Machine Learning in Asset Management
As an asset manager, the assessment of risk, needs, goals, and progress is crucial for success. Thus, many asset managers are turning to new technologies to build on the existing knowledge and systems that they have for their needs. Such types of technologies being utilized today include machine learning and artificial intelligence, tools that could help generate new insights to be at the disposal of asset managers.
How do Asset Managers Leverage Machine Learning?
Identifying Outperforming Assets→ By finding new patterns in existing data sets, machine learning may be used to generate insights that range from the reliability of forecasts to estimations on the performance of firms in the same sector. These valuable insights may then be translated into decisions that may improve asset performance.
Analyzing New Forms of Data → Traditionally, certain formats of information such as sounds or images could only be analyzed and understood by humans. However, modern-day machine learning technologies may now allow computers to identify certain elements within these forms of data, giving asset managers an edge when it comes to decision making. For example, computer systems are able to use GPS locations through mobile phones to understand foot traffic at certain retail locations, which may provide asset managers with crucial predictive insights on company performance.
Removing Human Biases on Investment Decisions → Human bias and error is inevitable. Furthermore, when it comes to high-risk investment decisions for asset managers, these mistakes may be highly detrimental. For instance, many investors exhibit biases such as loss aversion, in which they have a preference for avoiding losses relative to generating equivalent returns, or confirmation bias, the tendency to interpret new evidence as opposed to affirming pre-exisitng beliefs. Thus, in order to combat these biases, machine learning may be deployed to analyze the historical trading record of asset managers to search for patterns that may have contributed to the formation of these biases. These critical insights may be used for training and the reform of decision making for asset managers.
What Are The Risks?
While machine learning has the potential to vastly improve the quality of data analysis for asset managers, it cannot replace the judgement of humans. For example, even if machine learning may find patterns for certain data, human judgement is required to utilize that information for profitable decision making. Furthermore, sometimes, correlations between data analyzed by machine learning technology may be loose and invaluable.
Conclusion
As technology continues to evolve and rapidly change, new use cases may be developed for asset management in the future. However, like with all tools and technologies, humans must be able to properly utilize them and avoid becoming solely dependent on these tools for insights.
