- Enhancing the research process. A defining feature of AI is its ability to synthesize vast amounts of data. Apply this skill to decades of a financial firm’s proprietary data and over 100 terabytes of public data, and an AI large-language model (LLM) can help research analysts separate the signal from the noise, improve forecasting models and augment their fundamental process with sophisticated quantitative and textual analysis. - Optimizing portfolio management. AI tools can “coach” investment professionals by analyzing their historical investment decisions and providing personalized and actionable insights, saving valuable time and mitigating biases. AI may also help optimize asset allocation by improving risk and return estimates and fine-tuning portfolios to highly customized targets and constraints.[2] - Making trading more efficient and informed. Given the increasing size, frequency and complexity of trades, AI can help traders reduce transaction costs and improve execution. For instance, an LLM interface can quickly provide traders with relevant pricing data across exchanges while helping identify the best time, size and venue for trades.
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