The rapid development of artificial intelligence (AI) is increasingly raising the question of the extent to which these technologies will change existing processes in the financial sector – particularly in the area of equity research. A survey of DVFA investment professionals has drawn a differentiated picture that highlights both the opportunities and challenges of using AI in research.
The assessments on the question of whether AI could replace traditional equity research methods in the next five to ten years show a clear opinion: respondents do not expect AI to completely replace human analysts. None of the participants see research as obsolete. Instead, 49% believe it is likely that AI will automate a significant proportion of the research process and that human analysts will focus on strategic tasks that cannot be automated. A further 49% see AI primarily as a supporting tool that supplements, but does not replace, human expertise. Only 2% assume that the technology will have a minor influence.
AI is seen as having particularly high potential in automated data processing as part of fundamental analysis, with 48% of respondents seeing the greatest effectiveness here. A further 31% see the strengths of the technology in sentiment and market data analysis, for example in recognising market sentiment and patterns in real time. 17% also see a useful application in the area of quantitative trading strategies. Only 4% consider the use of AI in this environment to be hardly expedient.
Illustration: In which areas of equity research do you consider the use of AI to be particularly effective? Source: DVFA e. V.
A sustainable competitive advantage through AI-supported research is considered likely by the majority of respondents, but not on its own. 65% see added value in the combination of algorithms and human expertise in particular. 17% even expect AI to be superior in terms of speed, precision and efficiency. This contrasts with 15% who continue to see long-term investment decisions as dominated by human judgement.
Despite the high potential, the respondents see various limitations. 35% rate the risk of misinformation due to distorted or incomplete training data as the greatest challenge. 33% emphasise the difficulties in interpreting qualitative factors such as company management or market dynamics, while 30% criticise the lack of transparency and traceability of results.
The analyst’s focus is changing
The future role of analysts is seen by the majority as continuing to be important, albeit with a different focus. 56% of survey participants expect analysts to focus more on non-automatable aspects such as company discussions or market interpretation in future. 24% see analysts primarily in the role of validating and interpreting AI-generated results. Only 3% assume that analysts will be replaced by AI in the long term, while 17% believe that human expertise will remain central in the future.
Regulatory and ethical issues take centre stage
A significant part of the discussion revolves around regulatory and ethical challenges. 37% of participants cited unclear liability issues and a lack of transparency as a key risk. 34% express concerns about potential market manipulation through algorithmic distortions or disinformation. Data protection issues are seen as critical by 21%. Only 8% see the use of AI in research as an unproblematic technological advancement.
In conclusion, the survey shows that AI already plays a role in the day-to-day work of many investment professionals, albeit to varying degrees.
Figure: How often do you use artificial intelligence in your everyday work? Source: DVFA e. V.
Conclusion: AI as an evolutionary, not revolutionary step
The survey results make it clear: Artificial intelligence will change equity research in the coming years, but not through a radical break, but through a gradual transformation. The integration of AI systems will primarily increase efficiency, automate standard processes and open up new ways of gaining insights. Nevertheless, humans remain irreplaceable as interpreters, decision-makers and ethical compass. The key to success lies in a symbiotic combination of technology and human judgement.
“It is important to identify risks and take appropriate measures to minimise the dangers of AI in financial research while reaping the benefits. However, a balance between human expertise and AI-supported analyses can help to make informed decisions,” says Thorsten Müller, Chairman of the DVFA Executive Board.
“Ethical issues play an important role alongside regulation. For example, AI models can exhibit unintentional biases that originate from the training data. This can lead to discriminatory judgements that are not only unethical, but can also have legal consequences.”
Peter Thilo Hasler, DVFA board member and recognised expert in the field, adds: “The quality of the underlying data is crucial, because the value of the prediction rises or falls with it. The underlying algorithms based on deep learning are also a black box and indicate a remarkable lack of transparency. The possibility of market distortion due to similar algorithms is also not discussed enough.”
translated by deepl.com
