Broken Markets: How High Frequency Trading and Predatory Practices on Wall Street Are Destroying Investor Confidence and Your Portfolio
by
Sal Arnuk
and
Joseph Saluzzi
Published 21 May 2012
One algorithm was pricing the book at 1.27 times the price of the other algorithm, which in turn would revise its price to 0.998 times the price of the first algorithm, creating a positive feedback loop. Momentum ignition is extremely damaging to long-term investors. It does not cost pennies; it costs them quarters, and it ferociously distorts the price discovery process. How the World Began to Learn About HFT While HFT has been steadily expanding since the millennium and exponentially since the implementation of the SEC’s Reg NMS in 2007, it stayed out of the mainstream media until mid-2009. Over the July 4 weekend, Sergey Aleynikov was arrested by the FBI at Newark Airport for stealing code from his prior employer, Goldman Sachs, and trying to bring it to his new job as an HFT programmer at Teza Technologies, where he was set to triple his $400,000 salary.
…
The proposals called for dark pool indications of interest to be treated like regular quotes and therefore required to be visible to all investors; real time disclosure of the identity of the dark pool that executed a trade; and any dark pool that traded more than 0.25% of a stock had to display those orders in the public quote.8, 9 Ironically, Reg ATS in 1998 was supposed to force more orders into the public quote to aid in the price discovery process. However, it resulted in almost one-third of orders being executed away from the public quote and in dark pools. With the flash order and dark pool proposals, it appeared that the SEC was serious about increasing transparency. The SEC received hundreds of comment letters, most by industry participants who wanted to maintain the status quo.
…
Today’s severe market drop should never have happened. The U.S. equity market had been hailed as the best, most liquid market in the world. The market action of May 6 has demonstrated that our equity market has major systemic risks built into it. There was a time today when folks didn’t know the true price and value of a stock. The price discovery process ceased to exist. High frequency firms have always insisted that their mini-scalping activities stabilized markets and provided liquidity, and on May 6 they just shut down. Money began pouring out of the equity market, as nervous investors lost confidence. The SEC responded almost five months later, issuing a report titled “Findings Regarding Market Events of May 6, 2010.”
Bitcoin for the Befuddled
by
Conrad Barski
Published 13 Nov 2014
From an economics standpoint, any asset that becomes newly available to an open market needs to first undergo a price discovery process. This was part of the reason for the Internet bubble in 2000: People simply didn’t know the value of the stocks of eBay, Yahoo!, and other tech companies because similar companies had not existed in the past. Eventually, as people became more familiar with Internet-focused corporations, it became clearer how to reasonably assign a price to each company’s stock. Bitcoin has been undergoing a similar price discovery process, which is still in its very early stages: The price of a bitcoin has been swinging wildly up and down since the currency’s inception.
…
program, 217–218, 220–222 hello-money starter project creating, 228–229 declarations, 231 hook for detecting money arrival, 234 running and testing, 235–236 writing code, 230–235 hierarchical deterministic wallets, 190 Hill, Austin, 120 history of Bitcoin, 112–116 homebrew (command-line tool), 219 hosted wallets online services, 36 vs. personal wallets, 34–35 hot storage, 47 vs. cold storage, 33–34 hot wallets, personal, 37–38 human-readable Bitcoin addresses, 10n hybrid wallets, 187 I illegal activity, Bitcoin and, 124 impedance mismatch, 57 importing private key, 17, 39, 193, 194–195, 237 installing SPV wallets vs. full wallets, 193 integer factorization, 131 Internet bubble, 120 InterruptedException exception type, 239 irreversibility, of transactions, 25–26, 56 superiority of, 57 J Java, 226 initializing objects, 231–233 installing, 226–227 java.io.File class, 231 Java JDK (Java Development Kit), 226 java.matho.BigInteger class, 231 JavaScript, 213–223 preparing machine for, 218–219 writing Bitcoin program in, 217–218 jelly-filled donut incident, 141–156 JSON-RPC API (JavaScript Object Notation - Remote Protocol Call), 222 limitations of writing Bitcoin programs using, 223 JSON-RPC protocol, 214 K Kaminsky, Dan, 118 Keynesian economics, 126 Kienzle, Jörg, 110–111 Koblitz curve, 151 Kraken, 64 Krugman, Paul, 117 L Landauer limit, 157 