For Adam, and anyone else interested in the shan zai manufacturers in China:
Here’s Ethereum, which the Economist mentioned in the “Bitcoin’s future” article I posted earlier. Ethereum is a project that describes itself as “a platform and a programming language that … can be used to codify, decentralize, secure and trade just about anything: voting, domain names, financial exchanges, crowdfunding, company governance, contracts and agreements of most kind, intellectual property, and even smart property thanks to hardware integration.” It uses the concept of the blockchain as a decentralized ledger for all manner of transactions.
Here’s the whitepaper. It discusses related approaches (colored coins, metacoins) and details what they think Ethereum will look like. I haven’t read the whole paper.
This Economist article, Money from nothing, discusses whether Bitcoin can be understood as money. Money is defined as follows:
Economists reckon money is anything that serves three main functions. It must be a “medium of exchange”, which can reliably be swapped for goods and services. It should be a stable store of value, enabling users to tuck some away and come back later to find its purchasing power more or less intact. And it should function as a unit of account: a statistical yardstick against which value in an economy is measured. The American dollar meets all three conditions. Bitcoin has some way to go.
Here’s how they reckon Bitcoin stacks up against that definition:
- “Bitcoin does best as a medium of exchange, thanks to its clever technical design… The combination of functionality and user interest means that people are finding it easier to swap coins for both goods and services and for other currencies. This rising credibility as a medium of exchange supports Bitcoin values.”
- “… Bitcoin is not exactly a stable store of value. It is technically equipped to do the job: coins saved in an encrypted wallet on a hard drive can be retrieved for later use in purchases. But the currency’s worth is prone to wild gyrations.”
- “Volatile values could prevent Bitcoin from ever establishing itself as a medium of account. Even the few retailers who accept Bitcoin use other currencies as their principal accounting unit. … until Bitcoin values are less volatile relative to the currencies that now dominate real economies, users are unlikely to change their monetary frame of reference.”
The article also discusses how Bitcoin’s inflexibility on the control of the money supply (encoding the money supply decisions into the protocol rather than trusting them to government fiat) will be unpopular with governments that have become accustomed to using the money supply as a tool, and unpopular with societies that have come to expect and rely on slow inflation.
There is also this Economist blog commentary on the article: Bitcoin: New money. The commentary touches on three interesting bits:
- Control of the money supply, and modest inflation, are vital to avoid economic shocks and collapses. By the article’s reckoning, these tools serve as shock absorbers to lessen the impact of major economic events.
- Bitcoin/cryptocurrency will need reputable banks and exchanges, combined with guarantee schemes to limit the risk of systemic disruption caused by the failure of one or more large actors (i.e. Mt. Gox) or very large bank heists (Mt. Gox, again).
- The Bitcoin protocol should be modified to permit control of the money supply, instead of setting a fixed cap on the total possible number of Bitcoins.
This Economist article, Bitcoin’s future: Hidden flipside, talks about BitCoin as a platform, rather than a currency. Here’s a great quote:
Some want ownership of devices–a car, say–to be represented by a Bitcoin, or a tiny fraction of it. The car would work only when turned on with a key that includes the Bitcoin token. This would make managing ownership of and access to physical assets much easier: the token could be sold or rented out temporarily, enabling flexible peer-to-peer car-rental schemes. Such “smart property” would turn the blockchain into a global registry of ownership in physical assets.
All that may sound like science fiction, but a growing number of startups are working on bringing such applications to market. Coloured Coins and Mastercoin will soon release software that enables trade in other financial assets, including stocks and bonds. The most ambitious project is Ethereum: it will launch a new blockchain, similar but unrelated to Bitcoin, with a programming language to encode financial instruments and other contracts.
Again, the focus is on use of the blockchain technology, either by large/reputable entities, or for non-Bitcoin purposes. The most interesting is Ethereum, which sounds like a manifestation of Lanier’s “formal financial expression” scheme (encoding financial instruments and contracts in code rather than words).
