Here are 5 AI tools to use in investing and how they can help

In this article, we take a look at 5 AI tools to use in investing. A few months ago, we wrote about how to use AI generally in investing, but we did not look at any specific tools avaliable to investors – whether on the ASX, Wall Street or elsewhere. Now we will.

What are the Best ASX Stocks to invest in right now?

Check our buy/sell tips

5 AI tools to use in investing

Kavout

Stock investing starts with idea generation and Kavout helps with that. It provides AI‑driven stock scoring (Kai Score), trading signals, stock screening, and tools to explore ideas and build and watch portfolios. It integrates alternative data + sentiment.

The lucrative aspect is that it processes very large data sets through machine learning models very quickly. What would take you days to do manually can now be done much faster.

It is good for retail or professional investors who want a relatively user‑friendly way to get advanced screening, signal ideas, and not build everything from scratch. But obviously, you’ll want to vet signals and combine with your own judgment.

Amenity Analytics

Recently, you may have heard something like ‘mentions of cost cutting this earnings season increased _%’. On what basis can you make such a claim? Well through machine learning tools that extract such insights and Amenity Analytics is ons such solution.

It uses machine learning to parse massive volumes of text, classify, tag, and score them. For example, in earnings or transcripts, detect language suggesting “cost pressures”, “guidance reduction”, or “executive tone shift”. It also tracks ESG too. In some ways it is similar to Kavout, but is more targeted at investors wanting to monitor general sentiment.

Amenity Analytics is very useful for fundamental analysts who want to monitor risk, catch early warnings or analyse ‘stories’ rather than number. The risks? Well, Interpretation can be tricky (tone does not equal outcome), and there can be false positives (text sounding bad but not affecting profits). Also there is a subscription cost, and you need to integrate these signals into your workflow; it’s less plug‑and‑play than a stock screener.

Numerai

The basis of this platform is hedge fund crowdsourcing AI-based models to predict returns. Crowdsourcing means the models are contributed by other users – yes, if you became a user, you yourself could submit your own models. And it is a tournament model – your model gets rewarded via a token system. There is integration with certain other tools (i.e. signal or model builders) to export your work immediately.

We will admit that this may be too complicated for you if you’re not a data scientist or ML engineer. Moreover the tournament setting often means participants optimise for the metric rather than the business‑risk; and encrypted data means less visibility (you don’t know exactly what all the features are), which can be good for fairness, bad for interpretability.

Portfolio Optimiser API

This is aimed at portfolio managers who are looking at it from a wholistic perspective – in other words, not at individual stocks but other things like factor exposure and turnover. It has generic tools, services or APIs (some open source, some paid) which help compute optimal portfolio weights given constraints.

It is useful for anyone wanting to avoid naive “equal weights” or naive sector tilts, and who wants to systematically optimise risk / return trade‑offs. It can be also helpful for backtesting and “what if” scenario tests. Obviously, the key is to get the inputs right to avoid bad allocations.

Dataminr

It is a real‑time event detection & alerts platform. It sifts through data (news, social media, signals) to flag events that could impact markets (such as breaking news, supply chain disruptions, disasters, regulatory announcements) often faster than traditional news sources. Then pushes alerts to users. Some signals might be “noise”, others high‑impact. Users often use for risk oversight, trading alerts, early warnings.

It is good for risk management, event‑driven trading strategies, macro watchers, portfolios that are sensitive to external events. It is also useful for “headline risk” monitoring. The risk include that false positives can be frequent or that events may be identified but not material.

Also, speed may matter: many get alerts, but acting on them in a way that yields profit is crucial. And also, subscriptions can be expensive – we say can be, because dependant on how much you use it and how much it saves/makes you, it could pay for itself.

Disclosure: We at Stocks Down Under have used these and other tools, but have no other relationship with them and have only recommended the ones we genuinely believe are worth our readers’ while.