wharton research data services - the supply and demand of … · 2018-09-14 · supply and demand...

The Supply and Demand of Liquidity: Understanding and Measuring Institutional Trade Costs

Donald B. Keim Wharton School

University of Pennsylvania

WRDS Advanced Research Scholar Program

August 21, 2018

Supply and Demand of Liquidity: Understanding & Measuring Institutional Trade Costs WARSP Aug 21, 2018

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Understanding, Measuring and Controlling Trade Costs

Measurement and control of trade (or implementation) costs is a fundamental issue facing both portfolio managers and those who evaluate their performance. It is important for portfolio managers to understand the magnitude and determinants of their trading costs.

Better able to measure and control their trade costs and, hence, improve investment performance. Investment performance can be defined by the identity: Investment Performance = Alpha from Investment Strategy − Implementation Costs The magnitude of Implementation Costs can easily be an order of magnitude larger than the magnitude of Alpha from Investment Strategy. Adding value by reducing implementation costs (which are indeed manageable) is far easier than adding value by increasing alpha (which is extremely difficult).


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The Components of Institutional Trading Costs (1) Commissions (Explicit Costs)

Payment to broker for execution – can range from less than 1¢ to 15¢ per share

In 2014, commissions were 0.146% of trade value for U.S. institutional equity trades (Greenwich Associates) (2) Market Maker (Bid-Ask) Spread

Compensation to market maker for providing immediacy of execution.

The less liquid the market in which the stock trades, the larger the spread.

Spreads can range from less than 0.05% for very liquid stocks to larger than 2% for very illiquid stocks. (3) Market Impact/Price Concession

The amount by which your trade moves the stock price away from its current level

Market impact is likely to increase with trade size and market illiquidity: (4) Opportunity Cost

The cost of not executing, and missing potential opportunity, once the decision to invest is made.

the information motivating the trade may become stale.


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Measurement and Magnitude of Implementation/Trade Costs

We use a measure of trade costs (Perold 1988, Keim and Madhavan 1997) that incorporates temporary, permanent, and opportunity costs, plus commissions:

Implementation Cost = [(Ptrade / Pd) – 1] + (Commissions / Pd)

where Ptrade is the trade price and Pd is the price at the time the decision to trade is made.

Implementation costs increase in trade illiquidity (e.g., market cap) and trade size. For example:

Average Total Trading Costs (in %) by Market‐Cap Quintile for Common Stock Trades by 21 Institutions

NYSE & AMEX Stocks Nasdaq Stocks Average Std Error # obs Average Std Error # obs Buyer‐Initiated Trades

Largest 0.31 0.02 10,960 0.24 0.11 1,155 2 0.43 0.03 7,989 0.54 0.09 1,934 3 0.64 0.06 4,137 0.92 0.08 2,929 4 1.00 0.07 2,115 1.52 0.09 2,720

Smallest 1.78 0.12 834 2.85 0.13 1,801

Seller‐Initiated Trades Largest 0.26 0.02 10,901 0.16 0.12 960

2 0.63 0.04 4,738 0.85 0.18 853 3 1.02 0.09 2,296 1.18 0.12 1,517 4 1.33 0.16 1,112 1.73 0.15 1,613

Smallest 2.03 0.23 568 2.91 0.23 1,106

Source: Keim and Madhavan (1998)


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Updated Estimates of Institutional Trade Costs (Frazzini, Israel, Moskowitz (2018))

─ Trade Costs (in basis points) are computed using the method described above

─ Pro: Sample is much larger and more recent: 11.0 million orders worth $1.7 trillion

─ Con: All trades made by one firm, and the firm has very good traders (how representative?)


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Demanding vs. Supplying Liquidity – A first look

Typically, think of institutional trades as being liquidity demanding, and hence costly

But depending on market conditions, investment style, trade ideas – might supply liquidity

lower, possibly negative, trade costs (pseudo market making)

Investment strategy/style tells a lot about whether liquidity is demanded or supplied (e.g., Keim and Madhavan (1997)). Consider three investment styles:

Momentum – trend chasers who buy stocks in rising markets and sell stocks in falling markets.

