Key takeaways from dinner with Jerry and Johnson at the Rosewood hotel

Types of money making opportunities

it is very important to know the type of opportunity you are tackling and to employ the right strategy. Opportunity shifts overtime between categories

  • fast money
  • slow money
  • big money
  • small money

Types of abilities

  • professional knowledge, intellect
  • social capital
  • Must important: steadfast perseverance


  • when dealing with opportunities pick your battles and don’t spread too thin
  • focus on the core and do it well

Parameters to optimize

  • Time
  • Effort
  • Quality


Reflections on bad trades – the narrative fallacy or apophenia

The main challenge with sticking to the strategy of loss aversion & reversion to mean is, as Charlie Munger has said it, being able to stand there and do nothing when no trading pattern emerges for multiple days in rows.

During this period of time, restlessness creeps in and the mind becomes more susceptible to the narrative fallacy or apophenia ( seeing patterns and trajectories when none exists). Inevitably one gets tricked into nursing grandiose visions of what the future might be and while leaving the physics of reality behind…

Actual costly examples:

  • Spotify 2nd May 2018 @168 when it continue its climbs
    • net loss on the swing -USD1,016.04
  • Tesla 10th May 2018 @ USD308 when it continue its climb
    • net loss on the swing -USD5138.85
  • DocuSign 18th June 2018 @ USD65.66 when it continued its climb
    • net loss on the swing -USD6023.37
  • Dropbox 18th June 2018 @ USD42.53 when it continued its climb
    • net loss on the swing -USD928.21

Accumulative screw ups: -USD12,986.72

Had I not succumb to the siren calls of the narrative fallacy and retained my ability to sit and down nothing, I might have the opportunity to enter on the dip instead of the up.

Nevertheless all hope is not lost, the path remains clear and apparent. That is to diligently practice the craft of adhering to the script while acknowledging the psychological biases.

Ironically, the practice of investment is synonymous with the practice of mindfulness.

Reflections on the decentralized multi-sided market place – GetData.IO

The beginning

The concept of GetData.IO was first conceived back in November 2012. I was rewriting one of my side project ( in NodeJS back then. Part of the rewrite required that I wrote up two separate crawlers each for a different site which I was getting data for.

Very soon after I was done with the initial rewrite, I was once again compelled to write a third crawler when I wanted to buy some stocks on the Singapore stock exchange. I realized while the data for the shares were available on the site, they were not presented in a way that facilitated my decision making process. In addition to that, the other part of the data I needed were presented on a separate site and unsurprisingly not in the way I needed.

I was on my way to write my fourth crawler when it occurred to me, if I structured my code by cleanly decoupling the declaration from underlying implementation details, it is possible to achieve a high level of code re-use.

Two weekends of tinkering and frenzied coding later, I was able to complete the first draft of the Semantic Query Language and the engine that would interpret this query language. I was in love. Using just simple JSON, it allowed anybody the ability to declare the desired data from any parts of web. This includes data scattered across multiple pages on the same site or data scattered across multiple domains which could be joined using unique keywords.

The Journey

Five years have past since, during this time, I brought this project through an incubator in Singapore with my ex-co-founder, tore out and rewritten major parts of the code-base that did not scale well, banged my head countless times on the wall  in frustration due to problems with the code and with product market fit, watched a bunch of well-funded entrants came and went. To be honest, quite a few times I threw in the towel. Always, the love for this idea would call out to me and draw me back to it. I picked up the towel and continued ploughing.

It’s now June 2018. Though it has taken quite a while, I am now here in the Bay Area, the most suitable home for this project given to the density of technological startups in this region. My green card was finally approved last month. I have accumulated enough runway to allow my full attention on this project for the next 10 years. Its time to look forward.

The vision

The vision of this project is a multi-sided market place enabled by a Turing complete Semantic Query Language. The Semantic Query Language will be interpreted and executed upon by a fully decentralized data harvesting platform that will the capacity to gather data from more than 50% of the world’s websites on a daily basis.

Members can choose to participate in this data sharing community by playing one or more of the 4 roles:

  • Members who need data
  • Members who maintain the data declarations
  • Members’ who will run instances of the Semantic Query Language interpreter on their servers to mine for data
  • Member’s who sell their own proprietary data

From this vantage point, given its highly decentralized nature, it feels appropriate to deploy the use of block chains. The final part that needs to be sorted out prior to the deployment of blockchain to operate in full decentralized mode is figure out the “proof of work”.

