Learning Module 7: Trading Costs and Electronic Markets

Learning Module 7 Trading Costs and Electronic Markets EXAMPLE 1 Transaction Cost Analyses for an Illiquid Stock Arapahoe Tanager, portfolio manager of a Canadian small-cap equity mutual fund, and his firm's chief trader, Lief Schrader, are reviewing the execution of a ticket to sell 12,000 shares of Alpha Company, limit C$9.95. The order was traded over the day. Schrader split the ticket into three orders that executed that day as follows: A. A market order to sell 2,000 shares executed at a price of C$10.15. Upon order submission, the market was C$10.12 bid for 3,000 shares, 2,000 shares offered at C$10.24. B. A market order to sell 3,000 shares executed at a price of C$10.11. Upon order submission, the market was C$10.11 bid for 3,000 shares, 2,000 shares offered at C$10.22. C. Toward the end of the trading day, Schrader submitted an order to sell the remaining 7,000 shares, limit C$9.95. The order executed in part, with 5,000 shares trading at an average price of C$10.01. Upon order submission, the market was C$10.05 bid for 3,000 shares, 2,000 shares offered at C$10.19. This order exceeded the quoted bid size and "walked down" the limit order book (i.e., after the market bid was filled, the order continued to sell at lower prices). After the market closed, Schrader allowed the order to cancel. Tanager did want to sell the 2,000 unfilled shares on the next trading day. Only two other trades in Alpha Company occurred on this day: 2,000 shares at C$10.20 and 1,000 shares at C$10.15. The last trade price of the day was C$9.95; it was C$9.50 on the following day. 1. For each of the three fund trades, compute the quoted spread. Also, compute the average quoted spreads prevailing at the times of each trade. Solution: The quoted spread is the difference between the ask and bid prices. For the first order, the quoted spread is C$10.24-CS10.12 = C$0.12. Similarly, the quoted spreads for the second and third orders are C$0.11 and C$0.14, respectively. The average quoted spread is (C$0.12 + C$0.11 + C$0.14)/3 = C$0.1233. 2. For each of the three fund trades, compute the effective spread (use the average fill price for the third trade). Also, compute the average effective spread Solution: The effective spread for a sell order is 2 x (Midpoint of the market at the time of order entry Trade price). For the first order, the midpoint of the market at the time of order entry is (C$10.12 + C$10.24)/2 = C$10.18, so that the effective spread is 2 x (C$10.18-C$10.15) C$0.06. The effective spread for the second order is 2 x [(C$10.11 + C$10.22)/2- C$10.11 C$0.11. The effective spread for the third order is 2 x [(C$10.05 + C$10.19)/2- C$10.01] C$0.22. The average effective spread is (CS0.06 C$0.11 + C$0.22)/3 = CS0.13. 3. Explain the relative magnitudes of quoted and effective spreads for each of the three fund trades. Solution: The first trade received price improvement because the shares sold at a price above the bid price. Therefore, the effective spread is less than the quoted spread. No price improvement occurred for the second trade because the shares sold at the bid price. Also, the second trade had no price impact beyond trading at the bid; the entire order traded at the quoted bid. Accordingly, the effective and quoted spreads are equal. The effective spread for the third trade is greater than the quoted spread because the large order size, which was greater than the bid size, caused the order to walk down the limit order book. The average sale price was less than the bid so that the effective spread was higher than the quoted spread. 4. Calculate the VWAP for all 13,000 Alpha Company shares that traded that day and for the 10,000 shares sold by the mutual fund. Compute the VWAP transaction cost estimate for the 10,000 shares sold. Solution: The VWAP for the day is the total dollar volume divided by the total number of shares traded. The dollar volume is 2,000 shares × C$10.153,000 shares C$10.11+5,000 shares × C$10.01 + 2,000 shares x CS 10.20+ 1.000 shares at C$10.15 C$131,230. Dividing this by the 13,000-share total volume gives a VWAP of C$10.0946. A similar calculation using only the sales made by the mutual fund gives a trade VWAP of CS10.0680. The VWAP trans-action cost estimate for the sale is the difference multiplied by the 10,000 shares sold: C$266.15 10,000 shares x (C$10.0946-C$10.0680) (differ- ences due to rounding. EXAMPLE 2 Latency 1. Explain why low latency is important to electronic traders. Solution: Electronic traders need a comparative speed advantage to 1) take advantage of market opportunities before others do, 2) receive time precedence that would allow them to trade sooner when offering liquidity to others, and 3) ensure order cancellation when they no longer want to fill the order. To gain a comparative advantage relative to others, electronic traders try to minimize the time between an event occurring and a subsequent action, typically the submission of an order instruction, based upon that event. To minimize latency, electronic traders invest in very fast communication systems and very fast computer systems. EXAMPLE 3 Use of Electronic Brokers 1. You have recently been hired recently as a junior buy-side analyst. Part of your training (onboarding) has been to sit with the trading desk to learn how the desk trades through its electronic brokers. In a meeting with your manager, she asks you to explain the use of electronic brokers for advanced orders, trading tactics, and algorithmic trading tools that your electronic brokers provide. What would you say? Solution: The use of electronic brokers and their systems is valuable for such advanced order types as pegged or floating limit orders, whose limit prices change as market conditions change. Traders use these order types to supply liquidity at a specified distance from the market. These orders require continuous real-time evaluation to determine if an order cancellation or replacement is needed as