“The Scam” – Debashis Basu and Sucheta Dalal – Was it the Beginning of the End?

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“India is a turnaround scrip in the world market.”

“Either you kill, or you get killed” 

— Harshad Mehta

“Though normally quite reasonable and courteous, there was one breed of brokers he truly detested. to him and other kids in the money markets, brokers were meant to be treated like loyal dogs.”

— Broker

The first two claims by Harshad Mehta could be said to form the central theme of the book, The Scam, while the third statement is testimony to the fact of how compartmentalization within the camaraderie proved efficacious to the broker-trader nexus getting nixed, albeit briefly. The authors Debasish Basu and Sucheta Dalal have put a rigorous investigation into unraveling the complexity of what in popular culture has come to be known as the first big securities scam in India in the early 90s. That was only the beginning, for securities scams, banking frauds and financial crimes have since become a recurrent feature, thanks to increasing mathematization and financialization of market practices, stark mismatches on regulatory scales of The Reserve Bank of India (RBI), Public Sector Banks and foreign banks, and stock-market-oriented economization. The last in particular has severed the myth that stock markets are speculative and had no truck with the banking system, by capitalizing and furthering the only link between the two, and that being banks providing loans against shares subject to high margins.  

The scam which took the country by storm in 1992 had a central figure in Harshad Mehta, though the book does a most amazing archaeology into unearthing other equally, if not more important figures that formed a collusive network of deceit and bilk. The almost spider-like weave, not anywhere near in comparison to a similar network that emanated from London and spread out from Tokyo and billed as the largest financial scandal of manipulating LIBOR, thanks to Thomas Hayes by the turn of the century, nevertheless magnified the crevices existing within the banking system and bridging it with the once-closed secretive and closed bond market. So, what exactly was the scam and why did it rock India’s economic boat, especially when the country was opening up to liberal policies and amalgamating itself with globalization? 

As Basu and Dalal say, simply put, the first traces of the scam were observed when the State Bank of India (SBI), Main Branch, Mumbai discovered that it was short by Rs. 574 crore in securities. In other words, the antiquated manually written books kept at the Office of Public Debt at the RBI showed that Rs. 1170.95 crore of an 11.5% of central government loan of 2010 maturity was standing against SBI’s name on the 29th February 1992 figure of Rs. 1744.95 crore in SBI’s books, a clear gap of Rs. 574 crore, with the discrepancy apparently held in Securities General Ledger (SGL). Of the Rs. 574 crore missing, Rs. 500 crore were transferred to Harshad Mehta’s account. Now, an SGL contains the details to support the general ledger control account. For instance, the subsidiary ledger for accounts receivable contains all the information on each of the credit sales to customers, each customer’s remittance, return of merchandise, discounts and so on. Now, SGLs were a prime culprit when it came to conceiving the illegalities that followed. SGLs were issued as substitutes for actual securities by a cleverly worked out machination. Bank Receipts (BRs) were invoked as replacement for SGLs, which on the one hand confirmed that the bank had sold the securities at the rates mentioned therein, while on the other prevented the SGLs from bouncing. BRs is a shrewd plot line whereby the bank could put a deal through, even if their Public Debt Office (PDO) was in the negative. Why was this circumvention clever was precisely because had the transactions taken place through SGLs, they would have simply bounced, and BRs acted as a convenient run-around, and also because BRs were unsupported by securities. In order to derive the most from BRs, a Ready Forward Deal (RFD) was introduced that prevented the securities from moving back and forth in actuality. Sucheta Dalal had already exposed the use of this instrument by Harshad Mehta way back in 1992 while writing for the Times of India. The RFD was essentially a secured short-term (generally 15 day) loan from open bank to another, where the banks would lend against Government securities. The borrowing bank sells the securities to the lending bank and buys them back at the end of the period of the loan, typically at a slightly higher price. Harshad Mehta roped in two relatively obscure and unknown little banks in Bank of Karad and Mumbai Mercantile Cooperative Bank (MMCB) to issue fake BRs, or BRs not backed by Government securities. It were these fake BRs that were eventually exchanged with other banks that paid Mehta unbeknownst of the fact that they were in fact dealing with fake BRs. 

By a cunning turn of reason, and not to rest till such payments were made to reflect on the stock market, Harshad Mehta began to artificially enhance share prices by going on a buying spree. To maximize profits on such investments, the broker, now the darling of the stock market and referred to as the Big Bull decided to sell off the shares and in the process retiring the BRs. Little did anyone know then, that the day shares were sold, the market would crash, and crash it did. Mehta’s maneuvers lent a feel-good factor to the stock market until the scam erupted, and when it did erupt, many banks were swindled to a massive loss of Rs. 4000 crore, for they held on to BRs that had no value attached to them. The one that took the most stinging loss was the State Bank of India and it was payback time. The mechanism by which the money was paid back cannot be understood unless one gets to the root of an RBI subsidiary, National Housing Bank (NHB). When the State Bank of India directed Harshad Mehta to produce either the securities or return the money, Mehta approached the NHB seeking help, for the thaw between the broker and RBI’s subsidiary had grown over the years, the discovery of which had appalled officials at the Reserve Bank. This only lends credibility to the broker-banker collusion, the likes of which only got murkier as the scam was getting unravelled. NHB did come to rescue Harshad Mehta by issuing a cheque in favor of ANZ Grindlays Bank. The deal again proved to be one-handed as NHB did not get securities in return from Harshad Mehta, and eventually the cheque found its way into Mehta’s ANZ account, which helped clear the dues due to the SBI. The most pertinent question here was why did RBI’s subsidiary act so collusively? This could only make sense, once one is in the clear that Harshad Mehta delivered considerable profits to the NHB by way of ready forward deals (RFDs). If this has been the flow chart of payment routes to SBI, the authors of The Scam point out to how the SBI once again debited Harshad Mehta’s account, which had by then exhausted its balance. This was done by releasing a massive overdraft of Rs. 707 crore, which is essentially an extension of a credit by a lending institution when the account gets exhausted. Then the incredulous happened! This overdraft was released against no security!, and the deal was acquiesced to since there was a widespread belief within the director-fold of the SBI that most of what was paid to the NHB would have come back to SBI subsidies from where SBI had got its money in the first place. 

The Scam is neatly divided into two books comprising 23 chapters, with the first part delineating the rise of Harshad Mehta as a broker superstar, The Big Bull. He is not the only character to be pilloried as the nexus meshed all the way from Mumbai (then Bombay) to Kolkata (then Calcutta) to Bengaluru (then Bangalore) to Delhi and Chennai (then Madras) with a host of jobbers, market makers, brokers and traders who were embezzling funds off the banks, colluded by the banks on overheating the stock market in a country that was only officially trying to jettison the tag of Nehruvian socialism. But, it wasn’t merely individuated, but the range of complicitous relations also grabbed governmental and private institutions and firms. Be it the Standard Chartered, or the Citibank, or monetizing the not-even in possession of assets bought; forward selling the transaction to make it appear cash-neutral; or lending money to the corporate sector as clean credit implying banks taking risks on the borrowers unapproved by the banks because it did not fall under the mainline corporate lending, rules and regulations of the RBI were flouted and breached with increasing alacrity and in clear violations of guidelines. But credit is definitely due to S Venkitaraman, the Governor of the RBI, who in his two-year at the helm of affairs exposed the scam, but was meted out a disturbing treatment at the hands of some of members of the Joint Parliamentary Committee. Harshad Mehta had grown increasingly confident of his means and mechanisms to siphon-off money using inter-bank transactions, and when he was finally apprehended, he was charged with 72 criminal offenses and more than 600 civil action suits were filed against him leading to his arrest by the CBI in the November of 1992. Banished from the stock market, he did make a comeback as a market guru before the Bombay High Court convicted him to prison. But, the seamster that he was projected to be, he wouldn’t rest without creating chaos and commotion, and one such bomb was dropped by him claiming to have paid the Congress Prime minister PV Narsimha Rao a hefty sum to knock him off the scandal. Harshad Mehta passed away from a cardiac arrest while in prison in Thane, but his legacy continued within the folds he had inspired and spread far and wide. 