Buys (sells) more expensive in rising (falling) markets (liquidity demanding)

Value – investment decisions are dependent on long-term fundamentals; buy stocks when “undervalued” (e.g., low P/E), sell when “overvalued.”

Allows for patient trading (and possibly liquidity provision)

Diversified/Index – objective to maintain a well-diversified “balanced” portfolio, or to minimize tracking error relative to an index;

Trades might range from liquidity demanding to liquidity supplying


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Average Price Impacts (implementation shortfall) vs. Investment Style:

_________________________________________________________________________________________

Prior excess stock return (PriorXRet) is the return on the traded stock, measured over the three weeks prior to the initiation of trade, less the market return over the same period.

Price impact is the ratio of the average trade price to the closing stock price on the day before the order was initiated, minus 1.0, in excess of the market return over the interval of the trade. Price impacts for sells are multiplied by (-1) so they can be interpreted as costs and compared to the impacts for buys. Aggregate investment flow is the sum of the dollar value of all trades in the respective category ($bill).

Number of orders is in parentheses. Results are for the 1996-97 and 2000 periods.

Buys Sells PriorXRet <0 PriorXRet > 0 PriorXRet <0 PriorXRet > 0 Diversified -0.197 0.908

1.413 -0.494

$9.44 $10.91 $8.81 $9.18 (11,842) (12,803) (9,316) (9,921)

Value -0.155 0.851 0.889 -0.366

$32.31 $24.77 $23.03 $34.82 (13,231) (12,301) (12,454) (13,598)

Momentum 0.494 1.892 2.238 0.319 $14.19 $30.08 $23.93 $17.10

(14,159) (17,959) (13,557) (12,218)


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Active Mutual Funds & the Generation of Investment Ideas

(From Passive Aggressive Trading by Christoffersen, Keim, Musto and Rzeźnik) We now consider how an inventory of investment ideas, and its depletion, can result in a tradeoff between demanding and supplying liquidity

and how this tradeoff affects trade costs and fund performance. We examine this through the lens of active stock mutual funds, with a special database my coauthors and I painstakingly constructed. Active mutual funds are generally viewed as liquidity demanders

– Develop a (presumably) profitable trade idea

– Pay the going price for immediacy to put it on

– Later, pay the going price for immediacy to take it off The generation of profitable trade ideas positive alpha The cost of implementing the idea is determined by the price of immediacy


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Dynamic inventory of trade ideas Active institutional investors build an inventory of trade ideas Research produces trade ideas, the value of which presumably expires quickly Putting these ideas to work requires some amount of immediacy

costly demand for liquidity Ex ante, willingness to incurring such costs the research ideas have value

high expected near-term return Alpha-generating ideas need to be replenished after prior ideas are used up by trades But inventory of ideas depletes when fund inflows exceed idea production

inflows need to be invested, but the fund has no positive-alpha ideas in which to invest


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Demanding vs. Supplying Liquidity, again What does a fund do when the cost of a trade exceeds its value? Demand immediacy/liquidity and make the trade anyway? → Not recommended Instead, supply immediacy/liquidity to someone else

(e.g., if buying, take the other side of someone’s sell) But have to consider: → Is the stock within your investable universe? → Adverse selection? (Does the seller know something that you don’t?)


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Testable Hypotheses Do inflows deplete buy ideas?

Do outflows deplete sell ideas? Increased inflows → shift from demanding to supplying immediacy when buying?

Similarly, do increased outflows lead to liquidity provision when selling? Does such shifting from demanding to supplying liquidity show up in trade costs? Does the demand for liquidity reflect profitable trade ideas that ultimately show up in near-term performance on individual stock trades? What is the net effect of profitable trade ideas, costly demand for immediacy, and the benefits of liquidity provision on fund performance?


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Existing Studies

Rough proxies for trades from quarterly SEC disclosures:

Grinblatt & Titman (1989); Wermers (1999,2000); Chalmers, Edelen & Kadlec (1999); Edelen, Evans & Kadlec (2013); Da, Gao & Jagganathan (2011)…

→ Able to assess fund-specific influences on inferred costs, and can compare inferred costs to fund performance.