market conditions change. The use of electronic brokers relieves the need for the trader to continuously monitor the market to cancel and resubmit orders when prices change. An electronic broker is also valuable for orders placed a few ticks outside the best market that will be among the last orders to supply liquidity to a large trader, hopefully at a good price. Electronic brokers also allow their clients to access order execution tactics (presented as another complex order type) that involve multiple submissions that may "sweep" through markets to uncover hidden liquidity. These tactics allow traders to submit multiple orders with a single instruction. Finally, electronic brokers also provide algorithmic trading tools. Algorithms are automated (programmed trading strategies for combinations of simple and single, advanced, or multiple orders and various trading tactics) to fill small orders efficiently based on various criteria. They often break up large orders into smaller pieces to minimize the market impact of filling the order. They may route the orders to multiple venues at the same time or to the same venue at various times. For example, VWAP algorithms attempt to fill orders at the volume-weighted average price (or better) of all trades over a specified interval. The systems running algorithms that place standing limit orders must be very fast to cancel orders in trading. In these cases, low latency is critical to ensure order cancellation before unfavorable executions occur. Fast systems also help ensure that traders are first to respond when market conditions change and to maintain time precedence. HISTORICAL EVENT: THE FLASH CRASH The 6 May 2010 Flash Crash was the most notable market structure event in recent memory. During the crash, which started at about 2:42 p.m. ET, the E-mini S&P 500 futures contract dropped approximately 5% in 5 minutes and then recovered nearly fully in the next 10 minutes. The price volatility spilled from the equity futures market into the stock market, where some stocks traded down more than 99% or up more than 1,000%. In the immediate aftermath of the crash, regulators decided that more than 20,000 trades in more than 300 securities that occurred more than 60% away from earlier prices would be broken (canceled). This extraordinary event raised many concerns about security market structure, in particular, how the adoption of electronic trading may have increased potential systemic risks. This subsection describes the events that led up to the crash, what happened during the crash, and the regulatory responses to the crash. The Event and Its Causes On Thursday, 6 May 2010, the stock market traded down throughout the day at an accelerating rate. By 2:30 p.m., it had lost about 4% from its previous close. Contemporaneous commentators attributed the fall to concerns about Greek sovereign debt and the implications of a Greek default for other markets. During the day, many traders who had been providing liquidity to the market were accumulating substantial long positions as people demanded to sell. As the day wore on, their willingness to continue to accumulate additional inventory decreased. Moreover, day traders, who do not normally carry inventory overnight, also were considering how and when they would sell their losing positions. Presumably, in response to the European concerns and perhaps other concerns, portfolio managers at Waddell & Reed Financial Inc. (W&R) decided to reduce US equity exposure in their $27 billion Asset Strategy Fund by selling 75,000 June 2010 E-mini S&P 500 futures contracts with a nominal value of approximately $4.1 billion. They gave this order to their buy-side trader, who proceeded to fill it using an algorithm that split the order into small pieces for execution. Although the order was the largest single order submitted to the E-mini futures market that year, it was not without precedent. Two earlier orders in the previous year were of similar size or larger, one of which had been submitted by W&R. Those orders had been filled in more stable markets and over longer periods of time than W&R's 6 May order. The order started to execute at 2:32 p.m. W&R's head trader, who normally would have handled such a large order, was out of the office that day. Instead, a less senior trader in his office handled the order. The trader set parameters on the algorithm to target an execution rate of 9% of the trading volume calculated over the previous minute without regard to price or time. This trading strategy was more aggressive than the one W&R had used to fill its large order from the previous year. The trader probably set an aggressive rate because he feared that the firm would obtain a worse execution if prices continued to fall. The more aggressive strategy contributed to the crash. When the initial trades depressed S&P 500 futures prices, trading volumes increased substantially as arbitrageurs and others started to trade, many of them trading with each other as they normally did. The arbitrageurs bought the futures and sold equities and equity ETFs (exchange-traded funds), such as the SPDR S&P 500 Trust (ticker SPY). Some arbitrageurs also sold call option contracts and bought put option contracts. The increase in trading volumes caused the algorithm to increase the rate of its order submissions as it tried to keep up with its mandate to participate in 9% of the market volume. The increasing order submission rate exacerbated the problem. Initially, high-frequency traders and other liquidity suppliers in the E-mini futures markets supplied liquidity to W&R's order and accumulated long positions. Between 2:41 p.m. and 2:44 p.m., these short-term traders sold these positions as the algorithm continued to pump more orders into the market. During this 4-minute period, the E-mini dropped 3%. By the end of this period, buy-side depth (total size of standing buy orders) in the E-mini contract dropped to only 1% of the average depth observed earlier in the day. The E-mini