I’m reading The Doomsday Cult of Bitcoin. Quote:
These may seem like isolated incidents, but together, they add up to a massive, damning breach of trust. I don’t doubt, as Nobel laureate Robert Shiller put it last week, that “something good can arise from [Bitcoin’s] innovations.” But Bitcoin itself will never recover from these initial pratfalls. Partly this is because lawmakers and regulators, spooked by early hype and the Mt. Gox disaster, are never going to afford Bitcoin services the kind of autonomy they’d need in order to flourish. Partly it’s because there are conceptual problems with the Bitcoin architecture itself. And partly it’s because Bitcoin’s anarchic roots are too fringe to draw in the masses.
The opinions in this article loosely fit my own opinion of the prospects of Bitcoin: Bitcoin will be a proving ground for the basic cryptocurrency infrastructure, but it wasn’t built with the governments’ or the banks’ interests at heart, so it is likely to fail. However, I will not be surprised to see it replaced with a similar cryptocurrency that is built by the governments and/or banks–one that uses the lessons of Bitcoin but replaces Bitcoin’s anarchic agenda with a pro-government and pro-bank agenda. The government will want a single electronic ledger with all users strongly authenticated–how else would taxation, regulation, or surveillance be possible? But the banks will want to preserve some of the obscurity and anonymity features for their own purposes. It’s interesting to think about how a government-blessed cryptocurrency would interoperate with the White House’s new National Strategy For Trusted Identities In Cyberspace.
I mentioned in class that De La Soul gave away their entire catalog on Valentine’s Day. This is fascinating in the context of Lanier’s work. Arguably, De La Soul is frustrated with their predicament where they have not been able to get their music released digitally. Their early work used many samples, so in order to release it digitally, their record label must get permission and negotiate a payment for the use of each sample. And digital sale of music that includes samples is frequently treated differently than physical sales. So their music has languished, available only in physical form, while music sales have shifted to digital.
This is caused in part by the lack of a “statutory rate” for music sample clearance. In covering another artist’s song, there is a Congressionally set rate that you must pay to the copyright holder of a song for each sale of your cover. This is a statutory rate. Along with that, after the first performance of a new song, the owner of the copyright cannot prohibit others from covering it (this is a “compulsory license”). And there is also a “performance” compulsory license and statutory rate (for using a song on the radio, or TV, or streaming), and several clearinghouses (ASCAP and BMI) that audit radio and TV stations to get them to pay the performance royalties to copyright holders. It’s basically a half-century old micropayment system. Given today’s NYT article on the Content Creators Coalition protesting the way these royalties get paid, it seems that the jury is still out on whether this system is a good thing.
However, sampling is not treated the same way. There is no set rate for sample use, so each sample use must be negotiated individually. Also, a copyright holder of a work does not have to agree to allow anyone to use the sample. The Beatles, for example, have never allowed anyone to use samples of their music. De La Soul happened to sample The Beatles, so I imagine that’s part of their non-starter.
Lanier might argue that sample clearance should be part of his universal micro payment system. It’s an interesting case, because supply and demand runs into a basic math problem: when supply is equal to zero, there is no way to set a price. Assuming The Beatles have a valuable and popular catalog, but they will not sell it for any amount of money, then their product is fundamentally unsuitable for the micropayment system unless the micropayment system can coerce them to provide it. In this regard, it’s a similar situation to the camera surveillance proposal that Lanier mentioned–many people will never consent to having their physical presence constantly monitored, and not for any amount of money, so their data is fundamentally unsuitable for the micropayment system. In order for a micropayment system like his to work on a broad scale, it practically must have compulsory licensing of the content that it sells. So a big question emerges: is compulsory licensing suitable for all kinds of data and content? Would you consent to compulsory licensing of your child’s physical location? Compulsory licensing of your psychological health records? Compulsory licensing of your art?
