Themis Trading refers me to a comment letter from R T Leuchtkafer which in turn referred me to an excellent paper by Andrei A. Kirilenko, Albert S. Kyle, Mehrdad Samadi and Tugkan Tuzun titled Flash Crash: The Impact of High Frequency Trading on an Electronic Market.
We define Intermediaries as those traders who follow a strategy of buying and selling a large number of contracts to stay around a relatively low target level of inventory. Specifically, we designate a trading account as an Intermediary if its trading activity satisfies the following two criteria. First, the account’s net holdings fluctuate within 1.5% of its end of day level. Second, the account’s end of day net position is no more than 5% of its daily trading volume. Together, these two criteria select accounts whose trading strategy is to participate in a large number of transactions, but to rarely accumulate a significant net position.
We define High Frequency Traders as a subset of Intermediaries, who individually participate in a very large number of transactions. Specifically, we order Intermediaries by the number of transactions they participated in during a day (daily trading frequency), and then designate accounts that rank in the top 3% as High Frequency Traders. Once we designate a trading account as a HFT, we remove this account from the Intermediary category to prevent double counting.
This seems like an entirely sensible division, although one might quibble about the 3% cut-off. Why not 2% or 4%? It might also be illuminating to make the division based on the technology used.
Some Fundamental Traders accumulate directional positions by executing many small-size orders, while others execute a few larger-size orders. Fundamental Traders which accumulate net positions by executing just a few orders look like Noise Traders, while Fundamental Traders who trade a lot resemble Opportunistic Traders. In fact, it is quite possible that in order not to be taken advantage of by the market, some Fundamental Traders deliberately pursue execution strategies that make them appear as though they are Noise or Opportunistic Traders. In contrast, HFTs appear to play a very distinct role in the market and do not disguise their market activity.
Naturally, the better you are at disguising your activity, the better you are going to do for your clients. This point is lost upon the regulators, who generally take the view that an order cancellation is an indication of fraudulent activity and spend their time crafting rules to penalize smart traders and their clients.
It will also be noted that the ultimate disguise consists of not showing your order publicly at all – which means using a dark pool.
In order to further characterize whether categories of traders were primarily takers of liquidity, we compute the ratio of transactions in which they removed liquidity from the market as a share of their transactions.[Footnote] According to Table 2, HFTs and Intermediaries have aggressiveness ratios of 45.68% and 41.62%, respectively. In contrast, Fundamental Buyers and Sellers have aggressiveness ratios of 64.09% and 61.13%, respectively. This is consistent with a view that HFTs and Intermediaries generally provide liquidity while Fundamental Traders generally take liquidity. The aggressiveness ratio of High Frequency Traders, however, is higher than what a conventional definition of passive liquidity provision would predict. [Footnote]
In order to better characterize the liquidity provision/removal across trader categories, we compute the proportion of each order that was executed aggressively.[Footnote] Table 3 presents the cumulative distribution of ratios of order aggressiveness.
Footnote: When any two orders in this market are matched, the CME Globex platform automatically classifies an order as ‘Aggressive’ when it is executed against a ‘Passive’ order that was resting in the limit order book. From a liquidity standpoint, a passive order (either to buy or to sell) has provided visible liquidity to the market and an aggressive order has taken liquidity from the market. Aggressiveness ratio is the ratio of aggressive trade executions to total trade executions. In order to adjust for the trading activity of different categories of traders, the aggressiveness ratio is weighted either by the number of transactions or trading volume.
Footnote: One possible explanation for the order aggressiveness ratios of HFTs is that some of them may actively engage in “sniping” orders resting in the limit order book. Cvitanic and Kirilenko (2010) model this trading behavior and conclude that under some conditions this trading strategy may have impact on prices. Similarly, Hasbrouck and Saar (2009) provide empirical support for a possibility that some traders may have altered their strategies by actively searching for liquidity rather than passively posting it. Yet another explanation is that after passively buying at the bid or selling at the offer, HFTs quickly reduce their inventories by trading aggressively if necessary.
Footnote: The following example illustrates how we compute the proportion of each order that was executed aggressively. Suppose that a trader submits an executable limit order to buy 10 contracts and this order is immediately executed against a resting sell order of 8 contracts, while the remainder of the buy order rests in the order book until it is executed against a new sell order of 2 contracts. This sequence of executions yields an aggressiveness ratio of 80% for the buy order, 0% for the sell order of 8 contracts, and 100% for the sell order of 2 contracts.