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Ketan Parekh forms a substantial character of Book 2 of The Scam. Often referred to as Midas in privy for his ability to turn whatever he touched into gold on Dalal Street by his financial trickery, he decided to take the unfinished project of Harshad Mehta to fruition. Known for his timid demeanor, Parekh from a brokers family and with his training as a Chartered Accountant, he was able to devise a trading ring that helped him rig stock prices keeping his vested interests at the forefront. He was a bull on the wild run, whose match was found in a bear cartel that hammered prices of K-10 stocks precipitating payment crisis. K-10 stocks were colloquially named for these driven in sets of 10, and the promotion of these was done through creating bellwethers and seeking support fro Foreign Institutional Investors (FIIs). India was already seven years old into the LPG regime, but still sailing the rough seas of economic transitioning into smooth sailing. This wasn’t the most conducive of timing to appropriate profits, but a prodigy that he was, his ingenuity lay in instrumentalizing the jacking up of shares prices to translate it into the much needed liquidity. this way, he was able to keep FIIs and promoters satisfied and multiply money on his own end. This, in financial jargon goes by the name circular trading, but his brilliance was epitomized by his timing of dumping devalued shares with institutions like the Life Insurance Corporation of India (LIC) and Unit Trust of India (UTI). But, what differentiated him from Harshad Mehta was his staying off public money or expropriating public institutions. such was his prowess that share markets would tend to catch cold when he sneezed and his modus operandi was invest into small companies through private placements, manipulate the markets to rig shares and sell them to devalue the same. But lady luck wouldn’t continue to shine on him as with the turn of the century, Parekh, who had invested heavily into information stocks was hit large by the collapse of the dotcom bubble. Add to that when NDA government headed by Atal Bihari Vajpayee presented the Union Budget in 2001, the Bombay Stock Exchange (BSE) Sensex crashed prompting the Government to dig deep into such a market reaction. SEBI’s (Securities and Exchange Board of India) investigation revealed the rogue nature of Ketan Parekh as a trader, who was charged with shaking the very foundations of Indian financial markets. Ketan Parekh has been banned from trading until 2017, but SEBI isn’t too comfortable with the fact that his proteges are carrying forward the master’s legacy. Though such allegations are yet to be put to rest. 

The legacy of Harshad Mehta and Ketan Parekh continue to haunt financial markets in the country to date, and were only signatures of what was to follow in the form of plaguing banking crisis, public sector banks are faced with. As Basu and Dalal write, “in money markets the first signs of rot began to appear in the mid-1980s. After more than a decade of so-called social banking, banks found themselves groaning under a load of investments they were forced to make to maintain the Statutory Liquidity Ratio. The investments were in low-interest bearing loans issued by the central and state governments that financed the government’s ever-increasing appetite for cash. Banks intended to hold these low-interest government bonds till maturity. But each time a new set of loans came with a slightly higher interest rate called the coupon rate, the market price of older securities fell, and thereafter banks began to book losses, which eroded their profitability,” the situation is a lot more grim today. RBI’s autonomy has come under increased threat, and the question that requires the most incision is to find a resolution to what one Citibank executive said, “RBI guidelines are just that, guidelines. Not the law of the land.” 

The Scam, as much as a personal element of deceit faced during the tumultuous times, is a brisk read, with some minor hurdles in the form of technicalities that intersperse the volume and tend to disrupt the plot lines. Such technical details are in the realm of share markets and unless negotiated well with either a prior knowledge, or hyperlinking tends to derail the speed, but in no should be considered as a book not worth looking at. As a matter of fact, the third edition with its fifth reprint is testimony to the fact that the book’s market is alive and ever-growing. One only wonders at the end of it as to where have all such journalists disappeared from this country. That Debashis Basu and Sucheta Dalal, partners in real life are indeed partners in crime if they aim at exposing financial crimes of such magnitudes for the multitude in this country who would otherwise be bereft of such understandings had it not been for them. 

Bullish or Bearish. Note Quote.

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The term spread refers to the difference in premiums between the purchase and sale of options. An option spread is the simultaneous purchase of one or more options contracts and sale of the equivalent number of options contracts, in a different series of the class of options. A spread could involve the same underlying: 

  •  Buying and selling calls, or 
  •  Buying and selling puts.

Combining puts and calls into groups of two or more makes it feasible to design derivatives with interesting payoff profiles. The profit and loss outcomes depend on the options used (puts or calls); positions taken (long or short); whether their strike prices are identical or different; and the similarity or difference of their exercise dates. Among directional positions are bullish vertical call spreads, bullish vertical put spreads, bearish vertical spreads, and bearish vertical put spreads. 

If the long position has a higher premium than the short position, this is known as a debit spread, and the investor will be required to deposit the difference in premiums. If the long position has a lower premium than the short position, this is a credit spread, and the investor will be allowed to withdraw the difference in premiums. The spread will be even if the premiums on each side results are the same. 

A potential loss in an option spread is determined by two factors: 

  • Strike price 
  • Expiration date 

If the strike price of the long call is greater than the strike price of the short call, or if the strike price of the long put is less than the strike price of the short put, a margin is required because adverse market moves can cause the short option to suffer a loss before the long option can show a profit.

A margin is also required if the long option expires before the short option. The reason is that once the long option expires, the trader holds an unhedged short position. A good way of looking at margin requirements is that they foretell potential loss. Here are, in a nutshell, the main option spreadings.

A calendar, horizontal, or time spread is the simultaneous purchase and sale of options of the same class with the same exercise prices but with different expiration dates. A vertical, or price or money, spread is the simultaneous purchase and sale of options of the same class with the same expiration date but with different exercise prices. A bull, or call, spread is a type of vertical spread that involves the purchase of the call option with the lower exercise price while selling the call option with the higher exercise price. The result is a debit transaction because the lower exercise price will have the higher premium.

  • The maximum risk is the net debit: the long option premium minus the short option premium. 
  • The maximum profit potential is the difference in the strike prices minus the net debit. 
  • The breakeven is equal to the lower strike price plus the net debit. 

A trader will typically buy a vertical bull call spread when he is mildly bullish. Essentially, he gives up unlimited profit potential in return for reducing his risk. In a vertical bull call spread, the trader is expecting the spread premium to widen because the lower strike price call comes into the money first. 

Vertical spreads are the more common of the direction strategies, and they may be bullish or bearish to reflect the holder’s view of market’s anticipated direction. Bullish vertical put spreads are a combination of a long put with a low strike, and a short put with a higher strike. Because the short position is struck closer to-the-money, this generates a premium credit. 

Bearish vertical call spreads are the inverse of bullish vertical call spreads. They are created by combining a short call with a low strike and a long call with a higher strike. Bearish vertical put spreads are the inverse of bullish vertical put spreads, generated by combining a short put with a low strike and a long put with a higher strike. This is a bearish position taken when a trader or investor expects the market to fall. 

The bull or sell put spread is a type of vertical spread involving the purchase of a put option with the lower exercise price and sale of a put option with the higher exercise price. Theoretically, this is the same action that a bull call spreader would take. The difference between a call spread and a put spread is that the net result will be a credit transaction because the higher exercise price will have the higher premium. 

  • The maximum risk is the difference in the strike prices minus the net credit. 
  • The maximum profit potential equals the net credit. 
  • The breakeven equals the higher strike price minus the net credit. 

The bear or sell call spread involves selling the call option with the lower exercise price and buying the call option with the higher exercise price. The net result is a credit transaction because the lower exercise price will have the higher premium.

A bear put spread (or buy spread) involves selling some of the put option with the lower exercise price and buying the put option with the higher exercise price. This is the same action that a bear call spreader would take. The difference between a call spread and a put spread, however, is that the net result will be a debit transaction because the higher exercise price will have the higher premium. 

  • The maximum risk is equal to the net debit. 
  • The maximum profit potential is the difference in the strike
    prices minus the net debit. 
  • The breakeven equals the higher strike price minus the net debit.

An investor or trader would buy a vertical bear put spread because he or she is mildly bearish, giving up an unlimited profit potential in return for a reduction in risk. In a vertical bear put spread, the trader is expecting the spread premium to widen because the higher strike price put comes into the money first. 

In conclusion, investors and traders who are bullish on the market will either buy a bull call spread or sell a bull put spread. But those who are bearish on the market will either buy a bear put spread or sell a bear call spread. When the investor pays more for the long option than she receives in premium for the short option, then the spread is a debit transaction. In contrast, when she receives more than she pays, the spread is a credit transaction. Credit spreads typically require a margin deposit. 

Bear Stearns. Note Quote.

