AI Prompts: Spotting Red Flags in Financial Statements

Welcome back to AI in Investment Research & Finance.

For new readers, previous editions have explored how fund analysts and strategists apply large language models (LLMs) to transform raw data into sharper insights. Topic have included value strategies, macro signals, and exchange-traded fund (ETF) positioning, with highlights from funds like MOAT, SPLV, JEPI, GLD, BITO, EDOG, AIQ, QGRW, OMAH, QAI, FLOT, IVV. We’ve also examined thematic comparisons such as SLV vs SIL.

This time, we shift focus to one of the most overlooked but essential skills in financial analysis: identifying red flags before they become front-page news. Subtle indicators, such as unstable earnings, negative operating cash flow or a spike in receivables, can point to deeper issues long before they hit the share price.

Table of Contents

• This Week's Focus: Spotting Red Flags in Financial Statements

  • Message From Our Partner: The AI Report

    • Three AI Prompts for Detecting Financial Stress

    • ETF Spotlight: Invesco S&P 500 High Beta ETF (SPHB)

    • AI Career Moves: Exciting AI Jobs This Week

    • AI in Markets: Upcoming Events & Key Developments

As automation accelerates workflows, AI prompts offer more than just speed. They help connect disparate warning signs, each seemingly harmless on its own, that together reveal early signs of financial strain.

Three Targeted AI Prompts for Detecting Financial Stress

The following prompts are designed for structured, AI-enhanced analysis across quarterly filings, earnings transcripts and footnotes. Each serves a different lens, like trend, disclosure and peer comparison, and helps analysts go beyond surface-level ratios.

To improve precision, assign the LLM a clear role (for example, “equity analyst” or “forensic accountant”), request source citations where applicable and specify an output format that aligns with your goals, such as a risk summary, table of flagged metrics or investment memo.

📌 Prompt 1: Cross-Statement Pattern Analysis

Acting as a forensic accountant, review [Company Name]’s last four quarterly filings [URL Links]. Compare: (1) Days sales outstanding vs reported revenue growth, (2) Inventory turns vs margin changes and (3) Capex as % of revenue vs depreciation trends. Highlight quarters where reported earnings improved but cash metrics deteriorated.

Why this works: Standard analysis looks at each statement separately. This prompt forces the AI model to track interdependencies across income, balance and cash flow statements; helping detect earnings quality issues that standalone screens miss.

📌 Prompt 2: Footnote Mining for Hidden Risks

As a credit analyst, review the footnotes from the last four 10-K or 10-Q filings for [Company Name]. Identify material disclosures related to accounting policy changes, off-balance-sheet arrangements, related-party transactions and contingent liabilities. Highlight any trends indicating increased financial complexity or reduced transparency.

Why this works: Footnotes often contain the early signals of risk, well before they show up in headline metrics. This prompt helps uncover what traditional screens might miss.

📌 Prompt 3: Peer-Relative Divergence Check

Compare [Company Name] to five industry peers over the last eight quarters. Flag divergences in working capital trends, capex discipline and earnings quality. Focus on metrics where [Company] improves while peers weaken or vice versa.

Why this works: Peer-relative prompts expose signs of potential manipulation or competitive decline that aren’t visible in absolute terms.

ETF Spotlight: Invesco S&P 500 High Beta ETF (SPHB)

Quick Overview:

• Current Price: $101.59

• 52-week range: $64.40 to $104.93

• Net Expense ratio: 0.25% per year

• Dividend yield: 0.58%

• Expense Ratio: 0.25%

• Assets Under Management: around $480 million

• Holdings: 102 securities

• Top Sectors: Information Tech (43.4%), Industrials (16.1%), Consumer Discretionary (12.3%)

The Invesco S&P 500 High Beta ETF (SPHB) tracks the 100 S&P 500 stocks with the highest beta over the past 12 months, companies most sensitive to broad market movements. Beta measures a stock’s volatility relative to the overall market; a beta above 1 means the stock tends to move more than the market, up or down. These high-beta stocks typically rally harder in bull markets but are also more vulnerable during volatility shocks, often due to cyclicality, leverage, or inconsistent earnings.

Recent top holdings include Super Micro Computer (SMCI), Advanced Micro Devices (AMD), Vistra (VST), NVIDIA (NVDA) and GE Vernova (GEV). While many of these names are growth leaders, they often carry heightened financial risk, especially when macro conditions tighten or funding costs rise.

Notably, SPHB doesn’t screen for quality, fundamentals or profitability. Its sole filter is price sensitivity, which makes it a compelling candidate for prompt-based red flag detection. The fund’s P/E ratio of 25.9 (TTM) and price-to-book of 3.2 reflect a strong growth tilt, yet the low dividend yield (0.58%) suggests limited cash flow generation at the portfolio level.

AI Prompt: Is SPHB a Good Fit for My Portfolio?

Acting as a portfolio analyst, evaluate whether the Invesco S&P 500 High Beta ETF (SPHB) fits my overall investment strategy. Assess: (1) sector and factor exposures vs. my existing holdings, (2) overlap or concentration risk, (3) potential for outperformance in bullish conditions and (4) vulnerability during market downturns. Based on this, summarize the ETF’s role (e.g., tactical growth, diversification, or risk amplifier) and recommend position sizing or alternatives if better suited.

💡 Why it matters: ETFs like SPHB can amplify returns, but also risks. This prompt helps determine whether it's a strategic addition or a volatility mismatch for your portfolio.

AI Career Moves: Exciting AI Jobs This Week

Looking for your next opportunity in AI? Explore these standout roles across industries and locations:

📩 Feel free to share this list with anyone looking for AI opportunities!

AI in Markets: Upcoming Events & Key Developments

🤖 Bloomberg–Columbia ML in Finance Conf – New York, Sep 25

This one-day event explores machine learning in asset pricing, forecasting, and credit modeling. Hosted by Columbia University and Bloomberg, it features speakers from Yale, PwC, and Morgan Stanley.

🤖 Behavioral/Cognitive Finance & AI Conference – Santa Clara, Sep 24–26

This event explores how AI shapes financial decision-making, behavioral modeling, and risk. It brings together researchers and practitioners to examine bias, trust, and the cognitive dimensions of AI in finance.

🏛️ SEC Launches AI Task Force

On August 1, 2025, the U.S. Securities and Exchange Commission (SEC) announced the formation of an AI Task Force to enhance innovation and efficiency across the agency. Valerie Szczepanik, the SEC’s first Chief AI Officer, will lead efforts to integrate AI into operations, oversight, and internal systems.

📊 Citi–CREATE Report: AI Rising, Adoption Lagging

A global survey of 269 asset managers ($37.7 trillion AUM) finds AI and GenAI widely seen as disruptors, but adoption remains limited, with just 5% calling their AI efforts mature. Focus is on core investment processes, while barriers include legacy tech and data quality. Key strategies include private markets, outsourcing, and personalization.

Stay tuned for next week’s edition, where we’ll explore new AI prompts for deeper sector analysis. To ensure our next newsletter lands in your Inbox, please add our email address [email protected] to your contacts.