This is a much better indicator of order intent than the puerile “Order Toxicity” metric, but remains flawed, as shown by the last footnote. If somebody needs to sell a large block, for instance, and places an offer well below the prevailing market price, the vast majority of it will execute as buyers take advantage of this low offer (this happens on a routine basis in the preferred share market). However, since this order is “resting”, these execution will indicate that the seller is providing liquidity and the buyers are taking it – when the actual situation is the other way around.
In fact, the first quoted section above explicitly demonstrates this fact of trading, with Fundamental Traders going to great lengths to look like Noise and Opportunistic traders.
According to Figure 4, HFTs do not accumulate a significant net position and their position tends to quickly revert to a mean of about zero. The net position of the HFTs fluctuates between approximately +/- 3000 contracts. Figure 5 presents the net position of the Intermediaries during May 3-6, 2010.
According to Figure 5, Intermediaries exhibit trading behavior similar to that of HFTs. They also do not accumulate a significant net position. Compared to the HFTs, the net position of the Intermediaries fluctuates within a more narrow band of +/- 2000 contracts, and reverts to a lower target level of net holdings at a slower rate.
We also find a notable decrease in the number of active Intermediaries on May 6. As the Figure 6 shows, the number of active Intermediaries dropped from 66 to 33, as the large price decline ensues.
In contrast, as presented in Figure 7, the number of active HFTs decreases from 13 to 10.
This demonstrates the position limits highlighted by the SEC report.
We interpret these results as follows. HFTs appear to trade in the same direction as the contemporaneous price and prices of the past five seconds. In other words, they buy, if the immediate prices are rising. However, after about ten seconds, they appear to reverse the direction of their trading – they sell, if the prices 10-20 seconds before were rising. These regression results suggest that, possibly due to their speed advantage or superior ability to predict price changes, HFTs are able to buy right as the prices are about to increase. HFTs then turn around and begin selling 10 to 20 seconds after a price increase.
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The Intermediaries sell when the immediate prices are rising, and buy if the prices 3-9 seconds before were rising. These regression results suggest that, possibly due to their slower speed or inability to anticipate possible changes in prices, Intermediaries buy when the prices are already falling and sell when the prices are already rising.
So in other words, part of the thing that differentiates HFT and Intermediaries is not simply the volume of trade, but also that the HFT guys can do it better. In many cases, HFT strategies attempt to predict the (short-term) future direction of the market by looking at the order book … if there’s a huge volume of offers compared to the bids, get out of the way! One method of counter-attack against this is, as mentioned above, the use of dark pools for trading.
We consider Intermediaries and HFTs to be very short term investors. They do not hold positions over long periods of time and revert to their target inventory level quickly. Observed trading activity of HFTs can be separated into three parts. First, HFTs seem to anticipate price changes (in either direction) and trade aggressively to profit from it. Second, HFTs seem to provide liquidity by putting resting orders in the direction of the anticipated the price move. Third, HFTs trade to keep their inventories within a target level. The inventory management trading objective of HFTs may interact with their price-anticipation objective. In other words, at times, inventory-management considerations of HFTs may lead them to aggressively trade in the same direction as the prices are moving, thus, taking liquidity. At other times, in order to revert to their target inventory levels, HFTs may passively trade against price movements and, thus, provide liquidity.
This is consistent with my speculation on October 25 that HFT acts as a capacitator that will discharge if a certain inventory level is breached.
We find that compared to the three days prior to May 6, there was an unusually level of HFT “hot potato” trading volume — due to repeated buying and selling of contracts accompanied a relatively small change in net position. The hot potato effect was especially pronounced between 13:45:13 and 13:45:27 CT, when HFTs traded over 27,000 contracts, which accounted for approximately 49% of the total trading volume, while their net position changed by only about 200 contracts.
We interpret this finding as follows: the lack of Opportunistic and Fundamental Trader, as well as Intermediaries, with whom HFTs typically trade, resulted in higher trading volume among HFTs, creating a hot potato effect. It is possible that during the period of high volatility, Opportunistic and Fundamental Traders were either unable or unwilling to efficiently submit orders. In the absence of their usual trading counterparties, HFTs were left to trade with other HFTs.
So in other words, it wasn’t the HFTs that left the market, it was the Opportunistic and Fundamental Traders.
Aggressiveness Imbalance is constructed as the difference between aggressive buy transactions minus aggressive sell transactions. Figure 8 shows the relationship between price and cumulative Aggressiveness Imbalance (aggressive buys – aggressive sells).
