AI-powered capital allocation
Lugn Avkastnaden analyzes your income pattern and real-time market data to turn irregular gig income into structured, long-term returns — without you having to time the market yourself.
The model is based on historical cash flow patterns and market volatility — not on promises of future returns.
The uncertainty of the gig economy
Irregular income creates decision fatigue, not just uncertain finances.
As a gig worker, consultant or freelancer, your monthly income often varies by 30-60%. That variation, called volatility (how much a value fluctuates over time), makes it difficult to plan regular savings.
At the same time, manual market timing requires constant decisions: when to invest, how much, and in what. Every decision under uncertainty eats away at mental capacity — a phenomenon commonly known as decision fatigue, where the quality of the decisions deteriorates the more you are forced to make.
The result is often that capital remains in an account, or is invested impulsively at the wrong time. Lugn Avkastnaden removes the manual assessment and replaces it with a rules-based, data-driven process.
Methodology
The system doesn't just buy according to a fixed schedule. It analyzes real-time data to optimize every krone invested through so-called smart entry points — times when the model judges that the price is relatively favorable.
Step 01
The platform collects your income flow, account balance and relevant market data (price movements, volume, volatility index) continuously and in real time.
Step 02
A machine learning model weighs together historical patterns and current market signals to estimate the likelihood of short-term price movement — not to predict the future with certainty.
Step 03
Based on the analysis, your available cash flow is automatically distributed over several smaller purchases, according to the principle of dollar-cost averaging, but adjusted according to identified smart entry points.
Technical basis
The model weighs each proposed purchase against current volatility and exposure in your existing holdings. The goal is not to eliminate risk — that's not possible — but to avoid investing large parts of the cash flow at times of heightened uncertainty.
You see exactly which data basis was the basis for each completed purchase: which signals weighed in, which percentage of the cash flow was allocated, and how it relates to your set risk framework.
Regardless of whether your gig income varies between SEK 2,000 and 20,000 a month, the same infrastructure handles the allocation — the model scales the distribution proportionally without having to rebuild for different volumes.
Method and security
The core of Lugn Avkastnaden is an enhanced version of dollar-cost averaging (DCA) — a method where capital is invested in fixed, smaller parts over time rather than as a lump sum. The AI layer adjusts the size and timing of each partial purchase based on current market signals, rather than following a completely static schedule.
The model's decisions are logged and can be recreated. This means that every completed purchase can be explained afterwards based on the data that was available at the time — as opposed to an intuitive, difficult-to-justify assessment.