When at the base rate of q1, we have TP1, FP1, TN1, and FN1. SN1, SP1, and NPV1 then can be drived. For example, NPV1 = TN1 / (TN1 + FN1).
Similarily, when at the base rate of q2, we have TP2, FP2, TN2, FN2, SN2, SP2, and NPV2.
Question: how can we calculate the NPV at base rate of q （q=12%）?
If we use SN1, SP1, and q=12%, we can get one value for the NPV at base rate of 12%.
But if we use SN2, SP2, and q=12%，we will get another value for the NPV at base rate of 12%.
Out of the two NPV at base rate of 12%, which one should we trust?
Why does the formula of calcualting NPV at base rate of 12% not consider the original base rate (for example q1 or q2) that it is derived from?
Related
I have the loan dataset below -
Sector
Total Units
Bad units
Bad Rate
Retail Trade
16
5
31%
Construction
500
1100
20%
Healthcare
165
55
33%
Mining
3
2
67%
Utilities
56
19
34%
Other
300
44
15%
How can I create a ranking function to sort this data based on the bad_rate while also accounting for the number of units ?
e.g This is the result when I sort in descending order based on bad_rate
Sector
Total Units
Bad units
Bad Rate
Mining
3
2
67%
Utilities
56
19
34%
Healthcare
165
55
33%
Retail Trade
16
5
31%
Construction
500
1100
20%
Other
300
44
15%
Here, Mining shows up first but I don't really care about this sector as it only has a total of 3 units. I would like construction, other and healthcare to show up on the top as they have more # of total as well as bad units
STEP 1) is easy...
Use SORT("Range","ByColNumber","Order")
Just put it in the top left cell of where you want your sorted data.
=SORT(B3:E8,4,-1):
STEP 2)
Here's the tricky part... you need to decide how to weight the outage.
Here, I found multiplying the Rate% by the Total Unit Rank:
I think this approach gives pretty good results... you just need to play with the formula!
Please let me know what formula you eventually use!
You would need to define sorting criteria, since you don't have a priority based on column, but a combination instead. I would suggest defining a function that weights both columns: Total Units and Bad Rate. Using a weight function would be a good idea, but first, we would need to normalize both columns. For example put the data in a range 0-100, so we can weight each column having similar values. Once you have the data normalized then you can use criteria like this:
w_1 * x + w_2 * y
This is the main idea. Now to put this logic in Excel. We create an additional temporary variable with the previous calculation and name it crit. We Define a user LAMBDA function SORT_BY for calculating crit as follows:
LAMBDA(a,b, wu*a + wbr*b)
and we use MAP to calculate it with the normalized data. For convenience we define another user LAMBDA function to normalize the data: NORM as follows:
LAMBDA(x, 100*(x-MIN(x))/(MAX(x) - MIN(x)))
Note: The above formula ensures a 0-100 range, but because we are going to use weights maybe it is better to use a 1-100 range, so the weight takes effect for the minimum value too. In such case it can be defined as follow:
LAMBDA(x, ( 100*(x-MIN(x)) + (MAX(x)-x) )/(MAX(x)-MIN(x)))
Here is the formula normalizing for 0-100 range:
=LET(wu, 0.6, wbr, 0.8, u, B2:B7, br, D2:D7, SORT_BY, LAMBDA(a,b, wu*a + wbr*b),
NORM, LAMBDA(x, 100*(x-MIN(x))/(MAX(x) - MIN(x))),
crit, MAP(NORM(u), NORM(br), LAMBDA(a,b, SORT_BY(a,b))),
DROP(SORT(HSTACK(A2:D7, crit),5,-1),,-1))
You can customize how to weight each column (via wu for Total Units and wbr for Bad Rates columns). Finally, we present the result removing the sorting criteria (crit) via the DROP function. If you want to show it, then remove this step.
If you put the formula in F2 this would be the output:
Hi I am not sure if this specific question has been asked but I am wondering if anyone can help me with this.
Sale Price
9.98
Fixed
Expenses %
0.35
Fixed
Expenses
3.49
Sale Price * Expenses %
Cost
4.99
Fixed
ROI %
0.40
Dynamic
Adjusted Cost
Result
Basically what we want to achieve is a cost recommendation based off of the ROI %, so as you change the ROI % then the Cost Adjustment should also change to be true if you took Sale Price - Cost - Expense.
Calc for ROI = ( Sale - Expense - Cost) / Sale
In the area where you have Adjusted Cost, where I figure you're trying to calculate what Cost you need for a 40% ROI. I'm assuming your ROI is a manual input.
Cost = [Sale Price] * ( 1 - [ROI] ) - [Expense]
I need to calculate the weighted median, average, sd of PE funds' returns. I weighted the sample according to the amount of committed capital of a fund, but I should consider negative products to analyze underperforming funds. However, I'm not sure if I can use neg/zero values to derivate these statistic measures.
Wμ = Σ(w,x)/Σw --> the formula i consider for wgt. average
w = Fund's size
x = net IRR
(w,x) = Neg & Pos values.
How can I calculate those measures, including negative/zero values? I'm doing it in Excel
My standpoint is the Kaplan and Schoar's approach (Private Equity Performance: Returns, Persistence, and Capital Flows)
Any help on this matter is really appreciated!
