The more we learned about the current crop of robo advisory firms, the more we realized we could do better. This brief blog post hits the high points of that thinking.
Not Just the Same Robo Advisory Technology
It appears that all major robo advisory companies use 50+ year-old MPT (modern portfolio theory). At Sigma1 we use so-called post-modern portfolio theory (PMPT) that is much more current. At the heart of PMPT is optimizing return versus semivariance. The details are not important to most people, but the takeaway is the PMPT, in theory, allows greater downside risk mitigation and does not penalize portfolios that have sharp upward jumps.
Robo advisors, we infer, must use some sort of Monte Carlo analysis to estimate “poor market condition” returns. We believe we have superior technology in this area too.
Finally, while most robo advisory firms offer tax loss harvesting, we believe we can 1) set up portfolios that do it better, 2) go beyond just tax loss harvesting to achieve greater portfolio tax efficiency.
A very astute professor of finance told our graduate finance class that the best way to become a bona fide quant is NOT to get a Ph.D. in Finance! It is better, he said, to get a Ph.D. in statistics, applied mathematics, or even physics. Why? Because a Ph.D in Finance is generally not sufficiently quantitative. A quant needs a strong background in Stochastic Calculus.
“Quants for Hire?”
Our company has been described as a “quants for hire” firm. That is flattering. While we currently have 4 folks with master’s of science degrees (and one close to finishing a master’s) what we do is probably more accurately described as “quant-like” or “quant-lite” software and services. However “Quants for Hire” definitely has a nice succinct ring to it.
Quant-like Tangents to Financial Learning
Most of our quant-like work has been fairly vanilla — back testing trading strategies in Excel, Monte Carlo simulations (also in Excel), factor analysis, options strategy analysis. So far our clients like Excel and are not very interested in R. The main application of R has been to double-check our Excel back tests!
We have attracted fairly sophisticated clients. They seem reasonably comfortable about talking about viewing portfolios as unit vectors that can be linearly combined. They tend to understand correlation matrices, Sortino ratios, and in some cases even relate to partial derivatives and gradients. But they tend to push back on explanations involving geometric Brownian motion, Ito’s lemma, and the finer points of Black-Scholes-Merton. They do, however, appear to appreciate that we “know our stuff.”
I’ve got a decent set of R skills, but I’m looking to take them to the next level. I’m taking a page from my professor in tackling non-financial quantitative problems. My current problem du jour image compression. I came up with an R script that achieves very high compression levels for lossy compression. It is shorter than 200 lines commented and shorter than 100 lines when stripped of comments and blank (formatting) lines.
It can easily achieve 20X or greater compression, albeit with a loss in quality. In my initial tests my R algorithm (IC_DXB1.1) was somewhat comparable to JPEG (GIMP 2.8) at 20X compression, though I the JPEG clearly looks better in general. I also found an elegant R compressor that is extremely compact R code… the kernel is about 5 lines! Let’s call this SVD (singular value decomposition) for reference. So here’s the bake off results (all ~20X compressed to ~1.5KB):
What’s interesting to me is that each algorithm uses radically different approaches. JPEG uses DCT (discrete cosine transform) plus a frequency “mask” or filter that reduces more and more high-frequency components to achieve compression. My ic_dxb1.1 algorithm uses a variant of B-splines. The SVD approach uses singular value decomposition from linear algebra.
Obviously tens of thousands of hours have been invested in JPEG encoding. And, unfortunately, 99%+ of JPEG images are not as compact as they could be due to a series of patent disputes around arithmetic coding. Even thought the patents have all (to the best of my knowledge) expired, there is simply too much inertia behind the alternative Huffman coding at the present. It is worth noting that my analysis of all 3 algorithms is based on Huffman coding for consistency. All three approaches could ultimately use either Huffman or arithmetic coding.
So this Image Stuff Relates to Finance How?
