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Business Times
3 days ago
- Business
- Business Times
Outperformed by AI: Time to replace your analyst?
SIX artificial intelligence (AI) models recently went head-to-head with seasoned equity analysts to produce Swot (strengths, weaknesses, opportunities and threats) analyses, and the results were striking. In many cases, the AI didn't just hold its own; it uncovered risks and strategic gaps the human experts missed. This wasn't theory. My colleagues and I ran a controlled test of leading large language models (LLMs) against analyst consensus on three companies – Deutsche Telekom (Germany), Daiichi Sankyo (Japan), and Kirby Corporation (the US). Each was the most positively rated stock in its region as of February 2025 – the kind of 'sure bet' that analysts overwhelmingly endorse. We deliberately chose market favourites because if AI can identify weaknesses where humans see only strengths, that's a powerful signal. It suggests that AI has the potential not just to support analyst workflows, but to challenge consensus thinking and possibly change the way investment research gets done. The uncomfortable truth about AI performance Here's what should make you sit up: With sophisticated prompting, certain LLMs exceeded human analysts in specificity and depth of analysis. Let that sink in. The machines produced more detailed, comprehensive Swots than professionals who have spent years in the industry. But before you eliminate the need for human analysts, there's a crucial caveat. While AI excels at data synthesis and pattern recognition, it can't read a CEO's body language or detect the subtext in management's 'cautiously optimistic' guidance. As one portfolio manager told us, 'Nothing replaces talking to management to understand how they really think about their business.' The 40 per cent difference that changes everything The most striking finding? Advanced prompting improved AI performance by up to 40 per cent. The difference between asking 'Give me a Swot for Deutsche Telekom' and providing detailed instructions is the difference between a Wikipedia summary and institutional-grade research. This isn't optional anymore — prompt engineering is becoming as essential as Excel was in the 2000s. Investment professionals who master this skill will extract exponentially more value from AI tools. Those who don't will watch competitors produce superior analysis in a fraction of the time. BT in your inbox Start and end each day with the latest news stories and analyses delivered straight to your inbox. Sign Up Sign Up The model hierarchy: Not all AI is created equal We tested and ranked six state-of-the-art models: 1. Google's Gemini Advanced 2.5 (Deep Research mode) – The clear winner 2. OpenAI's o1 Pro – Close second with exceptional reasoning 3. ChatGPT 4.5 – Solid but notably behind the leaders 4. Grok 3 – Elon Musk's challenger showing promise 5. DeepSeek R1 – China's dark horse, fast but less refined 6. ChatGPT 4o – The baseline for comparison The reasoning-optimised models (those with 'deep research' capabilities) consistently outperformed standard versions such as ChatGPT-4o. They provided more context, better fact-checking, and fewer generic statements. Think of it as hiring a senior analyst versus a junior analyst – both can do the job, but one needs far less handholding. Timing matters too. The best models took 10 to 15 minutes to produce comprehensive Swots, while simpler models delivered in less than a minute. There's a direct correlation between thinking time and output quality – something human analysts have always known. The European AI deficit: A strategic vulnerability Here's an uncomfortable reality for European readers: Of the models tested, five are American and one is Chinese. Europe's absence from the AI leadership board isn't just embarrassing – it's strategically dangerous. When DeepSeek emerged from China with competitive performance at a fraction of Western costs, it triggered what some called a 'Sputnik moment' for AI. The message was clear: AI leadership can shift rapidly, and those without domestic capabilities risk technological dependence. For European fund managers, this means relying on foreign AI for critical analysis. Do these models truly understand European Central Bank communications or German regulatory filings as well as they grasp US Federal Reserve statements? The jury's out, but the risk is real. The practical integration playbook Our research points to a clear four-step approach for how investment professionals should use these tools: 1. Hybrid, not replacement: Use AI for the heavy lifting – initial research, data synthesis, pattern identification. Reserve human judgment for interpretation, strategy, and anything requiring genuine insight into management thinking. The optimal workflow: AI drafts, humans refine. 