Predicting future assets, is this recursion or not?

Hi IsmoO 

I finished reading this blog post and I think the method described by the author is probably the most useful for now.

Previous Value (“Recursion”) in DAX - Microsoft Fabric Community

So in response to your question, I ran some tests:

Here's some dummy data

"Table"

First, go to the power query and add an index column.

Create measures.

Measure 1 = 
IF(
    SELECTEDVALUE('Table'[Index]) <> 1,
    CALCULATE(
        SELECTEDVALUE('Table'[Assets at end of year after gains]),
        FILTER(
            ALL('Table'),
            'Table'[Year] = MAX('Table'[Year]) - 1
        )
    ),
    SELECTEDVALUE('Table'[Assets at beginning of year])
)

Measure 2 = 'Table'[Measure 1] * 0.05

Measure 3 = 
IF(
    ISBLANK(SELECTEDVALUE('Table'[Wages])),
    CALCULATE(
        MAX('Table'[Wages]),
        FILTER(
            ALL('Table'),
            'Table'[Index] = MAX('Table'[Index]) -1)
        ),
    SELECTEDVALUE('Table'[Wages])
)

Measure 4 = 
IF(
    ISBLANK(SELECTEDVALUE('Table'[Costs])),
    CALCULATE(
        MAX('Table'[Costs]),
        FILTER(
            ALL('Table'),
            'Table'[Index] = MAX('Table'[Index]) -1)
        ),
    SELECTEDVALUE('Table'[Costs])
)

Measure 5 = 
'Table'[Measure 1] + 'Table'[Measure 2] + 'Table'[Measure 3] - 'Table'[Measure 4]

Measure 6 = 'Table'[Measure 5] * 0.05 

Measure 7 = 'Table'[Measure 5] + 'Table'[Measure 6] 

Create a new table.

New Table = 
SELECTCOLUMNS(
    'Table',
    "year",
    'Table'[Year],
    "Assets at beginning of year",
    'Table'[Measure 1],
    "Divididends 5%",
    'Table'[Measure 2],
    "Wages",
    'Table'[Measure 3],
    "Costs",
    'Table'[Measure 4],
    "Assets end of year before gains",
    'Table'[Measure 5],
    "Assets value gain 5%",
    'Table'[Measure 6],
    "Assets at end of year after gains",
    'Table'[Measure 7]
    )

Here is the result.

Regards,

Nono Chen

If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.