In recent weeks, the public feud between the U.S. President Donald Trump and tech billionaire Elon Musk has escalated from cryptic posts to open barbs. What looks like celebrity drama to some is something else entirely to those paying closer attention: a narrative battleground that can move markets.
At Aisot Technologies, we’re not in the business of political commentary – but our research and models are deeply focused on how information flows, sentiment shifts, and narrative velocity impact the way capital behaves.
Because in a data-driven world, drama isn’t just noise. It can be used as signals that influence weekly and monthly predictions.
The Trump–Elon effect
When two of the world’s most influential voices clash – across platforms, ideologies, policies and industries – the ripple effects are measurable:
- Social media sentiment swings rapidly.
- Retail investor activity can spike, often erratically.
- Asset classes from tech to defense can see unusual volatility.
While traditional financial models often overlook these dynamics, at aisot we embrace them as input. Powered by Large Language Models, our technology and research focus on capturing narrative momentum – such as news and sentiment – and transforming it into actionable forecasts on weekly and monthly horizons.
From feud to forecast
What happens when Musk criticizes Trump’s policies or vice versa?
Many systems would mark it as unstructured noise.
Our models and research focus on:
- Detecting anomaly patterns in social sentiment and correlate them with the market
- Adjusting signal strengths based on influencer amplification (yes, some people move markets more than others)
- Delivering weekly and monthly forecasts derived not just from financials – but from the velocity of narrative
In essence, we aim to quantify the unstructured noise and its impact on the markets.
Why this matters now
In today’s market environment – marked by geopolitical tensions, AI disruption, and increasing retail participation – the line between media, perception, and price action is blurred.
You can no longer afford to rely solely on lagging indicators and historical data. You also need tools that read the market’s mood as well as its metrics.
Whether it’s a Musk tweet picked up in the media, a Trump interview, or a viral headline – aisot’s AI news models and research aim to turn these unstructured data into valuable intelligence for long-only investors.
Thought leadership in a new era of market news sentiment
We believe the future of asset intelligence lies not just in what’s measurable, but in what’s interpretable. This involves building portfolio models that leverage:
- A foundation in established financial and economic knowledge: We blend machine learning or AI-derived signals with prior beliefs rooted in financial theory, such as the Arbitrage Pricing Theory (APT) and the Capital Asset Pricing Model (CAPM).
- Data-driven real-time predictive models:
- Data-driven machine learning models provide corrections to the classical financial models
- Contextual AI model – Large Language Model (LLM)-powered AI tools excel at understanding context, rather than simply relying on word frequencies. These models can read headlines or articles and interpret whether the sentiment is positive, negative, or neutral within the broader context. For Example: President Trump said: “The easiest way to save money in our budget, billions and billions of dollars, is to terminate Elon’s governmental subsidies and contracts.” Older AI might see it as purely negative due to words like “terminate.” But contextual AI models powered by LLMs aim to recognize the political condition and separate personal or policy threats from company performance.
- Explainable AI for Finance – not only that AI needs to be explainable, but also it needs to have grounding to economic and financial features and factors.
- Portfolio risk – One should avoid using models that fail to take portfolio risk into account when making decisions. Specifically, it is important to consider various sources of risk: both systematic risk factors and idiosyncratic sources of risk, such as asset-specific or company-specific risks.
- Joint model: News should not be used in isolation, without the financial context of other factors and features, and without incorporating a portfolio risk model.
The Trump–Elon feud is a headline today. Tomorrow, it might be a catalyst.
Our question is always the same: Can you see it before the market reacts?
If you're a professional investor who wants to discover how we integrate news into our models and use this for weekly and monthly predictions, we invite you to schedule a call with us.
