Creating novel databases for out-of-sample testing adds real value as people seldom take the time to perform such a task. We discuss this and other topics with quant investment specialist Bart van Vliet.
“The main goal was to test for equity factors using an out-of-sample dataset that covered a period that no one had looked at before. More specifically, we were curious to see if the documented factor patterns in the post-1926 era also held up in the preceding 61 years. At the onset and throughout the duration of the project, we expected to face a number of data problems given the nature of the exercise and this turned out to be the case. That is why the research1 paper took us over five years to wrap up.”
“To give you some context, even before I got involved in the project during my internship, two students from Erasmus University were working on the data. Luckily for me, they did a lot of the heavy lifting. So by the time I looked at it, I already knew which stock exchanges to ignore and which stocks to exclude from our sample data. But the most difficult aspect in my opinion was accounting for liquidity.”
We manually captured company market cap data that we sourced from digitized old newspapers
“For example, we had to think about how to consider stocks with small market capitalizations that traded infrequently. If we took the equally-weighted sorting approach, then a bank with market cap of USD 1 million would have the same weight as a railroad business with a market cap of USD 500 million. To solve for this bias, we manually captured company market cap data that we sourced from digitized old newspapers. Overall, our biggest challenge was making sense of the data and this definitely brought about a few surprises along the way.”
“Myself and the two students from Erasmus University clocked up countless hours on the project. They were instrumental in creating the dataset and ensuring that it was clean, which made my task of adding market cap data a lot easier. I remember spending weeks just looking at newspapers and capturing values in numerous Excel sheets. At some points, I really felt like throwing my keyboard away given how time-consuming the task was.”
“The FRASER digital library has an archive of all the Commercial & Financial Chronicle newspapers from the 1860s to the 1960s, which I became very familiar with. Our research starts from 1866 as we located the first market cap datapoints in December 1865. These newspapers contain historical market data such as stock outstanding and par values. So as a part of my role in the project, I manually captured over 60 years’ worth of data. And this effort really adds value because no one takes the time to collect data manually.”
“The key takeaway was that the results validate the research that has been done over the 1926 to 2020 period. There is a lot of evidence in the academic literature that attributes the existence of established equity factors to behavioral biases. In our analysis, we found similar patterns relating to factor premiums in the pre-1926 era. In our view, it was not strange to come across these results as human behavior does not change overnight. In fact, it probably doesn’t over decades or even centuries.”
“Another interesting observation was that markets were quite efficient back in the 19th century. Based on our own analysis and other academic studies, we saw that the transaction costs were not as high as we initially expected. It is normal to assume that markets are much more efficient nowadays given that we have daily trading and market makers. But in reality, this was not necessarily the case, at least not to the extent that is assumed. On a lighthearted sidenote, I wasn’t really fond of history while I was in high school. But I soon found out that 30% of this project was based on history and the other 70% on economics. That being said, I really enjoyed the whole process — even the history. In fact, I found myself speaking about 19th century railroad companies in conversations with my friends.”
“To begin with, we took into account the nature of our database to determine which equity factors we could actually test for given the information on hand. Thereafter, we set out to limit our degrees of freedom. We therefore refined our list by only focusing on established academic factors and this led us to our final selection. For value, we used dividend yield as a proxy as there are no book-to-market values for that era. This is because companies were not obligated to report on such information before the 1930s. While a few did in the 1920s, there are not enough cross-sectional observations for testing purposes.”
“The common element with all the factors we tested is that they are return driven. So you can assess them by just looking at either total returns, price returns or dividend yields. If you look at quality, for instance, the data related to the characteristics that define it are only readily available from 1963 onwards, let alone the pre-1926 period. So, a combination of focusing on key established factors as well as taking into account certain constraints led us to our choice of factors.”
Behavioral biases are largely behind the existence and persistence of established equity factors
“For one, it reaffirms our existing beliefs as long-term quant investors. In our view, behavioral biases are largely behind the existence and persistence of established equity factors. Therefore, seeing the same patterns in the 19th century suggests there is strong evidence of this. The results also underline that factor premiums are not very dependent on specific market regimes, nor specific market structures. Moreover, the era is not as different as we think. It was characterized by technological disruptions and the stock market played an important role in financing the innovation. This is somewhat similar to what we have seen in recent times.”
“Machine learning techniques are typically used on broad datasets with lots of variables. A prominent academic paper2 on machine learning demonstrates that they can take into account 100 or so predictive variables to construct portfolios with good risk-return characteristics. But what we also see is that when we apply these techniques to our pre-1926 database, which has a smaller cross-section than what we have become accustomed to nowadays, they also produce good results.”
“This outcome is interesting given that it is based on out-of-sample data that was not available beforehand. This really signals the potential these methods have. Another interesting observation was that these techniques picked the same predictive variables for the pre-1926 era as they did for the 1926 to 2020 period. Indeed, the same academic study2 shows that the random forest technique allocates the highest weighting to dividend yields, while the neural network approach doesn’t. And this is the same result we got when we analyzed our dataset. This is quite remarkable in our view.”
“The most important takeaway is that aside from creating a high quality dataset, we wanted to ensure that it was of high economic quality. We achieved this by adding market cap data and applying liquidity screens as there are a lot of small companies that trade infrequently. One of the key objectives was to put together a dataset that resembles an investable universe from a practical sense. So when we applied these filters, we saw that some factor premiums became smaller, more specifically for the size factor. This is intuitive as our screening process excludes a lot of small companies from our dataset.”
“Although this shrinks our sample, we believe it is important to take this approach as the results also consider the typical liquidity constraints that investors face. We think this is even more appropriate to take into account for the pre-1926 era, given that there were other limitations affecting trading activity back then. So, our results are based on stocks that had a fair amount of liquidity, which makes the outcomes more meaningful in our opinion.”
1 Baltussen, G., Van Vliet, B. P., and Van Vliet, P., November 2021, “The cross-section of stock returns before 1926 (and beyond)”, working paper.
2 Gu, S., Kelly, B., and Xiu, D., February 2020, “Empirical asset pricing via machine learning”, Review of Financial Studies.
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.