Another of my professors explained that, fundamentally, finance is about information. One set of financial interview questions start with the premise that you have immediate (light-speed, real-time) access to all public information. Generally how would you make use of this information to make money trading? Alternatively you are to assume (correctly) that information costs money… how would your prioritize your firm’s information access? How important is frequency and latency?
Having boat loads of real-time data and knowing what to do with it are two different things. I use R to back test strategies, because it easy to write readable R code with a low bug rate. If I had to implement those strategies in a high-frequency trading environment, I would not use R, I would likely use C or C++. R is fast compared to Excel (maybe 5X faster), but is slow compared to good C/C++ implementations (often 100X slower).
My thinking is that while knowledge is important, so is creativity. By dabbling in areas outside of my “realm of expertise”, I improve my knowledge while simultaneously exercising my creativity.
Both signal processing and quant finance can reasonably be viewed as signal processing problems. Signal processing and information theory are closely related. So I would argue that developing skills in one area is cross-training skills in the other… and with greater opportunity for developing creativity. Finance is inextricably linked to information.
The Future of Finance Requires Disruptive (Software) Technology
It aint gonna be pretty for traditional financial advisors, hybrid advisors, broker/dealers, etc. Not with the rapid market acceptance of robo advisors.
Robo advising will have at least three important disruptive impacts:
Accelerating downward pressure on advisory fees
Taking of market share and AUM
Increasing market demand for investment tax management services such as tax-loss harvesting
Are you ready for the rise of the bots? We at Sigma1 are, and we are looking forward to it. That is because we believe we have the software and skills to make robo advisors work better. And we are not resting on our laurels — we are focusing our professional development on software, computer science, advanced mathematics, information theory, and the like.
As I continue to explore patterns in beta-client data, I clearly see one common difference. For globally-diversified, and asset-diversified ETF- and mutual-fund-based portfolios 36-month, monthly modified-semivariance and variance based portfolios tend to converge to produce similar results. This is in sharp contrast to stock-based portfolios, where variance (MVO) and semivariance (PMPT) portfolios display a significant trade-off between Sharpe and Sortino ratios.
My preliminary conclusion, based on poring through individual optimized-portfolios, is that variance and semivariance are closely correlated for portfolios based on sufficiently-diversified ETFs. On the other hand, the difference between variance-optimized, semivariance-optimized, and hybrid (blend of variance- and covariance-optimized) portfolio is significantly different if individual stocks and bonds are analyzed. [Sufficiently-diversified in this context does not mean diversified per se. It only means relative diversification within a given ETF or set or ETFs/ETNs/Mutual Funds.)
These preliminary findings suggest that semivariance and variance based optimizations are highly correlated for certain asset classes (and expected returns) while differing for other asset classes (and expected returns). Stock-pickers are more likely to see benefits from semivariance-based optimization than are those who select from relatively-diverse ETFs.
These preliminary findings are causing a shift in the approach taken by Sigma1. Since, so far, Sigma1 beta partners are primarily interested in constructing portfolios based primarily or exclusively around ETFs, ETNs, and mutual funds, our company is focusing more on Sharpe ratios (because they are quicker to optimize for than Sortino ratios).
Because Sigma1 HAL0 portfolio-optimization is tuned to optimize for 3 objectives this presents an interesting question: “Your investment company wishes to optimize portfolios based on 1) expected return, 2) minimal variance, and 3) <RISK MEASURE 3>?”
Sigma1 is posing questions: What is your third criterion? What is your other risk measure? Answer these questions, and Sigma1 HAL0 software will optimize your portfolio accordingly; showing the trade-offs between Sharpe ratios and your other chosen risk metric.
Sigma1’s 3-objective-optimization is causing a few financial-industry players to ask the question of established optimization engines, “Can you do that?” Sigma1 Software can. Can your current portfolio-optimization software do the same?
I am presently in Placencia Belize. I love to learn about how the economy and culture of a country and how regions within a country contribute to its place in the world.
