Category Archives: Finance

Lessons from Super-Traders

Interesting retrospective from Traders Magazine, telling the stories of nine hot-shot traders that featured in a book 20 years ago and where they are now.

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Primer on Credit Default Swaps

This is an excellent introduction to CDS’s, aimed at developers.

Now consider the case where we buy a General Motors bond. We then enter into a credit default swap to the maturity of the bond as well. This acts as insurance against General Motors defaulting on the bond. We receive interest from the General Motors bond, but pay some of it away in insurance on the credit default swap. Suppose the interest rate on a US Government bond is 5% and the interest rate on a General Motors bond of the same maturity is 8%. We’d expect the premium on a credit default swap on General Motors for the same period to be about 3%. Note that the 3% premium is also called the ‘spread’ on a credit default swap, since it is the spread between the government bond and the corporate bond interest rates.

In summary, a credit default swap is a contract where one party to the contract pays a small periodic premium to the other, in return for protection against a credit event (financial difficulty) of a known reference entity (company).

I wish there were more articles on financial products like this, written in plain English for non-Quants.

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Latest “Fat Finger” trade reports that a trade input error caused an inflated sell order to be posted on Nasdaq OMX Stockholm, leading to ABN AMRO being fined this week.

The sponsored access client put in a negative value of -5,000 shares in the volume field for a sell order, which its own trading system erroneously converted to more than 294 million shares. This order, for shares in Swedish manufacturer SKF, was sent to the market despite constituting around 70% of outstanding SKF shares and resulted in the execution of 800,000 shares.

It’s amazing that this was a manually entered trade, yet didn’t have sufficient data validation, let alone pre-trade controls.

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Alleged insider trading in Heinz options

CNBC reports on an alleged case of insider trading:

The SEC says a trader purchased 2,500 out-of-the-money call options on shares of Heinz for $95,000 on Feb. 13. The options give the purchasers the right to acquire 250,000 shares at $65 each until June. The stock was trading at just over $60 a share at the time. The out-of-the-money call options weren’t very popular. On Feb. 12, only 14 $65 June call options were traded. On the day before, none at all.

When Berkshire Hathaway and 3G Capital Management announced a buyout, the stock rose to about $72. The price of the June 65 call options, now very much in the money, surged 1,700 percent. The $90,000 investment had been turned into $1.8 million.

The article points out that the SEC has frozen the Zurich-based trading account, leaving the beneficiary a dilemma – though their identity is confidential under Swiss-banking rules, they would have to go to court in the US to unfreeze the assets:

At that point, the SEC will likely publicly brand this person a securities fraud. If the trader doesn’t come forward by June, his investment goes to zero.

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The Rise of Dark Pools reports that institutional traders are leaving traditional displayed market venues for dark pools:

Experts like Justin Schack, partner and managing director at Rosenblatt Securities, has seen the overall market share of dark liquidity pools rise from 6.55% in 2008, just after the adoption of Regulation NMS and the introduction of its Trade Reporting Facility – a source of off-exchange trading data – to 13.36% at the end of 2012.

However, fleeing to dark pools wasn’t enough – other strategies have been put in place to mitigate against the high-frequency traders:

Some dark liquidity pool operators responded to this change in order size by introducing ‘order bunching’, where they would aggregate a series of smaller orders to create the other half of an institutional trade. In theory, an institutional trade could interact with four or five high-frequency traders on a 2,000-share trade instead of a single high-frequency trader in a 200-share trade.

Other dark pools define a variety of trader profiles and are allowing participants to specify with which profiles they are prepared to trade.

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Excel and The London Whale

The problems of using Excel within financial institutions are well-known – the control risks are huge because it’s so easy for a rogue trader to manually edit trade data/market data and re-save the sheet. This article describes the role Excel played in under-estimating the risks involved in financing The London Whale’s trading strategies:

JPMor­gan’s Chief Invest­ment Office need­ed a new value-at-risk (VaR) model for the syn­thet­ic cred­it port­fo­lio (the one that blew up) and assigned a quan­ti­ta­tive whiz (“a London-based quan­ti­ta­tive expert, math­e­mati­cian and model devel­op­er” who pre­vi­ous­ly worked at a com­pa­ny that built ana­lyt­i­cal mod­els) to cre­ate it. The new model “oper­at­ed through a series of Excel spread­sheets, which had to be com­plet­ed man­u­al­ly, by a process of copy­ing and past­ing data from one spread­sheet to another.”

