  • Rise in protectionism around the world in response to income inequality
    • Slowing trade volumes around the world
  • Demand saturation at the upper income segments
    • Slowing demand for housing in South Bay
    • Too much money chasing after too little deals

Related sources

Insights from Josh

Product

  • Starts with search
  • once query and corresponding is good visualize using graph which users can easily zoom in and out

Key APIs

Key data pipeline technology

  • Apache Spark
  • Apache Kafka
  • Elastic Search
  • GPL visualization

Acquisition strategy

  • Enterprise sales
  • Hire users from customers to act as salesman to other users who are in their network

Key challenge

  • Business is stuck at USD30 million ceiling per year right now
  • Trying to build more granular apps to target verticals to generate more profits

Key highlights of Fed Chair Jerome Powell’s testification before the congress

Primary mandate

  • Stable prices at 2% inflation rate
  • Low unemployment rate

Highlights of the US economy

  • US unemployment is at 3.7% the lowest in 50 years
  • Longest period of economic expansion at 11 years
  • Philips curve is dead – Inflation rate and unemployment rate seems to be  decoupled and no longer correlated

Key concerns within the environment

  • US inflation rate has fell below 2%
  • slowing global growth
    • manufacturing
    • investments
    • trade
    • business fixed investments
  • international trade volume slow down
  • slowing US business investments
  • lots of central bank with low interest rates have little wiggle room if recession were to occur

Federal Reserve as an institution

  • US economy is 70% consumer driven
  • Responds to congress policies
  • Responds to fiscal policies
  • reopens market when economy grinds to a halt
  • resilient to president’s (executive branch) meddling
  • utilizes framework and the tools available to achieve symmetrical 2% inflation rate
  • Fiscal policy is more powerful than monetary policy
    • becomes critical during a severe downturn
  • 4 key pillars of monitor
    • Asset price
    • leverage in financial system
    • leverage away from banks
    • funding risks
  • Fed will negotiate with other countries and will reject terms if international terms cannot be expected to work within the US system – Insurance Capital Standards
  • Will look for more tools to pursue mandate since interest rate is already very low
  • Can handle counter cyclical policies
    • buying US treasuries to increase supply of money in circulation

On the labor market

  • Income inequality
    • 1% owns 40% of wealth in America
  • US has less social mobility than other more developed countries
    • since 40 years ago US education system has not be able to train people to capitalize on technology and globalization
  • Median and lower income has stagnated (those without a degree)
    • Only increased 3% wage increase despite low unemployment rates
    • does not make up 2% inflation rate and productivity gains
    • chief causes
      • technology: automation has severely increased individual productivity and thus throughput capacity while production demand has not yet caught up with this increased capacity
      • globalization: more and more qualified workforce is coming online around the world that can be employed
  • community in the fringes finally getting to join the workforce in recent years after 2008 recession
  • employers having problems finding qualified people to fill positions – 7 million open positions within the US
    • good people skills
      • service economy
      • manufacturing economy
    • opioid crisis

Uneven development

  • US 3.7% unemployment rates
  • Michigan
    • layoffs due to trade war
    • 2018 April – 4.2% unemployment
    • 2019 April – 4.6% unemployment
    • cannot raise bonds at competitive rates
    • infrastructure on the rocks
  • Wisconsin – 2.8% unemployment rates
  • Economic development is increasing moving to the coast
    • young people are moving to the coast and to cities
    • able people are doing the same
  • Policies need to solve problem by increasing productivity
    • technology savviness
    • basic research skills and aptitude

On minimum wage

  • concerns over job loss when USD15 minimum wage is imposed
  • some will lose their jobs
  • others will make more money