Like many of its competitors, Bear Stearns saw the rise of the hedge fund industry during the 1990s and began managing its own funds with outside investor capital under the name Bear Stearns Asset Management (BSAM). Unlike its competitors, Bear hired all of its fund managers internally, with each manager specializing in a particular security or asset class. Objections by some Bear executives, such as co-president Alan Schwartz, that such concentration of risk could raise volatility were ignored, and the impressive returns posted by internal funds such as Ralph Cioffi’s High-Grade Structured Credit Strategies Fund quieted any concerns.

Cioffi’s fund was invested in sophisticated credit derivatives backed by mortgage securities. When the housing bubble burst, he redoubled his bets, raising a new Enhanced Leverage High-Grade Structured Credit Strategies Fund that would use 100 leverage (as compared to the 35 leverage employed by the original fund). The market continued to turn disastrously against the fund, which was soon stuck with billions of dollars worth of illiquid, unprofitable mortgages. In an attempt to salvage the situation and cut his losses, Cioffi launched a vehicle named Everquest Financial and sold its shares to the public. But when journalists at the Wall Street Journal revealed that Everquest’s primary assets were the “toxic waste” of money-losing mortgage securities, Bear had no choice but to cancel the public offering. With spectacular losses mounting daily, investors attempted to withdraw their remaining holdings. In order to free up cash for such redemptions, the fund had to liquidate assets at a loss, selling that only put additional downward pressure on its already underwater positions. Lenders to the fund began making margin calls and threatening to seize its $1.2 billion in collateral.

In a less turbulent market it might have worked, but the subprime crisis had spent weeks on the front page of financial newspapers around the globe, and every bank on Wall Street was desperate to reduce its own exposure. Insulted and furious that Bear had refused to inject any of its own capital to save the funds, Steve Black, J.P. Morgan Chase head of investment banking, called Schwartz and said, “We’re defaulting you.”

The default and subsequent seizure of $400 million in collateral by Merrill Lynch proved highly damaging to Bear Stearns’s reputation across Wall Street. In a desperate attempt to save face under the scrutiny of the SEC, James Cayne made the unprecedented move of using $1.6 billion of Bear’s own capital to prop up the hedge funds. By late July 2007 even Bear’s continued support could no longer prop up Cioffi’s two beleaguered funds, which paid back just $300 million of the credit its parent had extended. With their holdings virtually worthless, the funds had no choice but to file for bankruptcy protection.

On November 14, just two weeks after the Journal story questioning Cayne’s commitment and leadership, Bear Stearns reported that it would write down $1.2 billion in mortgage- related losses. (The figure would later grow to $1.9 billion.) CFO Molinaro suggested that the worst had passed, and to outsiders, at least, the firm appeared to have narrowly escaped disaster.

Behind the scenes, however, Bear management had already begun searching for a white knight, hiring Gary Parr at Lazard to examine its options for a cash injection. Privately, Schwartz and Parr spoke with Kohlberg Kravis Roberts & Co. founder Henry Kravis, who had first learned the leveraged buyout market while a partner at Bear Stearns in the 1960s. Kravis sought entry into the profitable brokerage business at depressed prices, while Bear sought an injection of more than $2 billion in equity capital (for a reported 20% of the company) and the calming effect that a strong, respected personality like Kravis would have upon shareholders. Ultimately the deal fell apart, largely due to management’s fear that KKR’s significant equity stake and the presence of Kravis on the board would alienate the firm’s other private equity clientele, who often competed with KKR for deals. Throughout the fall Bear continued to search for potential acquirers. With the market watching intently to see if Bear shored up its financing, Cayne managed to close only a $1 billion cross-investment with CITIC, the state-owned investment company of the People’s Republic of China.

Bear’s $0.89 profit per share in the first quarter of 2008 did little to quiet the growing whispers of its financial instability. It seemed that every day another major investment bank reported mortgage-related losses, and for whatever reason Bear’s name kept cropping up in discussions of the by-then infamous subprime crisis. Exacerbating Bear’s public relations problem, the SEC had launched an investigation into the collapse of the two BSAM hedge funds, and rumors of massive losses at three major hedge funds further rattled an already uneasy market. Nonetheless, Bear executives felt that the storm had passed, reasoning that its almost $21 billion in cash reserves had convinced the market of its long-term viability.

Instead, on Monday, March 10, 2008, Moody’s downgraded 163 tranches of mortgage- backed bonds issued by Bear across fifteen transactions. The credit rating agency had drawn sharp criticism for its role in the subprime meltdown from analysts who felt the company had overestimated the creditworthiness of mortgage-backed securities and failed to alert the market of the danger as the housing market turned. As a result, Moody’s was in the process of downgrading nearly all of its ratings, but as the afternoon wore on, Bear’s stock price seemed to be reacting far more negatively than those of competitor firms.

Wall Street’s drive toward ever more sophisticated communications devices had created an interconnected network of traders and bankers across the world. On most days, Internet chat and mobile e-mail devices relayed gossip about compensation, major employee departures, and even sports betting lines. On the morning of March 10, however, they were carrying one message to the exclusion of all others: Bear was having liquidity problems. At noon, CNBC took the story public on Power Lunch. As Bear’s stock price fell more than 10 percent to $63, Ace Greenberg frantically placed calls to various executives, demanding that someone publicly deny any such problems. When contacted himself, Greenberg told a CNBC correspondent that the rumors were “totally ridiculous,” angering CFO Molinaro, who felt that denying the rumor would only legitimize it and trigger further panic selling, making prophecies of Bear’s illiquidity self-fulfilling. Just two hours later, however, Bear appeared to have dodged a bullet. News of New York governor Eliot Spitzer’s involvement in a high-class prostitution ring wiped any financial rumors off the front page, leading Bear executives to believe the worst was once again behind them.

Instead, the rumors exploded anew the next day, as many interpreted the Federal Reserve’s announcement of a new $200 billion lending program to help financial institutions through the credit crisis as aimed specifically toward Bear Stearns. The stock dipped as low as $55.42 before closing at $62.97. Meanwhile, Bear executives faced a new crisis in the form of an explosion of novation requests, in which a party to a risky contract tries to eliminate its risky position by selling it to a third party. Credit Suisse, Deutsche Bank, and Goldman Sachs all reported a deluge of novation requests from firms trying to reduce their exposure to Bear’s credit risk. The speed and force of this explosion of novation requests meant that before Bear could act, both Goldman Sachs and Credit Suisse issued e-mails to their traders holding up any requests relating to Bear Stearns pending approval by their credit departments. Once again, the electronically linked gossip network of trading desks around the world dealt a blow to investor confidence in Bear’s stability, as a false rumor circulated that Credit Suisse’s memo had forbidden its traders from engaging in any trades with Bear. The decrease in confidence in Bear’s liquidity could be quantified by the rise in the cost of credit default swaps on Bear’s debt. The price of such an instrument – which effectively acts as five years of insurance against a default on $10 million of Bear’s debt – spiked to more than $626,000 from less than $100,000 in October, indicating heavy betting by some firms that Bear would be unable to pay its liabilities.

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Internally, Bear debated whether to address the rumors publicly, ultimately deciding to arrange a Wednesday morning interview of Schwartz by CNBC correspondent David Faber. Not wanting to encourage rumors with a hasty departure, Schwartz did the interview live from Bear’s annual media conference in Palm Beach. Chosen because of his perceived friendliness to Bear, Faber nonetheless opened the interview with a devastating question that claimed direct knowledge of a trader whose credit department had temporarily held up a trade with Bear. Later during the interview Faber admitted that the trade had finally gone through, but he had called into question Bear’s fundamental capacity to operate as a trading firm. One veteran trader later commented,

You knew right at that moment that Bear Stearns was dead, right at the moment he asked that question. Once you raise that idea, that the firm can’t follow through on a trade, it’s over. Faber killed him. He just killed him.

Despite sentiment at Bear that Schwartz had finally put the company’s best foot forward and refuted rumors of its illiquidity, hedge funds began pulling their accounts in earnest, bringing Bear’s reserves down to $15 billion. Additionally, repo lenders – whose overnight loans to investment banks must be renewed daily – began informing Bear that they would not renew the next morning, forcing the firm to find new sources of credit. Schwartz phoned Parr at Lazard, Molinaro reviewed Bear’s plans for an emergency sale in the event of a crisis, and one of the firm’s attorneys called the president of the Federal Reserve to explain Bear’s situation and implore him to accelerate the newly announced program that would allow investment banks to use mortgage securities as collateral for emergency loans from the Fed’s discount window, normally reserved for commercial banks.

