To conclude and sum up the last five articles, I would like to introduce this video, which is a debate about the advantage and disadvantage of the high frequency trading.
Sunday, May 6, 2012
Friday, May 4, 2012
The Flash Crash
“On May 6, 2010, in the course of about 30 minutes, U.S. stock market indices, stock-index futures, options, and exchange-traded funds experienced a sudden price drop of more than 5 percent, followed by a rapid rebound. This brief period of extreme intraday volatility, commonly referred to as the “Flash Crash”, raises a number of questions about the structure and stability of U.S. financial markets”.
The video is a recording of a trader from the S&P 500 futures during the flash crash on May 6 2010
A survey conducted by Market Strategies International between June 23-29, 2010 reports that over 80 percent of U.S. retail advisors believe that “overreliance on computer systems and high-frequency trading” were the primary contributors to the volatility observed on May 6”
Source: The Flash Crash: The Impact of High Frequency Trading on an Electronic Market- October 1, 2010
The reason for the the Flash Crash of may 6, 2010 still a mystery. No evidence shows that the responsible are the high frequency traders. However, the investigation conducted by the SEC and The CFT, concluded that a trading firm, which the name has not been mentioned in the investigation report, executed a computerized trade of 4.1 billion dollar. (Read more)
Some argued that the trade was not intentional, but a bug in one of the firm's machines was responsible for this heavy sell-off. On this topic, staff at Nanex, a computer programing company, made a comment on the event: : “On the subject of HFT systems, we were shocked to find cases where one exchange was sending an extremely high number of quotes for one stock in a single second -- as high as 5,000 quotes in 1 second! During May 6, there were hundreds of times that a single stock had over 1,000 quotes from one exchange in a single second. Even more disturbing, there doesn't seem to be any economic justification for this. In many of the cases, the bid/offer is well outside the National Best Bid/Offer (NBBO). We decided to analyze a handful of these cases in detail and graphed the sequential bid/offers to better understand them. What we discovered was even more bizarre and can only be evidence of either faulty programming, a virus or a manipulative device aimed at overloading the quotation system.”
Even if the name has not been mentioned on the official reports, Waddel and Reed, a investment company has been the target of many blames because of its likelihood to execute these kind of orders.
Some argued that the trade was not intentional, but a bug in one of the firm's machines was responsible for this heavy sell-off. On this topic, staff at Nanex, a computer programing company, made a comment on the event: : “On the subject of HFT systems, we were shocked to find cases where one exchange was sending an extremely high number of quotes for one stock in a single second -- as high as 5,000 quotes in 1 second! During May 6, there were hundreds of times that a single stock had over 1,000 quotes from one exchange in a single second. Even more disturbing, there doesn't seem to be any economic justification for this. In many of the cases, the bid/offer is well outside the National Best Bid/Offer (NBBO). We decided to analyze a handful of these cases in detail and graphed the sequential bid/offers to better understand them. What we discovered was even more bizarre and can only be evidence of either faulty programming, a virus or a manipulative device aimed at overloading the quotation system.”
Even if the name has not been mentioned on the official reports, Waddel and Reed, a investment company has been the target of many blames because of its likelihood to execute these kind of orders.
The massive sell-off was not the only reason that lead to the Flash Crash. The sudden injection of 4.1 billion to the stock market created a great deal of stress and panic, which lead all computers on the network to sell positions dragging the stock market down. In reaction to this, many high frequency trading firms just turned of their computers and left the office. Manoj Narang, the CEO of a high frequency trading firm, said: "Flash Crash was worst day for high frequency traders...We hit our stop-loss as the market was going down,... we turned our systems off... We did not turned the high frequency trading system on for the rest of the day "
The video is called the truth about May 6,2010 and express an opinion about the linkage of the Flash Crash and High Frequency Trading.
As a response to all the criticisms against high frequency trading, Manoj Naranj argued against these accusations during an interview conducted by Bloomberg, the content of the interview is available on the following
video
As a response to all the criticisms against high frequency trading, Manoj Naranj argued against these accusations during an interview conducted by Bloomberg, the content of the interview is available on the following
video
Thursday, May 3, 2012
The Dark Side of High Frequency Trading
The video is about Senator Ted of Delaware explaining high High Frequency Trading became a large bubble that dominate the market and even too big to be regulated.