→ But changes in holdings are poor proxy for actual trades, and do not allow precise measurement of price impacts and other costs associated with actual trades. Anonymous transaction-level proprietary data:

Chan & Lakonishok (1995); Keim & Madhavan (1997); Anand et al (2012); Frazzini, Israel & Moskowitz (2018); DiMascio, Lines & Naik (2016); Abel-Noser studies

→ Can assess trade-specific and invest-style influences on costs and performance, but not able to examine fund-specific influences (e.g., flows, fund size).

→ Fund anonymity precludes comparisons of fund performance with trade costs. Forensic matching of anonymous (Abel-Noser) transaction data with actual funds:

Busse, Chordia, Jiang & Tang (2016); Xing (2017)


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The Trade Data

Until June 2005, Ontario Securities Commission required a “Statement of Portfolio Transactions” from all Canadian funds – disclosed all trades plus periodic holdings.

→ All funds reported all individual trades

But the required form was ambiguous and, as a result, there was no consistency in the way institutions filled out the form. These public filings are available from SEDAR (www.sedar.com) in pdf form.

→ Had to assemble the database ourselves from pdfs – very painful. Limitations of the data:

− No (within-day) time-stamp

− No indication of whether a trade was part of a larger desired order quantity

→ But we can observe whether there were nearby trades by the same fund in the same stock in the same direction, thereby permitting us to classify a trade as part of a possibly larger package


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Building the Database

Detailed trade data for January 2001 through December 2003

− identity of traded stock, number of shares traded, trade price (less commissions) Merge the individual stock trade data with CRSP and TAQ data from U.S., and Datastream and TAQ data from Canada, using Cusips to match.

→ Sample includes only Canadian and U.S. equities All values are converted to Canadian dollars (daily rate from Bank of Canada)

Merge the above data at the fund level with fund-specific data from Canadian Morningstar using Morningstar id numbers.

− Share classes aggregated within funds

Of 293 initial sample funds, we are able to match 199 with Canadian Morningstar

− 103 Canadian Equity Funds, 43 US Equity, 18 International, 35 Specialty

− represents 30% of TNA of entire Canadian mutual fund industry in 2004 After filtering and matching: 107,058 buys ($34.7 bill) and 80,202 sells ($34.5 bill)


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Summary Statistics for Mutual Funds and Implementation Costs (Passive Aggressive Trading by Christoffersen, Keim, Musto and Rzeźnik)

Total Value Mean Std Dev # obs ($bill)

Implementation Costs (%) Buy Trades 0.11 0.23 107,058 34.7 Sell Trades 0.39 0.25 80,202 34.5

Mutual Fund Characteristics - Our Sample

Total Net Assets ($mill) 406.28 862.99 Total Net Assets - Family ($bill) 20.83 20.03 Net Returns (%) 0.29 4.81 # of Funds 199

Mutual Fund Characteristics - Morningstar Universe

Total Net Assets ($mill) 349.83 862.96 Total Net Assets - Family ($bill) 17.09 14.61 Net Returns (%) 0.41 4.01 # of Funds 677


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Variables Used in the Analysis: Stock-Specific Variables LogMktCap – log of the market capitalization of the traded stock CanUS – an indicator variable that equals one if a Canadian stock, zero if a U.S. stock Amihud – impact of dollar trading volume on stock price → illiquidity (Amihud (2002)) Spread – average % spread over prior 20 trading days XSRet Pre 1 Mo – excess return on stock over month ending the day before the trade XSRet Post 1 Day – excess return on stock for the day after the trade XSRet Post 1 Wk – excess return on stock over week beginning the day after the trade


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Trade-Specific Variables

Trade cost (TC) – implementation shortfall, using same-day open price as benchmark

→ decision to trade occurred between prior-day close and before same-day open

(No intra-day time stamp for trade submission or transaction)

Tr Size/Vol – size of the trade relative to average shares traded over 20 trading days prior to the trade.