The trickle of withdrawals that had begun earlier in the week turned into an unstoppable torrent of cash flowing out the door on Thursday. Meanwhile, Bear’s stock continued its sustained nosedive, falling nearly 15% to an intraday low of $50.48 before rallying to close down 1.5%. At lunch, Schwartz assured a crowded meeting of Bear executives that the whirlwind rumors were simply market noise, only to find himself interrupted by Michael Minikes, senior managing director,

Do you have any idea what is going on? Our cash is flying out the door! Our clients are leaving us!

Hedge fund clients jumped ship in droves. Renaissance Technologies withdrew approximately $5 billion in trading accounts, and D. E. Shaw followed suit with an equal amount. That evening, Bear executives assembled in a sixth-floor conference room to survey the carnage. In less than a week, the firm had burned through all but $5.9 billion of its $18.3 billion in reserves, and was still on the hook for $2.4 billion in short-term debt to Citigroup. With a panicked market making more withdrawals the next day almost certain, Schwartz accepted the inevitable need for additional financing and had Parr revisit merger discussions with J.P. Morgan Chase CEO James Dimon that had stalled in the fall. Flabbergasted at the idea that an agreement could be reached that night, Dimon nonetheless agreed to send a team of bankers over to analyze Bear’s books.

Parr’s call interrupted Dimon’s 52nd birthday celebration at a Greek restaurant just a few blocks away from Bear headquarters, where a phalanx of attorneys had begun preparing emergency bankruptcy filings and documents necessary for a variety of cash-injecting transactions. Facing almost certain insolvency in the next 24 hours, Schwartz hastily called an emergency board meeting late that night, with most board members dialing in remotely. Bear’s nearly four hundred subsidiaries would make a bankruptcy filing impossibly complicated, so Schwartz continued to cling to the hope for an emergency cash infusion to get Bear through Friday. As J.P. Morgan’s bankers pored over Bear’s positions, they balked at the firm’s precarious position and the continued size of its mortgage holdings, insisting that the Fed get involved in a bailout they considered far too risky to take on alone.

Its role as a counterparty in trillions of dollars’ worth of derivatives contracts bore an eerie similarity to LTCM, and the Fed once again saw the potential for financial Armageddon if Bear were allowed to collapse of its own accord. An emergency liquidation of the firm’s assets would have put strong downward pressure on global securities prices, exacerbating an already chaotic market environment. Facing a hard deadline of credit markets’ open on Friday morning, the Fed and J.P. Morgan wrangled back and forth on how to save Bear. Working around the clock, they finally reached an agreement wherein J.P. Morgan would access the Fed’s discount window and in turn offer Bear a $30 billion credit line that, as dictated by a last-minute insertion by J.P. Morgan general counsel Steven Cutler, would be good for 28 days. As the press release went public, Bear executives cheered; Bear would have almost a month to seek alternative financing.

Where Bear had seen a lifeline, however, the market saw instead a last desperate gasp for help. Incredulous Bear executives could only watch in horror as the firm’s capital continued to fly out of its coffers. On Friday morning Bear burned through the last of its reserves in a matter of hours. A midday conference call in which Schwartz confidently assured investors that the credit line would allow Bear to continue “business as usual” did little to stop the bleeding, and its stock lost almost half of its already depressed value, closing at $30 per share.

All day Friday, Parr set about desperately trying to save his client, searching every corner of the financial world for potential investors or buyers of all or part of Bear. Given the severity of the situation, he could rule out nothing, from a sale of the lucrative prime brokerage operations to a merger or sale of the entire company. Ideally, he hoped to find what he termed a “validating investor,” a respected Wall Street name to join the board, adding immediate credibility and perhaps quieting the now deafening rumors of Bear’s imminent demise. Sadly, only a few such personalities with the reputation and war chest necessary to play the role of savior existed, and most of them had already passed on Bear.

Nonetheless, Schwartz left Bear headquarters on Friday evening relieved that the firm had lived to see the weekend and secured 28 days of breathing room. During the ride home to Greenwich, an unexpected phone call from New York Federal Reserve President Timothy Geithner and Treasury Secretary Henry Paulson shattered that illusion. Paulson told a stunned Schwartz that the Fed’s line of credit would expire Sunday night, giving Bear 48 hours to find a buyer or file for bankruptcy. The demise of the 28-day clause remains a mystery; the speed necessary early Friday morning and the inclusion of the clause by J.P. Morgan’s general counsel suggest that Bear executives had misinterpreted it, although others believe that Paulson and Geithner had soured both on Bear’s prospects and on market perception of an emergency loan from the Fed as Friday wore on. Either way, the Fed had made up its mind, and a Saturday morning appeal from Schwartz failed to sway Geithner.

All day Saturday prospective buyers streamed through Bear’s headquarters to pick through the rubble as Parr attempted to orchestrate Bear’s last-minute salvation. Chaos reigned, with representatives from every major bank on Wall Street, J. C. Flowers, KKR, and countless others poring over Bear’s positions in an effort to determine the value of Bear’s massive illiquid holdings and how the Fed would help in financing. Some prospective buyers wanted just a piece of the dying bank, others the whole firm, with still others proposing more complicated multiple-step transactions that would slice Bear to ribbons. One by one, they dropped out, until J. C. Flowers made an offer for 90% of Bear for a total of up to $2.6 billion, but the offer was contingent on the private equity firm raising $20 billion from a bank consortium, and $20 billion in risky credit was unlikely to appear overnight.

That left J.P. Morgan. Apparently the only bank willing to come to the rescue, J.P. Morgan had sent no fewer than 300 bankers representing 16 different product groups to Bear headquarters to value the firm. The sticking point, as with all the bidders, was Bear’s mortgage holdings. Even after a massive write-down, it was impossible to assign a value to such illiquid (and publicly maligned) securities with any degree of accuracy. Having forced the default of the BSAM hedge funds that started this mess less than a year earlier.

On its final 10Q in March, Bear listed $399 billion in assets and $387 billion in liabilities, leaving just $12 billion in equity for a 32 leverage multiple. Bear initially estimated that this included $120 billion of “risk-weighted” assets, those that might be subject to subsequent write-downs. As J.P. Morgan’s bankers worked around the clock trying to get to the bottom of Bear’s balance sheet, they came to estimate the figure at nearly $220 billion. That pessimistic outlook, combined with Sunday morning’s New York Times article reiterating Bear’s recent troubles, dulled J.P. Morgan’s appetite for jumping onto what appeared to be a sinking ship. Later, one J.P. Morgan banker shuddered, recalling the article. “That article certainly had an impact on my thinking. Just the reputational aspects of it, getting into bed with these people.”

On Saturday morning J.P. Morgan backed out and Dimon told a shell-shocked Schwartz to pursue any other option available to him. The problem was, no such alternative existed. Knowing this, and the possibility that the liquidation of Bear could throw the world’s financial markets into chaos, Fed representatives immediately phoned Dimon. As it had in the LTCM case a decade ago, the Fed relied heavily on suasion, or “jawboning,” the longtime practice of attempting to influence market participants by appeals to reason rather than a declaration by fiat. For hours, J.P. Morgan’s and the Fed’s highest-ranking officials played a game of high-stakes poker, with each side bluffing and Bear’s future hanging in the balance. The Fed wanted to avoid unprecedented government participation in the bailout of a private investment firm, while J.P. Morgan wanted to avoid taking on any of the “toxic waste” in Bear’s mortgage holdings. “They kept saying, ‘We’re not going to do it,’ and we kept saying, ‘We really think you should do it,’” recalled one Fed official. “This went on for hours . . . They kept saying, ‘We can’t do this on our own.’” With the hours ticking away until Monday’s Australian markets would open at 6:00 p.m. New York time, both sides had to compromise.

On Sunday afternoon, Schwartz stepped out of a 1:00 emergency meeting of Bear’s board of directors to take the call from Dimon. The offer would come somewhere in the range of $4 to 5 per share. Hearing the news from Schwartz, the Bear board erupted with rage. Dialing in from the bridge tournament in Detroit, Cayne exploded, ranting furiously that the firm should file for bankruptcy protection under Chapter 11 rather than accept such a humiliating offer, which would reduce his 5.66 million shares – once worth nearly $1 billion – to less than $30 million in value. In reality, however, bankruptcy was impossible. As Parr explained, changes to the federal bankruptcy code in 2005 meant that a Chapter 11 filing would be tantamount to Bear falling on its sword, because regulators would have to seize Bear’s accounts, immediately ceasing the firm’s operations and forcing its liquidation. There would be no reorganization.