2. Prompt libraries are your new alpha source: Develop standardised prompts for common tasks. A well-crafted Swot prompt is intellectual property. Share best practices internally, but guard your best prompts like trading strategies. 3. Model selection matters: For deep analysis, pay for reasoning-optimised models. For quick summaries, standard models suffice. Using GPT 4o for complex analysis is like bringing a knife to a gunfight. 4. Continuous evaluation: New models launch almost weekly. Our six-criteria evaluation framework (structure, plausibility, specificity, depth, cross-checking, meta-evaluation) provides a consistent way to assess whether the latest model truly improves on its predecessors. Beyond Swot: The expanding frontier While we focused on Swot analysis, the implications extend across the entire investment process. We list a few of these below, but there are many more: Earnings call summarisation and analysis in minutes, not hours ESG red flag identification across entire portfolios Regulatory filing analysis at scale Competitive intelligence gathering Market sentiment synthesis Each application frees human analysts for higher-value work. The question isn't whether to adopt AI – it's how quickly you can integrate it effectively. The uncomfortable questions Let's address what many are thinking: 'Will AI replace analysts?' Not entirely, but it will replace analysts who don't use AI. The combination of human plus AI will outperform either alone. 'Can I trust AI output?' Trust but verify. AI can hallucinate facts or miss context. Human oversight remains essential, especially for investment decisions. 'Which model should I use?' Start with Gemini Advanced 2.5 or o1 Pro (or the successors) for complex analysis. But given the pace of change, reassess quarterly. 'What if my competitors use AI better?' Then you'll be playing catch-up while they're finding alpha. Staying on the sidelines while competitors build AI advantage means ceding ground in an increasingly competitive landscape. The path forward The genie is out of the bottle. LLMs have demonstrated they can perform analytical work in seconds that once took days. They bring speed, consistency, and vast knowledge bases. Used effectively, they're like having a tireless team of junior analysts who never sleep. But here's the key: Success requires thoughtful integration, not wholesale adoption. Treat AI output as you would a junior analyst's draft – valuable input requiring senior review. Master prompt engineering. Choose models wisely. Maintain human oversight. For European professionals, there's an additional imperative: Push for domestic AI development. Technological dependence in critical financial infrastructure is a strategic vulnerability no region can afford. Master the tools – or be outpaced by them Embrace these tools intelligently, or watch competitors leave you behind. The winners in this new landscape will be those who combine AI's computational power with human insight, intuition, and relationship skills. The future of investment analysis isn't human or AI – it's human and AI. Those who recognise this and act accordingly will thrive. Those who don't will find themselves outperformed not by machines, but by humans who learned to work with them. Your next analyst hire might still need that coffee break. But they'd better know how to prompt an LLM, evaluate its output, and add the human insight that transforms data into alpha. Because in 2025, that's the new standard. The tools are here. The frameworks exist. The winners will be the ones who know how to use them. This content has been adapted from an article that first appeared in Enterprising Investor at The full study can be found here The writer, CFA, is chief investment officer at MHS CapInvest, where he employs advanced AI tools to enhance allocation, stock selection, portfolio construction, and risk management for different market capitalisations, He trains teams at DAX-listed companies on generative AI integration and helps investment professionals leverage tools like ChatGPT and Gemini to enhance their performance.
