One oft-heard criticism of explicit allocation to factors is that it inevitably leads to high, or even excessive, portfolio turnover. Indeed, while following a cap-weighted market index can essentially be seen as a ‘buy-and-hold’ approach, with limited portfolio activity, explicit allocation to factors necessarily leads to more dynamic trading.
In cap-weighted indices, stock weights fluctuate naturally with the prices of constituent securities, and changes in the portfolio composition are only triggered by large changes in free-float capitalizations or corporate actions such as mergers, splits or new listings or delistings.
On the contrary, factor-based investment strategies generate turnover from the periodic rebalancing required to maintain optimal exposure to the targeted premiums, for example value, momentum, low volatility or quality. This necessary turnover has led many academics and investors to question whether factor-based solutions are really worthwhile, given the higher trading costs associated with these strategies.
For example, a recent study1 by Research Affiliates warned about the significant slippage between the factor returns realized by mutual fund managers and the theoretical factor returns that would have been achieved by virtual portfolios, over the 1991–2016 period. The authors attributed this gap to a number of costs related to implementation, including trading costs.
In a 2016 white paper2, Joop Huij and Georgi Kyosev, of Robeco’s Quant research team, warned specifically about the high rebalancing costs implied by the replication of some common smart beta indices. Analyzing the impact of composition changes for two popular indices, they found that these costs are actually higher than they might appear, as the rebalancing process also leads to lower index returns. This is because strategies that follow publicly available indices, for which changes are announced in advance, tend to buy stocks that have just had a price run-up, and sell stocks that have just suffered a price decrease.
More generally, an FTSE Russell survey carried out in 2016 suggested that avoiding excessive portfolio turnover ranked fourth among investor concerns, when considering factor-oriented allocation.
“
Turnover can be reduced without lowering gross returns too much, but only up to a certain point.
Reducing, not minimizing, turnover
But while the risk of excessive turnover should not be overlooked, it should not be exaggerated, either. In fact, it is possible to considerably reduce turnover without hampering performance too much. Robeco’s in-house research shows that when investors start keeping securities with less attractive factor qualities in their portfolios for longer, trading costs tend to decrease faster than the gross return. As a result, the net return/risk ratio tends to increase when turnover starts to decline.
This finding does not mean that portfolio changes should be minimized. Turnover can be reduced without lowering gross returns too much, but only up to a certain point. And gross returns also tend to drop rapidly once we allow unattractive securities to remain in the portfolio for too long or rebalance too infrequently. Investors must therefore find the optimal trade-off between factor exposure and rebalancing costs, in order maximize after-cost performance.
Common techniques
There are many ways to reduce and control portfolio turnover, and that can be applied to all kinds of factor-based strategies. The most obvious one is setting and adjusting fixed rebalancing intervals, in order to reassess factor exposures more or less frequently. Another option is allowing a portfolio to deviate more or less from its ideal composition, if only factor exposures were taken into account and implementation costs were neglected. The greater the deviation tolerance, the lower the turnover will tend to be.
In addition to these general techniques, which are widely used by investment managers and index providers, there are also more strategy-specific ways to reduce turnover. Empirical studies carried out on the short-term reversal phenomenon, which has been extensively documented in the academic literature, provide a good illustration of this.
Short-term reversal strategies exploit the fact that stocks that experience huge gains or losses during one month tend to reverse that trend the following month. However, many investors remain skeptical about this kind of approach because they involve huge turnover, as signals typically change completely every month.
But a 2011 paper3 by Wilma de Groot, Joop Huij and Weili Zhou, of Robeco’s Quant Equity research team, showed that the high transaction costs incurred in many these investment strategies implemented in the US stock market could largely be attributed to excessive trading in small caps. Trading costs could therefore be significantly reduced by limiting the stock universe to large caps. Similarly, comparable ways to reduce turnover can often be found for different kinds of quantitative strategies.
All of Robeco’s quantitative strategies use portfolio-construction processes designed to keep trading low and trading costs under control, using a securities-ranking approach. This kind of method is less sensitive to changing market inputs. Moreover, for credit markets, which lack the immediacy seen in equity markets and where keeping transaction costs under control proves more challenging, we have developed a specific investment process, in which liquidity management is actually embedded in the portfolio construction process itself. This enables us to send only those orders which have a high probability of being executed.
免責聲明
本文由荷宝海外投资基金管理(上海)有限公司(“荷宝上海”)编制, 本文内容仅供参考, 并不构成荷宝上海对任何人的购买或出售任何产品的建议、专业意见、要约、招揽或邀请。本文不应被视为对购买或出售任何投资产品的推荐或采用任何投资策略的建议。本文中的任何内容不得被视为有关法律、税务或投资方面的咨询, 也不表示任何投资或策略适合您的个人情况, 或以其他方式构成对您个人的推荐。 本文中所包含的信息和/或分析系根据荷宝上海所认为的可信渠道而获得的信息准备而成。荷宝上海不就其准确性、正确性、实用性或完整性作出任何陈述, 也不对因使用本文中的信息和/或分析而造成的损失承担任何责任。荷宝上海或其他任何关联机构及其董事、高级管理人员、员工均不对任何人因其依据本文所含信息而造成的任何直接或间接的损失或损害或任何其他后果承担责任或义务。 本文包含一些有关于未来业务、目标、管理纪律或其他方面的前瞻性陈述与预测, 这些陈述含有假设、风险和不确定性, 且是建立在截止到本文编写之日已有的信息之上。基于此, 我们不能保证这些前瞻性情况都会发生, 实际情况可能会与本文中的陈述具有一定的差别。我们不能保证本文中的统计信息在任何特定条件下都是准确、适当和完整的, 亦不能保证这些统计信息以及据以得出这些信息的假设能够反映荷宝上海可能遇到的市场条件或未来表现。本文中的信息是基于当前的市场情况, 这很有可能因随后的市场事件或其他原因而发生变化, 本文内容可能因此未反映最新情况,荷宝上海不负责更新本文, 或对本文中不准确或遗漏之信息进行纠正。