Even as Cayne raged against the $4 offer, the Fed’s concern over the appearance of a $30 billion loan to a failing investment bank while American homeowners faced foreclosures compelled Treasury Secretary Paulson to pour salt in Bear’s wounds. Officially, the Fed had remained hands-off in the LTCM bailout, relying on its powers of suasion to convince other banks to step up in the name of market stability. Just 10 years later, they could find no takers. The speed of Bear’s collapse, the impossibility of conducting true due diligence in such a compressed time frame, and the incalculable risk of taking on Bear’s toxic mortgage holdings scared off every buyer and forced the Fed from an advisory role into a principal role in the bailout. Worried that a price deemed at all generous to Bear might subsequently encourage moral hazard – increased risky behavior by investment banks secure in the knowledge that in a worst-case scenario, disaster would be averted by a federal bailout – Paulson determined that the transaction, while rescuing the firm, also had to be punitive to Bear shareholders. He called Dimon, who reiterated the contemplated offer range.

“That sounds high tome,” Paulson told the J.P. Morgan chief. “I think this should be done at a very low price.” It was moments later that Braunstein called Parr. “The number’s $2.” Under Delaware law, executives must act on behalf of both shareholders and creditors when a company enters the “zone of insolvency,” and Schwartz knew that Bear had rocketed through that zone over the past few days. Faced with bankruptcy or J.P. Morgan, Bear had no choice but to accept the embarrassingly low offer that represented a 97% discount off its $32 close on Friday evening. Schwartz convinced the weary Bear board that $2 would be “better than nothing,” and by 6:30 p.m., the deal was unanimously approved.

After 85 years in the market, Bear Stearns ceased to exist.

Option Spread. Drunken Risibility.

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The term spread refers to the difference in premiums between the purchase and sale of options. An option spread is the simultaneous purchase of one or more options contracts and sale of the equivalent number of options contracts, in a different series of the class of options. A spread could involve the same underlying:

  •  Buying and selling calls, or
  •  Buying and selling puts.

Combining puts and calls into groups of two or more makes it feasible to design derivatives with interesting payoff profiles. The profit and loss outcomes depend on the options used (puts or calls); positions taken (long or short); whether their strike prices are identical or different; and the similarity or difference of their exercise dates. Among directional positions are bullish vertical call spreads, bullish vertical put spreads, bearish vertical spreads, and bearish vertical put spreads.

If the long position has a higher premium than the short position, this is known as a debit spread, and the investor will be required to deposit the difference in premiums. If the long position has a lower premium than the short position, this is a credit spread, and the investor will be allowed to withdraw the difference in premiums. The spread will be even if the premiums on each side results are the same.

A potential loss in an option spread is determined by two factors:

  • Strike price
  • Expiration date

If the strike price of the long call is greater than the strike price of the short call, or if the strike price of the long put is less than the strike price of the short put, a margin is required because adverse market moves can cause the short option to suffer a loss before the long option can show a profit.

A margin is also required if the long option expires before the short option. The reason is that once the long option expires, the trader holds an unhedged short position. A good way of looking at margin requirements is that they foretell potential loss. Here are, in a nutshell, the main option spreadings.

A calendar, horizontal, or time spread is the simultaneous purchase and sale of options of the same class with the same exercise prices but with different expiration dates. A vertical, or price or money, spread is the simultaneous purchase and sale of options of the same class with the same expiration date but with different exercise prices. A bull, or call, spread is a type of vertical spread that involves the purchase of the call option with the lower exercise price while selling the call option with the higher exercise price. The result is a debit transaction because the lower exercise price will have the higher premium.

  • The maximum risk is the net debit: the long option premium minus the short option premium.
  • The maximum profit potential is the difference in the strike prices minus the net debit.
  • The breakeven is equal to the lower strike price plus the net debit.

A trader will typically buy a vertical bull call spread when he is mildly bullish. Essentially, he gives up unlimited profit potential in return for reducing his risk. In a vertical bull call spread, the trader is expecting the spread premium to widen because the lower strike price call comes into the money first.

Vertical spreads are the more common of the direction strategies, and they may be bullish or bearish to reflect the holder’s view of market’s anticipated direction. Bullish vertical put spreads are a combination of a long put with a low strike, and a short put with a higher strike. Because the short position is struck closer to-the-money, this generates a premium credit.

Bearish vertical call spreads are the inverse of bullish vertical call spreads. They are created by combining a short call with a low strike and a long call with a higher strike. Bearish vertical put spreads are the inverse of bullish vertical put spreads, generated by combining a short put with a low strike and a long put with a higher strike. This is a bearish position taken when a trader or investor expects the market to fall.

The bull or sell put spread is a type of vertical spread involving the purchase of a put option with the lower exercise price and sale of a put option with the higher exercise price. Theoretically, this is the same action that a bull call spreader would take. The difference between a call spread and a put spread is that the net result will be a credit transaction because the higher exercise price will have the higher premium.

  • The maximum risk is the difference in the strike prices minus the net credit.
  • The maximum profit potential equals the net credit.
  • The breakeven equals the higher strike price minus the net credit.

The bear or sell call spread involves selling the call option with the lower exercise price and buying the call option with the higher exercise price. The net result is a credit transaction because the lower exercise price will have the higher premium.

A bear put spread (or buy spread) involves selling some of the put option with the lower exercise price and buying the put option with the higher exercise price. This is the same action that a bear call spreader would take. The difference between a call spread and a put spread, however, is that the net result will be a debit transaction because the higher exercise price will have the higher premium.

  • The maximum risk is equal to the net debit.
  • The maximum profit potential is the difference in the strike
    prices minus the net debit.
  • The breakeven equals the higher strike price minus the net debit.

An investor or trader would buy a vertical bear put spread because he or she is mildly bearish, giving up an unlimited profit potential in return for a reduction in risk. In a vertical bear put spread, the trader is expecting the spread premium to widen because the higher strike price put comes into the money first.

So, investors and traders who are bullish on the market will either buy a bull call spread or sell a bull put spread. But those who are bearish on the market will either buy a bear put spread or sell a bear call spread. When the investor pays more for the long option than she receives in premium for the short option, then the spread is a debit transaction. In contrast, when she receives more than she pays, the spread is a credit transaction. Credit spreads typically require a margin deposit.

High Frequency Traders: A Case in Point.

Events on 6th May 2010:

At 2:32 p.m., against [a] backdrop of unusually high volatility and thinning liquidity, a large fundamental trader (a mutual fund complex) initiated a sell program to sell a total of 75,000 E-Mini [S&P 500 futures] contracts (valued at approximately $4.1 billion) as a hedge to an existing equity position. […] This large fundamental trader chose to execute this sell program via an automated execution algorithm (“Sell Algorithm”) that was programmed to feed orders into the June 2010 E-Mini market to target an execution rate set to 9% of the trading volume calculated over the previous minute, but without regard to price or time. The execution of this sell program resulted in the largest net change in daily position of any trader in the E-Mini since the beginning of the year (from January 1, 2010 through May 6, 2010). [. . . ] This sell pressure was initially absorbed by: high frequency traders (“HFTs”) and other intermediaries in the futures market; fundamental buyers in the futures market; and cross-market arbitrageurs who transferred this sell pressure to the equities markets by opportunistically buying E-Mini contracts and simultaneously selling products like SPY [(S&P 500 exchange-traded fund (“ETF”))], or selling individual equities in the S&P 500 Index. […] Between 2:32 p.m. and 2:45 p.m., as prices of the E-Mini rapidly declined, the Sell Algorithm sold about 35,000 E-Mini contracts (valued at approximately $1.9 billion) of the 75,000 intended. [. . . ] By 2:45:28 there were less than 1,050 contracts of buy-side resting orders in the E-Mini, representing less than 1% of buy-side market depth observed at the beginning of the day. [. . . ] At 2:45:28 p.m., trading on the E-Mini was paused for five seconds when the Chicago Mercantile Exchange (“CME”) Stop Logic Functionality was triggered in order to prevent a cascade of further price declines. […] When trading resumed at 2:45:33 p.m., prices stabilized and shortly thereafter, the E-Mini began to recover, followed by the SPY. [. . . ] Even though after 2:45 p.m. prices in the E-Mini and SPY were recovering from their severe declines, sell orders placed for some individual securities and Exchange Traded Funds (ETFs) (including many retail stop-loss orders, triggered by declines in prices of those securities) found reduced buying interest, which led to further price declines in those securities. […] [B]etween 2:40 p.m. and 3:00 p.m., over 20,000 trades (many based on retail-customer orders) across more than 300 separate securities, including many ETFs, were executed at prices 60% or more away from their 2:40 p.m. prices. [. . . ] By 3:08 p.m., [. . . ] the E-Mini prices [were] back to nearly their pre-drop level [. . . and] most securities had reverted back to trading at prices reflecting true consensus values.

