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Interview with Sida Zhu


Here is an interview with a professional investment analyst. As a bit of background information, in the interest of preserving anonymity, he is in Equity Research at a Chinese mutual fund with <10 Billion USD. We asked him questions about the future of investing.

Q: How do you use quantitative strategies to help determine your investment decisions?

1) Construct a database for investment opportunities analysis, trying to get a historical record related to some special events. For example, when the swine flu broke out, how would the price of the pork react? And how about the price of stocks in the pork industry?

2) Try to figure out some more information besides the financial statements or guidance given by the listed companies. This is more common in retail stocks. For example, we may try to use the crawler technique to get some retailing data through an e-commerce website, to get ahead of the financial data release of the stock companies.

3) Quantitative research on the price or other data. This is more about pattern recognition of a series of data or some series of data, like the stock prices. We may model the data based on some assumptions, namely, lognormal or something, aiming to find ‘signals’ for investment decisions(entry, exit, hold)

Q: Are some asset classes better suited to fundamental research than others?

A: Yes, definitely. For my company, we focus on assets with high liquidities, like equities, bonds, commodity futures. Equities and bonds are more fundamental related, while a technical trading strategy of commodity futures may work as well. However, what trading is all about is to absorb more information than other traders to have an edge in the market, so quantitative research, especially the research about raw data(like the sales on e-commerce of some products)  can have an effect on improving the performance of traders.

Q: Where do you see the current trends in investment research changing the investment process in 10 years?

A: In China, the research trends may not have too much to change, especially in equity. In other words, fundamental research is and will be the mainstream (let’s say that quantitative research is the alternative way). The reasons may be various. Quantitative research requires a lot of techniques and skills, and it is not very easy for the PMs to master such a thing. What’s more, most PMs in China are 30-40 years old, which means they may work for another 20 years, without quantitative research skills. Since PMs do not know how to use it or even read the quantitative research reports, the demand for such research is not high. There are some teams with a background of Wallstreet quant funds indeed, but most of them do not generate a satisfactory performance. So the investors of funds do not want to put money into Quant style funds. However, I do think data mining techs like e-commerce retailing data(scrawler) will contribute to research, which has shown a proofed record for investment performance enhancement.

Q: As more of the research process gets automated, how can investors differentiate themselves and generate new unique ideas?

A: PMs are people to take money out of others’ pockets. So no matter how automated the process is, people are still there to make money. It is about decision, tradeoff, and judgment, not about information. Automated research is just about information.

Q: How have you taken advantage of developing technologies in your research process?

A: Generating reports with python, especially for the quarterly or annual reports analysis. I downloaded the data and compiled tableaus for the growth rate or other standard analysis. This is huge save of time for tedious work. I then pay a lot more time on trend analysis.

Commentary: The investment analyst interestingly noted that there was a separation between “Quant funds” and other money managers. He looks forward to new types of alternative data as ways to generate an edge.


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