Operations available in other database technologies will get ported over where appropriate as and when we encounter relevant use cases surfaced by our community members.

Why now and how is it important?

More as I dwell in this space, I see very clearly why it is only going to become increasingly important to have this piece of infrastructure in place. There are namely 3 reasons for this.

Leveling the playing field

The next phase of our computing will rely very heavily on machine learning. It is a very data intensive activity. Given that established data siren’s like Facebook, Google, Amazon and Microsoft have over the past years aggregated huge tons of data, this have given them a huge unfair advantage which might not necessarily be good for the eco-system. We need to level the playing field by making it possible for other startups to gain easy access to training data for their machine learning work.

Concerns about data ownership

GDPR is a cumulation of concerns of data ownership that has been building for the past 10 years. People will increasing want to establish ownership and control over their own data, independent of the data siren’s use to house them. This means a decentralized infrastructure which people can trust to manage their own data.

Increasing world-wide need for computing talents

Demand for engineering talent will only continue to increase as the pervasiveness of computing in our lives increase. The supply of engineering talents does not seem like it will be catching up and short fall is projected to continue widening till 2050. A good signal is the increasingly high premium paid to engineering talents in the form of salaries over the recent years. It’s just plain stupidity as a civilization to devote major portions of this precious engineering resource to the writing and rewriting of web crawlers for the same data sources over and over again. Their time should be freed up to do more important things.

The first inning

Based on historical observation, I believe we are on the cusp of the very first inning in this space. A good comparison to draw upon is the early days of online music streaming.

Napster versus the music publishers is similar to how the lay of the land was back 5 years ago when Craigslist was able to successfully sue 3Tap.

Last year, LinkedIn lost the law suit against folks who were scraping public data. This is a very momentous inflection point in this space. Even the government is starting to the conclusion that public data is essentially public and Data Siren’s like any of the big Tech should have no monopoly over data that essentially belongs to the users who generated them.

Drawing further upon on the music industry analogy, the future of this space should look like how Spotify and ITunes operate in the modern day online music scene

What about recumbents?

Further readings

Evening reflections on the importance of story telling

If you want to scale beyond your own physical efforts, you will need to be able to convince others the importance of what you are doing. When you are successful at that you will be able to elicit their muscles to work for your own cause.

To be able to elicit their muscles, you will need to be able to tell a good story. If you read the book titled “Sapiens” by Yuval Harari, story telling is a technological innovation by the human species that has enabled large numbers of people to coordinate their efforts based around a single endeavor. It is one of the main causes for the human species’ predominance in our environment.

This innovation is enabled by our limbic brain which understands things based on narratives. Compelling narratives elicit an emotional response resulting in human motivation and corresponding action.

As a product manager working on the core product as opposed to growth, the primary focus is the narrative as opposed to the metrics.

The primary job of the executive is to “fight” for resources to further his agenda. The way he does so is by telling a compelling narrative to the company.

As opposed to a demagog, a good product manager tells a story, backs it up with data and delivers his promise. The demagog gets his resources by telling a story but never delivers anything of substance.

Learn to tell a story. That is a very important skill set acquire if you haven’t done so.

Inspired by conversations with Ved.

Related readings

M1 versus MyRepublic – 6th July 2017

  • A scenario of company versus company
  • M1 was one of three players within the Singapore telecommunications market which was considered to have high legal barriers to entry
  • On 6th July 2017, MyRepublic announced plans to launch new mobile service in Singapore
  • Key areas used to assess potential impact of new entrant
    • How rapid can new entrant build out distribution channel to start disrupting new entrant
    • Market segment differentiation through analysis of product features
  • Short term: Loss aversion, reversion to mean scenario
    • Market reacted by falling 16%
    • Market reverted to mean by 5%
  • Mid/Long term: share prices recovered to historic levels when major third party attempted a take over bid

Related references

Lessons from Saudi Arabia’s 2014-2015 flooding of market

Saudi Arabia held oil price at artificial low levels

  • curbing Russian involvement in Syria
  • Collateral damage:
    • small shale Oil companies
      • required USD$80/ barrel to breakeven operations
      • 35 shale oil companies filed for chapter 11
      • caught in precarious position of being over-leveraged and unprofitable for extensive periods of time
    • Large oil companyBP
      • trading at close to Net Book Value for more than a year (2016-2017)
      • price still recovering after 2 years (2018)
  • Countries can hold an industry suppressed longer than impacted companies can stay liquid
  • large cap companies will trade for extensive periods at close to net book value. Might be a good buy when markets turn around
  • If industry is directly affected, cut losses and bail


