DPS (Income+ETF)
As we were creating the DPS Summary for our Income and ETF Funds, we utilized AI for the consolidation and preparation of the data. We’ve first started with the Income Funds. DPS were correctly stated for each fund, but the Ex-dividend dates were incorrect, and AI couldn’t pull the prices for those dates. We’ve decided to update the information ourselves. Only thing AI did correctly was DPS and calculating DY%. As for the ETFs, AI failed to pull the correct DPS information. Granted, ETFs pay quarterly, but AI hallucinated on all of the DPS. They couldn’t source the data. However, AI managed to pull the CP correctly for all of the Funds and even showed the source data which is interesting. We have confidence in AI that can provide us with CP and live data, but not historical pricing or DPS announcements. We’ve updated our TAPP from our Power BI dashboard.
In addition to creating the DPS summary, we told AI to determine Fund Assessments based on Ex-CP, CP, and TAPP. We prompted AI to focus on generating metrics using %’s variances for all of the prices and to create three bullet points showing % variances as well as their insights. Due to the lengthy information, we separated into two tables: Income and ETFs. We can utilize the AI funds assessments and determine if we would need to adjust our targets, or to increase or decrease our positions allocations.
Funds Assessment (Income) / Funds Assessment (ETF)
Although we were hoping AI could’ve done a better job, this report still needed to be done so that we can sync our target amounts in our Portfolio. Since AI couldn’t be reliant on updating this report, we might consider updating this report in Excel quarterly going forward and populate this in Power BI once we have sufficient metrics.
