Short-term gains, long-term losses: the tradeoff in alternative data use
The financial data market, valued at $42 billion, and the alternative data market, which is growing to nearly $10 billion, highlight the increasing role of information in financial markets. Thierry Foucault’s research, “Does Alternative Data Improve Financial Forecasting? The Horizon Effect”, published in The Journal of Finance in 2024, sheds light on the impact of a growing alternative data market.
It demonstrates that, while this market enhances short-term forecasting accuracy, it also diverts attention from long-term projections, thus undermining analysts’ ability to assess strategic value over time. Foucault argues that alternative data’s immediate availability and real-time nature improve short-term stock forecasts.
However, its overwhelming volume can lead to information overload, diminishing analysts’ capacity for in-depth, long-term analysis. This imbalance has profound implications, including misaligned stock valuations and reduced incentives for firms to pursue long-term investments.
Technological innovations offer potential solutions
Fortunately, there are emerging technological innovations that offer potential solutions to the problems identified in Foucault’s research. Synthetic data and alternative data sources can improve data quality issues, while large language models (LLMs), generative adversarial networks (GAN) and reinforcement learning provide tools to analyze complex relationships in financial markets. By integrating synthetic data, reinforcement learning, and LLMs into financial systems, institutions can bridge the gap between short-term performance and long-term reliability.
Fortunately, there are emerging technological innovations that offer potential solutions to the problems identified in Foucault’s research.
Challenges persist
Yet, challenges persist. Simplistic applications of models fail to address weak signals, and neural networks struggle with unstructured or dynamic data. To counter these limitations, specialized datasets tailored to capital markets, advanced training methodologies, and modular, scalable systems are necessary. These approaches can better capture hidden relationships and adapt to rapid market changes.
Start-ups could pave the way for the future of finance
HEC has the good fortune to benefit from the Creative Destruction Lab (CDL) programs. As a CDL’s mentor, I can identify promising startups such as Lemon AI, Synthera AI and Revaisor offering solutions.
- Lemon AI is exploring advancements in synthetic data to curate high integrity datasets and bridge short-term and long-term data analyses.
- Synthera AI develops proprietary AI models to generate synthetic financial market data, enabling investors to simulate and analyze complex market dynamics.
- And Revaisor is addressing compliance and transparency challenges as part of the governance.
As with any innovation, the initial outcomes may not be perfect. However, at HEC, the ability to foster collaborations between carefully selected startups and researchers is a key differentiator. This helps financial professionals embrace innovation while maintaining vigilance to safeguard long-term strategic decisions. The research conducted by Professor Foucault can also enhance these safeguards.
Between 2020 and 2022, Claire Clamejane was nominated one of the 100 most influential women in Finance in Europe by the Financial News. She began her career in 2006 at the Capgemini Consulting's Technology Transformation department before joining Lloyds Banking Group in 2012, where she led digital delivery and risk transformation. As Innovation Director at Société Générale, she then drove Digital P&L, AI and fintech investments through SG Ventures.
