New tools such as big and alternative data, AI,1 and cloud computing have emerged as major developments for the financial industry. A 2019 survey by the Bank of England and the UK’s Financial Conduct Authority found, for instance, that two-thirds of all British financial firms were already using machine learning.2 Many of these parties expected the number of areas in which they use it to more than double in the next three years.
In asset management, although many players have publicly embraced these innovations and been beating their chests about it, practical applications have so far remained focused on areas such as process automation, and sales and marketing. Other domains, in particular investments, still stand to benefit more broadly from this kind of innovation.
According to a 2019 survey by the CFA Institute among global investment professionals,3 only 10% of the portfolio managers who responded had used AI or machine learning4 (ML) techniques to improve their investment process in the previous 12 months. In contrast, almost half of them indicated that they had used regression analysis to find a linear relationship.
But while most of these techniques are still in their infancy, a growing number of players – primarily but not exclusively hedge funds – have taken important steps to investigate how they can be used in an effort to design better quantitative investment strategies, heralding what some experts have called “the next wave of quant investing”.
At Robeco, for instance, we have invested significant resources over the past few years, leading to concrete advances in the integration of these innovative technologies into our investment processes. A case in point is the ‘news sentiment signal’ derived from advanced event-based text analytics, which is now used to enhance the momentum factor in our quantitative equity strategies.5
Other uses of AI and alternative and big data reported by asset managers and other investment service providers include the analysis of earnings conference calls, equity trading volumes predictions, and the use of publicly available geospatial data to estimate local market share in the aggregates industry – the mining of sand, gravel and crushed rock for the production of concrete.6
This leaves investors with a burning question, though. Should these tools be seen as a mere extension of traditional quant investment approaches, which are primarily based on decades of empirical research on factors, using signals such as accounting information, financial analyst estimates and past prices from equity, fixed income, options or lending markets? Or do they mean the drivers behind most of the existing quantitative strategies are at risk of becoming obsolete?
Innovation enthusiasts obviously argue that the latter is true.7 One common explanation is that, in a world where most active quantitative managers have access to the same data, such as stock prices or macroeconomic fundamentals, and apply the same methods, including classic linear regression analysis and mean-variance optimizations, such techniques have become the only way to stand out from both market indices and direct competitors.
At the other end of the spectrum, skeptics argue that while these innovations may be able to add marginal improvements to existing investment strategies, they should be viewed with a fair amount of caution8 and do not fundamentally call into question more traditional and transparent quantitative investment approaches.
These skeptics frequently contend that while a solid investment strategy requires extensive empirical testing and falsification on broad data samples and over long periods of time, the evidence for big and alternative data remains largely anecdotal. Alternative datasets generally have a very short history and often lack the necessary breadth and quality to draw strong conclusions.9 Sometimes, it is even questionable whether the data provider will still exist in five or ten years’ time.
Another common criticism is the lack of interpretability or ‘auditability’ of AI algorithms and machine learning models.10 As a result, investment strategies based mainly on these techniques often also lack the necessary foundation of a clear economic rationale that’s normally required of more traditional quantitative approaches.
This divide illustrates the struggle asset managers face in order to maintain their edge over time: stick to time-tested methods and eventually risk becoming obsolete, or embrace change and risk a major misstep into ill-fated innovation. This dilemma is exacerbated by the recent disappointing performance of several broadly accepted factors, in particular value.
The current drawdown has brought established quantitative managers under severe scrutiny, with many investors wondering whether factor investing might need a complete overhaul. In the meantime, however, the live investment results achieved by most hardcore AI and alternative data advocates remain largely unimpressive.11
This leaves investors with no obvious robust alternative to more traditional factors, for now at least. Things could of course change, as alternative datasets available to investors will inevitably improve over time and AI algorithms could become reliable enough to deliver on their goal of long-term outperformance on a standalone basis.
In the past, the issues surrounding the datasets that are now widely used by quantitative asset managers were similar to those surrounding big and alternative data today. Over the years the quality, breadth and history of these datasets have improved, and they have become usable. With the passage of time, and as more data becomes available, big and alternative data will likely also become increasingly usable.