My high-school education in history went deeper than most. I studied AP history and my score exempted me from university history requirements. The approach to history was, in part, a story of wars, victories, and defeats. Differentials in leadership, technology, and manpower were used to explain, ex post, how wars were won and lost.
I have learned that these are only part of the story. The parallel, kindred theme is economic. Superior economies provide great advantage. This advantage is reflected in both income and the interest rate paid on bonds. If a side loses the confidence of the bond market, the outcome of the conflict becomes largely predetermined.
I have a working theory about how people and governments respond to events. At the core of my working theory is positive feedback. Despite the name “positive” feedback is not necessarily positive in the emotional sense. “Positive” feedback simply means self-reinforcing. Virtuous cycles and vicious cycles are both examples of self-reinforcing positive feedback.
Strangely, mindset is crucial. There are two mindsets that favor positive outcomes: 1) economic self-confidence, 2) patient self-development and willingness to defer economic self-gratification (spending). The United States seems to be losing both of these factors. The key word is “self”, as in individual.
US growth, anemic as it is, is based largely on government leverage. As long as this leverage can be financed at historically-low rates, this largely superficial growth can occur… until real, organic growth resumes, or the debt bubble bursts or begins deflating (signaled by erosion of the value of the USD).
Simply put, I have become bearish on the USD and am looking for safer harbors for my “non-risk” capital. Alternatives include CAD, AUD, and NOK (kr) government bonds. TIPs provide a degree of protection, if you believe CPI-U stats accurately reflect US inflation. CASH in USD is suspect.
Back to Belize. Belizean currency is pegged to the USD on 2:1 basis. Belize’s unemployment rate is approximately 14.4%, and the trend is upward. With the shear beauty and wonder of the country, reasonable English-proficiency, and a tiny 360,000 population, tourism should translate more effectively to income/capita, GDP/capita, and overall economic growth.
I am studying dissonant economic stagnation first hand. Beauty surrounded by poverty. Economic stagnation. Hope concentrated outward on government, on divine intervention, on anything but self.
I study with objectivity laced with sadness. These people could be much more, but they will likely not be any time soon.
This is a lesson for the US. The US government’s fiscal situation is unsustainable. The general outlook is looking outward for hope rather than inward (to oneself) for strength and betterment. Until this attitude changes I see anemic US growth as the future. This is likely to manifest in US inflation and USD devaluation. Federal Reserve policy seems to concur.
My macro outlook can be reflected in the numbers I put into expected-return data for global market ETFs. Sigma1 HAL0 software uses these projections and variance and semivariance optimization (or other metrics) to help me revise my personal portfolio.
Feel free to disagree with my outlook. Sigma1 HAL0 optimization will incorporate your projections, and build an optimized portfolio based upon them. HAL0 portfolio optimization is algorithmic and objective. Individual security expected-returns are typically based on user inputs. HAL0 software optimizes within the provided framework. User predictions matter, as does human discretion in using the HAL0 results.
Because my projections for expected-return may vary widely from yours, your company’s, and your analysts, the resulting portfolios will vary widely.
The bottom line is that Sigma1 portfolio-optimization software uses your hard-won security analysis and projections to build an accordingly-optimized investment portfolio. If you believe in your analysis, so will Sigma1. Keep that in mind. Your analysis matters.
This marks the first month (30 days) of engagement with beta financial partners. The goal is to test Sigma1 HAL0 portfolio-optimization software on real investment portfolios and get feedback from financial professionals. The beta period is free. Beta users provide tickers and expected-returns estimates via email, and Sigma1 provides portfolio results back with the best Sharpe, Sortino, or Sharpe/Sortino hybrid ratio results.
HAL0 portfolio-optimization software provides a set of optimized portfolios, often 40 to 100 “optimal” portfolios, optimized for expected return, return-variance and return-semivariance. “Generic” portfolios containing a sufficiently-diverse set of ETFs produce similar-looking graphs. A portfolio set containing SPY, VTI, BND, EFA, and BWX is sufficient to produce a prototypical graph. The contour lines on the graph clearly show a tradeoff between semi-variance and variance.