High Frequency Trading has dramatically grown since its beginning in the late nineties. Today it became the ultimate force that control and manipulate the stock market. With their sophisticated computers, High Frequency Traders influence and anticipate slow traders moves and even behaviors. In july 2009, an example of this type of manipulation happened. The issue of the New York Time released on 07/24/2009 discussed this.
“It was July 15, and Intel, the computer chip giant, had reporting robust earnings the night before. Some investors, smelling opportunity, set out to buy shares in the semiconductor company Broadcom. (Their activities were described by an investor at a major Wall Street firm who spoke on the condition of anonymity to protect his job.) The slower traders faced a quandary: If they sought to buy a large number of shares at once, they would tip their hand and risk driving up Broadcom’s price. So, as is often the case on Wall Street, they divided their orders into dozens of small batches, hoping to cover their tracks. One second after the market opened, shares of Broadcom started changing hands at $26.20.
The slower traders began issuing buy orders. But rather than being shown to all potential sellers at the same time, some of those orders were most likely routed to a collection of high-frequency traders for just 30 milliseconds — 0.03 seconds — in what are known as flash orders. While markets are supposed to ensure transparency by showing orders to everyone simultaneously, a loophole in regulations allows marketplaces like Nasdaq to show traders some orders ahead of everyone else in exchange for a fee.
In less than half a second, high-frequency traders gained a valuable insight: the hunger for Broadcom was growing. Their computers began buying up Broadcom shares and then reselling them to the slower investors at higher prices. The overall price of Broadcom began to rise.
Soon, thousands of orders began flooding the markets as high-frequency software went into high gear. Automatic programs began issuing and canceling tiny orders within milliseconds to determine how much the slower traders were willing to pay. The high-frequency computers quickly determined that some investors’ upper limit was $26.40. The price shot to $26.39, and high-frequency programs began offering to sell hundreds of thousands of shares.
The result is that the slower-moving investors paid $1.4 million for about 56,000 shares, or $7,800 more than if they had been able to move as quickly as the high-frequency traders.”
In addition to action and behavior manipulation of slower investors, High Frequency Trading can create a crash on the stock market like the one happened on may 6, 2010, and more popular as “The Flash Crash”*, in which the Dow Jones Industrial Average plunged about 900 points (about 9%) and recovered those losses within minutes. This kind of situation can occur given that all the transactions made by the High Frequency Traders are done through machines and robots that use very complex algorithms. As opposed to a human being, a robot is only able to perform the tasks it has been programmed for. Also, robots, computers, and machines can “bug”, which can be dramatic for the market given the access of computers to liquidities, and the network that relates all of them to the stock exchange. "The interaction between automated execution programs and algorithmic trading strategies can quickly erode liquidity and result in disorderly markets," said The the U.S. Securities and Exchange Commission and the Commodity Futures Trading Commission report about the Flash Crash of May 6 2010 .
Monday, April 30, 2012
Are Computers Better Than Humans?
This video features a debate between Steve Kroft, 60 minutes presenter, and Larry Leibowitz, the chief operating officer of the New York Stock Exchange (NYSE). The point of Mr Leibowitz is that HFT is beneficial for the market and investors. However, Mr Kroft counters this argument by stating that computers are not humans and do not have the capacity to analyze of the companies and stocks that they trade objectively.
Also, an important point related by William H. Donaldson, former chairman and chief executive of the NYSE and current adviser to a big hedge fund, was that individual investors are losing their advantage compared to bigger institutions that have the financial and technical resources to use high frequency trading methods. Indeed, when Mr Donaldson said “This is where all the money is getting made,” ... “If an individual investor doesn’t have the means to keep up, they’re at a huge disadvantage,” he was referring to the edge that high frequency specialists have over ordinary investors.