At the same time, a growing body of academic literature confirms that AI techniques can be helpful tools to improve investment strategies.12 So, while machines will probably never fully replace humans, they can – under human supervision – help detect and explain new patterns. Machines can also make research production much more scalable.
Ultimately, investors should remain open-minded about new ideas. The fundamental issue for them may not necessarily be about choosing between one approach or the other. There is a wide array of possibilities, from sticking to traditional price and financial statement information at one extreme, to relying solely on information sources such as satellite imagery of parking lots and deep learning algorithms.
The answer could well be in using a blend of information resources. For example, big data and AI signals could be very useful to fundamental credit and equity analysts. This would feed through into our quantitative strategies that take analyst revisions into account. In this case, we would be using big data and AI information in an indirect manner. Figure 1 provides an overview of how leading asset managers use such advanced analytics.
That said, it is important to remember that while innovation can help, it should be applied carefully and sensibly. Basic principles – such as ensuring that investment decisions are evidence based, prudent and with a clear economic rationale – should always apply, even when considering avant-garde techniques like alternative data or AI.
1AI can be defined as the use of computational tools to perform tasks that traditionally required human thinking. As a scientific field of research, AI is far from new. The term was coined in the mid-1950s by computer scientist John McCarthy, then assistant professor at Dartmouth College. However, recent improvements in computational power and the dramatic surge in the amount of data available in the digital age have significantly increased the scope of potential applications for these technologies.
2Jung, C., Mueller, H., Pedemonte, S., Plances, S. and Thew, O., October 2019, “Machine learning in UK financial services”, Bank of England and Financial Conduct Authority report.
3Cao, L., 2019, “AI pioneers in investment management”, CFA Institute report.
4Machine learning refers to the use of computer algorithms that improve their predictions automatically through experience. It can therefore be considered a subsegment of artificial intelligence.
5Marchesini, T. and Swinkels, L., July 2019, “Integrating news sentiment in quant equity strategies”, Robeco client note.
6The CFA Institute report mentioned in footnote 3 provides an interesting overview of pioneering, concrete applications.
7See for instance: Calvello, A., 15 January 2020, “Fund managers must embrace AI disruption”, Financial Times. See also: Rajan, A., 27 January 2020, “AI will rewrite the future of fund management”, Financial Times.
8Kirk, E., 3 March 2020, “Don’t believe the hype about AI and fund management”, Financial Times.
9See for example: Arnott, R., Harvey, C. R. and Markowitz, H., 2019, “Backtesting protocol in the era of machine learning”, The Journal of Financial Data Science.
10FSB 2017. Artificial intelligence and machine learning in financial services – Market developments and financial stability implications.
11See: Fletcher, L., 7 September 2020, “AI hedge fund Voleon suffers in choppy markets”, Financial Times.
12Simonian, J., Lopez de Prado, M., Fabozzi and F. J., 2018, “Order from chaos: How data science Is revolutionizing investment practice”, Invited editorial comment, The Journal of Portfolio Management. See also: Snow, D., 2020, “Machine learning in asset management – Part 1: Portfolio construction – Trading strategies”, The Journal of Financial Data Science. See also: Snow, D., 2020, “Machine learning in asset management—Part 2: Portfolio construction—Weight optimization”, The Journal of Financial Data Science.
The contents of this document have not been reviewed by the Securities and Futures Commission ("SFC") 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 SFC 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. Please refer to the relevant offering documents for details including the risk factors before making any investment decisions.
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.
Please read this information carefully.
This website is prepared and issued by Robeco Hong Kong Limited ("Robeco"), which is a corporation licensed by the Securities and Futures Commission in Hong Kong to engage in Type 1 (dealing in securities); Type 4 (advising in securities) and Type 9 (asset management) regulated activities. The Company does not hold client assets and is subject to the licensing condition that it shall seek the SFC’s prior approval before extending services at retail level. This website has not been reviewed by the Securities and Futures Commission or any regulatory authority in Hong Kong.
