In the “Golden Age” of tech stocks, how do we use tech itself to assess risk and evaluate the markets?
The tech-heavy Nasdaq told the story in 2024, with Palantir up 340.5%, Nvidia up 171.2% and Broadcom up 107.7%. The latter two are among the so-called BATMMAAN stocks whose success has led some worried commentators to point out the concentration risk now present in US share markets: the top 10 stocks in the S&P 500 hit 37.3% of the value of the entire index in mid-July, just shy of the highest level on record, which was 38% in January this year according to Reuters.
In the current Golden Age of the tech sector, it is the emergent AI and analytics tools being created by top-performing companies that are proving some of the best virtual assistants for evaluating stocks in the tumultuous 2020s, generally by using in combination with traditional analytical techniques. Here's how:
1. Going beyond the basics. We have access to enormous amounts of research and data about every tradable stock, but traditional statistics, like revenue or P/E ratio, don't always tell the whole story with tech companies, especially those rapidly reinvesting for growth. Rather, one can look for:
-
Price-to-sales (P/S) ratio: Especially useful for high-growth, pre-profit tech firms.
-
Free cash flow (FCF) growth: Indicates whether a company is capable of self-funding continued innovation.
-
R&D expense growth: Is the business consistently investing in future products and features?
-
Scale and market cap: Is the company large enough to weather market challenges?
-
SG&A (selling, general, and admin expenses) to revenue: Offers clues about efficiency in scaling operations.
2. Using technical analysis for trends. The rise of quantitative trading and algorithmic strategies means technical analysis is an important supplementary lens for active traders, though not a substitute for deep research. Traders are looking at markers such as:
-
Volatility metrics: Identifying periods where momentum or reversals are likely.
-
Advanced charting: Using visual tools to spot levels of investor support or resistance.
-
Changes in options implied volatility and ratios of puts to calls in analysis of both the indexes and individual stocks.
3. Leveraging quantitative and AI tools. The next generation of evaluation involves AI and big data. These tools filter vast amounts of information from financial reports, market sentiment, news, web analytics. Some of my preferred research platforms and AI-driven tools include:
-
Tiger Trade App's AI-powered chatbot TigerAI. Its features allow investors to research stocks, summarise key insights from earnings calls and releases, and extract pertinent company news and sentiment analysis based on the nature of the questions asked, all within seconds. TigerAI can be accessed through the Tiger Trade app, so everything is in one place.
-
Perplexity: This AI-powered research co-pilot synthesises web results and provides live monitoring, trend analysis, Q&A to feed back to users.
-
ChatGPT: The biggest name brand in LLMs to date, this is conversational AI for brainstorming and quick synthesis, and a good tool to test investment ideas and pull data summaries.
-
AlphaSense: Offers AI search for business/financial filings and news; users can deep dive for company and sector insights.
-
Google Gemini: This is multimodal AI (text and images) for competitive research; users can scan public information fast.
4. Developing valuation frameworks. Valuing tech stocks is both an art and a science. Of course, getting it right or wrong can make a big difference in ROI terms for traders and clients. Key techniques one can use:
-
Discounted cash flow (DCF): Projects future value but is highly sensitive to assumptions.
-
Relative valuation: Compares companies' multiples within the sector.
-
Premium for growth: Sometimes justified if a company is truly dominant or highly innovative.
5. Making qualitative assessments. Without context, numbers can be misleading – and in an age of massive data volume, investors need to figure out which context is actually relevant. One can evaluate:
-
Leadership quality: Track record, vision, and ability to execute.
-
Innovation pipeline: New products/services and IP protection.
-
Industry ecosystem position: Is the business a vital cog in a rising sector like AI, cloud computing, or cybersecurity?
-
ESG practices: Environmental, social, and governance disclosures, especially around climate responsibility, are highly relevant. For companies involved in AI, the conversation is becoming increasingly heated around the vast energy consumption of data centres.
6. Finding practical uses for AI in research. AI can change the scope of intense periods such as earnings season. It can be used in reporting and analysis in a few ways:
-
Hourly news alerts: Using Perplexity or AlphaSense for customisable updates on specific tech companies.
-
Rapid data summarisation: With ChatGPT, one can parse lengthy earnings calls or filings quickly.
-
Scenario analysis: running "what if" scenarios via AI, such as how a new product might reshape a market, the expected effects of tariffs on sector X or Y, or what headwinds a new regulation could create.
-
Monitoring social trends: AI tools aggregate social media sentiment and web traffic, offering another layer of insight into a company's traction.
-
Idea validation: When considering a trend or hypothesis, cross-examine it using multiple AI platforms to find the weak points.
7. Remembering the risks. AI can give the impression that there is a final right answer to everything, but any tool can only digest the data it is designed to process. And like any tool, it is only as good as the person using it. Given AI's complexity and known pitfalls (like "hallucinations"), the risk of relying on its output is for the user to bear – it should not be taken as nor is a substitute for professional advice. Only experienced users of AI should use it for financial analysis. It is well known that AI can be, and often is, wrong in its analysis and every finding of AI needs to be verified and double checked.
No research method guarantees anything, and the risks include:
-
Extreme volatility: Of course, tech stocks can swing wildly, and AI can't tell you for sure when or by how much. AI can be a predictor, but it is not a perfect one.
-
Disruption risk: Share market leaders today can lag tomorrow if innovation slows.
-
Overvaluation: High hopes can lead to painful corrections. These can be sudden or extreme.
-
Regulatory changes: New rules on data or antitrust can shift the landscape overnight.
-
Behavioural bias: Even seasoned investors can be swayed by hype or groupthink.
There are investors who think the current Golden Age of tech is another bubble and the only question is when it will burst, not if.
Disclaimer:
This article is presented by Tiger Fintech (NZ) Limited and is for information only. It does not constitute financial advice. Investing involves risk. You should always seek professional financial advice before making any investment decisions.
