‘Factors are still the foundation of financial markets’

‘Factors are still the foundation of financial markets’

08-06-2022 | 訪問

New data sources and techniques can help us to better understand factors. We discuss this and other topics with quant fixed income researcher Philip Messow who recently joined Robeco from Quoniam Asset Management.

  • Lusanele Magwa
    Investment Writer

Speed read

  • Increasing interest in possibilities offered by novel data sources or techniques
  • Factors are the return and risk drivers for financial instruments
  • The best days for quant fixed income investing are yet to come

What inspired you to enter the field of quant investing?

“The close connection initially developed through my studies and PhD journey. Being able to combine econometric theory with real-world investments is fascinating to me. At university, the approach is academic by nature and the focus is predominantly on theoretical aspects as opposed to their practical application. Quant investing is essentially an interconnection between the two or a mixture of both worlds. It provides a platform for us to really undertake interesting research which can make a tangible difference for investors.”

Why did you decide to join Robeco?

“When I joined the industry back in 2014 following my PhD, I was aware that Robeco was one of the flagships in terms of quant investing. Naturally, I read a lot of Robeco publications and found their research to be cutting-edge and ahead of the curve. Therefore, I was genuinely pleased when I was approached with the opportunity to join the Robeco Quant Fixed Income team. Moreover, I had previously met several of my present colleagues at different conferences and those interactions left a lasting impression on me. Not only did they come across as smart, but they were also nice, regular people. So I had already gathered that it would be a good professional fit for me.”

“This view has been reinforced in my short time here so far. In particular, the flat hierarchical structure has impressed me, as decisions are made from a bottom-up perspective. So, as an individual and a team as a whole, you have an influential stake in the entire investment process. In my brief experience, I have found that the corporate culture at Robeco encourages people to take ownership of responsibilities, as they are not constrained by top-down decision-making.”


Does this help to create an environment that is conducive to innovation?

“Absolutely. For example, the main projects in our 2022 research agenda were chosen by majority vote. This approach allows team members to come up with their own ideas and, if they can convince the team of their value-add, they stand a good chance of being taken onboard and included in the agenda. This encourages innovation, and the majority vote approach also ensures collective buy-in from the team, which is helpful when carrying out the project.”

How do you see the future of quant investing?

“The field of quant investing is constantly evolving. While the broad, fundamental framework of factor investing generally ties everything together, some of the individual elements frequently change or new ones emerge. For example, investors are increasingly interested in the possibilities offered by novel data sources or techniques and they are also more involved with sustainable investing. But if you are ahead of the curve, perform cutting-edge research, have state-of-the-art infrastructure and employ smart people, then the questions that are posed by constant change are fun to solve. In fact, keeping abreast of the evolution is what gives certain quant investors an edge. If you fail to take into account the future needs of investors, it is likely that you will be left behind.”

“But because of the constant evolutionary process, quant investors have to adapt. They have to reassess their previous findings, come up with new ideas and maybe draw different conclusions. As Robeco quant investors, this makes our job challenging, yet interesting. And I believe the company has the ingredients in place to successfully navigate the changing terrain, as it has a culture that promotes innovation, the right infrastructure, institutional knowledge in quantitative and sustainable investing and a great mix of people who challenge the status quo.”

Can novel techniques such as machine learning fit into an investment process?

“It is important to note that machine learning (ML) techniques were not originally developed for financial data. These methods typically work really well when you have lots of independent data observations and a high signal-to-noise ratio. But the problem with trying to explain future returns, for example, is that you don’t necessarily have a vast set of independent observations. Securities generally move in the same direction due to their correlation, while the time series return data for individual securities is also typically highly correlated. Moreover, datasets usually have a lot of noise and few signals.”

“That said, ML techniques are useful for specific applications such as liquidity or risk measurement as well as return or volatility forecasting. However, you also need to blend these techniques with traditional statistical approaches and incorporate economic insights into the relevant models to reduce the dimensionality of the problems you are solving. This is easier than solving an unconstrained problem. Therefore, it is important to refrain from blindly applying ML techniques to these applications, but to also incorporate your financial expertise.”

