Data-driven investing involves analysing data sources to provide investors with specific investment insights. As more data gets created every day, institutional investors are increasingly looking towards alternative data sets to leverage for trend analysis, patterns, and risks in a bid to outperform their competitors.
“Data really is everything,” said Murray Keir, head of systemic equities and global portfolio manager at Aware Super at AsianInvestor’s webinar 'Turning market sentiment into investment strategy’, in partnership with Refinitiv on April 25.
“Finding ways to extract and utilise data is really the core of my process and it's the work of my entire team,” said Keir whose team manages around $13 billion of international and domestic equities leveraging a quantitative systemic process.
Quantum and systemic processes leveraged in investing is a well-developed style that has been around for over 20 years, according to Keir. The process involves taking on as much data as possible and extracting a series of alpha and risk factors, and then applying those factors to their investment strategy in a systemic way.
As this process has been well explored and is understood by most institutional investors, investing through traditional data sets has become quite a saturated market, explained Keir.
“One of the big things that we're constantly exploring is how to get new data sources that aren't already essentially priced out by the market?” he said.
The hunt for alternative data - sentiment data which includes information from filings, news reports and social media - is becoming an increasingly fertile ground for quantitatively-orientated investment managers to find alpha and mitigate risk.
“Alpha and risk are just two sides of the same coin; the difference is just direction. In both cases, we're looking for factors that contribute to price volatility and correlation,” said Keir.
Within this emerging style of data analysis, Aware Super’s team continues to place great importance on the fundamentals when assessing which factors are reliable and relevant enough to apply to their investment strategy. While there are many advanced statistical techniques that can be used to reduce risk, ultimately you’re still using history to inform your opinion of the future, said Keir.
“Unless there is that understandable linkage between the factors and the stock price, it will mean that we don't really understand why something would work, what to do if it stops working, and we can't really get any confidence of its effectiveness. That is a key determinant when looking at this sort of data,” he said.
“One of the things I would say about news flow as an alternative data source, is that it’s very easy to look at it and see what the economic linkages should be,” he said.
“You're trying to predict the next quarter's earnings, you’re looking at information that's coming out about sales or credit card data and you can see very clearly where that's going to have an impact.”
When it comes to risk factors, Aware Super’s team is looking for data that contributes to volatility.
“I think with some of the risk factors, while you can see the economic linkage — it's harder, to tease that out of the data. So that's an area that I think is quite an interesting within this particular space.”
CHINA SENTIMENT DATA
In China, social media is the most important sentiment data to analyse according to Steve Zhang, deputy chief investment officer at Ping An, China Asset Management, who spoke at the same AsianInvestor webinar.
However, collecting this data has proved challenging for Zhang and his team due to a lack of standardisation and the fact that it involves a long process using several sophisticated tools to extract and analyse the data.
After collecting all this data and doing the analysis, Ping An’s team found a number of areas that China’s social media platforms were driving alpha generation.
“In order of importance to us, the first area is public views on stock return. This can be extracted from social media to assess public investment willingness on particular stocks. The second one is news sentiment, whether positive or negative information, about a particular company,” said Zhang.
The third set of data that Zhang’s team has been able to effectively leverage in their investment strategy, is information and investor sentiment towards many Chinese companies’ environment, social and governance (ESG) practices.
“That has helped us to assess a company from a pure ESG perspective, and has been generating extra alpha for our current model,” said Zhang.
Despite Ping An’s success in using alternative data thus far, Zhang said that it remains very limited in China which is still a developing market.
“Using new alternative data known by others, does not necessarily make you unique or competitive in investments,” he said.
“But it doesn't mean that you can't or shouldn't do it, because everybody else is doing it and if you’re not then you're lagging behind.