I am trying to solve an iterative problem in Excel. I want to be able to calculate the sum of rent for x years. The rent is increasing at a rate of 10 percent every year. I quickly came up with this python code on a REPL for clarity:
year = 6
rent = 192000
total_rent = rent
for x in range(1 , year):
rent= rent + .1*rent
total_rent = total_rent + rent
print(total_rent) # 1481397.12 is what it prints
This is a trivial problem in programming but I am not sure the best way to achieve this in excel.
In excel I am doing it this something like this:
But all the intermediate rent amount(s) are not really needed. I guess there should be a for loop here as well too, but is there a mathematical representation of this problem which I can use to create the expected result?
If you have a financial problem, you might try the financial functions of excel.
=-FV(0.1, 6, 192000)
or
=FV(0.1, 6, -192000)
the detail: FV on Office Support
Description
FV, one of the financial functions, calculates the future value of an investment based on a constant interest rate. You can use FV with either periodic, constant payments, or a single lump sum payment.
Syntax
FV(rate, nper, pmt, [pv], [type])
For a more complete description of the arguments in FV and for more information on annuity functions, see PV.
The FV function syntax has the following arguments:
Rate Required
The interest rate per period.
Nper Required
The total number of payment periods in an annuity.
Pmt Required
The payment made each period; it cannot change over the life of the annuity. Typically, pmt contains principal and interest but no other fees or taxes. If pmt is omitted, you must include the pv argument.
Pv Optional
The present value, or the lump-sum amount that a series of future payments is worth right now. If pv is omitted, it is assumed to be 0 (zero), and you must include the pmt argument.
Type Optional
The number 0 or 1 and indicates when payments are due. If type is omitted, it is assumed to be 0.
Your problem is a geometric series where the initial term is a = 192000 and the common ratio is r = 1.1. (The ratio is not just the 10% added, it includes the 100% that is added to.) To refresh your Algebra II memory, a geometric series is
total = a + a*r + a*r**2 + ... + a*r**(n-1)
The closed-form formula for the sum of the geometric series is
total = a * (r**n - 1) / (r - 1)
(using Python syntax), or, using something closer to Excel syntax,
total = a * (r^n - 1) / (r - 1)
where n is the number of years. Just substitute your values for a, r, and n.
As the question is about excel it is possible by
Or by using the FV function.
FV returns the future value of an investment based on regular payments and a constant interest rate.
Attributes of the FV function;:
Rate: The interest rate per period.
Nper: The total number of payment periods in an annuity.
Pmt: The payment made each period; it cannot change over the life of the annuity. Typically, pmt contains principal and interest but no other fees or taxes. If pmt is omitted, you must include the pv argument.
Pv: The present value, or the lump-sum amount that a series of future payments is worth right now. If pv is omitted, it is assumed to be 0 (zero), and you must include the pmt argument.
Type: The number 0 or 1 and indicates when payments are due. If type is omitted, it is assumed to be 0.
Yet another way is computing it as a geometric series with the non-financial function SERIESSUM:
=SERIESSUM(1.1,0,1,192000*{1,1,1,1,1,1})
The rate multiplier is 1.1, starting from 1.1^0 == 1 and increasing by 1 each year. The result is 1*a + 1.1*b + 1.1^2*c.... The array 192000*{1,1,...} provides the coefficients a, b, c, ... : one array value for the initial total_rent = rent, and one for each subsequent year 1..5 (from range(1,year)).
let us suppose we have following data with binary response output(coupon)
annual spending is given in 1000th unit, my goal is to estimate whether if customer spend more then 2000 and has Simmons card, will also have coupon, first of all i have sorted data according to response data, i got following picture
at next stage i have calculated logit for each data, for those initially i choose following coefficient
B0 0.1
B1 0.1
B2 0.1
and i have calculated L according to the following formula
at next stage i have calculated e^L (which in excel can be done easily by exp function )
=EXP(D2)
after that i have calculated probability
=E2/(1+E2)
and finally using formula
i have calculated log likelihood function
then i have calculated sum and using solver i have calculated coefficient that minimize this sum( please pay attention that values are given in negative value) , but i have got all coefficient zero
i am wrong ? or does it means that i can'predict buying of coupon on the base of Annual spending and owning of Simmons card? thanks in advance
You can predict the buying of a coupon on the base of Annual spending (and knowing Simmons card doesn't help).
Admittedly I didn't solve it in Excel, but I suspect the problem might be that your optimization didn't converge (i.e., failed to reach the correct coefficients through the solving process) -- the correct coefficients are B0 = 5.63, B1 = -2.95, and B2 = 0. I found an online reference for the Excel logistic regression procedure at http://blog.excelmasterseries.com/2014/06/logistic-regression-performed-in-excel.html.
I ran the logistic regression myself and found that Annual spending is significant (at the 0.05 level) whereas Simmons card is not. Re-running the model with Simmons card removed yields the following equations:
L = 5.63 - 2.95 * Annual spending
P(1) = exp(L)/(1 + exp(L))
If P(1) > 0.5 => coupon = 1
Although the entropy Rsquare is low at 0.39 (and the number of data points is very low), the model is statistically significant.