Magazine Issues » March 2019

Interview: Money needs a place to go

Peng_FeiPeng Fei, chief investment officer at Wanwei Asset Management, tells Romil Patel about allocating capital across risk factors when asset performance is uncertain and unpredictable.

Last year was a difficult year with the US-China trade war, US Federal Reserve rate hikes and Brexit, to name some issues. Can you tell us a bit about Wanwei and how it outperformed the markets as they fell?
Wanwei was incubated by Bopu Technologies, a quantitative hedge fund in China. Wanwei is a fintech solution provider, which provides services such as robo-advisory, an intelligent risk management platform and online/mobile wealth management platform customisation to institutional and retail clients. We have a very unique asset allocation framework to help us perform in different market regimes. In China there are not as many asset class options as in overseas markets, but we have very good absolute return strategies to choose from, such as arbitrage strategies, which delivered a return of more than 10% in 2018 and are very stable.

Our asset allocation is based on a new idea of asset risk management, so we are not looking at allocating capital across assets; we are focusing on allocating capital across risk factors. Asset performance is uncertain and very difficult to predict, but the factor performance over the long run is more predictable, so we are transforming from the uncertain allocations to more likely certain allocations on risk factors that will bring us good protection on the downside. While we have good downside protection, our risk factors are selected based on our investment experience, industry research papers and artificial intelligence (AI) algorithm search as they can deliver long-term risk premium. We also have different factors that provide diversification, so the overall drawdown is much smaller compared to concentrating on a single asset. That is how we protect our capital and delivered solid performance in 2018.

Last year, China’s interest rate bond market did very well. Our overweight position captured that premium, which provided some good returns and compensated the drawdown from the equity side.

Wanwei is delivering bespoke robo-advisory models to clients – are you seeing greater interest from foreign institutional investors or is it mainly from Chinese investors?
Yes, the interest is definitely universal, and we have been observing the migration from fintech 1.0 to fintech 2.0 due to the growing demand globally. Financial companies have been implementing technology long before ‘fintech’ came about as a term, but they only bought or developed technology services for their own purposes. That is sometimes referred to as ‘fintech version 0’.

During fintech 1.0, however, start-ups and venture capital companies sensed that technology can serve people that large organisations cannot cover, so they provided the user-friendly tools or apps to underserved individuals. But that group has limited room to penetrate further, so the focus of fintech 2.0 has shifted to the majority of investors; however, those are mainly covered by large organisations such as large banks, trust companies, wealth management centres and so on. Those organisations have domain expertise, marketing capabilities and resources, but less fintech skills. Now, they are willing to enhance their service by partnerships and outsourcing to companies like Wanwei. Before, when people spoke about fintech, they referred to it bringing disruption, but fintech 2.0 is the future and is about collaboration.

How do you see fintech 2.0 evolving over the next five years and how will you stay at the forefront of developments?
AI needs data and so far the financial industry lacks large data. There are some areas like anti-fraud and risk management where the industry does have some data, but compared to large data sets it is still not enough. What we foresee is industry growth by connecting more and more individuals’ data. What kind of data? Investment behaviour data.

Amazon has tons of customer data – that is the transaction data. It is useful to describe the customer’s appetite through their purchasing patterns. For investments, what we care about is the investment behaviour data, which in the future will be used to provide a personalised solution, like a more intelligent decision-making engine for each individual. We also need this type of data for marketing – if they know people’s appetite, they can feed them more accurately and efficiently. This business model will not replace people, it will help managers to make wiser decisions and make the work more efficient, so it is a good supplement.

What impact is China’s economic slowdown having on your allocation, if any?
I do not think it will affect asset allocation in China too much because money has to find a place to go. That is the beauty of asset allocation – especially in China, because asset rotation is wild and it provides very good opportunities for active managers to capture it due to market inefficiencies. In one year, an asset may go from one extreme to the other and that is very typical in China, so if we have good risk management then we may avoid a large drawdown and while capturing the opportunities during the capital rotation.

On the other hand, the economic slowdown in China is also a good lesson for retail investors to learn from, forcing them to change their investment style from concentrated bets to more diversified asset allocation

Are you adding any new features to your service?
Yes. For the past two years, we focused on fintech application in asset allocation. Asset allocation is an efficient way to achieve stable performance in wealth management. But we do see a growing demand on the risk management side from large organisations. Last year we saw a lot of credit risk and high yield bond default in China, so the credit risk assessment is critical in the current financial environment in China, and there is a huge demand for quantitative ways to identify risks.

We collaborate with the fixed income investment team to develop our in-house model, credit risk model such that we can capture some risky bonds one year in advance. If we track our performance over the last seven months, we captured almost all defaulted bonds, missing only one.

We built an online service similar to Google Search where by typing the company or bond name, users can find out whether it is on a watch list, and the reason is that is one way to provide some protection to the investment. We could also do financial diagnosis to all companies, not necessarily just listed companies. If the financial data is provided, we can do the diagnosis, which is similar to a body check, but it gives an assessment on the company’s financial health instead. We also have an AI-driven media search engine, which is a good addition to the credit risk analysis. This AI NLP [natural language process] search yields immediate information, so if bad news around a company comes up, then this tool will capture that right away, which is good for immediate reaction. It is not about investment, but risk management instead and it has developed over the past six months.

What are your expectations for 2019?
In the short run, we can enjoy the market rally while the sentiment is recovering, but fundamentally we still see some risks down the road. Some of them are already there, such as the US-China trade war, and we have seen a lot of effort from China to mitigate these risks, but we see political uncertainties in the US now and there is a lot of noise there.

Overall, I feel that there will be less volatility this year compared to last. The reason is that people always underestimate or overestimate, and this results in an overreaction. At the beginning of 2018, most investors underestimated the risks because of the good run in 2017. Because 2018 was so bad, people tend to overreact with pessimism, which means that most investors or institutions already had light positions in the markets – and that provides a limited drawdown.

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