Once the set of optimized portfolios has been generated the user can select the “best” portfolio based on their selection criteria.
So far I have learned that many financial advisers and fund managers are aware of post-modern portfolio theory (PMPT) measures such as semivariance, but also a bit wary of them. At the same time, some I have spoken with acknowledge that semivariance and parts of PMPT are the likely future of investing. Portfolio managers want to be equipped for the day when one of their big investors asks, “What is the Sortino ratio of my portfolio? Can you reduce the semi-variance of my portfolio?”
I was surprised to hear that all of Sigma1 beta partners are interested exclusively in a web-based interface. This preliminary finding is encouraging because it aligns with a business model that protects Sigma1 IP from unsanctioned copying and reverse-engineering.
Another surprise has been the sizes of the asset sets supplied, ranging from 30 to 50 assets. Prior to software beta, I put significant effort into ensuring that HAL0 optimization could handle 500+ asset portfolios. My goal, which I achieved, was high-quality optimization of 500 assets in one hour and overnight deep-dive optimization (adding 8-10 basis points of additional expected-return for a given variance/semi-variance). On the portfolio assets provided to-date, deep-dive runtimes have all been under 5 minutes.
The best-testing phase has provided me with a prioritized list of software improvements. #1 is per-asset weighting limits. #2 is an easy-to-use web interface. #3 is focused optimization, such as the ability to set max variance. There have also been company-specific requests that I will strive to implement as time permits.
Financial professionals (financial advisers, wealth managers, fund managers, proprietary trade managers, risk managers, etc.) seem inclined to want to optimize and analyze risk in both old ways (mean-return variance) and new (historic worst-year loss, VAR measures, tail risk, portfolio stress tests, semivariance, etc.).
Some Sigma1 beta partners have been hesitant to provide proprietary risk measure algorithms. These partners prefer to use built-in Sigma1 optimizations, receive the resulting portfolios, and perform their own in-house analysis of risk. The downside of this is that I cannot optimize directly to proprietary risk measures. The upside is that I can further refine the HAL0 algos to solve more universal portfolio-optimization problems. Even indirect feedback is helpful.
Portfolio and fund managers are generally happy with mean-return variance optimization, but are concerned that semivariance-return measures are reasonably likely to change the financial industry in the coming years. Luckily the Sharpe ratio and Sortino ratio differ by only the denominator (σp versus σd) . By normalizing the definitions of volatility (currently called modified-return variance and modified-return semivariance) HAL0 software optimizes simultaneously for both (modified) Sharpe and Sortino ratios, or any Sharpe/Sortino hybrid ratios in-between. A variance-focused investor can use a 100% variance-optimized portfolio. An investor wanting to dabble with semi-variance can explore portfolios with, say, a 70%/30% Sharpe/Sortino ratio. And an investor, fairly bullish on semivariance minimization, could use a 20%/80% Sharpe/Sortino hybrid ratio.
I am very thankful to investment managers and other financial pros who are taking the time to explore the capabilities of HAL0 portfolio-optimization software. I am hopeful that, over time, I can persuade some beta partners to become clients as HAL0 software evolves and improves. In other cases I hope to provide Sigma1 partners with new ideas and perspectives on portfolio optimization and risk analysis. Even in one short month, every partner has helped HAL0 software become better in a variety of ways.
Sigma1 is interested in taking on 1 or 2 additional investment professionals as beta partners. If interested please submit a brief request for info on our contact page.
Let’s start with the idea that CAPM (Capital Asset Pricing Model) is incomplete. Let me prove it in a few sentences. Everyone knows that, for investors, “risk-free” rates are always less than borrowing (margin) rates. Thus the concept of CAL (the capital asset line) is incomplete. If I had a sketch-pad I’d supply a drawing showing that there are really three parts of the “CAL” curve…
The traditional CAL that extends from Rf to the tangent intercept with the efficient-frontier curve.