Are traditional factors old-fashioned or outdated?

“Absolutely not. Factors defined by traditional data and methods are still the foundation of financial markets. They are the return and risk drivers for corporate bonds, government bonds, equities and many more financial instruments. But new techniques, such as ML, help us to better understand how factors work and interact with each other. They can extract information from large datasets that would otherwise remain hidden when using traditional techniques.”

“This additional information can provide more clarity on how to exploit factors more effectively to achieve better risk-adjusted performance. Moreover, it can reveal some of the shortcomings of factor investing. Although factors deliver a premium over the long term, they are susceptible to periods of underperformance. Thus, these methods can uncover some of the less understood risks associated with factors.”

Why are new data sources integrated slowly into quant models?

“Alternative datasets are still relatively new and need to be studied carefully. They usually do not have a long history, so it is really hard to test whether they are useful or not. To do so, you need at least a full investment cycle to evaluate whether new data signals work well as standalone factors or complement your model. Furthermore, it is important to understand whether these signals perform better or worse in varying economic conditions and why that is the case. Also, many datasets offer limited breadth, as they are only relevant for small parts of the investment universe – like satellite images. This makes them less applicable for quant models, which require breadth and long-term empirical evidence. Thus, being cautious is better than being wrong.”

What is your outlook on quant fixed income investing over the next five years?

“The best days for quant fixed income investing are yet to come. Firstly, the number of bonds is constantly increasing. As a result, bond indices are growing in size and this development is suitable for our data-hungry quant models. As I mentioned before, a higher number of data observations can allow us to apply more sophisticated models to generate superior risk-adjusted performance.”

“Secondly, liquidity in fixed income markets has constantly improved over the last few years. This is really important for quant investing, particularly concerning credit markets. Indeed, if you compare volatility to transaction costs, the ratio is significantly higher for credit markets than it is for equity markets. Therefore, the hurdle for factor investing is much higher for the former than for the latter. But with improving liquidity, it is becoming easier to implement systematic strategies in credit markets.”

“Thirdly, it has been shown empirically that combining fundamentally and quantitatively managed credit portfolios usually leads to significant tracking error reduction without weighing on the alpha potential. This style diversification can therefore benefit clients who have fundamentally managed portfolios, but zero or limited exposure to their quantitatively managed counterparts. Thus, increased appreciation of these diversification benefits should result in rising interest in quant fixed income strategies.”

“Lastly, lots of investors typically adopt a buy-and-hold approach to credit markets. But managers such as Robeco have now built track records longer than five years which show that quant investing also works in the credit space. This has established the view that it is possible to manage credit strategies in a systematic manner by exposing portfolios to factors. With more and more evidence of this coming to the fore, we expect some buy-and-hold investors to change their stance by shifting some of their assets to quant credit strategies.”

Important information

The contents of this document have not been reviewed by any regulatory authority in Hong Kong. If you are in any doubt about any of the contents of this document, you should obtain independent professional advice. This document has been distributed by Robeco Hong Kong Limited (‘Robeco’). Robeco is regulated by the Securities and Futures Commission in Hong Kong.
This document has been prepared on a confidential basis solely for the recipient and is for information purposes only. Any reproduction or distribution of this documentation, in whole or in part, or the disclosure of its contents, without the prior written consent of Robeco, is prohibited. By accepting this documentation, the recipient agrees to the foregoing
This document is intended to provide the reader with information on Robeco’s specific capabilities, but does not constitute a recommendation to buy or sell certain securities or investment products. Investment decisions should only be based on the relevant prospectus and on thorough financial, fiscal and legal advice.
The contents of this document are based upon sources of information believed to be reliable. This document is not intended for distribution to or use by any person or entity in any jurisdiction or country where such distribution or use would be contrary to local law or regulation.
Investment Involves risks. Historical returns are provided for illustrative purposes only and do not necessarily reflect Robeco’s expectations for the future. The value of your investments may fluctuate. Past performance is no indication of current or future performance.



