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.

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.

High Frequency Markets and Leverage

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Leverage effect is a well-known stylized fact of financial data. It refers to the negative correlation between price returns and volatility increments: when the price of an asset is increasing, its volatility drops, while when it decreases, the volatility tends to become larger. The name “leverage” comes from the following interpretation of this phenomenon: When an asset price declines, the associated company becomes automatically more leveraged since the ratio of its debt with respect to the equity value becomes larger. Hence the risk of the asset, namely its volatility, should become more important. Another economic interpretation of the leverage effect, inverting causality, is that the forecast of an increase of the volatility should be compensated by a higher rate of return, which can only be obtained through a decrease in the asset value.

Some statistical methods enabling us to use high frequency data have been built to measure volatility. In financial engineering, it has become clear in the late eighties that it is necessary to introduce leverage effect in derivatives pricing frameworks in order to accurately reproduce the behavior of the implied volatility surface. This led to the rise of famous stochastic volatility models, where the Brownian motion driving the volatility is (negatively) correlated with that driving the price for stochastic volatility models.

Traditional explanations for leverage effect are based on “macroscopic” arguments from financial economics. Could microscopic interactions between agents naturally lead to leverage effect at larger time scales? We would like to know whether part of the foundations for leverage effect could be microstructural. To do so, our idea is to consider a very simple agent-based model, encoding well-documented and understood behaviors of market participants at the microscopic scale. Then we aim at showing that in the long run, this model leads to a price dynamic exhibiting leverage effect. This would demonstrate that typical strategies of market participants at the high frequency level naturally induce leverage effect.

One could argue that transactions take place at the finest frequencies and prices are revealed through order book type mechanisms. Therefore, it is an obvious fact that leverage effect arises from high frequency properties. However, under certain market conditions, typical high frequency behaviors, having probably no connection with the financial economics concepts, may give rise to some leverage effect at the low frequency scales. It is important to emphasize that leverage effect should be fully explained by high frequency features.

Another important stylized fact of financial data is the rough nature of the volatility process. Indeed, for a very wide range of assets, historical volatility time-series exhibit a behavior which is much rougher than that of a Brownian motion. More precisely, the dynamics of the log-volatility are typically very well modeled by a fractional Brownian motion with Hurst parameter around 0.1, that is a process with Hölder regularity of order 0.1. Furthermore, using a fractional Brownian motion with small Hurst index also enables to reproduce very accurately the features of the volatility surface.

hurst_fbm

The fact that for basically all reasonably liquid assets, volatility is rough, with the same order of magnitude for the roughness parameter, is of course very intriguing. Tick-by-tick price model is based on a bi-dimensional Hawkes process, which is a bivariate point process (Nt+, Nt)t≥0 taking values in (R+)2 and with intensity (λ+t, λt) of the form

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Here μ+ and μ are positive constants and the functions (φi)i=1,…4 are non-negative with associated matrix called kernel matrix. Hawkes processes are said to be self-exciting, in the sense that the instantaneous jump probability depends on the location of the past events. Hawkes processes are nowadays of standard use in finance, not only in the field of microstructure but also in risk management or contagion modeling. The Hawkes process generates behavior that mimics financial data in a pretty impressive way. And back-fitting, yields coorespndingly good results.  Some key problems remain the same whether you use a simple Brownian motion model or this marvelous technical apparatus.

In short, back-fitting only goes so far.

  • The essentially random nature of living systems can lead to entirely different outcomes if said randomness had occurred at some other point in time or magnitude. Due to randomness, entirely different groups would likely succeed and fail every time the “clock” was turned back to time zero, and the system allowed to unfold all over again. Goldman Sachs would not be the “vampire squid”. The London whale would never have been. This will boggle the mind if you let it.

  • Extraction of unvarying physical laws governing a living system from data is in many cases is NP-hard. There are far many varieties of actors and variety of interactions for the exercise to be tractable.

  • Given the possibility of their extraction, the nature of the components of a living system are not fixed and subject to unvarying physical laws – not even probability laws.

  • The conscious behavior of some actors in a financial market can change the rules of the game, some of those rules some of the time, or complete rewire the system form the bottom-up. This is really just an extension of the former point.

  • Natural mutations over time lead to markets reworking their laws over time through an evolutionary process, with never a thought of doing so.

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Thus, in this approach, Nt+ corresponds to the number of upward jumps of the asset in the time interval [0,t] and Nt to the number of downward jumps. Hence, the instantaneous probability to get an upward (downward) jump depends on the arrival times of the past upward and downward jumps. Furthermore, by construction, the price process lives on a discrete grid, which is obviously a crucial feature of high frequency prices in practice.

This simple tick-by-tick price model enables to encode very easily the following important stylized facts of modern electronic markets in the context of high frequency trading:

  1. Markets are highly endogenous, meaning that most of the orders have no real economic motivation but are rather sent by algorithms in reaction to other orders.
  2. Mechanisms preventing statistical arbitrages take place on high frequency markets. Indeed, at the high frequency scale, building strategies which are on average profitable is hardly possible.
  3. There is some asymmetry in the liquidity on the bid and ask sides of the order book. This simply means that buying and selling are not symmetric actions. Indeed, consider for example a market maker, with an inventory which is typically positive. She is likely to raise the price by less following a buy order than to lower the price following the same size sell order. This is because its inventory becomes smaller after a buy order, which is a good thing for her, whereas it increases after a sell order.
  4. A significant proportion of transactions is due to large orders, called metaorders, which are not executed at once but split in time by trading algorithms.