contract then dropped 1.7% in the next 15 seconds. The arbitrage trades caused the equity markets to drop. In many securities, especially the ETFs, falling prices triggered stock loss market orders, which further depressed prices. The levered ETFs were particularly affected because their high volatilities make them popular with technical traders and retail traders, many of whom routinely place stop orders to protect their positions. As the prices changed quickly, many traders who were providing liquidity in the futures and equity markets dropped out because they were unwilling to trade in the face of such extreme volatility. Many also had already accumulated large inventory positions from earlier in the day and did not want to buy more. Interestingly, researchers later discov ered that the largest and most active high-frequency trading firms did not withdraw. Nonetheless, limit order books thinned out-especially on the buy side as traders canceled standing orders and as sellers filled those buy orders still standing. In some stocks, all standing buy orders were exhausted and trading stopped. In other stocks, all buy orders except those placed with a limit price of only a cent or two were exhausted. In these stocks, exchange trading systems blindly filled market sell orders at extraordinarily low prices. In a few other stocks, the withdrawal of liquidity suppliers from the market also removed essentially all liquidity from the sell side of the market. Some stocks then traded at prices as high as $100,000 when market buy orders were filled against sell orders placed at extraordinarily high prices. The slide stopped at 2:45:28 p.m. when a Chicago Mercantile Exchange trading rule called Stop Logic Functionality caused the exchange's computers to halt trading briefly in the E-mini S&P 500 futures contract and to clear the limit order book of all standing limit orders. The rule is triggered when it becomes apparent that pending order executions would cause prices to jump too far. The futures contract dropped about 5% from when the algorithm started to trade at 2:32 p.m. to the market halt at 2:45 p.m. The algorithm sold about 35,000 contracts during this period. When trading resumed 5 seconds later, the buy-side algorithm continued to trade, but many liquidity suppliers were now willing to provide liquidity. Prices rose quickly in orderly markets. The episode largely ended when the big W&R order completed filling at around 2:51 p.m., about 20 minutes after it started. However, the market remained quite volatile during the remainder of the day as traders adjusted their positions and responded to the extreme volatility. Following the crash, regulators broke all trades that had occurred more than 60% away from the previous close. Implications for Traders The Flash Crash provided three important lessons for observant traders: First, market orders are incompatible with electronic order-matching systems that do not curb trading when prices move too quickly. Had traders priced all their orders, no trades would have taken place at unreasonably high or low prices. Following the crash, many retail brokers adopted a policy of converting all customer market orders into marketable limit orders with limit prices set about 10% above the current ask for buy orders and 10% below the current bid for sell orders. Second, institutional traders using algorithms must be careful not to demand more liquidity than orderly markets can provide. Most buy-side investors probably immediately recognized that W&R lost a substantial amount of its clients' money owing to the extraordinarily high transaction costs associated with the trade. To obtain a crude estimate of this loss, assume that the algorithm traded all $4.1 billion of its order at a uniform rate throughout the 5% price reversal. The average market impact of the trade would have been 2.5%, which implies total transaction costs of about $100 million, or 0.37% of the $27 billion in assets of the W&R Asset Strategy Fund. Such significant losses attract attention. Within a week, many algorithm writers probably coded limits into their algorithms to help prevent them from being used irresponsibly. Finally, algorithm writers and the traders who use algorithms must pay much more attention to the dangers of using algorithms that can create destructive feedback loops. They particularly must understand how algorithms respond to market conditions that they may create themselves. Regulatory Responses Following the Flash Crash, regulators adopted new rules to prevent a similar crash from happening again. They placed curbs that halt trades in a stock for 5 minutes if prices move up or down by more than 10% for large stocks and 20% for smaller stocks. This rule ensures that prices cannot move too quickly, but it does not prevent traders from behaving foolishly. Had it been in effect during the Flash Crash, the rule would have stopped trades from occurring at ridiculously low or high prices, but it would not have stopped the W&R trader from submitting an unrealistically aggressive order. Regulators also adopted rules to establish when and which trades will be broken in the event of another extreme price change. Such rules should help ensure that liquidity suppliers who are afraid that their trades may be broken do not withdraw from the market prematurely. EXAMPLE 4 Electronic Trading and Transaction Costs 1. Describe the impact of electronic trading on transaction costs. Solution: Growth in electronic trading has resulted in greater trade process efficien- cies and reduced transaction costs for investors. Electronic systems are much cheaper to operate than floor-based systems (requiring less physi- cal space and fewer exchange personnel). These systems can operate on a close-to-continuous basis at far greater scale and scope and at much faster speeds than humans. Process efficiencies from electronic trading have led to significant decreases in bid-ask spreads, which have lowered transaction costs for investors.