Why? Because the CAML has it’s own tangent point based on the borrower’s marginal rate. Because the efficient frontier is monotonically-increasing the CAL and CAML points will be separated by a section of the EF curve I call the CAC.
All of this is so obvious, it almost goes without saying. It is strange, then, that I haven’t seen it pointed out in graduate finance textbooks, or online. [If you know of a reference, please comment on this post!] In reality, the CAL only works for an unleveraged portfolio.
Higher risk, higher return, right? Maybe not… at least on a risk-adjusted basis. Empirical data suggests that high-beta stock and portfolios do not receive commensurate return. Quite to the contrary, low-beta stocks and portfolios have received greater returns than CAPM predicts. In other words, low-beta portfolios (value portfolios in many cases) have had higher historical alphas. Add leverage, and folks like Warren Buffett have produced high long-term returns.
Black Swans and Grey Swans
On the fringe of modern-portfolio theory (MPT) and post-modern portfolio theory (PMPT), live black swans. Black swans are essentially the most potent of unknown unknowns, also known as “fat tails”.
At the heart of PMPT is what I call “grey swans.” This is also called “breakdown of covariance estimates” or, in some contexts, financial contagion. Grey-swan events are much more common, and somewhat more predictable… That is if one is NOT fixated on variance.
Variance is close, semivariance is closer. I put forth the idea that PMPT overstates its own potential. Black swans exists, are underestimated, and essentially impossible to predict. “Grey swans” are, however, within the realm of PMPT. They can be measured in retrospect and anticipated in part.
Assets are Incorrectly Priced
CAPM showed a better way to price assets and allocate capital. The principles of semivariance, commingled with CAPM form a better model for asset valuation. Simply replacing variance with semivariance changes fifty years of stagnant theory.
Mean-return variance is positively correlated with semivariance (mean semi-variance of asset return), but the correlation is far less than 1. Further, mean variance is most correlated when it matters most; when asset prices drop. The primary function of diversification and of hedging is to efficiently reduce variance. Investors and pragmatists note that this principle matters more when assets crash together — when declines are correlated.
The first step in breaking this mold of contagion is examining what matter more: semivariance. Simply put, investors care much less about compressed upward variance than they do about compressed downward variance. They care more about semivariance. And, eventually, they vote with their remaining assets.
A factor in retaining and growing an AUM base is content clients. The old rules say that the correct answer the a key Wall Street interview question is win big or lose all (of the client’s money). The new rules say that clients demand a value-add from their adviser/broker/hybrid. This value add can be supplied, in part, via using the best parts of PMPT. Namely semivariance.
That is the the end result of the of the success of semivariance. The invisible hand of Sigma1, and other forward-looking investment companies, is to guide investors to invest money in the way that best meets their needs. The eventual result is more efficient allocation of capital. In the beginning these investors win. In the end, both investors and the economy wins. This win/win situation is the end goal of Sigma1.
Saturday, I had a fascinating morning starting a 10-week entrepreneurship program. I sat face-to-face with a couple of people who could likely write 7-figure checks that would clear the bank.
Tuesday was day 2 of the entrepreneurship class. I can sum up the general mood of the class with two words: energy and optimism. Speakers, coaches, and students were increasingly energized as the 3.5 hour class progressed. There are some talented students in my class who have already have revenue, employees, and venture capital. Others have a Ph.D. thesis and novel ideas from their post-doc work. I am presently somewhere in between: I am beyond the idea phase and well into the software product development phase. I have demo-able software and am self-funded to the tune of approx. $50,000.
Apparently my ideas and software are starting to get traction within the group. It is possible that some college students will write the Sigma1 Financial business plan as a for-credit exercise. If this pans out, I get a free first-draft business plan, and they get college credit. That spells “win-win” to me.
