In with the New, Out with the Old: Integrating Risk Management and Data
Traditional risk management for hedge fund portfolio management has had a singular focus in looking back at historical data, models and metrics to draft an assumption about the future. Now, thanks to advancements in technology and data analysis, the “old” way of doing business with traditional risk management is getting a facelift.
Today, a combination of near real-time portfolio risk management and enhanced data analytics is changing the game for early investment.
The Traditional Approach
Before the onset of big data and the associated computing capabilities required to sort and manage massive amounts of data, traditional risk management was limited to calculating risk metrics based on distributional assumptions using historical data and financial models. This approach was slow to react to real-time market events, making it challenging to update forecasts/metrics as the events unfolded. Risk management was predominantly backward-looking with historical correlations informing future analysis.
As technology has advanced and data analysis has improved, the ability to leverage alternate data sets to inform risk management has become more accessible. By focusing on the underlying drivers of asset performance instead of waiting for markets to price it in before beginning analysis, risk management is presented with a unique opportunity to be more proactive and forward-looking.
Although new technologies are accessible to front and middle office employees, it is imperative that risk managers partner closely with portfolio managers to identify the correct questions when monitoring investments. Done effectively, technology and data analysis can transform the risk function from one that is focused on minimizing the downside using historical data and distributional assumptions to one that can generate alpha by analyzing alternate datasets that can capture preliminary warning signs.
The Marriage of Risk and Data
Despite increasing confidence and reliance on data, no investment is ever without risk. The key to forward-thinking risk management strategies lies in balancing return and risk with appropriate data tracking and analysis tools. Early risk advisory helps improve traditional risk analysis by providing clarity around drivers of traditional risk metrics. Instead of choosing one over the other, marrying risk management and data management holds the potential to yield the most returns. Near real-time portfolio risk analysis has the potential to be a game changer for data and risk insight generation.
By focusing in on the idea generation stage, risk managers can help structure a rigorous and analytic framework for the investment selection process. On an ongoing basis, analyzing alternate data to monitor key metrics at the company/security level can greatly enhance risk management’s ability to provide early and timely warnings.
Managing Risk from the Inside Out
As available data continues to increase along with analysis techniques, the type of information that can be used in risk management is also evolving. Along with strategies that focus on big data, such as transaction data, satellite imagery, weather pattern data, package delivery data and social media, micro-level data analysis is being utilized as well. Not only can alternate data sets like web traffic, point-of-sales data and news flow be used, but different metric sets on micro levels can also enhance risk management strategies. In fact, some hedge funds are strategically choosing to focus on “little data” in order to drive bigger-picture investment decisions.
Thinking “outside the box” can uncover new factors through data, such as supplier information or exposure in geographical regions. Data can also help provide advance warning for security gaps that could lead to vulnerabilities in the system and build out stress tests and scenario analyses to predict portfolio performances in new markets.
Achieving sustainable outperformance and retaining investors also means understanding the inherent risks with the discipline itself. The strategies for a risk management analysis come with their own inherent hurdles that portfolio and risk managers must be prepared to address. For instance, focusing on solutions that address managing organizational data flow, identifying and correcting data discrepancies and bridging the gap between IT systems and portfolio managers will be an important part of future risk management strategies that integrate with data technology.
Risk management in hedge funds will continue to be an ongoing process as demands, technology and regulatory compliance evolve. However, by refocusing their risk management strategies to integrate data analysis technology, portfolio managers can feel confident in gaining a more comprehensive view for driving investment decisions to serve their clients.