financial independence post

Author: godotalgorithm

_posts/2021-01-07-independence.md

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+---
+layout: post
+title: "Intellectual independence requires financial independence"
+categories: career
+---
+
+This is a very belated follow-up to [my post last year about career problems]({% post_url 2020-03-27-career-hopelessness %}).
+It was a very long and cathartic post right as the coronavirus pandemic began to engulf the United States,
+ and it largely pinned my future research career hopes on being able to monetize research software of some kind.
+Such a venture is very unlikely to succeed, thus the general feelings of hopelessness.
+However, I regained hope again a few months later after giving myself a crash course in investment and financial planning.
+The bittersweet reality that I have come to accept is that through a few more years of careful saving, planning, and investing,
+ I should be able to finance a future independent research career for the remainder of my life through the average long-term
+ expected returns from investments into financial markets (e.g. stocks, bonds, and commodities, but mostly stocks).
+The modern US scientific enterprise has not been willing to support my research despite me devoting my entire adult life so far to it
+ (rather than seeking out more lucrative jobs), and I no longer have any real expectations of it ever doing so.
+It was simultaneously very uplifting and frustrating to realize that independent research opportunities are realistically something
+ that I will be able to afford to give myself in the not too distant future rather than something I have to be given by someone else
+ (but likely never will be).
+To be fair, while the scientific enterprise has failed to give me any explicit independent research opportunities since graduate school,
+ it also operates in a relatively lax and inefficient manner so that I have certainly managed to carry out
+ many independent research projects on the margins, albeit much more slowly than I'd like,
+ with my research priorities still heavily distorted, and carrying the guilt of mis-using work time.
+
+A major point of frustration for me was that I've been struggling with and focusing on career problems for most of the last decade,
+ while I've had no financial problems at all and thus have paid little attention to financial matters.
+If I had been more mindful of my finances in the past, then I would have been investing my growing non-retirement savings
+ in a prudent manner, and I would now be a lot closer to a financial solution to my career problems.
+Also, it was a bit unfortunate to learn about investing in a time of high economic uncertainty,
+ negative real interest rates, and overinflated asset prices (the crown jewel of overvaluation right now being Tesla, Inc.).
+These frustrations aside, it was very comforting to read about the Financial Independence, Retire Early (FIRE) movement,
+ which shows that financial independence is within reach for frugal people who can maintain highish-paying jobs for long enough.
+It was also very encouraging to see the inflation-adjusted total return of the S&P 500 index (and its precursors) over the last century
+ on a semilog plot against an exponential fit - it is an impressive historical record of American prosperity
+ that persists and is more accessible than ever through exchange-traded index funds.
+Stock markets have some disconcerting behavior - the extreme correlations between all liquid assets
+ make it effectively impossible to fully mitigate the volatility of stocks with other stocks (i.e. the volatility of an index fund
+ relative to its components is not reduced in accordance to the averaging of independent random variables),
+ and the time correlations of volatility mean that deviations from the robust long-time exponential growth trend can persist for a decade or more.
+Still, the good very clearly outweighs the bad, and my past inattention to investment was a regrettable mistake.
+
+I obsessively read about investing in mid-2020 and messed around a bit with fitting historical financial data to simple models
+ to get more comfortable with investment, and I could definitely imagine an alternate reality in which I pursued a career in economics or finance.
+Nothing profound came out of that exercise, but I can share a representative exercise that might be of broader interest.
+For the most part, I plan to invest only in large exchange-traded funds with low expense ratios,
+ and so the main impactful decision that I have to make is the allocation of my money between assets with low and high risk/volatility.
+For this exercise, I'll model the low-risk asset as having fixed value with no volatility (e.g. some mix of cash and high-grade bonds)
+ and the high-risk asset as a historical time series of the inflation-adjusted total return of the US stock market between 1870 and now.
+The simple-minded investment strategy is to adjust the cash/stock ratio based on the immediate value of stock market relative
+ to its uniform exponential growth baseline of around 6.5%.
+This ignores economic indicators that are surely much better than deviations from a uniform baseline,
+ but it illustrates the basic exercise of balancing the risks and rewards of volatile assets.
+The optimization of this investment strategy is a straightforward numerical exercise conducted and plotted with Python,
+ which shows a very limited benefit from reallocating assets (blue curve) relative to the underlying risk asset (red curve).
+
+![investment optimization](/assets/invest.pdf){:height="80%" width="80%"}
+
+Inevitably, the optimal investment strategy obtained from this numerical exercise is an artifact of the specific details of the historical data,
+ and its details do not reflect a useful forward-looking investment strategy.
+However, a useful observation is that deviations from full stock market exposure only occur above a 50% overvaluation.
+With a more uniform strategy of completely removing stock market exposure above 50% overvaluation,
+ the growth of investments is unchanged from full exposure to the stock market under all conditions.
+This suggests that overvalued stock markets have already realized all meaningful profits for investors,
+ and whether you pull money out early or ride the overvaluation back down, your outcome will be roughly the same on average.
+This is consistent with general advice against timing the stock market,
+ although this exercise suggests that it is more futile than actually harmful.
+I will eventually settle on an allocation strategy much like this for my own money,
+ but I still need to learn more about various economic indicators to decide on which ones to use in practice.
+While market crashes and bear markets cannot be predicted,
+ economic indicators can identify periods of increased risk (such as right now)
+ when stock market exposure should probably be reduced.
+
+Because I still need more money before I am financially independent with a comfortable buffer,
+ I am trying to shift my discretionary research interests and activities in a more profit-seeking direction for the foreseeable future.
+Of my established skills and interests, I have decided that the most likely to be lucrative is quantum computing.
+I have two recent quantum computing preprints that I need to revise and publish -
+ a [quantum Metropolis algorithm](https://arxiv.org/abs/1903.01451) and a
+ [finite-temperature variational Monte Carlo method](https://arxiv.org/abs/2003.04171) -
+ that might help me find a higher-paying job in quantum computing.
+I took my present job for the potential opportunity to continue my electronic structure research rather than compelling financial compensation,
+ but that opportunity has unfortunately not panned out.
+Quantum computing is a very hot topic right now with numerous scientists working at high-compensation tech companies like Microsoft, Google, and Amazon.
+Also, I have some very promising unpublished ideas in quantum error correction (QEC)
+ that I will attempt to patent when they are sufficiently developed.
+There are some very inefficient aspects of existing QEC protocols (e.g. logical non-Clifford gates),
+ and patents on more efficient methods could end up being very lucrative.
+I've read up enough about patents to appreciate the difficulty of their monetization (their main use is as a legal defense, not a monetizable asset),
+ but I have the opportunity to file patents with my employer covering the costs in exchange for half of any future profits.
+While I could probably make more money if I tried to pivot away from research altogether for a while,
+ I see quantum computing research as a reasonable compromise for now.
+
+While I still have a long road ahead of me on my path to intellectual independence through financial independence,
+ it is overwhelmingly more plausible than the prospect of finding a job that will allow me to pursue my research interests.
+The modern US scientific enterprise is simply not supportive of intellectual independence -
+ it tightly controls the active topics of research through the narrow flow of funding and the very low success rates of grants.
+This works for scientists with very malleable interests or whose narrow interests align with funding priorities,
+ but career starvation starts to set in when a scientist's interests fall out of favor
+ and they aren't willing to choose from a narrow pool of acceptable new interests.
+I've seen this happen multiple times so far in my career,
+ and I consider it to be extremely shameful that the scientific enterprise does this to people.
+In some cases, the unfortunate scientist has had enough savings that they could afford to continue their research on their own dime.
+In other cases, they burned through their life savings and then gave up on science altogether.

