My Investment Research Checklist

I was talking to family and friends about various investments made (and some missed out on) over the years. Have gotten decent at picking winners (using a Value-Investment framework inspired by this book that mixes intuition, selection, and analysis). Here’s the base-level rubric for what I look into before I investment into any company. Some analyses go further. Generally, 9 months to 1 year is taken before any action…essentially driving the associations of the entity into muscle memory and relying on intuition, alongside any other company/product/market, to make the call.


  • Read on 3-6 months of articles on stock and target company
  • Read on 3-6 months of analyst coverage on stock
  • Find Market Cap potential of target company
  • Find Market Leader
  • Identify Competitor Set
  • Identify Lead Competitor(s)
  • Find Growth Darkhorse(s)
  • Identify any market regulation


  • Determine CEO capabilities and habits
  • Determine CEO’s style of communications (past 2 years)
  • Identify Product Roadmap
  • Read last 5 product announcements
  • Linkedin Lookup on Corporate/Employee Composition (specifically product/marketing/engineering)


  • Read past 10 10-Q, 2 10-K
  • Identify, if possible, Sales & Marketing + R&D spend
  • Identify, if possible, Growth of S & M + R & D spend

Would love to hear your thoughts! Feel free to DM via LinkedIn.

Elon Musk & 2018

I’ve written about Tesla’s growing community of advocates back in 2013…well, it’s now becoming real. Though is it happening because of an explicit push or something happening naturally due to increased sales?

My hypothesis is that I think Elon is being Elon and going on a major PR push in 2018.

Been tracking since February and it keeps on confirming along the way. With the Starman launch (February), Grimes (May), and “Pedo” (sigh) (July), I think there’s something going on in terms of media coverage on various issues in the US/Worldwide, and Elon Musk is either trying to take advantage of it or doing his own thing. Feel free to add the search term “pedo” to this Google Trends chart…I won’t.

Maybe this is Elon “taking control of the narrative”?


I haven’t done a multivariate analysis with Tesla’s share price, but it might be worthwhile now. Once the shorts/media started their campaign last year, it became clear that Tesla’s share price was going to be affected in a hedge-fund kind of way. I think this company should be valued, at least, at 2-2.5x higher than the current by end of 2018, if the market is accurate and corrects itself.

SJ Data Charts – Open Sourced

Thanks to some love over at Code for San Jose, SJ Charts is now SJ Data Charts and fully open-sourced here! has been fun and the documentation, as well as the community forums, have been great to work with. Wish there was more in-grained analytics support via Google Analytics and Mixpanel, but that seems to be a work-in-progress. Here’s what it looks like now:



The charts now have time-based charts for Housing Prices, Unemployment rate, and Jobs by Sector for the City of San Jose. The idea behind this app is to show various analyses via a top-level tabbed navigation from the Code for San Jose community. Basically, to build out useful analyses for the open data available from the City of San Jose.

SJ Charts

After dabbling with Pandas and asking around for better methods of building out interactivity, lo’ and behold: arrived. As a side project, I’ve been doing some data analysis on Open Data from the City of San Jose. Here’s the output: SJ Charts.

SJ Charts

It’s a fun, interactive dashboard to view Job-by-Sector data between 2008-2015 for the City of San Jose found via the source here or in a translated form, here. The intention is to expand with other analyses on this dashboard app and apply some checkbox love, but that might change depending on feedback. Looking to give back to the city I grew up in.

To give feedback, you can reach me via all the normal social channels. Github repo coming soon!

San Jose City Data Analysis Fun

So, I took a few weeks to get on-ramped with Pandas 0.23.0 and Python and am still learning the ropes. Though, I wanted to share some output. Namely, using the San Jose Open Data Portal, there were some good insights to be had from looking at economic data pushed out by the city for 2006-2016.


What did I do?


  • Looked at unemployment rates across various housing prices (condos/townhomes & single-family homes) using ordinary-least-squares regression.
  • Looked at, over time, labor force and housing price changes.


SJ Economic Changes, 2006-2016



Condo/Townhome prices vs. SJ Metro Unemployment



Single Family Homes prices vs. SJ Unemployment


What did I find? Namely, housing prices are increasing rapidly compared to labor change (i.e. job growth) in San Jose over the course of 2006-2016. Also, there’s a strong correlative relationship between something that we all take for granted and as obvious: increased housing prices in San Jose runs with lowered unemployment rates.

If you’d like to look at the data analysis done, here’s the GitHub repo. If you have any questions/comments/suggestions on more analysis, feel free to contact me via Twitter, GitHub, or LinkedIn.