Investing Process and Tools
Process below is iterative and feeds back on itself.
- Do research to identify trends – long, medium, short term – then prospects that fit the trends;
- Make investments based on investment criteria;
- Learn from successes and failures and document;
- Adjust investment criteria, then invest again based on research.
Most of the links below require permission to access.
This process is for all levels of investing, from early-stage startups, later-stage VC/PE backed pre-IPO companies, to public companies.
- Info Sources Dataset. Where research and data comes from. This dataset includes many free and paid sources, which I maintain and I cross-reference most of the items below with their original source listed in this dataset.
- Data Lake. Structured and unstructured data where all base research gets deposited.
- Company Dataset. Structured data on companies, which contains structured info on them and links to that info in the Data Lake.
- Business ideas Dataset. General dump of business ideas I come across or think up on my own.
- Trends Dataset. From research, trends roll into this dataset as a master overview. These are long-term trends.
- Focus Dataset. Based on long-term trends (Trends Dataset), this dataset helps to narrow focus where I think I should be in terms of sectors, industries and specific markets for investment.
- Narratives Dataset. This dataset includes current thinking and perception across sectors, industries and markets in which I look for opportunities, and specific investment opportunities that I am considering. The narratives in this list can be considered trends but are short-to-medium term focused and help provide more current context to further narrow the focus of investment.
- Future Planning. Short-term outlook (less than 1-yr) that attempts to put the trends, focus, and narratives, along with possibilities and events (known or anticipated) to a calendar that affects me and requires action from me. This helps me think through and list actions to do ahead of time based on upcoming potential outcomes. This is a graphic like a Gant chart. While trends, focus and narratives datasets help narrow the where and why, this Gant chart attempts to layer on timing, or when to do the where/why. This link shows the Gant chart for me, and I suggest you do the same for yourself.
- Q & A/Discussion Board. Add Q&A and provide comments and discussion around any research in the datasets above.
Investment Principles and Criteria
- Securities Investment Principles. A dataset of timeless principles to help with securities investing which I add to and maintain.
- Investment Criteria. This details my investment criteria, including style, trading strategies and screens.
- Decision Analysis. This dataset includes investment decisions with actions, results and analysis if successful or not and why.
- Securities Investing Tracker. This spreadsheet tracks positions and near real-time results. See past weeks archives for positions (amounts have been removed to keep confidential). This sheet is not real-time and is supposed to update every 15 minutes but I find sometimes it does not, which is probably network related interference. For real-time pricing, I go direct to the trading platform.
- Monitoring. For public companies, positions are automatically added to Seeking Alpha which then will pull news and articles from Seeking Alpha on those issues that I can review. I also add to WSJ Watch List which pulls more general news. I can create price alerts as needed sent to mobile devices via Yahoo Finance.
- Watch list and investments quick summary: click here for a quick summary.
- Securities Investing Tracker. When I sell investment, its row data gets copied below in transactions sheet, as well as in the net income sheet to track overall profits and losses. See past weeks archives for what was sold and profit per share.
- Decision Analysis. Once exited from an investment, I include why sold, outcome, and analysis. For long-term holds that not yet exited, I might update each prospect’s record entry with results to date and learnings.
- Focus Dataset. Items get archived in same dataset where they live when they are no longer relevant.
- Narratives Dataset. Items get archived in same dataset where they live when they are no longer relevant.
- Investing Log. A Google doc that includes daily updates on what I do, learn, and what happens. Keeping a daily log helps me track the minutia of what I am doing, which I roll up as needed into the above datasets. See past weeks archives here.
- Info Sources Report. I track where all my information originates. This report counts the number of times an info source is referenced in my datasets. This helps me see which info sources are most valuable to track and keep my attention.
- Dataset/Database Documentation: description of fields and database tasks for above.
- Soehnel Family Trust. See this page for a link to the data lake for all trust administration folders and files.
- Securities Investing Tracker: this link is the template for the Google Sheets/Excel file.