laptops, private keys on, 44 ledger, 11 length extension, 171n liability, for stolen bitcoins, 34 lightweight wallets, 192 limit orders, 66 Linux installing Git, 227 installing Maven, 227 OpenJDK version of Java, 227 setting up Bitcoin Core server, 219 live Bitcoin exchanges, 71 LocalBitcoins.com, 67, 68 escrow service, 70 M Mac OS installing Git, 227 installing Maven, 227 setting up Bitcoin Core server, 219 man-in-the-middle attacks, 216 market orders, 65–66 MasterCard, 112 master private key, 188 master public key, 188 generating Bitcoin address with, 190 Maven empty starter project created with, 228 installing, 227 mBTC (millibitcoins), 9 MD5 (message digest algorithm), 132 meeting places, for Bitcoin transactions, 68 MemoryBlockStore function (bitcoinJ), 237 merchant services, 214 Merkle trees, 192 mesh networks, 169 message digest algorithm (MD5), 132 microbitcoins (µBTC), 9 middleman, buying bitcoins from, 52–57 Miller-Rabin primality test, 90 millibitcoins (mBTC), 9 mining, 5, 20, 26–27, 96, 99, 161–180 in 2030, 201–202 decentralization of, 179–180 difficulty of, 173 distributing new currency with, 167–168 hardware, 174–175 2030 requirements, 202 energy efficiency of, 178 profitability threshold curves for comparing, 179 need for, 162–168 nodes, 170 pooled, 175–176 practicality, 50 preventing attacks with, 166–167 process for, 168–176 for profit, 176–177 proof-of-work in, 138–139 solving a block, 171 modular arithmetic, 131n “m of n” private key, 42 money laundering, 112–113 Moore’s law, 179n Moxie Jean, 67 Multibit, 38 multi-signature addresses, and fragmented private keys, 41–42 multi-signature transactions, 57, 69–70 mvn install command, 230 My Wallet Service, 37 N Nakamoto, Satoshi, 3, 110, 211 identity, 113 last comment, 114 white paper on Bitcoin, 112 network effect, 120 NetworkParameters structure, 232 newbiecoins.com, 13 newly minted bitcoins, 26–27 Newton, Isaac, Principia, 210–211 node-bitcoin, installing, 218 Node.js library, 217, 221 installing, 218 Node Package Manager, 218 nodes broadcast only, 169 full, 191 relay, 170 nominal deflation, 126 nonprofit organizations, accepting bitcoins, 18 NXT, 125 O off-chain transactions, 201 offline transaction signing, 40–41 onCoinsReceived function, 234–235 online wallet services hosted, 36 personal, 34, 37 Oracle Corporation, 226 orders, placing to buy bitcoins, 65 order of curve, elliptic curve cryptography, 152–153 orphaned blocks, 24–25 P paper money, color copiers as threat, 110 paper wallets, 39 encrypted, 39–40 passwords, 14, 40 for brain wallet, 45 function of, 40 loss of, 37 Peercoin, 125 PeerGroup object, 233–234, 240 peer-to-peer architecture, 119 pegging, 120 pending transaction, 18 Perrig, Adrian, 110–111 personal wallets vs. hosted wallet, 34–35 hot storage, 37–38 online services, 37 person-to-person bitcoin purchases, 52, 67–71 point multiplication, 150, 158–159 point-of-sale terminals, watch-only wallet for, 187 polling, Bitcoin programming, 223 pom.xml file, 229, 236–237 pooled mining, 175–176 portability, of currency, 117 Preneel, Bart, 140 price discovery process, 120 privacy, 11n and criminals, 124 multiple addresses and, 12 private currencies, 2 private key, 11–12, 150 compromise of, 41 extra protection for, 139 fragmented, and multi-signature addresses, 41–42 generating, 37 importing, 237 master, 188 memorizing, 45 parable on, 141–145 reversing function of, 136 security for, 39, 186 signing transaction with, 156 SPV wallets vs. full wallets, 194 storing, 33 profit, mining for, 176–177 programming languages, for Bitcoin network connection, 225–226 proof-of-stake, 125 proof-of-work, 125, 166 and blockchain, 165 in mining, 138–139 protecting bitcoins, 61.
Adaptive Markets: Financial Evolution at the Speed of Thought
by
Andrew W. Lo
Published 3 Apr 2017
But in 1934, eight years after his research was published, Ezekiel came across a possible solution to the hog cycle mystery in a review by the British economist Nicholas Kaldor.34 Economics, then as now, was an international enterprise: Kaldor communicated the results of two recent German-language papers written separately by an American and an Italian, Henry Schultz and Umberto Ricci. Schultz and Ricci had independently examined what might happen if the price discovery process along the supply and demand curves wasn’t smooth or instantaneous, but if it instead took place in discrete lumps of time, like the turns in a game. Figure 1.3. The “cobweb” model of the hog cycle with oscillations that eventually converge to the equilibrium (P*, Q*). Start at Q0, with expected price P0; actual price is P1, which yields supply Q1; Q1 yields actual price P2, and so on; the spiral continues until supply equals demand at (P*, Q*).