In the ordinary course of business, HFTs use their technological advantage to profit from aggressively removing the last few contracts at the best bid and ask levels and then establishing new best bids and asks at adjacent price levels ahead of an immediacy-demanding customer. As an illustration of this “immediacy absorption” activity, consider the following stylized example, presented in Figure and described below.

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Suppose that we observe the central limit order book for a stock index futures contract. The notional value of one stock index futures contract is $50. The market is very liquid – on average there are hundreds of resting limit orders to buy or sell multiple contracts at either the best bid or the best offer. At some point during the day, due to temporary selling pressure, there is a total of just 100 contracts left at the best bid price of 1000.00. Recognizing that the queue at the best bid is about to be depleted, HFTs submit executable limit orders to aggressively sell a total of 100 contracts, thus completely depleting the queue at the best bid, and very quickly submit sequences of new limit orders to buy a total of 100 contracts at the new best bid price of 999.75, as well as to sell 100 contracts at the new best offer of 1000.00. If the selling pressure continues, then HFTs are able to buy 100 contracts at 999.75 and make a profit of $1,250 dollars among them. If, however, the selling pressure stops and the new best offer price of 1000.00 attracts buyers, then HFTs would very quickly sell 100 contracts (which are at the very front of the new best offer queue), “scratching” the trade at the same price as they bought, and getting rid of the risky inventory in a few milliseconds.

This type of trading activity reduces, albeit for only a few milliseconds, the latency of a price move. Under normal market conditions, this trading activity somewhat accelerates price changes and adds to the trading volume, but does not result in a significant directional price move. In effect, this activity imparts a small “immediacy absorption” cost on all traders, including the market makers, who are not fast enough to cancel the last remaining orders before an imminent price move.

This activity, however, makes it both costlier and riskier for the slower market makers to maintain continuous market presence. In response to the additional cost and risk, market makers lower their acceptable inventory bounds to levels that are too small to offset temporary liquidity imbalances of any significant size. When the diminished liquidity buffer of the market makers is pierced by a sudden order flow imbalance, they begin to demand a progressively greater compensation for maintaining continuous market presence, and prices start to move directionally. Just as the prices are moving directionally and volatility is elevated, immediacy absorption activity of HFTs can exacerbate a directional price move and amplify volatility. Higher volatility further increases the speed at which the best bid and offer queues are being depleted, inducing HFT algorithms to demand immediacy even more, fueling a spike in trading volume, and making it more costly for the market makers to maintain continuous market presence. This forces more risk averse market makers to withdraw from the market, which results in a full-blown market crash.

Empirically, immediacy absorption activity of the HFTs should manifest itself in the data very differently from the liquidity provision activity of the Market Makers. To establish the presence of these differences in the data, we test the following hypotheses:

Hypothesis H1: HFTs are more likely than Market Makers to aggressively execute the last 100 contracts before a price move in the direction of the trade. Market Makers are more likely than HFTs to have the last 100 resting contracts against which aggressive orders are executed.

Hypothesis H2: HFTs trade aggressively in the direction of the price move. Market Makers get run over by a price move.

Hypothesis H3: Both HFTs and Market Makers scratch trades, but HFTs scratch more.

To statistically test our “immediacy absorption” hypotheses against the “liquidity provision” hypotheses, we divide all of the trades during the 405 minute trading day into two subsets: Aggressive Buy trades and Aggressive Sell trades. Within each subset, we further aggregate multiple aggressive buy or sell transactions resulting from the execution of the same order into Aggressive Buy or Aggressive Sell sequences. The intuition is as follows. Often a specific trade is not a stand alone event, but a part of a sequence of transactions associated with the execution of the same order. For example, an order to aggressively sell 10 contracts may result in four Aggressive Sell transactions: for 2 contracts, 1 contract, 4 contracts, and 3 contracts, respectively, due to the specific sequence of resting bids against which this aggressive sell order was be executed. Using the order ID number, we are able to aggregate these four transactions into one Aggressive Sell sequence for 10 contracts.

Testing Hypothesis H1. Aggressive removal of the last 100 contracts by HFTs; passive provision of the last 100 resting contracts by the Market Makers. Using the Aggressive Buy sequences, we label as a “price increase event” all occurrences of trading sequences in which at least 100 contracts consecutively executed at the same price are followed by some number of contracts at a higher price. To examine indications of low latency, we focus on the the last 100 contracts traded before the price increase and the first 100 contracts at the next higher price (or fewer if the price changes again before 100 contracts are executed). Although we do not look directly at the limit order book data, price increase events are defined to capture occasions where traders use executable buy orders to lift the last remaining offers in the limit order book. Using Aggressive sell trades, we define “price decrease events” symmetrically as occurrences of sequences of trades in which 100 contracts executed at the same price are followed by executions at lower prices. These events are intended to capture occasions where traders use executable sell orders to hit the last few best bids in the limit order book. The results are presented in Table below

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For price increase and price decrease events, we calculate each of the six trader categories’ shares of Aggressive and Passive trading volume for the last 100 contracts traded at the “old” price level before the price increase or decrease and the first 100 contracts traded at the “new” price level (or fewer if the number of contracts is less than 100) after the price increase or decrease event.

Table above presents, for the six trader categories, volume shares for the last 100 contracts at the old price and the first 100 contracts at the new price. For comparison, the unconditional shares of aggressive and passive trading volume of each trader category are also reported. Table has four panels covering (A) price increase events on May 3-5, (B) price decrease events on May 3-5, (C) price increase events on May 6, and (D) price decrease events on May 6. In each panel there are six rows of data, one row for each trader category. Relative to panels A and C, the rows for Fundamental Buyers (BUYER) and Fundamental Sellers (SELLER) are reversed in panels B and D to emphasize the symmetry between buying during price increase events and selling during price decrease events. The first two columns report the shares of Aggressive and Passive contract volume for the last 100 contracts before the price change; the next two columns report the shares of Aggressive and Passive volume for up to the next 100 contracts after the price change; and the last two columns report the “unconditional” market shares of Aggressive and Passive sides of all Aggressive buy volume or sell volume. For May 3-5, the data are based on volume pooled across the three days.

Consider panel A, which describes price increase events associated with Aggressive buy trades on May 3-5, 2010. High Frequency Traders participated on the Aggressive side of 34.04% of all aggressive buy volume. Strongly consistent with immediacy absorption hypothesis, the participation rate rises to 57.70% of the Aggressive side of trades on the last 100 contracts of Aggressive buy volume before price increase events and falls to 14.84% of the Aggressive side of trades on the first 100 contracts of Aggressive buy volume after price increase events.

High Frequency Traders participated on the Passive side of 34.33% of all aggressive buy volume. Consistent with hypothesis, the participation rate on the Passive side of Aggressive buy volume falls to 28.72% of the last 100 contracts before a price increase event. It rises to 37.93% of the first 100 contracts after a price increase event.

These results are inconsistent with the idea that high frequency traders behave like textbook market makers, suffering adverse selection losses associated with being picked off by informed traders. Instead, when the price is about to move to a new level, high frequency traders tend to avoid being run over and take the price to the new level with Aggressive trades of their own.

Market Makers follow a noticeably more passive trading strategy than High Frequency Traders. According to panel A, Market Makers are 13.48% of the Passive side of all Aggressive trades, but they are only 7.27% of the Aggressive side of all Aggressive trades. On the last 100 contracts at the old price, Market Makers’ share of volume increases only modestly, from 7.27% to 8.78% of trades. Their share of Passive volume at the old price increases, from 13.48% to 15.80%. These facts are consistent with the interpretation that Market Makers, unlike High Frequency Traders, do engage in a strategy similar to traditional passive market making, buying at the bid price, selling at the offer price, and suffering losses when the price moves against them. These facts are also consistent with the hypothesis that High Frequency Traders have lower latency than Market Makers.

