Tag
#ai
29 articles
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If Everyone Has AI, Who Wins?
If every investor has AI, the model stops being the edge. The advantage moves to proprietary context, reliable infrastructure, repeatable workflows, feedback loops and judgement.
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Investing Then vs Now: What AI Actually Changed
AI did not remove the need to understand a business. It changed the unit of research from one company at a time to one sourced question across the market.
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Building Trustworthy AI for Investing
Trustworthy financial AI rests on four disciplines: grounding every claim in filings, showing provenance, computing numbers deterministically, and knowing what it does not know.
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Analysis Paralysis in the AI Era
When AI makes analysis nearly free, the hard part is no longer producing it, it is deciding. Here is why more output can deepen paralysis and how to keep AI in service of a decision.
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Building an AI That Understands Financial Statements
Understanding a financial statement is not reading its words. It means normalising the data, respecting the accounting identities, and cross-checking every number against the other statements.
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Why Every Investment Team Will Have an AI Operating System
Research teams will move from scattered point tools to one shared, always-on layer: clean sourced data, queryable documents, and continuous monitoring, so analysts spend their time on judgement.
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Financial Modelling with AI: What It Does and What Stays Human
AI speeds up the mechanical parts of financial modelling (gathering inputs, spreading history, checking consistency, drafting), while assumptions, judgement, and the forecast stay with you.
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From Reading Documents to Asking Questions
Research is shifting from reading whole filings front to back to interrogating them with specific questions and getting sourced answers, which changes where an analyst spends time and attention.
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How AI Compresses a Week of Research Into an Hour
AI collapses the grunt work of primary research, gathering, reading, and spreading numbers, from days to minutes. The judgement, the part that decides the outcome, still takes a human.
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Investing Before AI and After AI: How the Research Day Actually Changes
Before AI, an analyst's day was manual reading and hand-spreading numbers. After AI, the reading is delegated and the human spends the day on judgement.
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Structuring Decades of Filings So an AI Can Actually Use Them
A language model cannot reason over a messy pile of filings. Labels drift, statements get restated, formats change, and history is not what it looks like today.
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The Death of Ctrl+F in Annual Reports
Keyword search finds strings, not meaning. It misses synonyms, ignores context, and cannot answer a question, which is why reading filings is shifting from searching words to asking questions.
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The Death of the Static Research Report
A research report is a snapshot that starts decaying the day it is filed. It is being replaced by living, queryable research that updates itself as the facts change.
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The Engineering Challenges Behind Institutional AI
Institutional-grade financial AI is hard for five reasons: data quality, point-in-time correctness, citations, deterministic outputs, and coverage at scale. Here is each one.
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Why Citations Are Non-Negotiable in Financial AI
Every number a financial AI reports must link to the source document. An unsourced but plausible figure is worse than no answer, because it invites a costly error.
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Why Deterministic Forecasting Beats LLM Guesses
A forecast used for capital must be reproducible and auditable. A language model's free-form guess is neither, which is why serious forecasts come from an explicit method, not a prompt.
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Why Every Analyst Will Have an AI Associate
An AI associate does the tireless first pass, pulling numbers and reading every page, while the human analyst keeps the judgement, conviction, and accountability.
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Why Research Coverage Is Becoming Obsolete
A fixed coverage list exists because analyst time was scarce and expensive. When reading and monitoring get cheap, that rationing breaks, and the narrow list of names a team follows stops making sense.
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AI for Equity Research: A Practical Guide
AI speeds up equity research by summarising filings, extracting data, monitoring events, and drafting notes, as long as you verify every figure against the source.
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The Hardest Part of AI in Finance Is Not the Model. It Is the Data.
In financial AI, the model is fast becoming a commodity. The durable edge lives in disciplined data work: units, restatements, point-in-time correctness.
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The AI Research Analyst Is Here: What Actually Works and What Is Just Marketing
AI can already read filings, extract data, and monitor events at scale. It cannot pick winners on command. Here is how to tell the tools apart before you buy.
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Can AI Predict Company Earnings? Separating Hype From Reality
AI can read filings and model earnings faster than any human, but it cannot see the future. Here is what the technology genuinely does, and where its limits are hard.
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Can AI Actually Read a Balance Sheet? Where LLMs Break on Financial Statements
Language models are strong at prose and weak at accounting. Here is exactly where they break on real filings, and what makes machine reading of statements reliable.
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Why AI Investing Apps Keep Getting Indian Stocks Wrong
Most AI investing tools are built for clean global data. Indian equities are full of local quirks that make those tools confidently wrong. Here is why.
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Why ChatGPT Hallucinates Financial Numbers, and How to Catch It
General chatbots predict plausible text, they do not look up facts, so they invent revenue and profit numbers. Here is why, and how to catch it.
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Why Earnings Call Transcripts Break Search, and What AI Must Do Instead
Transcripts hide their most important signals from keyword and even semantic search. The fix is structured extraction of management commentary, tracked over time.
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Why RAG Alone Fails for Equity Research
Retrieval-augmented generation reads filings like prose. Equity research lives in tables, footnotes and vintages, where one wrong digit is a wrong answer.
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AI Will Not Replace the Analyst. It Will Replace the Grunt Work.
The threat to equity research is not the analyst's judgement, it is the hours spent gathering filings and re-keying numbers. AI is coming for the grunt work first.
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Data Quality Beats Model Quality: A Year Reading Indian Filings
After a year building AI to read Indian company filings, the biggest gains came from boring data discipline, not from a better model. Here is what actually moved the needle.
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