Hi, I'm Matt. Welcome to AI Street, your weekly brief on AI + finance. Here’s the rundown: how AI can find changes in executive tone, Fed warning on AI systemic risk, Writer’s latest fundraising and more.

Wall Street has been parsing text for years looking to spot changes. Bloomberg publishes a side-by-side comparison of Fed announcements to show whether the central bank’s language is becoming more hawkish or dovish.

AI can now listen in on how executives deliver company updates.

Alex Kim, a Ph.D. candidate in accounting at the University of Chicago, has pioneered ways to quantify subtle vocal cues using AI. Research in communications has long shown that up to 70% of information in oral communications comes through voice rather than text.

Kim's work on AI and LLMs has caught the attention of dozens of hedge funds worldwide, including some of Wall Street's biggest names. 

The paper, "Vocal Delivery Quality in Earnings Conference Call," has been conditionally accepted for publication in the Journal of Accounting and Economics. Companies like Markets EQ, and Speech Craft Analytics are marketing this new way of understanding tone.

Just a few years ago, real-time transcription and vocal analysis tools weren't readily available. But now, with advances in large language models and speech recognition, Kim says the hurdles to accessing this type of data have dropped dramatically.

"It's admittedly more difficult than analyzing text at this moment, but this hurdle is way lower compared to 3 years before, and it's going to get lower and lower over time," Kim says. "So within a couple of years, I'm pretty sure that everybody will have access to these vocal analysis features."

As these new tools become more accessible, Kim expects to see a shift, with investors increasingly using vocal delivery scores to make trading decisions in the days following earnings calls. And he anticipates that companies might also start coaching executives to improve their delivery, similar to how textual disclosure practices have evolved.

This interview has been edited for clarity and length.

MR: So you found that when executives deliver uncertain or negative news, their vocal delivery quality diminishes. Is this typically an unconscious reaction due to nervousness?

AK: Yes, yes, that’s actually one story that we write in the paper. We studied the psychology literature, which says that when you’re trying to deliver uncertain negative news, even though you don’t try, your vocal delivery gets lower. That could be one of the causes of the results. As you said, there might be some managers who try to obfuscate negative news by mumbling or not pronouncing appropriately. If you look at the examples we include in the paper, there are audio clips of managers delivering bad news, and it just seems like they’re kind of mumbling more than when they’re delivering positive news. We randomly picked five examples from the bottom decile, and it seems like they mumble a lot compared to when they’re delivering positive news.

…

MR: For investors who aren’t experts, what can they do with this information to make better decisions?

AK: If you’re looking to use our signals in real time to make investment decisions during conference calls within minutes, that’s not realistic for most retail investors. Even we can’t generate delivery scores during the calls. Tech giants or providers might have the means to provide real-time scores, but it’s not feasible for most. 

However, the paper shows that there are longer-term effects. For instance, there are one- to two-day market reactions in response to vocal delivery scores. The market reacts positively when a call delivers positive news, and this reaction is amplified if the manager speaks clearly. There’s an amplification effect over the one or two days following the calls, and we also show that journalists’ and analysts’ reactions are impacted by vocal delivery scores. So, it’s not just about real-time market reactions. If a data provider offered vocal delivery scores during conference calls, retail investors could potentially use those scores to make trading decisions in the following days.

Click here to read the full interview

ICYMI
Recent Interviews:

• MDOTM’s Peter Zangari, PhD, on AI for portfolio management

• Arta’s Chirag Yagnik on AI-powered wealth management

• Finster’s Sid Jayakumar on AI agents for Wall Street

• Sov.ai's Dr. Derek Snow on AI for fundamental investors

• Bain’s Richard Lichtenstein on AI adoption in private equity

REGULATION

Philadelphia Fed President Patrick Harker raised concerns about AI adoption in finance during a recent speech, highlighting both opportunities and risks.

The Fed is worried about herd behavior in AI models:

• Banks using similar AI systems and datasets could amplify errors across institutions

• Shared models could create new forms of systemic risk

• Input data bias could spread widely through the system

"If institutions are sharing data or using the same programs to evaluate risks, these errors can have widespread impacts," Harker warned.

SEC Urges Companies to Disclose AI Impact in Year-End Filings

SEC officials will be watching closely to see what details companies provide investors about their reliance on AI.

AI isn’t explicitly mentioned in SEC rules, but they could still trigger corporate disclosures throughout the financial report from the description of the business, to its risks, to management’s discussion and analysis, said Sarah Lowe, deputy chief accountant with the SEC’s Division of Corporation Finance. (Bloomberg Law)

FUNDRAISING
Writer Raises $200M, Hits $1.9B Valuation for Enterprise AI

Writer, which develops AI tools for large organizations, secured $200 million in Series C funding led by Premji Invest, Radical Ventures, and ICONIQ Growth, with participation from Salesforce, Adobe, and Citi Ventures. Writer has raised a total of $326 million to date, per Bloomberg.

• Focus on "agentic AI" - autonomous systems for complex enterprise workflows

• Strong financial services presence: Clients include Franklin Templeton, Ally Bank, Prudential

• Clients report up to 9x ROI through work hour savings

Citi Ventures' investment highlights Writer's appeal to financial institutions, citing their proprietary language models' strength in "accuracy, security, privacy, compliance, and ethical considerations."

Big Picture

The raise shows strong demand for enterprise AI that can handle complex workflows while meeting strict compliance requirements.

MOVERS
Here’s a snapshot of the week’s top AI-driven stock movers:

• Skyworks Solutions (SWKS) -8.1%: Beat 3Q24 earnings but weak guidance; sees AI-driven smartphone upgrade cycle as growth catalyst despite heavy Apple reliance.

• Arista Networks (ANET) -6.6%: Strong AI cloud demand in 3Q24 but margins compress; AI networking expanding from trials to broader pilots.

• Chegg (CHGG) -10.2%: Revenue down 13% as Google's AI features in Search directly impact organic traffic.

Data provided by Linq Alpha, an AI copilot for hedge funds. 

RESEARCH

A new study examined whether Wall Street firms should use one AI analyst or a team to analyze companies. The researchers tested AI analysts on real-world data - 2023 annual reports from 30 Dow Jones companies. They assigned both simple tasks (analyzing fundamentals and sentiment) and complex ones (assessing investment risks).

Key Findings:

• For basic financial analysis, one AI works best. Multiple AIs tend to overcomplicate straightforward calculations.

• For complex risk assessment, teams of AIs perform better, especially with clear leadership. Different AIs catch different potential problems.

The researchers tested three team structures:

• Boss-and-Workers: One AI leads, others follow

• Equal Partners: AIs collaborate as peers

• Hybrid: Mix of both

The most effective approach is to use single AIs for basic analysis but deploy teams for complex risk assessment. This combination achieved ~67% accuracy in predicting stock movements.

Paper: Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research
Authors: Xuewen Han, Neng Wang (AI4Finance Foundation), and a team from Tsinghua University and University of Maryland

🗽 I’ll be at the International Conference on AI in Finance (ICAIF 2024) today. Reach out if you’re attending: [email protected]