Intuition might suggest that Fundamental Buyers would tend to place the Aggressive trades which move prices up from one tick level to the next. This intuition does not seem to be corroborated by the data. According to panel A, Fundamental Buyers are 21.53% of all Aggressive trades but only 11.61% of the last 100 Aggressive contracts traded at the old price. Instead, Fundamental Buyers increase their share of Aggressive buy volume to 26.17% of the first 100 contracts at the new price.

Taking into account symmetry between buying and selling, panel B shows the results for Aggressive sell trades during May 3-5, 2010, are almost the same as the results for Aggressive buy trades. High Frequency Traders are 34.17% of all Aggressive sell volume, increase their share to 55.20% of the last 100 Aggressive sell contracts at the old price, and decrease their share to 15.04% of the last 100 Aggressive sell contracts at the new price. Market Makers are 7.45% of all Aggressive sell contracts, increase their share to only 8.57% of the last 100 Aggressive sell trades at the old price, and decrease their share to 6.58% of the last 100 Aggressive sell contracts at the new price. Fundamental Sellers’ shares of Aggressive sell trades behave similarly to Fundamental Buyers’ shares of Aggressive Buy trades. Fundamental Sellers are 20.91% of all Aggressive sell contracts, decrease their share to 11.96% of the last 100 Aggressive sell contracts at the old price, and increase their share to 24.87% of the first 100 Aggressive sell contracts at the new price.

Panels C and D report results for Aggressive Buy trades and Aggressive Sell trades for May 6, 2010. Taking into account symmetry between buying and selling, the results for Aggressive buy trades in panel C are very similar to the results for Aggressive sell trades in panel D. For example, Aggressive sell trades by Fundamental Sellers were 17.55% of Aggressive sell volume on May 6, while Aggressive buy trades by Fundamental Buyers were 20.12% of Aggressive buy volume on May 6. In comparison with the share of Fundamental Buyers and in comparison with May 3-5, the Flash Crash of May 6 is associated with a slightly lower – not higher – share of Aggressive sell trades by Fundamental Sellers.

The number of price increase and price decrease events increased dramatically on May 6, consistent with the increased volatility of the market on that day. On May 3-5, there were 4100 price increase events and 4062 price decrease events. On May 6 alone, there were 4101 price increase events and 4377 price decrease events. There were therefore approximately three times as many price increase events per day on May 6 as on the three preceding days.

A comparison of May 6 with May 3-5 reveals significant changes in the trading patterns of High Frequency Traders. Compared with May 3-5 in panels A and B, the share of Aggressive trades by High Frequency Traders drops from 34.04% of Aggressive buys and 34.17% of Aggressive sells on May 3-5 to 26.98% of Aggressive buy trades and 26.29% of Aggressive sell trades on May 6. The share of Aggressive trades for the last 100 contracts at the old price declines by even more. High Frequency Traders’ participation rate on the Aggressive side of Aggressive buy trades drops from 57.70% on May 3-5 to only 38.86% on May 6. Similarly, the participation rate on the Aggressive side of Aggressive sell trades drops from and 55.20% to 38.67%. These declines are largely offset by increases in the participation rate by Opportunistic Traders on the Aggressive side of trades. For example, Opportunistic Traders’ share of the Aggressive side of the last 100 contracts traded at the old price rises from 19.21% to 34.26% for Aggressive buys and from 20.99% to 33.86% for Aggressive sells. These results suggest that some Opportunistic Traders follow trading strategies for which low latency is important, such as index arbitrage, cross-market arbitrage, or opportunistic strategies mimicking market making.

Testing Hypothesis H2. HFTs trade aggressively in the direction of the price move; Market Makers get run over by a price move. To examine this hypothesis, we analyze whether High Frequency Traders use Aggressive trades to trade in the direction of contemporaneous price changes, while Market Makers use Passive trades to trade in the opposite direction from price changes. To this end, we estimate the regression equation

Δyt = α + Φ . Δyt-1 + δ . yt-1 + Σi=120i . Δpt-1 /0.25] + εt

(where yt and Δyt denote inventories and change in inventories of High Frequency Traders for each second of a trading day; t = 0 corresponds to the opening of stock trading on the NYSE at 8:30:00 a.m. CT (9:30:00 ET) and t = 24, 300 denotes the close of Globex at 15:15:00 CT (4:15 p.m. ET); Δpt denotes the price change in index point units between the high-low midpoint of second t-1 and the high-low midpoint of second t. Regressing second-by-second changes in inventory levels of High Frequency Traders on the level of their inventories the previous second, the change in their inventory levels the previous second, the change in prices during the current second, and lagged price changes for each of the previous 20 previous seconds.)

for Passive and Aggressive inventory changes separately.

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Table above presents the regression results of the two components of change in holdings on lagged inventory, lagged change in holdings and lagged price changes over one second intervals. Panel A and Panel B report the results for May 3-5 and May 6, respectively. Each panel has four columns, reporting estimated coefficients where the dependent variables are net Aggressive volume (Aggressive buys minus Aggressive sells) by High Frequency Traders (∆AHFT), net Passive volume by High Frequency Traders (∆PHFT), net Aggressive volume by Market Makers (∆AMM), and net Passive volume by Market Makers (∆PMM).

We observe that for lagged inventories (NPHFTt−1), the estimated coefficients for Aggressive and Passive trades by High Frequency Traders are δAHFT = −0.005 (t = −9.55) and δPHFT = −0.001 (t = −3.13), respectively. These coefficient estimates have the interpretation that High Frequency Traders use Aggressive trades to liquidate inventories more intensively than passive trades. In contrast, the results for Market Makers are very different. For lagged inventories (NPMMt−1), the estimated coefficients for Aggressive and Passive volume by Market Makers are δAMM = −0.002 (t = −6.73) and δPMM = −0.002 (t = −5.26), respectively. The similarity of these coefficients estimates has the interpretation that Market Makers favor neither Aggressive trades nor Passive trades when liquidating inventories.

For contemporaneous price changes (in the current second) (∆Pt−1), the estimated coefficient Aggressive and Passive volume by High Frequency Traders are β0 = 57.78 (t = 31.94) and β0 = −25.69 (t = −28.61), respectively. For Market Makers, the estimated coefficients for Aggressive and Passive trades are β0 = 6.38 (t = 18.51) and β0 = −19.92 (t = −37.68). These estimated coefficients have the interpretation that in seconds in which prices move up one tick, High Frequency traders are net buyers of about 58 contracts with Aggressive trades and net sellers of about 26 contracts with Passive trades in that same second, while Market Makers are net buyers of about 6 contracts with Aggressive trades and net sellers of about 20 contracts with Passive trades. High Frequency Traders and Market Makers are similar in that they both use Aggressive trades to trade in the direction of price changes, and both use Passive trades to trade against the direction of price changes. High Frequency Traders and Market Makers are different in that Aggressive net purchases by High Frequency Traders are greater in magnitude than the Passive net purchases, while the reverse is true for Market Makers.

For lagged price changes, coefficient estimates for Aggressive trades by High Frequency Traders and Market Makers are positive and statistically significant at lags 1-4 and lags 1-10, respectively. These results have the interpretation that both High Frequency Traders’ and Market Makers’ trade on recent price momentum, but the trading is compressed into a shorter time frame for High Frequency Traders than for Market Makers.

For lagged price changes, coefficient estimates for Passive volume by High Frequency Traders and Market Makers are negative and statistically significant at lags 1 and lags 1-3, respectively. Panel B of Table presents results for May 6. Similar to May 3-5, High Frequency Traders tend to use Aggressive trades more intensely than Passive trades to liquidate inventories, while Market Makers do not show this pattern. Also similar to May 3-5, High Frequency Trades and Market makers use Aggressive trades to trade in the contemporaneous direction of price changes and use Passive trades to trade in the direction opposite price changes, with Aggressive trading greater than Passive trading for High Frequency Traders and the reverse for Market Makers. In comparison with May 3-5, the coefficients are smaller in magnitude on May 6, indicating reduced liquidity at each tick. For lagged price changes, the coefficients associated with Aggressive trading by High Frequency Traders change from positive to negative at lags 1-4, and the positive coefficients associated with Aggressive trading by Market Makers change from being positive and statistically significant at lags lags 1-10 to being positive and statistically significant only at lags 1-3. These results illustrate accelerated trading velocity in the volatile market conditions of May 6.