As the the article also mentioned, it is interesting that (so far) De La Soul has not been sued. It’s possible that this is an industry stunt, engineered by their labels for maximum effect. But there are certainly other recent cases of artists making saleable music that samples music without clearance (e.g. Girl Talk). Is this just the race to the bottom that Lanier predicts will happen to all content as it is devalued by our system, or is it something else, something better?
A quick search in the NYU Library’s Journals system for articles from The Economist that contained the words “dark pool” yielded the following:
- “Big headache or Big Bang?” 2007
- “The battle of the bourses,” 2008
- “Attack of the clones,” 2009
- “Rise of the machines,” 2009
- “Some like it not,” 2011
- “The fast and the furious,” 2012
- “Going broke in stocks,” 2013
- “Code blue,” 2013
- “The end of the street,” 2013
Over approximately seven years, dark pools evolved from a curiosity to a major upheaval in securities trading, and may actually be on the wane lately. Let’s take a minute to define the term. From “Rise of the machines” in 2009: “dark pools [are] electronic trading venues that conceal an order’s size and origin.” Here’s a longer definition:
Dark pools are places where anonymous buyers and sellers can trade directly with each other away from normal, “lit” exchanges. Because data are published only once trades are complete, institutional investors such as pension funds are meant to be able to take, or offload, large positions in quoted companies without alerting the wider market. (“Some like it not,” 2011)
Before dark pools, a buyer/seller with a large position in a stock would have the following options:
- just put the bid/ask on the stock market, taking any adverse price movement on the chin–this is the riskiest option because a large supply/demand shift can make a stock temporarily unsaleable (or permanently unsaleable in the worst case)
- have a broker or specialist do the bid/ask on the exchange but in small batches, say 100 shares at a time over a longer period, to avoid a sudden shift in supply/demand that sends the price rocketing in the wrong direction
- write an algorithm that does the bid/ask in small batches on the electronic markets (possibly on many electronic markets)
- call up colleagues that are known to have large positions in similar stocks and work out a private deal (a practice known as the “third market” or “block desk”)
Dark pools can be thought of as an automation of #4. Basically, these large traders form a consortium and agree to use custom software to search through their internal order books to match potential buyers with sellers. The stock bids/asks are never posted to the public exchanges, and the identities of the buyer and seller are not known to each other until the sale is reported, which happens after the sale is completed.
Several interesting things can be teased out of this list of options. Option #1 presented a critical difficulty: moving large positions without negatively affecting the price you get; that difficulty was ameliorated by option #2. But option #2 forced investors to rely on expensive brokers and specialists, whose intentions were never assured:
Institutional investors may complain about being forced into “dark pools” (off-exchange venues where they can deal anonymously) to avoid HFTs, but these pools existed before HFTs and were set up in part to avoid being scalped by brokers or floor traders. (“The fast and the furious,” 2012)
Along came option #3, once the rise of electronic trading made it easier to move large positions by clever automation of the role formerly occupied by the people on the trading floor. But electronic trading also made it easier for other algorithms to analyze activities like the series of small orders that indicated an attempt to move a large position:
The basic idea of HFT is to use clever algorithms and superfast computers to detect and exploit market movements. To avoid signalling their intentions to the market, institutional investors trade large orders in small blocks-often in lots of 100 to 500 shares-and within specified price ranges. High-frequency traders attempt to uncover how much an investor is willing to pay (or sell for) by sending out a stream of probing quotes that are swiftly cancelled until they elicit a response. The traders then buy or short the targeted stock ahead of the investor, offering it to them a fraction of a second later for a tiny profit. (“Rise of the machines,” 2009)
So dark pools evolved as a sort of peer to peer stock trading model, avoiding the risk and scrutiny of public exchanges. However, it wasn’t long before banks got in on the game:
Dark pools have proliferated over the past five years, winning volume from exchanges. Broker-dealers including Goldman Sachs have muscled in, setting up their own dark pools to capture transaction fees their clients would otherwise pay to exchanges. Pools owned by broker-dealers now dominate the sector, but as a result many of them have become much less attractive to large investors. (“Some like it not,” 2011)
And it wasn’t long before HFT got into the game:
Many dark pools that are owned by broker-dealers now welcome high-frequency traders (HFTs), who can use their speed to exploit price differences between exchanges and dark pools . An HFT might, for instance, lock in a high price by submitting a large sell order to a dark pool just as the exchange price of a share begins to fall. (“Some like it not,” 2011)
And now dark pools are showing various signs of declining popularity:
There are signs of a backlash from institutional investors. After several years of steep rises, trading volumes on dark pools have started to level off this year, according to the TABB Group, which tracks the industry. Some investors are only routing orders to pools that exclude HFTs. (“Some like it not,” 2011)
Alongside this is the decline of the public exchanges, and the increasingly marginal role of floor-based trading. In 2007, the New York Stock Exchange was riding high as a profitable, publicly traded company, buying its competitors, basking in the light of its deregulators. But fast forward to November 2013 to see the NYSE bought by an upstart electronic market competitor (ICE).
What does this brief history of dark pools tell us? Well:
- investors are not satisfied with limited liability–they want anonymity too
- large investors want to obscure the details of trades as much as possible
- HFT will infiltrate any electronic trading system that becomes available, particularly any that achieve high volume or high liquidity
- in the absence of real time public exchanges, banks will move to implement their own trading exchanges
From these trends, it seems clear to me that some form of crypto currency will succeed in this space because it has the two investor goals (anonymity and obscured transaction details) built-in.
It also seems clear that the plain old stock market used by regular people has become a second class place to trade. HFT (which can be thought of as meta-trading) and dark pools (analogous to Internet peering agreements) are the first class trading venues now. Regular people exist to pour money into the system by buying and selling, but also to pay the fund manager commissions, provide the HFT algorithms with liquidity rebates, and to pay HFT systems the “penny toll” that they extract from most trades they participate in. These new first class trading platforms will only be accessible by large players who have access to the best market data, research, and technology. Those large players will use these platforms to limit their risk, while the systemic risks are borne by the aggregated small investors on public exchanges/markets.
Finally, the typically cited primary goal of the stock market system (capital formation) has been pushed so far aside that one must wonder whether the model will persist. Stock prices have a diminishing connection to the performance of the company that issued the stock. Investors typically care only about making good bets–mostly short term bets, at that.
Let’s assume the stock market doesn’t exist. How will a company get capital without it? One answer is that they won’t, at least not in the millions and billions of dollars. Micro loans and other peer to peer lending systems seem like entirely more sustainable options for small business. Just as companies formed consortia to allow them to trade in dark pools, it is entirely possible that small businesses could form similar mutual lending arrangements within their geographic area, or even within their industry networks. For example, the owner of a local trucking company might recognize the benefit of a nearby warehousing company or a wholesaler, and might be willing to assist those other parts of the economic ecosystem with small loans.
As for regular investors, the question is how they’ll get the sort of investment returns they have come to expect (and plan their retirement around). Again, the answer might be that they won’t, at least not at the scale of the boom parts of our stock market cycle. Again, micro lending or geographically/economically local lending systems might be a viable option if they are able to pool enough capital, manage the overhead costs of a lending program, structure for small purposeful loans, and insure against default risks.
I’m not convinced that micro loans and peer to peer lending are the right path for a post stock market age, but they are an interesting start. My next dive will probably be into the micro loan world, starting with the work of Muhammad Yunus.