Data breach case study – Facebook (March 2018)


Observed commonalities

  • Technical behavior
    • continuous 7 day drops
    • valuation drops of above 10%
    • elevated trading volume during period of drop
  • significantly entrenched positions in their respective industries
    • return on Equity of above 10%
    • profit margin of above 20%
  • public outrage
  • government launched investigations

Outcome of Facebook to be verified on 1st April 2018

  • Non-conclusive regulation on the part of government
  • public outrage diffused and back to business as usual

Mid to long term follows ups 

  • To study period required to recover to original levels


  • Devised strategy to capitalize on increasing prevalence of hyper media coverage of  tech companies for bleeding edge technology trends
    • data breaches on Mega Data Silos
    • contains links to other related case studies

Tesla self driving cars casualty incident March 2018

Observed commonalities with other related case studies

  • Technical behavior
    • continuous 10 day drops
    • valuation drops of above 15%
    • elevated trading volume during period of drop
  • Fundamentals
    • Technology leader in its industry
    • Growing B2B business
      • Amazon
      • FedEx
      • Walmart
  • public outrage
  • government launched investigations

Mid to long term follows ups 

  • To study period required to recover to original levels


  • Devised strategy to capitalize on increasing prevalence of hyper media coverage of  tech companies for bleeding edge technology trends
    • self driving car casualties
    • contains links to other related case studies

Trading Strategy which capitalizes on loss aversion triggered off by hyper media coverage

Trading algorithm

  • Long position strategy
    • exit at +5% or -2.5%
  • Short position strategy
    • exit at +2.5% or -2.5%

Large Dip Scenarios

MOther nature versus company
  • Pre-requisite: Disrupted company must be state protected with while exhibiting monopolistic characteristics. Disruptions must be attributed to natural disasters
  • Trigger: Actual natural disaster occurrence or negative PR due to actions to avert natural disasters
  • Pattern
    • trigger threshold 10% drop
    • expected exit level 5%
GOVERNMENT versus Government
  • Pre-requisite: Company’s industry impacted by political event
  • Action: Short position with exit at 2.5% capital gain
  • Trigger:
    • Company explicit modifies earnings guidance based on macro political event
    • 2018/2019 – US/China trade war
    • 2014-2016 – Saudi Arabia/Iran/Russia/US oil crisis
  • Pattern
company versus Government
  • Pre-requisite: Company has strong fundamentals
  • Action: Long position with exit at 5% capital gain
  • Trigger: Threat of potential regulation
    • No negative macro event
    • Bug in software
    • Accidents not directly related to them
  • Pattern
    • trigger threshold 30-40% drop
    • expected exit level 20-25%
    • exit principal and level profit of 20% in position for risk free bonus
company versus Wall Street
company versus company
  • It is important to understand the underlying market structure to figure out if the new entrant will have significant long term impact on existing player in the market. Areas to consider for analysis (from Micheal Porter’s 5 forces model)
    • No negative macro event
    • disruption of existing distribution channels
    • market segment differentiation
    • customer lock in cost
  • Warning don’t do it if you don’t understand the dynamic clearly
  • Examples:

General restrictions

  • Figure out what the macro economic trend is.
  • know clearly the exit prices before entering into position
    • don’t be greedy
    • helps guard inability to execute caused by procrastination
  • Long positions: strictly trade only on loss aversion and recovery to mean pattern in companies with strong fundamentals
  • Short positions: strictly trade only on companies within industry directly impacted by political event

Portfolio Allocation

  • Application of rule of 7 +/-2
    • hold no more than 9 open positions at any time
  • Micheal Porter’s 5 forces
  • Kelly model: determine percentage of fund allocated to position
    • Equation: ( (expected win amount * probability of win ) – (probability of lose) / ( expected win amount )
  • Proposed portfolio allocation levels for each large dip occurrence
    • 5%
    • 10%
    • 25%
    • 50% – large dip + 5 positive Micheal porter’s 5 forces check offs