In addition, we calculate Aggressiveness Imbalance for each category of traders over one minute intervals. For illustrative purposes, the Aggressiveness Imbalance indicator for HFTs and Intermediaries are presented in Figures 9 and 10, respectively.
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According, to Figures 9 and 10, visually, HFTs behave very differently during the Flash Crash compared to the Intermediaries. HFTs aggressively sold on the way down and aggressively bought on the way up. IN contrast, Intermediaries are about equally passive and aggressive both down and up.
As suggested above, this could simply be a result of HFT looking at the order book and taking a view, in addition to the considerations implied by their inventories.
I have added emphasis below to what I suggest is the central conclusion to be drawn from the Flash Crash.
We believe that the events on May 6 unfolded as follows. Financial markets, already tense over concerns about the European sovereign debt crisis, opened to news concerning the Greek government’s ability to service its sovereign debt. As a result, premiums rose for buying protection against default on sovereign debt securities of Greece and a number of other European countries. In addition, the S&P 500 volatility index (“VIX”) increased, and yields of ten-year Treasuries fell as investors engaged in a “flight to quality.” By midafternoon, the Dow Jones Industrial Average was down about 2.5%.
Sometime after 2:30 p.m., Fundamental Sellers began executing a large sell program. Typically, such a large sell program would not be executed at once, but rather spread out over time, perhaps over hours. The magnitude of the Fundamental Sellers’ trading program began to significantly outweigh the ability of Fundamental Buyers to absorb the selling pressure.
HFTs and Intermediaries were the likely buyers of the initial batch of sell orders from Fundamental Sellers, thus accumulating temporary long positions. Thus, during the early moments of this sell program’s execution, HFTs and Intermediaries provided liquidity to this sell order. However, just like market intermediaries in the days of floor trading, HFTs and Intermediaries had no desire to hold their positions over a long time horizon. A few minutes after they bought the first batch of contracts sold by Fundamental Sellers, HFTs aggressively sold contracts to reduce their inventories. As they sold contracts, HFTs were no longer providers of liquidity to the selling program. In fact, HFTs competed for liquidity with the selling program, further amplifying the price impact of this program.
Furthermore, total trading volume and trading volume of HFTs increased significantly minutes before and during the Flash Crash. Finally, as the price of the E-mini rapidly fell and many traders were unwilling or unable to submit orders, HFTs repeatedly bought and sold from one another, generating a “hot-potato” effect. Yet, Opportunistic Buyers, who may have realized significant profits from this large decrease in price, did not seem to be willing or able to provide ample buy-side liquidity. As a result, between 2:45:13 and 2:45:27, prices of the E-mini fell about 1.7%.
At 2:45:28, a 5 second trading pause was automatically activated in the E-mini. Opportunistic and Fundamental Buyers aggressively executed trades which led to a rapid recovery in prices. HFTs continued their strategy of rapidly buying and selling contracts, while about half of the Intermediaries closed their positions and got out of the market. In light of these events, a few fundamental questions arise. Why did it take so long for opportunistic buyers to enter the market and why did the price concessions had to be so large? It seems possible that some opportunistic buyers could not distinguish between macroeconomic fundamentals and market-specific liquidity events. It also seems possible that the opportunistic buyers have already accumulated a significant positive inventory earlier in the day as prices were steadily declining. Furthermore, it is possible that they could not quickly find opportunities to hedge additional positive inventory in other markets which also experienced significant volatility and higher latencies. An examination of these hypotheses requires data from all venues, products, and traders on the day of the Flash Crash.
I suggest that the reason this happened is because Opportunistic traders are simply not very smart people. They’re prep-school smiley-boys who got their jobs through Daddy’s connections and can make a fat living without the necessity of labour or thought. This will not change until performance genuinely becomes a desirable metric in the marketplace (as opposed to consumer-goods style branding) and regulators dispose of their fixation on turnover, which is simply a hangover from legitimate concern regarding commission-driven churning.
But a lot of it is simply ultimate investors’ desire for a good story. In general, investors want to hear “I bought it because Bernanke this and Buffet that and in-depth macro-economic analysis the other thing”, not “I bought it because somebody really, really wanted to sell it and it was outside its fair-value range compared to what I sold. I think. Maybe. This type of trade works about 60% of the time.”
That being said, however, I will also suggest that it is possible that the Opportunistic Buyers were dissuaded from entering the market through the quote-stuffing identified by Nanex, which has yet to be explained in a satisfactory manner.