2. Important risk disclosures
2. Important risk disclosures Robeco Capital Growth Funds (“the Funds”) are distinguished by their respective specific investment policies or any other specific features. Please read carefully for the risks of the Funds:
3. Local legal and sales restrictions
The information contained in the Website is being provided for information purposes.
Neither information nor any opinion expressed on the Website constitutes a solicitation, an offer or a recommendation to buy, sell or dispose of any investment, to engage in any other transaction or to provide any investment advice or service. The information contained in the Website does not constitute investment advice or a recommendation and was prepared without regard to the specific objectives, financial situation or needs of any particular person who may receive it. An investment in a Robeco product should only be made after reading the related legal documents such as management regulations, prospectuses, most recent annual and semi-annual reports, which can be all be obtained free of charge at www.robeco.com/hk/en and at the Robeco Hong Kong office.
4. Use of the Website
The information is based on certain assumptions, information and conditions applicable at a certain time and may be subject to change at any time without notice. Robeco aims to provide accurate, complete and up-to-date information, obtained from sources of information believed to be reliable. Persons accessing the Website are responsible for their choice and use of the information.
5. Investment performance
No assurance can be given that the investment objective of any investment products will be achieved. No representation or promise as to the performance of any investment products or the return on an investment is made. The value of your investments may fluctuate. The value of the assets of Robeco investment products may also fluctuate as a result of the investment policy and/or the developments on the financial markets. Results obtained in the past are no guarantee for the future. Past performance, projection, or forecast included in this Website should not be taken as an indication or guarantee of future performance, and no representation or warranty, express or implied, is made regarding future performance. Fund performance figures are based on the month-end trading prices and are calculated on a total return basis with dividends reinvested. Return figures versus the benchmark show the investment management result before management and/or performance fees; the fund returns are with dividends reinvested and based on net asset values with prices and exchange rates of the valuation moment of the benchmark.
Investments involve risks. Past performance is not a guide to future performance. Potential investors should read the terms and conditions contained in the relevant offering documents and in particular the investment policies and the risk factors before any investment decision is made. Investors should ensure they fully understand the risks associated with the fund and should also consider their own investment objective and risk tolerance level. Investors are reminded that the value and income (if any) from shares of the fund may be volatile and could change substantially within a short period of time, and investors may not get back the amount they have invested in the fund. If in doubt, please seek independent financial and professional advice.
6. Third party websites
This website includes material from third parties or links to websites maintained by third parties some of which is supplied by companies that are not affiliated to Robeco. Following links to any other off-site pages or websites of third parties shall be at the own risk of the person following such link. Robeco has not reviewed any of the websites linked to or referred to by the Website and does not endorse or accept any responsibility for their content nor the products, services or other items offered through them. Robeco shall have no liability for any losses or damages arising from the use of or reliance on the information contained on websites of third parties, including, without limitation, any loss of profit or any other direct or indirect damage. Third party off-site pages or websites are provided for informational purposes only.
7. Limitation of liability
Robeco as well as (possible) other suppliers of information to the Website accept no responsibility for the contents of the Website or the information or recommendations contained herein, which moreover may be changed without notice.
Robeco assumes no responsibility for ensuring, and makes no warranty, that the functioning of the Website will be uninterrupted or error-free. Robeco assumes no responsibility for the consequences of e-mail messages regarding a Robeco (transaction) service, which either cannot be received or sent, are damaged, received or sent incorrectly, or not received or sent on time.
Neither will Robeco be liable for any loss or damage that may result from access to and use of the Website.
8. Intellectual property
All copyrights, patents, intellectual and other property, and licenses regarding the information on the Website are held and obtained by Robeco. These rights will not be passed to persons accessing this information.
10. Applicable law
The Website shall be governed by and construed in accordance with the laws of Hong Kong. All disputes arising out of or in connection with the Website shall be submitted to the exclusive jurisdiction of the courts of Hong Kong.