Expected value calculation

  • A3MG = average maximum gains during entire 3 month period for all large dips
  • A3MD = average maximum drawdown during entire 3 month period for all larges
  • PG = probability of successful exit
  • AVW = average wait time in days before successful exit
  • Daily expected value of holding a position = (A3MG X PG + A3MD X (1-PG) ) / AVW

Execution Time Line

Short term: loss aversion

  • 1-2 weeks time horizon
  • Magic number 5: social proofing threshold beyond which 100% conversion occurs within population
    • in this case, we watch for coverage of bad news by at least 5 major news network
  • Capitalize misalignment of stimulus/news’s negative impact with business model fundamental
    • Company versus Government
    • Company versus Wall Street
    • Company versus Company – no longer term impact
  • Be very wary not to take long positions when conflict is between
    • Government versus Government where tactics directly impact
    • Company versus Company – with long term impact
  • Don’t be too eager to jump in too early
    • Ok to wait till the coast is clear
    • Ok to make less on the rebound
  • if entry was done too early exit at 2.5% capital losses
    • Johnson: Much easier to buy at a cheaper price later than to ride it all the way down and then back up. Latter requires a lot of crowd confidence which is not common.
      • a 20% down is a 25% up
    • Dan Kwon: Don’t catch a falling knife
    • Don’t double down capital on a bad decision
      • lesson from bad trade with BreitBurn  in 2015
  • Exit strategy
    • long positions: at 5% capital gain
    • short positions: at 2.5% capital gain
    • Reasons
      • save yourself from unnecessary mental anguish by running various what if scenarios for not going along with the rest of the ride
      • a good gauge is when information parity has been achieved between main street and wall street

Main detractors of ability to stick to strategy during execution phase

  • Inability of manage symptoms of craving – greed
  • Inability of manage symptoms of aversion – fear

Long term: lessons from Berkshire Hathaway

  • Warren Buffet:
    • When management with good reputation meets industry with bad reputation, its the reputation of the latter that remains intact
    • A rising tide raises all boats, it’s only when the tide goes down when u know who is not wearing pants
    • When two companies make the same returns, the one that needs borrow less is the more capital efficient one
  • Charlie Munger
    • Don’t just do something, stand there

Warren Buffet Portfolio Strategy – Kelly Model

  • Expect to utilize a long time horizon when using the Kelly model
  • don’t use leverage as it will force u out of a position when price tanks
  • don’t over bet when using the Kelly Model – penalties of over betting is much worst than under betting
  • holding more than 15 positions is suboptimal
  • study models in multiple domains which will help triangulate probability better
    • Models from field of Psychology
    • Models from economics
    • Models from Physics
  • read widely. It’s cheaper than making own mistakes
  • stick to domains you are very familiar with and have a decisive advantage versus the average investors – even tech which is becoming a bigger sector over time

Advice from Johnson on trading

  • Remember clearly the outcome you were expecting when you enter a position
  • Don’t be greedy and expect to ride all the way to the top to levels beyond your expectation. The fall usually occurs beyond that level.
  • Be conservative and hop off just slightly before your expectations are met
  • changing strategy (self-narrated story) in-flight makes it hard to measure the actual effectiveness of the strategy over prolonged periods of time
  • Thinking that a strategy is perfect and can be repeatedly applied without micro-calibrations and occasional drastic changes assumes environment holds constant. That is wishful thinking.

Advice from Jake on trading

  • Entry at a 52 week low level is ideal during a scare
  • Historical context based analysis is really useful.
    • PCG was trading at USD7 during bankruptcy proceedings back in 2001 and was trading at the same levels period prior to bankruptcy proceedings in Jan 2019

Related Case Studies

Useful frameworks for dissecting companies

Useful resources worthy of reference

Data breach case study – Equifax (September 2017)

Observed commonalities

  • Technical behavior
    • continuous 7 day drops
    • valuation drops of above 10%
    • elevated trading volume during period of drop
  • significantly entrenched positions in their respective industries
    • return on Equity of above 10%
    • profit margin of above 20%
  • public outrage
  • government launched investigations

Mid to long term follows ups 

  • To study period required to recover to original levels


  • Devised strategy to capitalize on increasing prevalence of hyper media coverage of  tech companies for bleeding edge technology trends
    • data breaches on Mega Data Silos
    • contains links to other related case studies