    In a Hawkes process framework, the first of these properties corresponds to the case of so-called nearly unstable Hawkes processes, that is Hawkes processes for which the stability condition is almost saturated. This means the spectral radius of the kernel matrix integral is smaller than but close to unity. The second and third ones impose a specific structure on the kernel matrix and the fourth one leads to functions φi with heavy tails.

{Securities Transaction Tax + Commodities Transaction Tax + Revenue Foregone} = A Recipe for Illusionary Scam

Are Transaction Tax and Securities Transaction Tax synchronous? If by chance these are used interchangeably, then the rate is not 0.5%, but in accordance to a slab where sale/purchase, or transaction effects on options or futures, where it is valued in premium in the case of options and actual trade price in the case of futures. Moreover, Securities Transaction Tax differs from intra-day to inter-day transactions. Two crucial factors are the distinguishing parameters here: buying securities and selling securities would attract different STT, and is often resorted to avail exemption in case of long-term capital gain. The rate of taxation is determined by the Government. All stock market transactions that involve equity or equity derivatives like futures and options are liable to be taxed under the STT. Now this last sentence is redundant, but points out to commodities and currencies that stand out exempted from STT. If one talks of stocks, bonds and commodities in the same breath, albeit preliminarily, as different from what the governmental figures exhibit – the latter dips the figures, while it should have been higher according to those in the opposition, then clearly commodities and currency trades should have different taxation structure in the stock exchanges. But, that isn’t the case, for commodities are never traded on the stock exchanges, but on the commodity exchanges, and regulated by Forward Markets Commission and not by SEBI. Further, commodity derivatives can be settled by delivery, unlike security derivatives, which could only be settled by payment or receipt of differences. So bearing this differentiation in mind, where does one see commodities vis-à-vis securities under the rubric of revenue receipt?

I understand it has no truck with revenue foregone (it actually has, and I won’t dismiss it so simplistically!! but for brevity I am presuming it merely). Revenue Foregone is more of a myth in regards to the common misunderstanding surrounding the same. Section 5A (1) of the Central Excise Act 1944 empowers the Union Government to lower tariff rates below levels prescribed in the schedules, and are specifically applicable to mass consumable goods and more often than not are not tax sops to corporations. On the other side, Customs duty concessions are mostly for imported goods and used as inputs for exports as defined under Section 25 (1) of the Customs Act, and thus many a times run the risk of being included on the revenue foregone side, while it is mainly to boost India’s exports more competitively on the global market scene. Taken these two, the myth of Revenue Foregone only proliferates either as giveaways to corporations, or as political decisions taken on behalf of corporations, while the real demand from these corporations seldom make such warrants.

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Taxation on the money made via share market trading depends largely on the purpose for which share transactions are done. An individual can trade shares for business purposes or as an investment activity. In both these cases, the STT that is levied by the Government, varies. Why this obsession with STT is the obvious question? Because: STT is fast, transparent and effective. Since tax is levied as soon as the transaction arises, instances of non-payment, wrong payment etc. are reduced to a minimum. The net result of this is, however, it pushes up the cost of transactions. Now, while calculating the estimated potential of revenues from such taxes, the possibility of migration of trade volume is generally not taken into account. Hence, actual revenue mobilized in most cases does not correspond with the estimated potential, because the revenue potential is a function of the elasticity of trading volume with respect to transaction cost/STT spread. On the issue of volatility, things settle down to an ambiguity, and as far as stock exchanges are concerned, one cannot overlook it. The impact of transaction tax on volatility is a function of market microstructures. If market fundamentalists out-number noise traders, then STT will not only affect the latter, but also have a disproportionate effects on the former, leading to a fall in the trade volume and liquidity and inversely rising volatility. This inconclusively questions the veracity of transaction tax and STT, and the two are synchronous with STT and others being merely under different headings of taxation.

Let us leave behind CTT for the time being, as that would complicate matters since we are talking stock exchanges here and not commodity exchanges. Only thing, I’d like to point out then is not using commodities for revenue receipts, if such receipts are generated from stock exchanges as there is a categorical error in bringing them on a similar platform. If one goes into different heads and nomenclatures, what good does it bring about looking at transaction tax stripped off its components is something I find difficult to fathom? Would it not defeat the purpose if one is looking at volume or volumes of trade and revenue receipts under these? Looking now briefly at CTT. First proposed in 2008, it was met with extreme opposition, and CTT was proposed again in the Budget in 2013, but only on Non-Agricultural Commodities such as Gold, Silver, Aluminium, Crude Oil among others. This time the Bill was passed & CTT was levied on Trades in Commodity Futures on & after 1 July 2013. With the introduction of CTT in commodity trading, trading volumes on the MCX and other commodity exchanges in India have seen a dip as high as 50% – 60%. It has also driven away smaller segments of the volume contributors away from the segments since scalping, jobbing, etc have become an unviable and expensive proposition.