Robert Engle and Robert Ferstenberg Microstructure in Paris December 8, 2014

Transcription

1 Robert Engle and Robert Ferstenberg Microstructure in Paris December 8, 2014

2 Is varying over time and over assets Is a powerful input to many financial decisions such as portfolio construction and trading algorithms Is never directly observed but is believed to be correlated with volatility and volume and bid ask spread Can be interpreted as the ease with which an investor can purchase or sell large quantities. Forecasting Transaction Costs

3 Create several measures of transaction costs based on market data with daily market volume orders (DMVO) Produce forecasts of these measures Compare these forecasts with transaction records from Abel Noser Solutions (ANS) Do this daily for many US equity names over a 15 year period.

4 IS is the difference between the market price at the time the order is entered and the price at which a trade is executed, measured as a return. IS =log(execution price/arrival price)*side Where side is 1 for a buy order and -1 for a sell order IS should be positive on average for all trades. IS has low ratio of signal to noise.

5 Perold, Kyle, Grinold and Kahn, Engle Ferstenberg Russell, Kyle and Obisheva, Pastore and Stambaugh, Hasbrouck, Roll, Amihud, Easley Lopez de Prado and O Hara, Russell, Jones and Lipson, Engle and Lange, Keim and Madhavan. Many industry studies, ITG, ANS, AQR Apologies for the authors omitted Forecast Transaction Costs

6 Use transaction data from ANS. These are parent orders which were typically filled with sequences of small child orders. We have execution price and fill quantity. We also have a buy sell indicator. We approximate the arrival price by the opening price. We do not have the order size for orders that are not completed. This is probably an important bias. Thus we can compute IS for more than 300 million executed orders since 1998.

7 We consider the entire daily market volume on a name as an order. The arrival price is then the open and the execution price is VWAP. We approximate with the close. The direction of the trade must be inferred from the price movement. If the price goes up, the trade is classified as a buy and conversely for a sell. An order which is a small fraction of daily volume will incur only a fraction of the daily impact.

8 Trade direction has always been inferred from prices with market data. After all, every execution has a willing buyer and a seller. The Lee and Ready algorithm offers two solutions to signing a single trade either the price change is used or the price relative to the recent midquote. Lee and Ready was motivated by the idea that a market order was active and executed by a specialist. Today there are no specialists and informed traders do not generally use market orders. Limit orders can have price impact so the direction of trade can best be inferred by the price movement.

9 Not all trades have equal price impact. More informative trades have more impact. Hence, it makes sense to measure impact on average. Information on the informativeness of trades can be used if available. Easley, Lopez de Prado, O Hara in a series of papers have introduced Bulk Volume Classification or BVC which uses inter daily data to classify trades by the price change. We take this to the daily limit. DMVO

10 Volatility Volume Average Daily Volume Bid Ask Spread The correlation of volume and volatility is central to price impact. Predictions of transaction cost are based on order size relative to predicted volume.

11 Amihud, Yakov(2002), Illiquidity and stock returns: Cross-section and time series effects, Journal of Financial Markets 5, Grinold,Richard and Ronald Kahn(1999) Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk, McGraw Hill Engle, Robert, Robert Ferstenberg and Jeffrey Russell (2012), Measuring and Modeling Execution Cost and Risk, The Journal of Portfolio Management.

12 Historical forecasts Lagged 21 day average of absolute return divided by daily dollar volume There are no unknown parameters in this system Forecasting can also be done econometrically by using MEM models as in VLAB Transaction costs are approximated by multiplying ILLIQ by the order in dollars

13 IS Spread Volatility Volume t t = b1 t- 1 + b2 t- 1 * ADVt - 1 Where Volatility is 21 day lagged standard deviation of open to close returns, Volume is todays dollar volume, ADV is 21 day average dollar volume and Spread is the bid ask spread divided by open price. Estimated with panel of CRSP names for a year and forecast with the same betas for the following year.

14 IS = e Volatility Volume ADV b b b b t t- 1 t t This specification ensures that expected IS is non-negative. It is a generalization of GK. Estimate with panel of one year data on all trades in the year. Forecast for the next years trades.