…
But what if there were biological limits to human rationality itself? Every first-year student in economics learns that price is determined by supply and demand. Every economic transaction has a buyer and a seller, each trying to come to a mutually satisfying agreement, Jevons’s “the double coincidence of wants.” Economists call this negotiation the price discovery process, as we saw in chapter 1 in the cobweb model of the hog cycle. However, this negotiation doesn’t always conclude with a consummated trade and a price. If a seller refuses to lower the asking price to a level a buyer wishes to pay, no transaction will take place. That potato chip shaped like Jay Leno’s head, currently offered on eBay for one hundred dollars, may never get sold if the seller is unwilling to lower the price.
…
That potato chip shaped like Jay Leno’s head, currently offered on eBay for one hundred dollars, may never get sold if the seller is unwilling to lower the price. This might be a rational decision on the seller’s part. On the other hand, it might reflect a lack of awareness of what a buyer is willing to pay. The price discovery process in a well-functioning market requires its participants to engage in a certain degree of cause-and-effect reasoning. “If I do this, then others will do that, in which case I’ll respond by …” This chain of logic presumes that individuals have what psychologists call a theory of mind, the ability to understand another person’s mental state.
High-Frequency Trading
by
David Easley
,
Marcos López de Prado
and
Maureen O'Hara
Published 28 Sep 2013
Yet, the markets remain largely stable, with bounded bid–ask spread and price volatility. This is primarily due to the heterogeneous return objectives and investment horizons of the market participants. Agent heterogeneity has also created the highfrequency trading (HFT) debate about the value that low latency machine trading adds to the investment and price discovery process. Here we take the point of view that HFT is a market fact. Our objective is to understand its potential and limitations. 21 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 22 — #42 i i HIGH-FREQUENCY TRADING From the point of view of an executing broker, there are four main types of clients or market agents: 1. institutional investors, such as pension funds and asset management firms; 2. quant funds, including market makers that seek to capture the bid–ask spread or exchange rebates; 3. retail investors, driven primarily by private liquidity demand and less by proprietary signals; 4. hedgers of equity exposures, such as derivatives desks.
…
On average, algorithmic trading participation reduces the degree of autocorrelation in high-frequency currency returns by posting quotes that reflect new information more quickly. Finally, Chaboud et al report highly correlated algorithmic trading behaviour in response to an increase in absolute value of the autocorrelation in high-frequency currency returns; this supports the concern that high-frequency traders have very similar strategies, which may hinder the price discovery process (Jarrow and Protter 2011). HFT during time of market stress The availability of liquidity has been examined in equity markets; academic studies indicate that, on average, high-frequency traders provide liquidity and contribute to price discovery. These studies show that high-frequency traders increase the overall market quality, but they fail to zoom in on extreme events, where their impact may be very different.
Flash Boys: Not So Fast: An Insider's Perspective on High-Frequency Trading
by
Peter Kovac
Published 10 Dec 2014
Overall, dark pools and broker internalization facilities aren’t unquestionably bad, but it’s hard to make a compelling case for any significant benefit. For professionals in particular, they make it easier to shoot oneself in the foot. For the public, the lack of transparency doesn’t inspire confidence. And for the markets themselves, there is a legitimate question about whether or not they detract from the price discovery process. For these reasons, I believe that the default destination for retail customer orders should always be the public markets. If customers want to “opt in” and select a dark pool or internalizer for their orders, that’s fine, but it should be a choice the customer makes – not a choice that the broker makes for them.
Quantitative Trading: How to Build Your Own Algorithmic Trading Business
by
Ernie Chan
Published 17 Nov 2008
Since April 9, 2001, all U.S. stocks have been quoted in decimals. This seemingly innocuous change has had a dramatic impact on the market structure, which is particularly negative for the profitability of statistical arbitrage strategies. The reason for this may be worthy of a book unto itself. In a nutshell, decimalization reduces frictions in the price discovery process, while statistical arbitrageurs mostly act as market makers and derive their profits from frictions and inefficiencies in this process. (This is the explanation given by Dr. Andrew Sterge in a Columbia University financial engineering seminar titled “Where Have All the Stat Arb Profits Gone?”
