Trading Cost Management Of Mutual Funds
Erasmus University Rotterdam (EUR)
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January 27, 2016
This paper documents the trading behaviour of actively managed equity mutual funds from the perspective of their trading cost management. Consistent with the predictions in the literature of portfolio choice with trading costs, I present three main findings. Firstly, mutual funds trade more liquid stocks than illiquid stocks when there are large fund flows. Secondly, these mutual funds spread their trades over stocks, especially for outflow-driven sales. Finally, they also spread their flow-driven trades over time and use cash buffers. Spreading trades over stocks and time is consistent with a trading cost increasing and convex in trading amount, which is also consistent with the key assumption in Berk and Green (2004) that costs are increasing and convex in fund size.
Trading Cost Management Of Mutual Funds – Introduction
Since Constantinides (1986), theories on portfolio choice with trading costs have developed rapidly. Recent works, such as Scholes (2000), Duffie and Ziegler (2003) and Brown, Carlin, and Lobo (2010), have suggested that financial institutions that have urgent liquidity needs should sell liquid assets first in order to reduce the trading costs. Garleanu and Pedersen (2013) recommend that investors “trade gradually towards the aim” in order to reduce price impact costs. However, there is little empirical evidence to support those claims, and there is still uncertainty regarding the extent to which institutional investors actually care about trading costs, and what they actually do to reduce them. In this paper, I attempt to address this knowledge gap by looking directly at the trading behavior of mutual funds. I conduct this analysis using the holding data of mutual funds and find that trading-cost-management behavior exists and is consistent with theoretical predictions. Specifically, using quarterly holding data of mutual funds from 1980 to 2009, I investigate how actively managed equity mutual funds trade in order to reduce trading costs and the price impact of trades.
The trading strategy is always a joint decision of maximizing the profits and minimizing the trading costs, and usually the trading motives1 are not observable in publicly available data2. Therefore, the biggest challenge of this study is to identify the trading cost management from other trading motives. In this paper, I use fund flows for this identification. Firstly, it is because fund flows are observable, which can be calculated as the changes of total net assets (TNA) adjusted by fund returns from the data. Secondly, mutual funds are forced to trade when there are large fund flows.3 In this paper, I define the trades caused by fund flows as ow-driven trades. Thirdly, fund flows are largely exogenous to their investment strategies.4 Fourthly, price-impact costs lay a crucial role for ow-driven trades since the size of ow-driven trades is usually very large. Flow-driven trades account for about 28% of all trading activities of active mutual funds, and the total amount of flow-driven trades every year is about 100% of their total net assets. Therefore, I focus my analysis on ow-driven trades.
I find evidence for three aspects of trading-cost-management behavior predicted by theories,
(1) They trade more liquid stocks than illiquid stocks when there are large fund flows;
(2) They spread their ow-driven trades over stocks (trade more stocks) to reduce price impact of trades;
(3) They use cash buffers to spread their ow-driven trades over time.
I do both portfolio analysis and regression analysis for each of these. For the portfolio analysis, I sort all fund-quarter observations into deciles based on their quarterly fund flows. I find that, firstly, mutual funds trade relatively more liquid stocks in their portfolios when they face large fund flows. Secondly, rather than scaling up or down their portfolio proportionally, most mutual funds only trade a small number of stocks when facing small fund flows. They trade more stocks when facing larger fund flows to reduce the price impact of trades, but they still trade only a fraction of stocks in their portfolios (about 50% to 60%) when facing extremely large fund flows. Thirdly, they have less stock holdings and more cash buffers when there are in flows and more stock holdings and less cash buffers when there are out flows.
Then, I do regression analysis using both fund level data and fund-stock level holding data. Firstly, using fund level data, I study the relation between the average liquidity of stocks traded and the size of fund flows. I find that the average liquidity of stocks sold increases with the magnitude of fund out flows, and the average liquidity of stock bought increases with the magnitude of fund in flows. This tendency is stronger for out ow-driven sales than in ow-driven purchases since out ow-driven sales are usually more urgent than in ow-driven purchases. Consistently, the analysis using fund-stock-level holding data shows that the ow-driven trade is on average 10% larger for a stock 1 standard deviation more liquid across individual stocks.
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