In addition, not only are individual investors facing challenges on the trading market, but firms, banks, asset managers, and hedge funds that do not equip themselves with the most sophisticated material are not able to compete. Even geographical location has an important role in the high trading process. In fact, the high trading professionals who are able to locate themselves closer to exchange servers reduce their time by microseconds when they place an order, increasing their benefit by millions of dollars. “By co-locating,” says Adam Afshar of Hyde Park Global, a high-speed trading firm, “we are able to take 21 milliseconds off our trades. In the past, 21 milliseconds was a trivial matter. Now it’s a pivotal matter.” Several academic studies have found that shaving even one millisecond off every trade can be worth $100 million a year to a large, high-speed trading firm.
To read more please click: Wall Street's secret advantage: High-speed trading. The week june 28, 2010
Friday, April 27, 2012
High Frequency Trading helps small investors!
On this video, the high frequency trader Manoj Narang argus on the benefit of the HFT on small investors.
To understand the advantage of HFT, it is interesting to review the history and see the reason of introducing this method to the market. In 1996, the justice department fond 24 cases of anticompetitive conduct from major market makers. That created many pressure on the stock market increasing trading commissions for individual and institutional investors.
Later, in 2003, New York Stock Exchange specialists executed trading orders for their own dealers prior the public customers order violating this way Federal Securities rules and exchange rules.
These two scandals made the SEC issuing regulations that took off from traditional market makers the privilege of ruling Wall Street.
A key SEC regulation was the creation and growth of all electronic trading alternative system known as ATSs that make market makers to display the best priced limited orders to buy or sell which gave the general public to enjoy better trading prices.
Please click to see: Regulation of Exchanges and Alternative Trading Systems
Please click to see: Regulation of Exchanges and Alternative Trading Systems
Ultimately, these reforms were a key of the emergence of new professionals traders that began competing against traditional Wall Street traders. These new professionals was are able to use automated computer programs to enter orders directly on the market, they are know today as “high frequency traders”.
According to the previous video, the High Frequency Trading provides to the financial market liquidities. In fact, if every trader has only long term investments, the market would frequently suffer from stock shortage. Also, HFT firms help to tighter the bid- ask spreads (difference between the bid and ask), reduce the trading costs, and triple the trade volumes.
Please click to read more
Tuesday, April 24, 2012
High Frequency Trading
Click here to see the Video
The video describe the new profession, high frequency trading, that newly appeared with the emergence of the ICT. The speed traders, which are computer robots ruled by mathematicians and engineers (quants), replaced the traditional trader who was ruling Wall Street for more than 150 years.
High frequency trading is no of the two type of electronic trading. The first type is the manual trading where orders are executed by humans using an electronic platforms, an the second type is automated trading, where instructions are executed by computers algorithm, with a little or no human intervention. This second type is also called high frequency trading or HFT because of the very short term holding of securities.
HFT is also divided into two type:
“Algorithmic execution: a human trader decides to trade but uses an electronic trading program to execute the trade. This is often used for larger orders. For example, the program may use smart order routing to choose where to best trade, or it may use a time- or volume-weighted method to execute the dealer’s trade to achieve the best price.2 Bank traders may use this type of approach to trade via an aggregator; real money investors may use a time-weighted approach to drip- feed a large order to the market.
Algorithmic trade decision-making: a firm builds a model to initiate a trade based on certain key input parameters such as order book imbalance, momentum, correlations (within or across markets), mean reversion, and systematic response to economic data or news headlines. Once a trade decision has been made, the algorithm also executes the trade. Banks’ automated risk management tools may also use this method to offset risk automatically. Hedge funds engaged in model- based strategies and specialized HFT funds operate in a similar fashion.”
(High-frequency trading in the foreign exchange market Report submitted by a Study Group established by the Markets Committee. This Study Group was chaired by Guy Debelle of the Reserve Bank of Australia. September 2011)
This article is going to emphasize on the Algorithmic trade decision making according to the video above, and talk about its impact on the players and the market.
The new players that are using this technics (mainly large banks, hedge funds, and mutual funds) make profits on basis of trading large number of positions in very short amount of time (second or micro second). By taking advantage from very fast and powerful computers, they are able to get any market information shortly before all other players (other firms, individual traders...), so they can detect any profitable trading opportunity in the marketplace and translate a micro-second to millions or even billions of dollars.