Trading and Exchanges: Market Microstructure for Practitioners
by
Larry Harris
Published 2 Jan 2003
Their order precedence rules determine which buyers trade with which sellers, and their trade pricing rules determine the trade prices. Most order-driven markets are auction markets. In an auction market, the trading rules formalize the process by which buyers seek the lowest available prices and sellers seek the highest available prices. Economists call this the price discovery process because it reveals the prices that best match buyers to sellers. In order-driven markets, traders can offer or take liquidity. Traders who offer liquidity indicate the terms at which they will trade. Traders who take liquidity accept those terms. Dealers can—and often do—trade in order-driven markets.
…
When dealer inventories are in balance, dealers want to buy and sell in equal quantities so that their inventories remain near their target levels. A two-sided order flow includes a mix of buyers and sellers who want to trade equal quantities. Dealers try to set their prices to obtain two-sided order flows. The search for prices that produce a two-sided order flow is called the price discovery process. Dealers try to discover the prices which ensure that buying and selling quantities are just in balance. At these prices, supply equals demand. Prices that balance supply and demand determine market values. Dealers try to discover market values. Dealing is most profitable when dealers can sell immediately after buying and buy immediately after selling.
…
See trading posts post-trade transparency, 101 power of test, 454–57 prearranged trading, 165, 166 precommitted liquidity suppliers, 400, 404, 406 preferencing, 161–63, 282, 514, 515, 520–22, 528–29 preferred stocks, 40 premiums, 75 pre-trade transparency, 101 price(s) accelerator, 556 ask, 5, 69, 280, 295 benchmark, 422, 423–32, 433 bid, 5, 69, 70, 280, 295 clustering, 91 correlations, 8 dealer mistakes, 291–92 derivative, 132–37 discrimination, 247, 323, 325–26, 332 formation in index markets, 489–91 and fundamental values, 403 indexes, 484–86 inferior, 70 informative, 4, 206–14, 218, 222, 224, 235, 237–39, 241, 243 and limit orders, 76–77 manipulation, 135–37, 256 in market-based economies, 208–9 market-if-touched orders, 80 for perishable commodities, 416 predicting, 442 public benefits from informative, 206–14 reflection of information, 229 stock, 211–12 and stop orders, 78–79 terminology, 69–70 tick-sensitive orders, 81 trade, 70 and trading exposure, 385–86 unexpected increases, 370, 372 volatility, 76 price and sale feeds, 98 price characterization of arbitrage, 375 price concessions, 72, 324 price continuity, 497, 498 price convergence, 348, 350 price discovery process, 94, 284 price impact. See market impact price improvement, 71, 72, 282, 515 price limits, 572, 573–75 price manipulators, 195, 196, 259 price priority, 113, 117, 334 price reversal, 432, 434, 497 price risk, 183 price-weighted index, 485, 486 primary capital markets, 209, 210, 211 primary government securities dealers, 58 primary listing markets, 48, 49 primary markets, 39 primary order precedence rules, 117 primary spread determinants, 311, 312–13 Primex Auction System, 309, 515 principal-agent problem, 8, 159 principal trading, 149 principal value, 40 printing a trade, 333 private benefits, 205–6 private information, 223 private services, 538 proactive traders, 383, 384 production/allocation decisions, 206–7, 208 profit-motivated traders, 177, 194–97, 198, 205, 206 ProFunds Ultra OTC Fund, 447 program trading, 368, 489 proprietary orders, 70 proprietary traders, 32 proprietary trading, 32, 149 pro rata allocation, 117, 134, 447 proxies asymmetric information, 314–15 for utilitarian trading interest, 316–17 volatility, 315–16 proxy variables, 312 pseudo-informed traders, 197, 229–30, 231 public benefits definition of, 205 of exchange, 214 of hedging, 214 from informative prices, 206–14 of liquid markets, 214–16 of risk sharing, 214–15 of trading, 206 public commodity pools, 474 public goods, 494–95 public information, 223, 241–43 public liquidity preservation principle, 499, 500 public order precedence rule, 113, 115, 500 public policy, 529–30, 535 public services, 538 public traders, 310–11, 313, 498 pure arbitrageurs, 194 pure discount bonds.
Finding Alphas: A Quantitative Approach to Building Trading Strategies
by
Igor Tulchinsky
Published 30 Sep 2019
MARKET MICROSTRUCTURE AND EXPECTED RETURNS Apart from allowing us to model the dynamics of liquidity, intraday data enables analysis of the interaction among market participants. In fact, the goal of the latter research direction is often set to find theoretical explanations for empirical patterns documented by the former. 212 Finding Alphas The theories of microstructure take into account the price discovery process and, in general, differentiate among informed traders, uninformed traders, and specialists. Informed traders are defined as rational entities that buy (or sell) if the “true value” of an asset is higher (or lower) than the bid (or ask) price. Uninformed traders, by contrast, act on no rational logic but trade purely for liquidity purposes.