I have also learned that my “elevator pitch” needs work. My off-the-cuff pitch on Day 1 was my best. Successive effort have been worse. Feedback examples include: I felt buried; too much financial jargon; too long. This is great feedback! Better ideas from the group include gems such as:
Financial Risk: If you can see it, you can avoid it. Understand risk, visualize risk, and using proprietary software decrease risk by X%.
Nobody likes risk, but almost nobody truly understands it. Sigma1 software does.
Helping financial advisers model and explain risk to their clients.
Visualizing risk… Really!
Here’s the revised elevator pitch based off of their feedback. “Hi, I founded Sigma1 Software. If you own an investment portfolio there is one think you should know: it was probably constructed with using flawed risk models. Sigma1 Software can analyze your existing portfolio using better risk models. It will also build alternative portfolios and display the results visually. Our software not only reduces risk; it helps you visualize risk.
Few people like financial risk, and fewer still truly understand risk. Sigma1 Software does, and it uses advanced risk models and proprietary algorithms to help you build a better portfolio.
That’s the pitch: Smarter risk models, risk reduction, and powerful visualization tools. It’s what we do at Sigma1 Software.”
Building a Solid Team
To take Sigma1 Software to the next level, I need a great team. At a minimum I need a salesperson. This person needs both sales experience and experience in the financial industry. Having a wide network of connections to financial professionals is a plus. I’m looking for someone will no only close deals, but work with me and our attorneys to construct standard contracts and pricing models. A person who will build and maintain long-term relationships with our customers while finding new customers. Finally a salesperson who listens to customer needs in order to help us improve our software and software support.
Next on my hiring list is a second software developer specializing in user interface development for Windows, Linux, and Web applications. I’m looking for a developer who also has the interpersonal skills to 1) contribute to a highly collaborative environment, 2) conduct customer training, 3) join sales visits to demo Sigma1 software and answer technical questions. Pluses would be Windows and/or Linux sysadmin and IT skills. The software developer position would also require occasional work performing unit, regression, and beta testing of the portfolio-optimization suite.
It is crucial that any employees, partners, or employee/partners believe in Sigma1’s mission: To build and sell revolutionary portfolio-optimization technologies which will revitalize and reinvent the financial industry. To that end, equity in the form of voting, non-voting, dilutable, and possibly non-dilutable stock are likely to be an important part of any hiring agreement.
Almost every stock chart presents incomplete data for a security’s total return. Simply put, stock charts don’t reflect dividends and distributions. Stock charts simply show price data. A handful of charts superimpose dividends over the price data. Such charts are an improvement, but require mental gymnastics to correctly interpret total return.
At the end of the year, I suspect the vast majority of investors are much more interested in how much money they made than whether their profits come from asset appreciation, dividends, interest or other distributions. In the case of tax-differed or tax-exempt accounts (such as IRA, Roth IRAs, 401k, etc. accounts) the source of returns is unimportant. Naturally, for other portfolios, some types of return are more tax-advantaged than others. In one case I tried to persuade a relative that MUB (iShares S&P National AMT-Free Muni Bd) was a good investment for them in spite of it’s chart, because the chart did not show the positive tax impact of tax-exempt income.
Our minds see what they want to see. When we compare two stocks (or ETFs) we often have a slight bias towards one. If we see what we want in a stock’s chart, we may look past the dividend annotations and make a incorrect decision.
This 1-year chart comparing two ETFs illustrates this point. These two ETFs track each other reasonably well until Dec 16th, where there is a sharp drop in PBP. This large dip reflects the effect of a large distribution of roughly 10%. Judging strictly by the price data, it at first appears that SPY beats PBP by 7%. When factoring the yield of PBP, about 10.1%, and SPY, roughly 1.9%, shows a 1.2% 1-year out-performance by PBP. First appearances show SPY outperforming; a little math shows PBP outperforming.