To earn this competitive advantage has attracted a considerable number of players. Five years ago, 30% of the traded stocks where performed by High Frequency Traders. Today, the number has increased to 70%. To have a vision on how the emergence of new technologies which make the use of HFT possible, I would like to see how new technology changed the structure of FX (Foreign Exchange) market.
To read more about the: Research Study
Friday, April 20, 2012
Overconfident Investors
This First video is an introduction for behavioral finance. Also, NBR's Dan Grech is examining some psychological factors that affects investment decision and one of them is overconfidence.
This Second video is illustrating the impact of overconfident investors on the market.
Barber & Odean (2000) and Glaser & Weber (2004) performed research to investigate if the new trend of individual online trading might affect the market. One of the conclusions was that the new investing method has increased the level of trust of the investors “by providing false sense of knowledge and an illusion of control, but also by modifying the fundaments of investment decision-making” (Voicu-Dorobanţu Roxana and Marinoiu Ana Maria 2009). This citation reflects that large amounts of information leads to overconfidence and rapid and flawed decision making methods.
Also, information collected by investors is meant to be used to make predictions and make decisions based on these predictions. However since the apparition of the Internet and the amount of information offered lead to a lower precise prediction because of the level of information saturation is reached, from that point on being created just the illusion of knowledge. Thus, investors think that they are able to pick stocks better than they really do. Confirmation of the information is a real time feedback method that also increases the overconfidence level. In this process investor enter in contact subconsciously with other investors that have the same pinion and ideas about stock piking, earning predictions, and investment ideas. The investors, since the proliferation of the Internet, can virtually meet in forums for example, and they share together the same analysis and conclusions and confirm them. This process increases the confidence and can lead to bad decision-making.
Internet is clearly a key to overconfidence of the investors Data providers encourage this belief with ads such as one (from eSignal) that promises: “You’ll make more, because you know more.”
Overconfident investors can be dangerous for the stock market. Theoretical models state that the over-confidence level will make the trading frequency and the speculation level higher. Additionally, over-confident investors will have a less diversified portfolios thus a higher risk for them self and higher volatility for the market.
Before the apparition on the online brokerage, trading a stock was not done without multiple phone conversation between the client and his or her broker, who is going to give advices, and market tendencies. Currently, with the ability to trade online, trading frequency has grown exponentially and so the speculation rate. Balasubramanian, Konana and Menon (1999) list “Feeling of empowerment” as on of the basic reason for switching to on-line trading.
The relation of online trading and its relationaship with overconfidence has been the topic of many researches.
Literature review:
“Frequently, in literature, the discussion about the increase in the confidence level of the investor while online appears as an answer to the question related to the increase in trading volume. In like situations, a two-pronged approach may be encountered: the ”over-confident” investor, previously described and the „opinion differences”. The former has already been discussed, while the latter is motivated, at least at a theoretical level, by the research from 1985 and 1989 of Hal Varian. Varian generalizes the paradigm average value volatility in order to allow the use of different previous probabilities. Each investor has his own probability distribution, previously created and subjective, for the value of a risky asset. It is assumed that the values are normally distributed and have different average values. In his research, Varian (1989) has proven the fact that the trading volume is essentially determined by the differences in opinion. The net equilibrium volume for an investor depends exclusively and directly on his deviation in opinion from the average values. Harris & Raviv (1993) assume the fact that the investors starts from the same assumptions, they receive public information, which they interpret in different ways (for example, by using different actualization functions for the probabilities), hence causing differences in opinion. Kandel & Pearson (1995) model the differences in opinion as it follows: the investors receive a public signal that is the sum of two variables: the liquidity of a risky asset and a random error factor, whose average value is the difference in opinion. Morris (1995) and van den Steen (2001) have shown in their research that the differences in opinion depend on rationality. Shiller (1999), Barberis & Thaler (2003), Hong & Stein (2003), and Diether, Malloy & Scherbina (2002) consider the differences in opinion to be yet another form of over-confidence: the investors assume that their information and their valuation abilities are better that the others.”
From all the studies that has been made, the face that overconfidence of the investors is clearly related to the the apparition of new trading technologies. The multiplication of these kind of investors, might create a devastation threat for the market and thus for the economy.
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