Introduction

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‍In the first article, we observed that rebalancing according to the variations of a risk indicator can under certain circumstances be more interesting than rebalancing on a fixed schedule. However, we noted that following a risk indicator can increase the rebalancing frequency, which may pose a problem for some asset managers. We also found that it is possible to smooth a signal to reduce this frequency, but such a process can significantly alter the indicator's effectiveness if it is not properly calibrated.

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In the second article, we saw that an excellent way to reduce noise and therefore the number of rebalancing actions for a signal is to combine it with other signals. This diversification allows for retaining most of the value provided by the underlying indicators while making the resulting signal more stable. We demonstrated this statement using a combination of Alquant homemade indicators.

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The last article will present a second case study, with equity-only portfolio and different rebalancing constraints. Indeed, many asset managers and fund managers have constraints regarding exposure to fixed asset classes. For example, some must maintain a constant 100% exposure to equities and hold their positions for a certain period. These are the types of constraints we will explore here.

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Full equity risk managed strategy

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Our analysis focuses on a portfolio strategy that maintains 100% equity exposure while incorporating a mandatory two-week waiting period between rebalancing actions. This setup is representative of equity asset managers who must maintain continuous market exposure and are limited in how frequently they can rebalance. To adapt to varying market conditions, the strategy involves shifting between low-beta and high-beta equities based on the assessed level of market risk. Specifically, the portfolio shifts towards low-beta stocks when intrinsic market risk is high and moves back to high-beta stocks when the risk level is perceived as low. This approach aims to optimize returns while managing risk effectively within the constraints of the mandate’s or fund's operational guidelines.

To simplify the modeling of such an investment strategy, we will utilize a universe composed of two specific assets: the Invesco QQQ ETF (QQQ), representing high-beta equities, and the Consumer Staples Select Sector ETF (XLP), representing low-beta equities. We will employ the same Combined Indicator from the second article, which integrates five risk indicators developed by Alquant that are available on Prisma. For a detailed description of the indicators, please refer to the previous article.

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Additionally, we have set a rule mandating a minimum two-week interval between rebalancings to mitigate frequent portfolio changes and thus avoid clients' concerns regarding too frequent adjustments in portfolio composition. This approach ensures the portfolio remains reactive enough by switching to the low-beta asset (XLP) when our Combined Indicator exceeds 50%. For simplicity, the portfolio will adopt binary weightings for each asset—either 100% or 0%. This could be further adapted to match specific turnover volume constraints of the asset or fund manager.

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The entire strategy, including these operational parameters and decision rules, is detailed in Figure 1 below.

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Figure 1: Equity strategy construction summary

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The following table summarizes the strategy we will test.

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Figure 2: Exposure in equity depending on the Combined Indicator’s values

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Our benchmark for this analysis will be a 50% XLP:US - 50% QQQ:US portfolio, rebalanced on a monthly basis. Let us now have a look at the results of this simple strategy, and assess the frequency of rebalancing it needs. Transaction costs of 0.05% are applied.

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Figure 3: Performance and exposure over time of the strategy tested, from 2008-01-15 to 2024-05-20, including 0.05% transaction costs.

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The major advantage of switching between these equity types is clearly illustrated in the accompanying graph. During prolonged bull market periods, the portfolio is fully invested in QQQ, thereby maximizing gains from its higher returns. The indicator responds promptly during periods of high volatility and increased risk, ensuring a timely shift to low-beta equities. This strategic switching not only mitigates risk but also contributes to a steady increase in outperformance over time.

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Figure 4: Statistics of the strategies tested, from 2008-01-15 to 2024-05-20, including 0.05% transaction costs.

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The statistics indicate that the resulting strategy outperforms an equally weighted portfolio of its assets. Returns are increased by nearly 1.5 times, the Sharpe ratio improves by 30%, and the portfolio requires only six rebalancing actions per year on average, translating to one rebalancing every two months. Additionally, the maximum drawdown is reduced by approximately 25%, and the benefit of each indicator is maintained without diminishing its effectiveness through excessive smoothing.

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In conclusion, this series of three articles has demonstrated the significant added value of basing rebalancing actions on risk indicators rather than adhering to a fixed schedule. While this approach requires flexibility and adaptability to market conditions, it proves worthwhile in terms of the alpha and information ratio generated per average number of rebalancings. We have also observed that, to optimize rebalancing frequency according to constraints, a portfolio manager should diversify and combine multiple indicators rather than smoothing a single signal, which can reduce its effectiveness.

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Disclaimer

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‍This content is advertising material. This content as well as all information displayed on any of Alquant’s websites does not constitute investment advice or recommendation, and shall not be construed as a solicitation or an offer for sale or purchase of any products, to effect any transactions or to conclude any legal act of any kind whatsoever. Past performance is not a guide to future performance.