Rigged Money: Beating Wall Street at Its Own Game
by
Lee Munson
Published 6 Dec 2011
It doesn’t matter if it is a mutual fund, registered investment adviser (RIA), or a hedge fund, the point is that when dealing with a pool of money, the trades get big. This affects your money regardless of whether you own individual stocks or pool it with a manager. The market doesn’t care who you are. Now we enter the point of price discovery. price discovery The process of determining the price of an asset in the marketplace through the interactions of buyers and sellers. Also known as reality versus what you think a security is worth. You may discover that nobody wants to buy your stock unless the price is very low. Or, you may discover that in order to buy a stock you must pay a premium to get someone to sell it to you.
The Invisible Hands: Top Hedge Fund Traders on Bubbles, Crashes, and Real Money
by
Steven Drobny
Published 18 Mar 2010
You need to have people in there chasing alpha to make markets more efficient. And by efficiency I am not talking about providing liquidity to the market. Rather, you need to have people constantly trying to evaluate the right price, who are ready to trade on that belief, pushing the market towards equilibrium in a price discovery process. That way we get better allocation of resources in the real economy and fewer bubbles. If there had been more John Paulsons in the market during the last few years, and fewer gullible institutional investors in subprime, the global economy would have been much better off. But alpha in a strict sense is a zero-sum game, although with beneficial externalities for society.
Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined
by
Lasse Heje Pedersen
Published 12 Apr 2015
While the market price (shown by the dashed line) moves up as a result of the catalyst, it initially underreacts and therefore continues to go up for a while. A trend-following strategy buys the asset as a result of the initial upward price move and therefore capitalizes on the subsequent price increases. At this point in the life cycle, trend-following investors contribute to the speeding up of the price discovery process. Figure 12.1. Stylized plot of the life cycle of a trend. Source: Hurst, Ooi, and Pedersen (2013). Research has documented a number of behavioral tendencies and market frictions that lead to this initial underreaction:3 i. Anchor-and-insufficient-adjustment. People tend to anchor their views to historical data and adjust their views insufficiently to new information.
On the Brink: Inside the Race to Stop the Collapse of the Global Financial System
by
Henry M. Paulson
Published 15 Sep 2010
Investors ran away from securities that made them nervous—driving the current yield of 30-day ABCP up to 6 percent (from 5.28 percent in mid-July)—and began to accumulate Treasury bonds and notes, long the safest securities on the planet. This classic flight-to-quality nearly resulted in a failed auction of four-week bills on August 21, when massive demand for government paper so muddied the price discovery process that, ironically, some dealers pulled back from bidding to avoid potential losses. As a result, there were barely enough bids to cover the auction, so yields shot up despite the strong real demand. Karthik Ramanathan, head of Treasury’s Office of Debt Management, had to reassure global investors that the problems stemmed from too much demand, not too little.
Twilight of Abundance: Why the 21st Century Will Be Nasty, Brutish, and Short
by
David Archibald
Published 24 Mar 2014
Paper Money Collapse: The Folly of Elastic Money and the Coming Monetary Breakdown
by
Detlev S. Schlichter
Published 21 Sep 2011
The time and space for some consumers and some producers to err and to develop the patterns described above is very restricted in the context of pure models. However, the more we move away from the unrealistic model assumptions and consider a real-life economy, in which spending is not ongoing but discontinuous and intermittent, and price-discovery therefore periodic, these processes will be unavoidable. In any case, the imperfections of a real-life economy, when compared to the purity of the theoretical model, enhance the phenomena we just described; they do not cause them. What causes the processes described here is the lack of full transparency, which leads to potential misinterpretation.
On the Edge: The Art of Risking Everything
by
Nate Silver
Published 12 Aug 2024
Prediction markets are highly regarded in the River because they are seen as promoting epistemic rationality, i.e., giving people an incentive to see whether their perceptions of the world are aligned with reality. Preflop: The betting round in poker where each player has two private hole cards but the flop and other community cards have not yet been dealt. Price discovery: The process of establishing a market price by letting people make bets or trades. Prior: In Bayesian reasoning, an initial belief that you’re prepared to revise as more information is revealed. Under Bayes’ theorem proper, a prior takes the form of a statistical estimate of the probability of an event.
Empirical Market Microstructure: The Institutions, Economics and Econometrics of Securities Trading
by
Joel Hasbrouck
Published 4 Jan 2007
file:///C:/Documents%20and%...
by
vpavan
Alpha Trader
by
Brent Donnelly
Published 11 May 2021
The Meat Racket: The Secret Takeover of America's Food Business
by
Christopher Leonard
Published 18 Feb 2014