ClosePastTrade – one if the fund traded the stock at any time during the week prior to the trade, zero otherwise → proxies for trade breakup Fund-Specific variables

logTNA – the log of the total net assets of the fund measured at the end of the month of the trade

logTNAsponsor – the log of the total net assets of the fund family measured at the end of the month of the trade

Net Flow – (TNAt – TNAt-1 • (1+Rt)) / TNAt-1 where TNAt is defined as above and Rt is the net-of-fee rate of return for the fund during month t

Performance – net-of-fee return on the fund during the month of the trade


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Determinants of Trade Costs: a (univariate) First Look

Expect: Larger trades cost more (but larger trades are broken up to camouflage intent)

Large inflows → depletion of trade ideas → shift to liquidity provision Trade costs are decreasing in flows

What we find (Fig 1):

Buy costs increasing in Tr Size/Vol up to ~7th decile, then decline

Sell costs decreasing in Tr Size/Vol

larger trades gravitate toward liquidity provision (or broken up) Buy costs decline w/ lagged flows (Fig 2 in paper)

larger flows result in shift to liquidity provision for buys

Sell costs unrelated to lagged flows


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Determinants of Trade Costs: Regression Model (Table 3) Trade costs → Tr Size/Vol, LaggedFlows, + stock-, fund-, and trade-specific variables

→ Estimated separately for Buys and Sells

Include specification with Tr Size/Vol “kink” at 75% (based on Figure 1 above)

Fixed effects for year/month


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Key Regression Findings: Trade Costs…

... decrease in lagged flows for buys ... unrelated to lagged flows for sells ... increase with trade size for “smaller” buys (< 75% kink) decrease with trade size for “larger” buys (> 75% kink)

... unrelated to trade size for sells ... increase in Amihud illiquid stocks costlier to trade, especially for buys ... decrease with ClosePastTrade breakup of large trades reduces costs ... increase w/ higher past returns for buys buys more expensive in rising markets

... increase w/ lower past returns for sells sells more expensive in falling markets ... decrease in fund size – both buys & sells greater trade efficiency

Liquidity demand increases in buy size; largest buys include liquidity provision

Inflows lead to substitution of liquidity supply for liquidity demand for buys


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Are Post-Trade Stock Returns Related to Trade Costs? (Tables 5 & 6) Valuable ideas → liquidity demand → higher trade cost → higher post-trade return Depleted ideas → liquidity supply → low or negative cost → flat post-trade returns How long a post-trade period? We measure post-trade returns for the traded stock in excess of the market return (TSX300 for Canadian shares, S&P500 for U.S. shares) for one day and one week We test this relation using similar regression design as above

→ regress post-trade stock returns on trade costs and our other explanatory variables. Main Result: When a fund pays more to buy a stock, the post-trade return is higher

When a fund pays more to sell a stock, the post-trade return is lower

→ In predicted direction; stronger at a day than at a week


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Relation between Trade Costs and Fund Performance (Table 7)

So far, we find:

Funds incur trading costs for buys when they have trading ideas, otherwise substitute into liquidity provision.

Providing liquidity not so feasible for sells, and we see little evidence of it.

For both buys and sells, higher trade costs predict better post-trade performance; What is the net effect of all this on fund performance? Regress fund’s month-t return on VW trade costs of fund’s trades during month t Results:

When estimated over all trades, no relation between performance and trade costs

When estimated separately for buys and sells:

− Performance is increasing in the costs of buys

− Performance is decreasing or flat in the costs of sells

Trade costs of buys can be viewed as an investment in performance


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Conclusion Fund builds up inventory of profitable trade ideas, flows deplete it Funds substitute from demanding to providing liquidity as inventory of ideas depletes → The effect of this tradeoff is strong for buy costs, not for sell costs Higher Trade Costs More informed trades Better post-trade returns → for both buys and sells Fund performance is increasing in trade costs of buys Fund performance is decreasing or flat in trade costs of sells

wharton research data services - the supply and demand of … · 2018-09-14 · supply and demand...

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