Alexandra Coym and Karl Ward
We all have this image of trading floors with hundreds of people yelling at each other while maniacally waving little sheets of paper in the air. Though this may have been the case 40 years ago, 82% of today’s trading is done electronically. The shift to electronic trading began in the late 1980’s and 1990’s, first to phone trading, then to electronic trading done in so-called “upstairs” offices close to the exchange. This new style of trading really took off in 1992 with Globex, the first global electronic futures trading platform. Traditional trading floor exchanges such as the New York Stock Exchange implemented their own electronic trading systems to compete with the heavily computerized and decentralized NASDAQ exchange.
Exchanges exist to bring potential buyers and sellers of securities together (either in physical or virtual form) and, through facilitating the process, reduce the risk of investing. There are two kinds of markets on which people trade – the primary and the secondary market. On the primary market, securities are created (i.e. through an IPO), whereas on the secondary market already-created securities are traded. There are still trading floors around the world that mainly deal with primary market trading and large institutional secondary trading, whereas most secondary market trading is done digitally.
During the ascendance of electronic trading, the traditional stock market roles of broker and specialist changed dramatically, as most markets minimized the role of brokers and some eliminated the role of specialists. Specialists (also called “designated market makers”) are similar to brokers, but only for a single stock, and with the additional responsibility of reducing volatility in that stock when supply or demand becomes unbalanced. They reduce volatility by selling more of their own stock when demand is very high, or buying that stock when supply is very high.
What works? What doesn’t?
As with many machine integrations that replace human activity, the advantages and disadvantages brought on by the change are less than clear. Proponents of electronic trading tout its role in reducing execution costs for trades via “straight through process” (i.e. removing the middleman). They also cite the increased liquidity it creates by bringing many more potential buyers and sellers into the market. Because electronic trading is accessible globally, trades have definitely become geographically independent, more competitive, and more anonymized. Arguably, there is also increased transparency into pricing (due to real time quoting and trade data) and accountability into nefarious practices by brokers (due to the electronic audit trail)–though the accuracy of any of this will be discussed later. Last but not least, tighter spreads (the difference between the ask and the bid price) allow for more successful trading due to prices not having to move far in one direction or the other.
Critics of electronic trading tend to express several systemic concerns. First, that the incredible volume of electronic trades by high frequency trading algorithms does tend to increase liquidity when markets are stable, but exacerbates volatility to a dangerous extent when the markets encounter a destabilizing event. One typical strategy for HFT algorithms is to withdraw all bids and offers when the market becomes unpredictable, which translates to a sudden disappearance of demand and usually a precipitous price drop. Second, the HFT traders flood the quote and order systems with fake trades in order to influence the price of stocks or to fool potential traders into believing the market is moving a particular direction–more than 90% of all quotes are currently fake. Third, the HFT computers are so fast and so close to the data coming out of the exchanges’ computers that they have an asymmetric advantage over human traders. These advantages make it trivial for them to get ahead of a human trader who has put in even a modestly lucrative bid, thus beating that trader to the purchase (a technique called adverse selection). As Dennis Dick from Bright Trading noted:
HFT’s are the new market makers without the traditional affirmative obligation of designated market makers to keep markets orderly. When uncertainty enters the picture, they cancel their orders and liquidity disappears. Without traditional market makers to step in and be the buyer of last resort, prices can fall quickly as we saw in the flash crash in May 2010.
With technological advancements, there is always the danger of unforeseen events exceeding human control. The 1987 “Black Monday” crash was the first hint of the pitfalls of electronic trading, when the Dow Jones index dropped 23% in a single day. “Program trading” (an early name for algorithmic and electronic trading) bore the brunt of the blame, and the SEC responded by establishing a system for halting trades when the market displays excessive volatility. A more recent and dramatic example of electronic trading risk is found in the 2010 “Flash Crash,” where the Dow dropped 9% within minutes. There are a lot of theories and explanations as to how this happened, but in the end they all point to computer error. One algorithm ‘decided’ to sell a large bulk which cause other computers to react by panic selling and buying, or aggressively short selling. It is hard to say how the situation might have played out differently if humans had been involved in the trades, but most likely the trades would not have occurred instantaneously, and multiple brokers/specialists on the floor would have caught the error before it was posted as an offer. Computerized trading works at such a high speed and volume that the severity of damage that can occur within minutes or even seconds is hard to fathom. The algorithms are set to search for certain patterns and react in certain ways, but that does not allow a lot of room for the kind of judgement calls that would have been helpful in the Flash Crash incident. Trading algorithms and proprietary trading platforms are well kept secrets that make it difficult to regulate the practices appropriately.