Forbes
05-06-2025
- Business
- Forbes
Rise Of The Machines: A Dividend Revolution Yielding Up To 9.7%
Army of robots. 3D illustration Big companies are about to make even more money. They have discovered they no longer need armies of new hires to grow—extremely bullish news for shareholders because human employees are expensive. Good ones can also be notoriously elusive. For example, I'm the longest-standing member of my kids' school marketing committee, and we're always scrambling for volunteers (what non-profit isn't?). Until now, that is. Over the weekend, we welcomed the most talented marketer I've ever worked with to our team: ChatGPT 4.5. 'GPT' graciously accepted our volunteer position, and we're already actively boosting online referrals for the school. I'm learning cutting-edge 'AI referral' techniques straight from the entity that invented them. It was the easiest recruitment effort I've ever experienced. GPT and I were already collaborating closely to market and sell several software products, so extending our teamwork to the non-profit world was seamless. The same dynamic is quietly playing out at for–profit companies, particularly the tech giants that dominate the cap-weighted S&P 500. A senior executive friend at Meta (META) recently confirmed to me that the company has essentially frozen hiring, pivoting entirely toward AI-driven growth. It already shows in the numbers. Over the past year Meta has increased revenues by 22% while only hiring 10% more people. Sales are growing faster than humans, a trend that I expect to accelerate in the months and years ahead. In fact, I wouldn't be surprised if Meta has already reached peak headcount—which means profits are set to surge even more. And Meta isn't alone in this 'growing without hiring' trend. Alphabet (GOOG) grew revenues by 14% without any net new hires. And Nvidia (NVDA) did grow headcount by 13%, but for good reason—sales exploded by 126%! Microsoft (MSFT) is likewise sailing along without the need for new engineers, with 16% revenue growth on just a 3% headcount increase: Tech Growth The AI adoption at these companies is just beginning. These profit machines are already selling $1 to $2 million in product per employee, but their profits are going to pop as they sell even more without the expense drag of adding new employees! This four-pack packs 20% of the S&P 500 index. When we combine Amazon (AMZN), Tesla (TSLA), Netflix (NFLX) and Apple (AAPL)—four more tech companies that are scaling without hiring—we have 32% of the index. Earlier in the year, I warned that the 'tech heaviness' of the S&P 500 was dangerous—and it sure was during the tariff troubles of March and April. But with trade tensions fading and tech profits exploding thanks to lean payrolls, these big 8 companies are now set to power the index higher. Plus, we have a weakening US dollar. Stocks are, of course, priced in dollars. So, a softer dollar is another bullish catalyst for the S&P 500. As income investors, we can tap this rising tide for steady income. To do so, we'll use covered calls—a strategy where we buy stocks and then sell ('write') call options to other investors. We earn income now from the option premiums we collect, paid upfront to agree to sell our shares at a higher price later. Market volatility from a tumultuous spring means these options pay generous premiums right now (covered call options pay more when things are bouncing around!). So, this is a good market moment to cash in on leftover fear. I'm talking about dividends up to 9.7% that will benefit from the S&P 500 soaring towards 9000. (Yes, it sounds wild—but with record profits plus a declining dollar, this is a potential price target before the end of Trump 2.0.) Eaton Vance Tax-Managed Global Diversified Equity Income Fund (EXG) yields 9.2% and trades at a 6% discount. That's a sweet deal because it holds big winners like Amazon, Alphabet, and Microsoft, then boosts income by selling covered calls on the S&P 500 and international indices. The income from these constantly expiring calls is the key to the EXG's sky-high 'synthetic yield.' The fund collects premiums from option buyers immediately after it writes these calls, generating steady income for shareholders. We can think of this as 'renting' out positions to generate extra cash. EXG owns the underlying shares behind the S&P 500. Each month it leases its collection of stocks and collects the option premiums. Rinse and repeat. Nuveen S&P 500 Buy-Write Income Fund (BXMX) pays 8.1% and trades at a 9% discount to its net asset value (NAV)—another good deal because we're talking Apple and Amazon for 91 cents on the dollar. Finally the Global X S&P 500 Covered Call ETF (XYLD) dishes a 9.7% dividend. It is an ETF, so it trades at par ('fair value'), as most do. XYLD owns the S&P 500 stocks and has also written calls on the S&P 500 that expire later in June. When that happens, the fund will write new calls for July—delivering more tasty income to its investors. Covered Call Funds As sellers of covered calls, they exult in market volatility that delivers high option premiums. Plus, their NAVs have a tailwind—tech profits popping! Brett Owens is Chief Investment Strategist for Contrarian Outlook. For more great income ideas, get your free copy his latest special report: How to Live off Huge Monthly Dividends (up to 8.7%) — Practically Forever. Disclosure: none