Yahoo! Finance provides raw data adjusted for dividends and distributions. Using the 1-year start and end data shows SPY returning a net 3.77%, and PBP returning a net 4.96%. The delta shows a 1.19% out performance by PBP. Yahoo! Finance’s table have all the right data; I would love to see Yahoo! add an option to display this adjusted-price data graphically.
Total return is not a new concept. Bill Gross was very insightful in naming PIMCO’s “Total Return” lineup of funds over 25 years ago. Many mutual funds provide total return charts. For instance, Vanguard provides total return charts for investments such as Vanguard Total Stock Market Index Fund Admiral Shares. I am pleased to see Fidelity offering similar charts for ETFs in research “performance” reports for its customers. Unfortunately, I have not found a convenient way to superimpose two total-return charts.
While traditional stock and ETF charts do not play a large roll in my investment decisions, I do look at them when evaluating potential additions to my investment portfolio. When I do look at charts, I’d prefer to have the option of looking at total return charts rather than “old fashioned” price charts.
That said, I prefer to use quantitative portfolio analysis as my primary asset allocation technology. For such analysis I compute total return data for each asset from price data and distribution data, assuming reinvestment. Reformatting asset data in this way allows HAL0 portfolio-optimization software to directly compare different asset classes (gold, commodities, stock ETFs, bond ETFs, leveraged ETFs, etc). Moreover, such pre-formatting allows faster computation of risk for various asset allocations within a portfolio.
A large part of my vision for Sigma1 is revolutionizing how investors and money managers visualize and conceptualize portfolio construction. The key pieces of that conceptual revolution are:
Rethinking return to always mean total return.
Rethinking risk to mean something other than variance or standard deviation.
Many already think of total return as the key measure of raw portfolio performance. It is odd, then, that so many charts display something other than total return. And some would like to measure, manage, and model risk in more robust ways. A major obstacle to alternate risk measures is a dearth of financial portfolio optimization tools that work with PMPT models such as semi-variance.
HAL0 is designed from the ground up to address the goals of optimizing portfolios based on total return and a wide variety of advanced, more-robust risk models. (And, yes, total return can be defined in terms of after-tax total return, if desired.)
Disclosure: I have long positions in SPY, the Vanguard Total Stock Market Index, and PBP.
Technologies Mature and Innovator’s Discontent Grows
Discontent, in moderation, can be a powerful motivator for entrepreneurial spirit. I have been continuously employed for 15 years at 3 different Fortune-500 technology companies. All three companies have excellent benefits such as stock grants, stock options, stock purchase plans, profit sharing and bonuses, 401k matching, quality health insurance, and paid vacations. These benefits are very tough to walk away from. In general, I am very content with my benefits and somewhat content with my salary.
My level of satisfaction with the job itself has fluctuated widely over time. The most satisfying times are when I have made lasting changes to how CPUs are designed. Most of these improvements are smaller than the round-off error in the multi-billion dollar corporate balance sheet. However, at least one innovation I worked on, that was part of a small team effort, will likely be large enough to measurably boost the company’s bottom line. The company was insightful enough to give an award to our team for this accomplishment.
Unfortunately, most of the work my coworkers and I do is simply about getting things done (aka execution). Much of my work is not technically challenging — the key challenge is managing the shear volume of work. This is a change from 15 years ago, when there were many new and interesting technical problems to be solved.
Today, we computer design engineers, have become at least 10X more productive than we were 10-15 years ago. We each deliver millions of transistors in less time than we would have delivered tens or occasionally hundreds of thousands of transistors a decade ago.
Ten years ago I was providing unique contributions to the companies I worked for on a fairly regular basis — about 5-6 times a year. Today, I am making unique contributions about once per year on average. This is not due to a lack of ideas; it is simply due to a lack of time. Unlike the “old days”, I seldom get permission to work on innovation. And when I do, it is in the realm of “focused innovation”, which is all-too-often an oxymoron.
