Year in Review: Finances
The Year In Review:
- Year In Review: Finances
- Year In Review: Fitness
- Year in Review: Gratitude
- Year In Review: Resolutions
- Year In Review: Sleep
So I’ve been filling in pretty much every transaction I’ve done since I first started working (December 2016, four years ago). I’ve used this old Google notebook I got from a hiring event back at Duke. And in December 2020, I finally filled out the entire book!
I chose to use a handwritten financial ledger because I didn’t really trust apps like Mint in order to be around when I’m 50 or keep my data safe (I already don’t trust online banking or credit card agencies but the convenience is much harder to pass up), and because I wanted to keep my handwriting skill to the point where I can still read it (a partial success). This comes with the tradeoff of not having that data digitized, which would really help in understanding my finances and where I’m going, and having a single source of failure (e.g. apartment burns down, I lose my notes forever).
So after I filled out the book, I’m probably going to put it in a dark corner of my apartment somewhere where the survivability aspect is a lot lower. I thought it was time to digitize this ledger so I have some data dump I won’t worry about losing. I’ll still keep the handwritten ledger for newer records in another book, but this gives me the ability to take a look back at three years worth of personal financial data.
What I did was, take the ledger in front of me, enter it into a Google sheet (because I don’t have my own spreadsheet app right now), export that sheet into a CSV file, then ingest that CSV file into a PostgreSQL instance. Then the database is dumped, and the dump is synchronized to S3.
Also, if you’re interested in doing the same as me, might I kindly dissuade you.
Digitizing this ledger took three full days of straight-up manual data entry
into a spreadsheet, and I still haven’t yet data munging for the plentiful
fat-fingering mistakes I’m sure to have made (I’ve caught three already) and
file transforms (because
psql ingests CSVs in a very strict manner).
Here’s some of the things I’ve noticed about my spending, just from punching in the data:
I do a good amount of repeatable spending: Like, to the point where I can identify “miscellaneous spending” of $15.70 to be my monthly Allstate renter’s insurance (in case my landlord’s apartment burns down with all my stuff in it). I think this fits in with my understanding of myself as a creature of habit.
My spending has increased over the years: I think I’ve tried to avoid falling into the trap of increasing incomes -> increasing spending as best as I can. So many of the major expenditures others have made, I haven’t (I haven’t purchased a car or a home or anything that might require a loan). I have spent more money on Amazon purchases and food. I’m extremely skeptical of purchasing something with a loan, unless it’s stock or something on margin because my money is in a separate bank account.
It’s a mistake to think that cash is king: Cash is more like trash. Own assets (things that make money for you), don’t just stuff it in a bank account and hope interest payments cover inflation (they don’t and they haven’t since quantitative easing and lowering of fed funds rate and such back in ‘08-09). I’m trying out a bimodal approach to investing now, with a (should be) safe-ish approach of fixed income securities and blue-chip stocks / mutual funds through WealthSimple (which keeps me investing even though I’m not doing anything), and really really risky stocks that I think have a good chance of making it because of my field (so tech stocks and stuff). Also green energy stocks, because governments around the world are starting to order people to purchase green cars and green energy and government orders are pretty stable.
So I used Metabase in order to tabulate these finances, and here’s some of the interesting SQL queries I’ve run and some of the interesting visualizations I’ve been able to generate:
SELECT category, sum(amount_in_usd) sum_amount_in_usd FROM expenditures WHERE is_expenditure = bool(1) AND EXTRACT(year FROM pay_period_begin) = '2020' GROUP BY category ORDER BY sum_amount_in_usd ASC;
In 2020, I paid exactly $16,500 in rent, while I invested or saved $15,158.40 (investing/saving is less as I invested some of the money I had previously saved, thus double-counting). By contrast in the year prior (2019), I had paid around $15,125 in rent, while saved/invested around $30,329.17. So I definitely didn’t do as good of a job investing or saving in 2020 as I did in the year before, where (surprisingly) I had made about the same.
SELECT category, sum(amount_in_usd) sum_amount_in_usd FROM expenditures WHERE is_expenditure = bool(1) AND EXTRACT(year FROM pay_period_begin) = '2019' GROUP BY category ORDER BY sum_amount_in_usd ASC;
I spent a horrendously large amount of money on eating out in the first half of December ($611.49), while in 10 pay periods I spent more on groceries than I did in eating out. Most of the summer was empty as I had spent time at home and didn’t purchase any food or groceries during that time. My typical grocery budget is around $200-300, while my typical eating out budget is around $200. I wanted to spend $2 on groceries for each $1 I spent on eating out, so I’m missing the mark here.
SELECT extract(year from pay_period_begin) payment_year, sum(amount_in_usd) sum_amount_in_usd FROM expenditures WHERE category IN ('actual_paycheck') GROUP BY payment_year ORDER BY payment_year ASC;
When I switched jobs between 2017 and 2018, my take-home salary increased by around 39.8%. This is extremely surprising to me, I thought the bump would be a good deal less. Looks like it pays to switch jobs every so often.