REPOST: More Evidence Why Becoming An Entrepreneur Is Less Scary Than You Think

This article by Paul B. Brown from Forbes.com talks about how aspiring entrepreneurs who are "risk adverse" are likely to have a successful business.

Because the conventional wisdom about entrepreneurs is that they always leap before looking and bet everything on one roll of the dice, people were surprised by the recent post that argued becoming an entrepreneur is less scary than you think.

But they shouldn’t have been. Successful entrereneurs, as we saw, have mastered the basics of risk management. Specifically, they know if you’re going to play in a game with uncertain outcomes—such as starting something new—then you never:

1) Pay/bet more than what you can expect as a return, and
2) Pay/bet more than you can afford to lose.

Both of those ideas can be summed up with the phrase “acceptable loss,” a concept where you consider the potential downside of whatever risk you are about to take—such as starting a new company or some other venture that is going to consume a lot of your time, capital, or other assets—and put on the line no more than you find it tolerable to lose should it not turn out the way you want.

Image Source: Forbes.com

And as we said in the post, what has worked for those entrepreneurs will work for you.
Judging by all the traffic on the social media sites, people are willing to consider that entrepreneurship might be less risky than they first thought, but they were looking for more evidence that the best entrepreneurs are as risk adverse as I said.

That's understandable.

Here’s one more piece of evidence.

The next time you meet an entrepreneur ask them this very personal question: “How do you invest your own money.”

Most people will expect them to say “I put every dime I have into the business” and for them to add “if I have anything left over then I invest in the start ups of my friends.”

The reality is far different.

Every entrepreneur I know–including the one I am married to–is extremely conservative with their own funds.  (Their investment portfolio’s look like your grandmother’s.)  They know starting a business has a certain amount of risk, no matter how careful you are, so they further offset that risk by being extremely conservative with their personal investments.

More proof that if you are naturally risk adverse, you could be the perfect entrepreneur.

Srikant Krishna is a financial technologist, quantitative analyst, and entrepreneur who grew up in NYC and Holmdel, NJ. He currently resides in Stamford, CT.  This Facebook page has more information about him, as well as links to articles on finance and technology.

In Defense of Automated Trading

In the past several years, there has been an onerous amount of invective hurled at "high-frequency trading", "algorithmic trading", and their synonymous brethren. The acerbic consideration afforded to this style of trading has been particularly exacerbated by the conflagration that is the global financial crisis, with no end in sight. A large part of the criticism originates from non-practitioners, not only in the capacity of automated trading, but with respect to capital markets in general. Even specialized financial media sources such as ZeroHedge interminably condemn both the electronic trading apparatus and those market regulators who face a daily supervisory penance.

As is the case with most aspects of the social world that is touched by technology, trading in the capital markets has experienced a thorough revolution, nay series of revolutions, over the course of the preceding decades. Should we be so bewildered that trading systems incorporate technological developments such as many-core GPU processing, reconfigurable hardware, and in-memory databases? Is it so mystifying that the ubiquitous (and relatively obese) financial services industry avails services such as co-location, data-mining, satellite communication, and microwave transmission? After all, a visit to the theater to watch the latest animated film, or to your local healthcare facility to procure an MRI will each demonstrate the same pervasive technological transformation. Why must institutions entrusted with the important task of growing investors' capital be precluded from availing cutting-edge methodology and devices?

It has become an invariant that the landscape of computerized trading systems is lumped into an opaque, monolithic entity that few on the planet seemingly comprehend. This perspective however is simply wrong, and masks the tremendous diversity and specific roles that the landscape actually details. For example, "high-frequency trading systems" which are usually operated in the context of a proprietary broker-dealer or market-making platform with direct connections to the exchanges are very different beasts than an algorithmic trading system, operating in an agency capacity at a broker-dealer, but also with direct connections to the exchanges and harboring a similar assortment of supporting tools and technology. Each of these, in turn, is different from the extensive use of computers in quantitative modeling and trading, often performed by a hedge-fund. This simple non-exhaustive list of automated trading examples reveals the diversity in purpose, timescale, and methodology employed by the trading firm operating the entity.

Let us first distinguish between what facets and consequences of automated trading should be important to the non-participants, and what should not. Firstly, let us immediately disregard the magnitude of (potential) profits experienced by these firms. In a world of "expert networks", calamitous multi-billion dollar CDS bets, and nefarious manipulation of global interest-rate benchmarks, this avenue
of criticism simply cannot be maintained with any modicum of integrity. The two most important aspects of the market from a participant's point of view should be transaction cost, and price discovery.