Quantitative easing

  • expected to happen again this year
  • has caused substantial buildup of federal reserves
  • 22 trillionUSD Federal debt in total
  • corporate tax rate cuts leads to another expected 1trillion USD deficit this year
  • interest on government debt expected to become largest category of federal government expense by 2026 – government has less money to spend on anything else expect paying debts
  • Higher debt should have lead to higher interest rates, due to the special case of US being a reserve currency, government has been able to continue borrowing at low interest rates
    • Japan has higher debt to GDP ratio and low interest rates
  • Debt will get past on to next generation
  • If does not occur will undermine USD as the world’s reserve currency

World’s reserve currency

  • A very stable equilibrium for a long time
  • Can potentially have multiple currencies
  • On going back to the gold standard
    • will undermine the Dual mandate
    • Federal reserve will be forced to managed the price of gold instead – which sometimes fluctuate wildly
  • Currently USD was the British Pound a while back
  • Conditions for a sustained reserve currency
    • being the largest economy
    • best institution
      • democracy
      • rule of law
      • open to commerce with international trading nations
    • fiscal sustainability

Banking sector concerns

  • Going off LIBOR rate at end of 2021
    • Banks will no longer be required to offer a LIBOR rate at the end of day
    • will present a coordination problem especially for assets that do not have a predefined rate – mortgages
  • potential reduction of banks stress buffer
  • banks prefer to hold reserves versus treasury
  • bank CEOs areas of worries
    • leveraged lending
    • shadow banking
      • CLO/Hedge funds/ Mutual funds – area Global Financial Stability board is actively looking into
  • works closely with the Federal reserves

Speed up payment systems

  • use case: allowing cheques to clear faster so that mum can cash in salary to pay for food at home
  •  participants
    • regional banks
    • big banks
    • technologists
  • Federal Reserve will enter the market directly to be the settler
  • inter-operability with legacy system is a concern

Wire Fraud

  • Currently wire to an account
  • proposes to wire to an account with a name match
    • UK based system
    • conflicts with some state laws
  • organized crime primarily carried out within the real estate sector

Facebook LIBRA

  • facing negative headwinds
  • seen as aiding money laundering
  • privacy and data protection concerns
  • huge impact due to 2 billion users on platform
  • will be a marathon instead of a sprint to implement and roll out
  • Facebook conferring with governments around the world
  • will become the world’s largest payment system

 

Thoughts on systemic risk

A bottle neck was observed this evening with the data pipeline for trends.getdata.io.
 
Gradual addition of more new data sources eventually lead to more jobs being added to the queue than could be processed per period time.
 
Phenomena of a similar nature was observed with getdata.io during the beginning of the year when our user base started ramping up.
 
The higher order principle is that bottlenecks are inherent within systems. They are emergent by nature. Situation will deteriorate exponentially once threshold is crossed.
 
The same principle applies with financial systems, flight systems and boat sailing.

Book summary AI super-powers, China, silicon valley and the new world order by Kai Fu Lee

The difference waves of AI

  • Internet AI – Facebook, Netflix, Google search
  • Business AI – Palantir
  • Perception AI – Tesla cars
  • Autonomous AI – Tesla self driving, Google self driving

Key locations

  • Silicon Valley
  • Zhong Guan Cun – Beijing

State of the Union

  • We are in the stage of implementation/application as opposed to RnD
    • having access to more data is more important than have expertise to do more RnD
    • having solid AI engineers is more important than AI researchers
  • We are still far from general AI
  • Key ingredients
    • data
    • computing
    • maybe work of strong AI algorithms engineers

Key differences between eco-systems

  • Silicon Valley businesses are mission and core values driven while Chinese businesses are pragmatically focused on profitability.
  • Silicon Valley businesses stay in bits and binaries offloading the brick and mortar to external vendors vendors while Chinese businesses extend their business model into the brick and mortar (online to offline)
  • Silicon valley prefers one size fit all strategy, Chinese businesses utilized localized solutions often investing/acquiring in local startups
  • Americans treat search engines like Yellow Pages (come and leave fast) while Chinese treat search engines like shopping mall (come to linger around long)
  • Silicon Valley is adversed to copying preferring to be unique Chinese business copy the heck out of each other

