Portfolio Manager
Sizing and executing new positions
What You Do Today
Determine position size based on conviction level, volatility, liquidity, correlation with existing holdings, and maximum drawdown tolerance. Then work with the trading desk on execution strategy.
AI That Applies
ML-driven position sizing models optimize allocation considering portfolio-level risk contribution, expected transaction costs, and liquidation horizon under stress scenarios.
Technologies
How It Works
For sizing and executing new positions, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The model suggests a size; you decide whether you believe enough to put it on.
What Changes
Position sizing becomes more precise and portfolio-context-aware. The model knows that adding this position increases your tech factor exposure by 150 bps, which may not be intended.
What Stays
Conviction. The model suggests a size; you decide whether you believe enough to put it on. And when the position moves against you, you decide whether to add or cut.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for sizing and executing new positions, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long sizing and executing new positions takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your VP Operations or COO
“What data do we already have that could improve how we handle sizing and executing new positions?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“Who on our team has the deepest experience with sizing and executing new positions, and what tools are they already using?”
They understand the workflow dependencies that AI tools need to respect
a frontline supervisor
“If we brought in AI tools for sizing and executing new positions, what would we measure before and after to know it actually helped?”
They see the daily reality that AI tools need to fit into
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.