Another question is, how do you test a spreadsheet? And how do you re-use fragments of a sheet?

the spread­sheets that peo­ple cre­ate with Excel are incred­i­bly frag­ile. There is no way to trace where your data come from, there’s no audit trail (so you can over­type num­bers and not know it), and there’s no easy way to test spread­sheets

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Regulating High Frequency Trading

Andrew Keller’s article Robocops: Regulating High Frequency Trading After the Flash Crash of 2010:

This emphasis on speed is the primary defining aspect to high frequency trading: it is the main difference between traditional investment management and HFT. It also distinguishes HFT from other algorithmic trading strategies. HFT is a subset of algorithmic trading, where both use programmed algorithms to execute automated order submissions and automated order management. However, it is common for a non-HFT algorithmic strategy to hold traded securities for days, weeks or months, whereas HFT traders usually end the trading day flat, with no significant holdings. Furthermore, ultra-fast trading speeds are not necessary in a non-HFT algorithmic strategy; HFT, on the other hand, uses strategies that require speed to gain advantages in the market.

“smoking” is an HFT scheme that exploits slow traders by offering attractive limit orders, then quickly revising these prices to take advantage of an unsuspecting slow trader’s market order.

Presumably, the exchanges know the players and have a better starting point than government regulators in attempting to understand HFT methods and strategies. HFT computers are also tied directly into the exchanges’ computer systems which provide the exchanges an advantage in compiling data. On the other hand, the exchanges have strong incentives to provide free reign to HFT traders: they earn high rents from co-location, and significant fees from large amounts of trading. The exchanges want HFT traders to continue playing a significant role in the markets, and may not be a reliable regulator.

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Human traders v robots

Mike O’Hara of HFTReview interviewed Prof Alex Preda about his research into how modern day retail investors are trading (free registration required to view the full article).

London traders are working and building models together with traders situated on the US East Coast or in the Mid West. They use social media intensively in order to combine and match their skills, to develop trading algorithms. This kind of work has become almost impossible for a single trader to sustain, so they build these small groups of maybe five or six people, situated in different locations, in different countries, all coordinating with each other in building and testing their robots. They’ve become very, very savvy.

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Time to apply the brakes to high-speed trading? posted a good article on the history behind high frequency trading and the potential consequences of some proposed changes to trading rules:

High-frequency trading might appear to pose threats on the horizon, notes Tabb, but hasty regulation is all but certain to trigger unintended consequences. “It could totally destroy the market,” he says. If rules lock a high-frequency investor into a bid of $102 for even half a second when the market value is $101, other investors could swoop in at $101 and make a dollar a share on the incorrect price. This will create incentives not to quote or provide liquidity, making it harder and much more expensive to invest.

Now the debate is getting political:

While the debate simmers, high-frequency traders are enlisting influential allies in Washington. Republican members of Congress Jeb Hensarling of Texas and Spencer Bachus of Alabama are advocating a slow approach to any regulatory initiatives. In letters to the SEC and the House Financial Services Committee, both congressmen warned not to “shoot the computers first and ask questions later.”

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BATS looks to compensate after long-term system error

BATS Global Markets has identified a software bug that has caused almost 450,000 trades to be executed at the wrong price over a period of 5 years, according to

The exchange group found that some short-sale orders were executed at a price that is equal to or less than the national best bid or offer, while non-displayed pegged orders may not have been executed at the most optimal price.

Under the SEC’s Reg NMS rules, trades must be routed to the market displaying the best price. As per short-selling rules, if a stock declines by 10% from the previous day’s close, traders can only sell short at a price one tick above the national best bid or offer until the end of the next trading day.

This sounds like an oversight in their understanding of the complex market rules rather than a bug – it wasn’t that the software was implemented correctly, this rule wasn’t implemented at all!

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