We further examine how high frequency trading activity is related to market prices. Figure below illustrates how prices change after HFT trading activity in a given second. The upper-left panel presents results for buy trades for May 3-5, the upper right panel presents results for buy trades on May 6, and the lower-left and lower-right present corresponding results for sell trades. For an “event” second in which High Frequency Traders are net buyers, net Aggressive Buyers, and net Passive Buyers value-weighted average prices paid by the High Frequency Traders in that second are subtracted from the value-weighted average prices for all trades in the same second and each of the following 20 seconds. The results are averaged across event seconds, weighted by the magnitude of High Frequency Traders’ net position change in the event second. The upper-left panel presents results for May 3-5, the upper-right panel presents results for May 6, and the lower two panels present results for sell trades calculated analogously. Price differences on the vertical axis are scaled so that one unit equals one tick ($12.50 per contract).

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When High Frequency Traders are net buyers on May 3-5, prices rise by 17% of a tick in the next second. When HFTs execute Aggressively or Passively, prices rise by 20% and 2% of a tick in the next second, respectively. In subsequent seconds, prices in all cases trend downward by about 5% of a tick over the subsequent 19 seconds. For May 3-5, the results are almost symmetric for selling.

When High Frequency Traders are buying on May 6, prices increase by 7% of a tick in the next second. When they are aggressive buyers or passive buyers, prices increase by increase 25% of a tick or decrease by 5% of a tick in the next second, respectively. In subsequent seconds, prices generally tend to drift downwards. The downward drift is especially pronounced after Passive buying, consistent with the interpretation that High Frequency Traders were “run over” when their resting limit buy orders were “run over” in the down phase of the Flash Crash. When High Frequency Traders are net sellers, the results after one second are analogous to buying. After aggressive selling, prices continue to drift down for 20 seconds, consistent with the interpretation that High Frequency Traders made profits from Aggressive sales during the down phase of the Flash Crash.

Testing Hypothesis H3. Both HFTs and Market Makers scratch trades; HFTs scratch more. A textbook market maker will try to buy at the bid price, sell at the offer price, and capture the bid-ask spread as a profit. Sometimes, after buying at the bid price, market prices begin to fall before the market maker can make a one tick profit by selling his inventory at the best offer price. To avoid taking losses in this situation, one component of a traditional market making strategy is to “scratch trades in the presence of changing market conditions by quickly liquidating a position at the same price at which it was acquired. These scratched trades represent inventory management trades designed to lower the cost of adverse selection. Since many competing market makers may try to scratch trades at the same time, traders with the lowest latency will tend to be more successful in their attempts to scratch trades and thus more successful in their ability to avoid losses when market conditions change.

To examine whether and to what extent traders engage in trade scratching, we sequence each trader’s trades for the day using audit trail sequence numbers which not only sort trades by second but also sort trades chronologically within each second. We define an “immediately scratched trade” as a trade with the properties that the next trade in the sorted sequence (1) occurred in the same second, (2) was executed at the same price, (3) was in the opposite direction, i.e., buy followed by sell or sell followed by buy. For each of the trading accounts in our sample, we calculate the number of immediately scratched trades, then compare the number of scratched trades across the six trader categories.

The results of this analysis are presented in the table below. Panel A provides results for May 3-5 and panel B for May 6. In each panel, there are five rows of data, one for each trader category. The first three columns report the total number of trades, the total number of immediately scratched trades, and the percentage of trades that are immediately scratched by traders in five categories. For May 3-6, the reported numbers are from the pooled data.

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This table presents statistics for immediate trade scratching which measures how many times a trader changes his/her direction of trading in a second aggregated over a day. We define a trade direction change as a buy trade right after a sell trade or vice versa at the same price level in the same second.

This table shows that High Frequency Traders scratched 2.84 % of trades on May 3-5 and 4.26 % on May 6; Market Makers scratched 2.49 % of trades on May 3-5 and 5.53 % of trades on May 6. While the percentages of immediately scratched trades by Market Makers is slightly higher than that for High Frequency Traders on May 6, the percentages for both groups are very similar. The fourth, fifth, and sixth columns of the Table report the mean, standard deviation, and median of the number of scratched trades for the traders in each category.

Although the percentages of scratched trades are similar, the mean number of immediately scratched trades by High Frequency Traders is much greater than for Market Makers: 540.56 per day on May 3-5 and 1610.75 on May 6 for High Frequency Traders versus 13.35 and 72.92 for Market Makers. The differences between High Frequency Traders and Market Makers reflect differences in volume traded. The Table shows that High Frequency Traders and Market Makers scratch a significantly larger percentage of their trades than other trader categories.

What’s a Market Password Anyway? Towards Defining a Financial Market Random Sequence. Note Quote.

From the point of view of cryptanalysis, the algorithmic view based on frequency analysis may be taken as a hacker approach to the financial market. While the goal is clearly to find a sort of password unveiling the rules governing the price changes, what we claim is that the password may not be immune to a frequency analysis attack, because it is not the result of a true random process but rather the consequence of the application of a set of (mostly simple) rules. Yet that doesn’t mean one can crack the market once and for all, since for our system to find the said password it would have to outperform the unfolding processes affecting the market – which, as Wolfram’s PCE suggests, would require at least the same computational sophistication as the market itself, with at least one variable modelling the information being assimilated into prices by the market at any given moment. In other words, the market password is partially safe not because of the complexity of the password itself but because it reacts to the cracking method.

Figure-6-By-Extracting-a-Normal-Distribution-from-the-Market-Distribution-the-Long-Tail

Whichever kind of financial instrument one looks at, the sequences of prices at successive times show some overall trends and varying amounts of apparent randomness. However, despite the fact that there is no contingent necessity of true randomness behind the market, it can certainly look that way to anyone ignoring the generative processes, anyone unable to see what other, non-random signals may be driving market movements.

Von Mises’ approach to the definition of a random sequence, which seemed at the time of its formulation to be quite problematic, contained some of the basics of the modern approach adopted by Per Martin-Löf. It is during this time that the Keynesian kind of induction may have been resorted to as a starting point for Solomonoff’s seminal work (1 and 2) on algorithmic probability.

Per Martin-Löf gave the first suitable definition of a random sequence. Intuitively, an algorithmically random sequence (or random sequence) is an infinite sequence of binary digits that appears random to any algorithm. This contrasts with the idea of randomness in probability. In that theory, no particular element of a sample space can be said to be random. Martin-Löf randomness has since been shown to admit several equivalent characterisations in terms of compression, statistical tests, and gambling strategies.

The predictive aim of economics is actually profoundly related to the concept of predicting and betting. Imagine a random walk that goes up, down, left or right by one, with each step having the same probability. If the expected time at which the walk ends is finite, predicting that the expected stop position is equal to the initial position, it is called a martingale. This is because the chances of going up, down, left or right, are the same, so that one ends up close to one’s starting position,if not exactly at that position. In economics, this can be translated into a trader’s experience. The conditional expected assets of a trader are equal to his present assets if a sequence of events is truly random.

If market price differences accumulated in a normal distribution, a rounding would produce sequences of 0 differences only. The mean and the standard deviation of the market distribution are used to create a normal distribution, which is then subtracted from the market distribution.

Schnorr provided another equivalent definition in terms of martingales. The martingale characterisation of randomness says that no betting strategy implementable by any computer (even in the weak sense of constructive strategies, which are not necessarily computable) can make money betting on a random sequence. In a true random memoryless market, no betting strategy can improve the expected winnings, nor can any option cover the risks in the long term.

Over the last few decades, several systems have shifted towards ever greater levels of complexity and information density. The result has been a shift towards Paretian outcomes, particularly within any event that contains a high percentage of informational content, i.e. when one plots the frequency rank of words contained in a large corpus of text data versus the number of occurrences or actual frequencies, Zipf showed that one obtains a power-law distribution

Departures from normality could be accounted for by the algorithmic component acting in the market, as is consonant with some empirical observations and common assumptions in economics, such as rule-based markets and agents. The paper.