assets/invest.pdf

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+# analysis of a simple investment strategy on historical stock market data
+
+import numpy as np
+import matplotlib.pyplot as plt
+import scipy.optimize as opt
+
+date, cpi, val = np.loadtxt('sp500.dat', unpack=True)
+
+# normalize stock market values for convenience
+val /= 90.0
+val0 = 1.32*np.exp(0.06395*(date - 1871))
+
+ratio = np.arange(0.3, 2.1, 0.05)
+strategy = [1.0] * len(ratio)
+interval = [(0.0,1.0)] * len(ratio)
+
+# forward simulation of investment strategy
+def invest(strategy):
+    worth = 1.0
+    i=1
+    while i < len(val):
+        ratio0 = val[i-1]/val0[i-1]
+        strategy0 = np.interp(ratio0, ratio, strategy)
+        worth = worth*(1.0 - strategy0) + worth*strategy0*val[i]/val[i-1]
+        i = i+1
+    return -worth
+
+def invest_plot(strategy):
+    worth = [1.0]*len(val)
+    i=1
+    while i < len(val):
+        ratio0 = val[i-1]/val0[i-1]
+        strategy0 = np.interp(ratio0, ratio, strategy)
+        worth[i] = worth[i-1]*(1.0 - strategy0) + worth[i-1]*strategy0*val[i]/val[i-1]
+        i = i+1
+    return worth
+
+invest0 = invest(strategy)
+
+scan0 = invest_plot(strategy)
+res = opt.minimize(invest,strategy,bounds=interval)
+
+optimal_strategy = res.x
+scan = invest_plot(optimal_strategy)
+
+safe_strategy = [1.0] * len(ratio)
+for (i,strat) in enumerate(safe_strategy):
+    if(ratio[i] > 1.5):
+        strat = 0.0
+scan2 = invest_plot(safe_strategy)
+
+fig, (ax1,ax2) = plt.subplots(2,1,tight_layout=True, figsize=(6.0,6.0))
+
+ax1.plot(ratio,optimal_strategy,color='blue')
+ax1.set_xlabel('value of risk asset relative to baseline')
+ax1.set_ylabel('allocation')
+
+ax2.semilogy(date,val0,color='black')
+ax2.semilogy(date,scan0,color='red')
+ax2.semilogy(date,scan,color='blue')
+#ax2.semilogy(date,scan2,color='green')
+
+ax2.set_xlabel('year')
+ax2.set_ylabel('worth')
+
+plt.savefig('invest.pdf', bbox_inches='tight', pad_inches=0.01)