1. 一般事項


此網站由Robeco Hong Kong Limited(「荷寶」)擬備及刊發,荷寶是獲香港證券及期貨事務監察委員會發牌從事第1類(證券交易)、第4類(就證券提供意見)及第9類(資產管理)受規管活動的企業。荷寶不持有客戶資產,並受到發牌條件所規限。荷寶在擴展至零售業務之前,必須先得到證監會的批准。本網頁未經證券及期貨事務監察委員會或香港的任何監管當局審閱。

2. 風險披露聲明

Robeco Capital Growth Funds以其特定的投資政策或其他特徵作識別,請小心閱讀有關Robeco Capital Growth Funds的風險:

  • 部份基金可涉及投資、市場、股票投資、流動性、交易對手、證券借貸及外幣風險及小型及/或中型公司的相關風險。
  • 部份基金所涉及投資於新興市場的風險包括政治、經濟、法律、規管、市場、結算、執行交易、交易對手及貨幣風險。
  • 部份基金可透過合格境外機構投資者("QFII")及/或 人民幣合格境外機構投資者 ("RQFII")及/或 滬港通計劃直接投資於中國A股,當中涉及額外的結算、規管、營運、交易對手及流動性風險。
  • 就分派股息類別,部份基金可能從資本中作出股息分派。股息分派若直接從資本中撥付,這代表投資者獲付還或提取原有投資本金的部份金額或原有投資應佔的任何資本收益,該等分派可能導致基金的每股資產淨值即時減少。
  • 部份基金投資可能集中在單一地區/單一國家/相同行業及/或相同主題營運。 因此,基金的價值可能會較為波動。
  • 部份基金使用的任何量化技巧可能無效,可能對基金的價值構成不利影響。
  • 除了投資、市場、流動性、交易對手、證券借貸、(反向)回購協議及外幣風險,部份基金可涉及定息收入投資有關的風險包括信貨風險、利率風險、可換股債券的風險、資產抵押證券的的風險、投資於非投資級別或不獲評級證券的風險及投資於未達投資級別主權證券的風險。
  • 部份基金可大量運用金融衍生工具。荷寶環球消費新趨勢股票可為對沖目的及為有效投資組合管理而運用金融衍生工具。運用金融衍生工具可涉及較高的交易對手、流通性及估值的風險。在不利的情況下,部份基金可能會因為使用金融衍生工具而承受重大虧損(甚至損失基金資產的全部)。
  • 荷寶歐洲高收益債券可涉及投資歐元區的風險。
  • 投資者在Robeco Capital Growth Funds的投資有可能大幅虧損。投資者應該參閱Robeco Capital Growth Funds之銷售文件內的資料﹙包括潛在風險﹚,而不應只根據這文件內的資料而作出投資。

3. 當地的法律及銷售限制




4. 使用此網站



5. 投資表現



6. 第三者網站

本網站含有來自第三方的資料或第三方經營的網站連結,而其中部分該等公司與荷寶沒有任何聯繫。跟隨連結登入任何其他此網站以外的網頁或第三方網站的風險,應由跟隨該連結的人士自行承擔。荷寶並無審閱此網站所連結或提述的任何網站,概不就該等網站的內容或所提供的產品、服務或其他項目作出推許或負上任何責任。荷寶概不就使用或依賴第三方網站所載的資料而導致的任何虧損或損毀負上法侓責任,包括(但不限於)任何虧損或利益或任何其他直接或間接的損毀。 此網站以外的網頁或第三方網站皆旨在作參考之用。

7. 責任限制




8. 知識產權


9. 私隠

荷寶保證將會根據現行的資料保障法例,以保密方式處理登入此網站的人士的數據。除非荷寶需按法律責任行事,否則在未經登入此網站的人士許可,不會向第三方提供該等數據。 請於我們的私隱及Cookie政策 中查找更多詳情。 

10. 適用法律


如果您已閱讀並理解本頁並同意上述免責聲明以及同意荷寶收集和使用您的個人資料,用於私隱及Cookie政策 所列的收集和使用個人資料的目的(包括用於直接推廣荷寶的產品或服務),請點擊“我同意”按鈕。否則,請點擊“我不同意”離開本網站。