I work at an unreasonably large hedge fund. When discussing the trading/gambling implications of the upcoming Donald Trump 2024-2028 American presidential term, I heard the phrase ‘deregulation without growth.’ I think that is an accurate description of the economic consequences of Trump’s previous term and what is like to come in 2025, but incomplete. It is more like “cruel without meaning.” TLDR: When someone at a hedge fund says that they expect “deregulation without growth” it basically means “no market fundamentals will change; there will be no major changes to productive capacity, but we will be able to siphon off more money from productive means than we did last year.”

Here is what this person meant by “deregulation without growth” – Some regulations prevent people from making stuff (e.x. the Indian/British violent enforcement of a monopoly on the production and taxation of salt) and removing certain kinds of regulations can lead to a society producing more goods and value with less work. The “growth” in “deregulation without growth” mainly refers to gross domestic product, but I will use the more abstract notion/phrase of “productive capacity” or production. You can make money off of the productive value-add or the expectation of more (or less) future value – for some definition of value that I won’t get into here. Short of that, your only choice to make even more money is to steal from the proletariat and whomever is on the other side of a future/option contract. And that is the kind of deregulation that atleast one hedge fund expects based on how it worked out in 2016-2020.

Consider the following.

I really like my datalake. Having almost unfathomable amounts of information about the world at my fingertips really is one of the best outcomes of building/running my home server . The sheer number of questions that I can answer with a few minutes of SQL and under one second of computation time is pretty amazing.

from pathlib import Path
import polars as pl
import plotly.express as px

datalake_base_path = Path("/mydata/datalake/")  # Modified for privacy
wdi_db_dir = datalake_base_path.joinpath(
    "world_bank_world_development_indicators",
    "wdi",
    "parquet",
)
wdi_db_df = pl.scan_parquet(wdi_db_dir)

gdp_growth_rates = wdi_db_df.sql("""
    SELECT country_name, year, value
    FROM self
    WHERE
        indicator_code='NY.GDP.MKTP.KD.ZG'
        AND country_code IN ('USA', 'IND', 'CHN')
    ORDER BY country_code, year"""
).collect()

fig = px.line(
    gdp_growth_rates,
    x="year",
    y="value",
    color="country_name",
    symbol="country_name",
    template="plotly_dark",
)
fig.update_layout(
    title="GDP Growth Rates -- World Bank World Development Indicators",
    xaxis_title="Year",
    yaxis_title="Percent Change",
    legend_title="Country",
)
fig.show()
fig.write_image("gdp_growth_1960-2023_world_bank_wdi.svg")

GDP Growth by Year

If I didn’t include the x-axis labels, it would be impossible to determine where the Trump presidency ended and the Biden presidency began. And that is the point.

As an aside, I understand the troubles with estimating GDP (growth) and think it is a fine enough proxy for “productive capacity” for our present purposes.

While these differences seem small, the effects really accumulate over time. One country being 1-2 percentage points/units below another for extended periods of time can be catastrophic. Really inspect the following values of $(1+r)^n$ for various $r$ that are common for GDP growth numbers and $n$ on a relevant time scale. The way to read this table is as follows: at 0.01 GDP growth (1%) the initial GDP will increase by 1.105x over 10 years (~11% increase), and by 1.645x over 50 years (~65% increase). The difference between 2% and 5% annual growth rate is massive over 25-50 years – 2% growth vs 5% growth over 100 years is the difference between production increasing by 7x and by 132x.

Number of Years 🡣 — Growth Rate 🡢 0.01 0.02 0.05 0.07 0.1
5 1.051 1.104 1.276 1.403 1.611
10 1.105 1.219 1.629 1.967 2.594
25 1.282 1.641 3.386 5.427 10.835
50 1.645 2.692 11.467 29.457 117.391
100 2.705 7.245 131.501 867.716 13780.612
import prettytable as pt
rs = (0.01, 0.02, 0.05, 0.07, 0.1)
ns = (5, 10, 25, 50, 100)
table = pt.PrettyTable(
    field_names=["Number of Years 🡣 --- Growth Rate 🡢"] + list(map(str, rs))
)
rows = [[n] + [round((1+r)**n, 3) for r in rs] for n in ns]
table.add_rows(rows)
table.set_style(pt.TableStyle.MARKDOWN)
print(table)

I know that GDP is not terribly important when it comes to describing the quality of life for most people and that it is peak economist move to place too much weight into it. I also understand that growth rates are never constant. However, when it comes to hedge funds making money, GDP is actually important. It is easier to steal ¥100 from a bank vault when there is ¥1000000 in the vault than when there is ¥1000. Hedge funds profit immensely in high growth times because there is more money rushing downstream and one can siphon off more money without anyone noticing.

Deporting millions of people will not improve these effects for the United States. It might feel good for white people, but while they put their attention towards the counterproductive ends of putting muslims and latinos in their place, and consenquently settling into the 1.5-2x production potential increases over the next 25-50 years, the Indians and the Chinese 3-10x their production. I think Trump goes after the culture war issues because this is a truly difficult problem to address and letting the police kill more unarmed black people is something he can actually accomplish. Keeping the proletariat distracted and divided is the best that Trump can deliver to people like those who work at hedge funds. But I think hedge funds understand what is going on and why.