From the perspective of transaction cost, it has never been cheaper, faster, and more convenient to execute trades in the U.S. and various other global capital markets. Whether from a web browser or a mobile device, placing a trade and receiving the execution can transpire in a matter of seconds for humans. Compare this with having a call or discussion over the telephone with a stockbroker, who then would have to contact their trading desk or floor brokers, and after a chaotic exchange that occurs through an outcry market (or even an electronic matching system controlled by a specialist or market maker), fills and executions are finally disseminated back to the client. Of course, a phone call, voicemail or snail mail would be the method of conveyance this transaction. Contrast this with the millisecond response on a simple handheld device, and instant and powerful accounting features. The reason that this is possible is because of technology, the same technology that is being castigated as an evil to be eliminated. Another component influencing transaction cost is the actual commissions, either direct or indirect in the form of markups and spreads paid to dealers. There was once a time when being a NYSE specialist or a Nasdaq market-maker at a leading firm was a highly coveted role, often bringing in bonuses on order of several $million per annum to those lucky few. In fact, war stories of complete unethical rip-offs of clients were copiously disbursed within trading circles at the evening session around the bar or dinner. It is because of these widespread unethical practices such as frontrunning, principal trading by specialists, fading away of quotes, expansion of spreads, and so forth that order handling rules had to be implemented, and decimalization was invited by the buy-side participants. Perhaps the culmination of the buy-side's (and other market participants') angst towards this process was the filing of the lawsuit against NYSE by Calpers in the early 2000s.

But the writing was already on the wall at this point for the high-flying specialists and market-makers, as sell-side algorithmic and program trading began to seriously eat into their monopolized businesses.
At this point, I must interject and state rather openly that I find it amusing that the most vocal opponents of "machines" and "algo trading" were often these very same individuals who were maintaining exceptionally comfortable livelihoods to the detriment of mutual funds, pensioners, and small retail investors. To reiterate, never in the history of the global capital markets has it been more efficient and cheaper for any institution or human to transact in the public capital markets (at least pertaining to liquid instruments). If a firm is engaged in procuring highly exotic and complex forwards or derivatives, let it be said that they are the mercy of their counterparty or dealer, and they may simply be digging a new grave at a different casino table.

The second fundamental aspect from a market participant's perspective is the notion of price discovery, which is to ask whether the market price of a particular instrument represents the true sentiment amongst all the participants as a whole. Counterexamples of this are the virtually daily millisecond "flash crashes" which occur in various stocks, often independent of characteristics such as liquidity or volatility. These indeed occur because the speed in which algorithms operate, and the inability of various machines to distinguish very atypical market conditions, creates positive feedback loops that result in small time interval "catastrophes". There are three important points that pertain to these phenomena. Firstly, the time intervals are so small as to be rather irrelevant to human traders. If a flash crash occurs on the order of half a second, I'd be rather impressed if a large number of human traders were to be affected during the physical process of placing the trade. Secondly, the exchanges have been very accommodative in busting the executions during those particular time intervals. Thirdly, the "flash crash" (or inverse price movement), is temporary, and reverts back to a non-absurd price relatively quickly. There is a dearth of permanent price impact produced by the cascade of erratic machine behavior and stop triggers. In fact, there are strategies that seek to profit precisely from these sorts of crashes, by quickly buying into extreme dislocations in price. As was the case for transaction costs, if buy-side firms or market-participants attempt to "defect" in a prisoner's dilemma by using dark pools or electronic liquidity providers (ELPs), then they run the risk as of being buried in a different grave, away from the quoted markets in which there is a semblance of discipline and transparency. This is not to say that "trading in the dark" is necessarily a dangerous notion; however just like the complex derivative example it is necessary for the market participant to understand the advantages and risks involved in doing so.

It has never been easier to engage in the capital markets. As I've traveled through the developing economies of Latin America and Asia, I see a tremendous difference from my prior visits in the market knowledge that is pursued by that young professionals who are earning money to invest. Wall Street has always been rife with bubbles and busts, without exception, throughout history. This, in fact, is true of any global marketplace. We have observed unethical and nefarious behavior at very high levels, involving complex instruments, illegal information dissemination, very large notional values, and outright fraud. But these evils employ centuries-old implements of deception and accounting gimmicks. Trading volumes have greatly decreased, and the residual volume is often machines trading against each other. Exchanges, in fact, would probably go out of business were it not for these automated trading systems attempting to gain tiny profits at a very frequent rate. To suddenly shift the blame towards technological advances that are, undoubtedly double-edged swords, and the talented people that attempt to learn and profit from them, is an argument that simply cannot be maintained vis-à-vis the broader picture.

Copyright © 2013, Srikant Krishna

Srikant Krishna is a financial technologist and quantitative trader. He has a background in biophysics, software development, and the capital markets.

You can follow him on Twitter @SrikantKrishna, and on LinkedIn athttp://www.linkedin.com/in/srikantkrishna/, or e-mail him at sri@srikantkrishna.com.

You can visit his blog as well.