Who gets left behind?
With so much of the trading dependent on technological solutions, it’s clear that smaller companies that could not keep up with the latest multi-million dollar tech were pushed out of the market. Even for the large players, there is a constant drive to develop new and faster technology for handling the trades, all proprietary and secret. It even goes as far as trading companies paying to have their servers in the same building as the exchanges’ computers, or even on the same floor. Computerized trading enables firms to post buy and sell prices they don’t intend to follow through on, misleading potential investors and increasing instability in the market. The same goes for traders rapidly selling and buying back and forth between two entities they control, making it appear as though there is a lot of interest in that stock.
A big problem that has arisen from electronic trading are the so-called ‘Dark Pools of Liquidity’. In essence, these are large trades that are offered anonymously and away from the public between big financial institutions. The reason people use these dark pools is to make large trades anonymously so as to not reveal their strategy and stir up the market. The problem here is that investors that aren’t participating in those trades are disadvantaged by not seeing the trade beforehand, and therefore not participating in the price discovery and auction occurring behind closed doors by the participants in the dark pool. This brings us back to the earlier assertion that computerized trading has not necessarily made the market more transparent, but instead created more opportunities for making it opaque.
Some would argue that the machines and their algorithms help remove any bias and emotions of human participation that could affect the process, yet others might say exactly the opposite: that this bias and emotion is necessary for successful trading beyond the short term. The fact that over the past years so many regulations have been set up for computerized trading demonstrates that the harmful potential is larger (and weirder) than originally anticipated. With technology advancing so quickly it is nearly impossible to control what is being used on the market, unless you standardize the process or tightly regulate the network and information flow, in which case all competitive advantage of trading firms would disappear. On the other hand, if more and more restrictions are applied it could happen that the restrictions themselves become loop holes that certain players can use to their advantage.
One restriction that makes sense is a ban on dark pool trading, to restore the market transparency essential to make computerized trading fair for all players.
To contain the potential damage done by errant electronic trading and HFT, regulators might enforce a limit on trading volumes, or (better) an automatic “rate limit” that slows down trading as volatility increases. The current system of halts is not fast enough to prevent incidents like the Flash Crash, which have nearly instant global market repercussions.
To deal with the volatility problems caused by fake trades, there should be a small penalty on canceled trades, as recommended by the SEC in their report on the Flash Crash.
- “RECOMMENDATIONS REGARDING REGULATORY RESPONSES TO THE MARKET EVENTS OF MAY 6, 2010,” Summary Report of the Joint CFTC-SEC Advisory Committee
on Emerging Regulatory Issues, http://www.sec.gov/spotlight/sec-cftcjointcommittee/021811-report.pdf
- “Raging Bulls: How Wall Street Got Addicted to Light-Speed Trading,” Wired,
- “A Brief History of the 1987 Stock Market Crash with a Discussion of the Federal Reserve Response,” Federal Reserve Board, http://www.federalreserve.gov/pubs/feds/2007/200713/200713pap.pdf
- “High Frequency Trading: Is It A Dark Force Against Ordinary Human Traders And Investors?,” Forbes, http://www.forbes.com/sites/richardfinger/2013/09/30/high-frequency-trading-is-it-a-dark-force-against-ordinary-human-traders-and-investors/
- Electronic vs. Floor Based Trading, Schwartz, Robert A., Byrne, John Aidan, Colaninno, Antoinette (eds.). Springer, 2006.