15 Mean by Year of Daily ADN Weighted Mean of Mean of 21 Day Lagged IILIQ 2,50E-08 2,00E-08 1,50E-08 1,00E-08 5,00E-09 0,00E

16 0,0005 0, ,0004 0,00035 Mean by Year of Daily ADN Weighted Mean of Out of Sample ILLIQ Forecast of 1% ADV 5,00E-06 4,50E-06 4,00E-06 3,50E-06 Mean by Year of Daily ADN Weighted Mean of Out of Sample ILLIQ Forecast of $1M 0,0003 0, ,0002 0, ,0001 0, ,00E-06 2,50E-06 2,00E-06 1,50E-06 1,00E-06 5,00E-07 0,00E NYU Stern Volatility Institute 12/22/

17

18 0,035 0,03 0,025 0,02 0,015 forecast realization prediction 0,01 0, NYU Stern Volatility Institute 12/22/

19 Mean by Year of Daily ADN Weighted Mean of Out of Sample CRSP GK Forecast of 1% ADV Mean by Year of Daily ADN Weighted Mean of Out of Sample CRSP GK Forecast of $1M 0,004 0,0025 0,0035 0,003 0,002 0,0025 0,0015 0,002 0,0015 0,001 0,001 0,0005 0, NYU Stern Volatility Institute 12/22/

20

21 0,035 0,03 0,025 0,02 0,015 forecast realization prediction 0,01 0, NYU Stern Volatility Institute 12/22/

22 Mean by Year of Daily ADN Weighted Mean of Out of Sample CRSP EFR Forecast of 1% ADV Mean by Year of Daily ADN Weighted Mean of Out of Sample CRSP EFR Forecast of $1M 0,008 0,0014 0,007 0,0012 0,006 0,005 0,004 0,003 0,002 0,001 0,001 0,0008 0,0006 0,0004 0, NYU Stern Volatility Institute 12/22/

23 ABEL NOSER SOLUTIONS ANCERNO TRANSACTION DATA SET

24 More than 300 million orders executed by ANS clients from Arrival price is the open Execution price is the average price of the executed shares. Fill is the number of shares executed. Data are cleaned, extremes are removed and bucketed in many ways. Data are matched with CRSP by date/cusip/symbol

25

26 Mean IS Mean of IS 50-51%tile 0,0045 0,003 0,004 0,0025 0,0035 0,003 0,002 0,0025 0,002 0,0015 0,001 0,0005 EW-IS VW-IS AW-IS NW-IS 0,0015 0,001 0,0005 EW-IS VW-IS AW-IS NW-IS 0 0-0,0005-0,001-0,0005 NYU Stern Volatility Institute 12/22/

27

28

29 ANS Summary Statistics By Year Comparison between: VWAP: Un-weighted average execution cost if ANS fills are priced at VWAP approximated as average of open and close price EW-IS: Un-weighted average reported by ANS Assumption is that impact of ANS executions are priced in the market data 0 0,0035 0,003 0,0025 0,002 0,0015 0,001 0,0005 Mean IS Fills Priced at VWAP EW-IS VWAP 12/22/2014 NYU Stern Volatility Institute 29

30 The ANS estimate of Implementation Shortfall shows some surprising features. The peak IS for medium to large trades occurs in 2009 and then it falls slowly. For smaller trades it is in 2011 and seems surprisingly large and 2002 are very low cost years and this seems surprising too.

31 For each trade in ANS data set, predict IS from model parameters in preceding year using ANS fill and market variables. Predict ANS IS as an intercept plus slope coefficient on forecast for all trades in a year. This is sometimes called Mincer-Zarnowitz regression. Consider the predictions of this model as the transaction cost model.

32 0,0025 0,002 0,0015 forecast realization 0,001 prediction 0, NYU Stern Volatility Institute 12/22/

33 0,0035 0,003 0,0025 0,002 0,0015 forecast realization prediction 0,001 0, NYU Stern Volatility Institute 12/22/

34 Better spread data Explore impact of estimation universe CRSP is ~5000 names ANS is ~2000 names Explore models of excess returns Seek alternative samples of executions.

35 A PROMISING APPROACH TO LIQUIDITY FORECASTING!