Chinese Advantage

  • Abundant data – quality and quantity aided by their online to offline initiatives
  • hungry entrepreneurs
  • AI scientist
  • AI friendly policy environment – strong emphasis by Chinese government
  • Hardware manufacturing know how – Shen Zhen
    • unparalleled supply chain flexibility – XiaoMi

Silicon Valley Advantage

  • Microchip manufacturing know-how

Trends within the Chinese eco-system

  • Darwinian eco-system has lead to extreme levels of competition
  • Chinese companies have already moved past the stage of clone Silicon Valley business models
  • Businesses innovate to build a defensive moat around themselves. Local businesses have advantage, with no timezone differences to deal with, decision making is relatively faster.
  • Online to offline
    • an essential ingredient to building strategic moats
    • caused the decline of cash use
  • Chinese government information systems will be able to leap frog US government information systems

Policy approaches

  • Google – impeccable safety
  • Tesla / China – trial by fire
  • key to winning the Autonomous AI race
    • is the bottleneck technology (Silicon Valley) or policy (China)?

Key concerns

  • having cheap labor is no longer going to be a source of advantage in a world heavily powered by automaton.  Developing countries hoping to employ this well tested strategy to progress will not be able to do so anymore
  • Estimated 60% potential job loss worldwide barring policy interventions
  • Job loss probability assessment
    • physical labor
      • environment – unstructured versus structure
      • tasks nature – level of dexterity versus high dexterity
    • cognitive labor
      • social – high versus low
      • cognitive – optimization based versus creativity/strategy based
  • AI replacement approach
    • single tasks approach
    • ground up rethink re-imagination
  • A population of irrelevant (no longer employable) as opposed to unemployed

Tackling Key concerns

  • Silicon valley – reduce, retrain and redistribute
  • Kai Fu Lee – stipends for care, service, education

New promise

  • Humans freed up from repetitive tasks can now focus on becoming more human oriented

Related readings

  • Disruptor, Zhou
  • www.Arvix.org – an online repository of scientific papers
  • Folding Beijing – Hao JingFang

Mark Zuckerberg chats with Yuval Noah Harrai on the Future of AI

Key take aways

  • Spread of inequality where some countries have the ability to harness AI while others don’t
  • AI based recommendation systems moving from being just an oracle to becoming a sovereign
  • AI as a tool is an amplifier
    • concerns that it will benefit totalitarianism more than democracy leading to totalitarianism becoming a more favorable governance model worldwide
    • surveillance
    • psychological manipulation – the inability to know your true self through your thoughts
    • what happens if morality and expediency diverge when it comes to governance
  • Effectiveness of curbing the negative effects of AI by encoding values within policy frameworks governing these AI based systems
    • Companies based in Democratic countries will encode democratic values within their systems vice versa for Totalitarian countries
  • Personalization versus Fragmentation
    • when everyone in a country chooses his own community that is mainly online there is no longer a glue holding the local community together
  • Long term versus short term
    • The long term benefits might come sooner than expected when taking a short term trade off

 

Hedge fund sector challenges and flock to alternative data seeking alpha

When taking into account different signals about a sector you get a very different perspective altogether.

This signals says that hedge fund managers are all flocking to alternative data to seek alpha

https://www.businessinsider.com/hedge-fund-data-buyers-at-alternative-data-conference-battlefin-2019-6

Taking into account the signal from these two articles, we can infer that the above trend has been mainly driven by the deterioration of the sector over the past few years.

https://www.highlandfunds.com/business-insider-hedge-fund-apocalypse-coming/

https://www.bloomberg.com/news/articles/2019-05-31/quant-hedge-fund-amplitude-returning-client-cash-after-outflows

When further taking into account this signal, we see a subset of the survivors are struggling to transit

https://www.businessinsider.com/hedge-funds-open-source-platforms-2019-5

Trends associated with GetData.IO

Alternative Data

Robotic Process Automation

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