Discontent Leads to Change, Sometimes to Entrepreneurship
My doctor is a former electrical engineer. He experienced his company rapidly outsourcing engineering work to India and other low-wage countries, and decided to go to medical school. He now works for a medical group, and thus I don’t see his career change as entrepreneurial.
My story is different than my doctor’s. I saw the similar writing on the wall that he saw, but I interpreted it a bit differently. I saw a future of wage stagnation rather than unemployment. I saw that I must climb the electrical engineering food chain to what many consider the top — CPU Hardware Design. And I changed companies to make that climb. Simultaneously I took grad school classes to boost my knowledge in electrical and computer engineering as well as in finance.
Why study finance? 1) I have passion for finance 2) After studying electrical engineering, the math is easy. 3) Finance is a field that does not compete with my employer. 4) To understand technology and money is to better understand what “makes the world go ’round.” 5) To become a better, or at least more knowledgeable, investor. 6) As a backup career option. 7) To become a financial entrepreneur.
Serial Entrepreneur Considers Leaving the Corporate Cocoon
It could be argued that I have been an entrepreneur since I managed a paper route at the age of 12. I sold subscriptions door-to-door, I collected money for subscriptions, and I delivered papers. In high school I had did high-tech work for a print and publishing company. I telecommuted at age 16, using a 2400 baud modem to receive raw data and send back publication-ready charts and graphs. I even turned the college scholarship effort into an entrepreneurial enterprise: submitting over 50 applications and being awarded over $100,000 in merit-based scholarships.
I won’t bore you with the details of my intervening entrepreneurial efforts, other than to say that I was trading and auctioning products in 1995 on the internet (and Usenet), the same year that eBay was founded.
My fiancée has been a successful entrepreneur for 3 and 1/2 years. Her company’s revenue and earnings growth has been explosive (and the trend continues), while my salary growth over 7 years has not even kept pace with inflation. I have little doubt I could literally walk across the street and get a similar-paying job at 2 different tech companies… tech companies that are not nearly as financially secure as my present company. I could probably even get a 10%-15% pay bump. However, I have connections at these companies who, in aggregate, tell me that work there is simply less rewarding relative to the work at my present employer. Simply put, I work on one of the best projects for one of the best companies in the industry. My coworkers are likeable and extremely talented. Why would I consider giving this up?
I can answer this question with a question: “Do you want to change the world (for the better)?” My answer is a resounding “YES!” Do I see that happening in my corporate job? Sadly, it seems very unlikely, a least not in any profound way.
I am young enough that I can take a multi-year stab at full-time entrepreneurship. I have saved enough, and positioned myself financially so that I can live for 3-5 years without any earned income. And, if my venture should not succeed, I believe I can re-enter the corporate electrical engineering, software development, or financial services job markets.
Caution and Calculated Risk in the Mind of an Entrepreneur
My heart wants to put in my two-weeks notice tomorrow and dive straight into making Sigma1 portfolio-optimization software into the premier financial software in the world. My rational side has a very different perspective; Sigma1 must generate sufficient revenue (or meet other financial criteria) to justify a radical career change. Scenario #1 involves achieving revenue equal to my current total compensation for present job… as a strictly self-financed undertaking. Scenario #2 envisions a large (>$1M) external capital investment out of which I retain significant equity ownership (at least 50.1% non-dilutable) and a 3-year salary contract for $100,000. per year. Scenario #3 entails a very large external capital investment (>$2.5M), a longer and/or larger salary and benefits guarantee, non-dilutable equity retention of at least 20%, a voting seat on the board for 3+ years, and a 5+ year contact to control software development with the title of CTO.
Obviously, I don’t know what the future holds. My employer should not have any immediate concerns. I fully intend to be model employee — out of personal responsibility and because if my ventures don’t work out, I want a chance to return in a few years on good terms.