And, I think, one piece of information we need is a look at the order book at the time – such as it was! It is possible that the selling by HFT was not due merely to a desire to square their books, but there was also the motivation supplied by a huge volume of resting sells relative to resting buys. Appendix IV.2 of the SEC Flash Crash Report gave order-book data for seven securites, but not the eMini contract. I republished two of the depth-charts (for Accenture) in my post regarding the report.
Based on our analysis, we believe that High Frequency Traders exhibit trading patterns consistent with market making. In doing so, they provide very short term liquidity to traders who demand it. This activity comprises a large percentage of total trading volume, but does not result in a significant accumulation of inventory. As a result, whether under normal market conditions or during periods of high volatility, High Frequency Traders are not willing to accumulate large positions or absorb large losses. Moreover, their contribution to higher trading volumes may be mistaken for liquidity by Fundamental Traders. Finally, when rebalancing their positions, High Frequency Traders may compete for liquidity and amplify price volatility. Consequently, we believe, that irrespective of technology, markets can become fragile when imbalances arise as a result of large traders seeking to buy or sell quantities larger than intermediaries are willing to temporarily hold, and simultaneously long-term suppliers of liquidity are not forthcoming even if significant price concessions are offered. We believe that technological innovation is critical for market development. However, as markets change, appropriate safeguards must be implemented to keep pace with trading practices enabled by advances in technology.
Update: This is probably as good a place as any to pass on some information about Stop-Loss orders, from Mary Schapiro’s September 7 speech to the Economic Club of New York, titled Strengthening Our Equity Market Structure:
To understand where individual investors are coming from, we must truly recognize the impact of severe price volatility on their interests: one example is the use and impact of stop loss orders on May 6. Stop loss orders are designed to help limit losses by selling a stock when it drops below a specified price, and are a safety tool used by many individual investors to limit losses.
The fundamental premise of these orders is to rely on the integrity of market prices to signal when the investor should sell a holding. On May 6, this reliance proved misplaced and the use of this tool backfired.
A staggering total of more than $2 billion in individual investor stop loss orders is estimated to have been triggered during the half hour between 2:30 and 3 p.m. on May 6. As a hypothetical illustration, if each of those orders were executed at a very conservative estimate of 10 percent less than the closing price, then those individual investors suffered losses of more than $200 million compared to the closing price on that day.
I disagree with her view of the fundamental premise of a stop-loss order. The purpose of a stop-loss order is to demonstrate that you’re an ignorant little turd who deserves to go bankrupt. If we consider an earlier section of Ms. Schapiro’s speech …:
Those who purchase stock in an initial public offering, for example, can have confidence that they will be able to sell that stock at a fair and efficient price in the secondary market when they need or want to. And of course, the values assigned to stocks in the secondary market play an important role in the ability of companies to raise additional funding.
Markets are powerful and they are the most efficient and effective tools for turning savings into capital and growth.
But, if the equity market structure breaks down — if it fails to provide the necessary and expected fairness, stability, and efficiency — investors and companies pull back, raising costs and reducing growth.
… we see that the fundamental premise of a market is to indicate a fair value of a listed company. I have no arguments with that. A stop-loss order says “I don’t want to sell this stock at $50. But if it goes down to $40, that’s the time I want to sell it.” – a sentiment completely divorced from the objective of fairly valuing a listed company.
Update: Despite all this – despite the complete lack of evidence that either HFT or algorithmic trading was in any way the root cause of the debacle – there are some who don’t want to be confused with facts:
“While I do not believe that the Flash Crash was the direct result of reckless misconduct in the futures market, I question what the CFTC could have done if the opposite were true. When does high frequency or algorithmic trading cross the line into being disruptive to our markets? And, along those same lines, who is responsible when technology goes awry? Do we treat rogue algorithms like rogue traders? These are the issues I hope to explore at our October 12th meeting,” stated Commissioner O’Malia.
Nothing wrong with the world that a few extra rules wouldn’t cure, eh Commisioner?
BCE.PR.R to Reset at 4.490%
November 11th, 2010BCE Inc. has announced that it:
The announcement of the Selected Percentage Rate was reported on PrefBlog. Data for the Pairs Equivalency Calculator have been updated, but as yet the RatchetRate complement to this issue, Series Q, does not exist.
BCE.PR.R is tracked by HIMIPref™ but is relegated to the Scraps index on credit concerns.
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