Indien de vergelijking [math]NPV(r)=0[/math] slechts één zinvolle oplossing heeft, dan noemt men deze oplossing de [i][b]interne rentabiliteit[/b][/i] IRR [“internal rate of return”] van dit project. [br][br]Voor [br] [img width=325,height=51]data:image/png;base64,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[/img][br]geldt IRR [math]\approx[/math] 15.24% (of 0.1524°/1).[br][br]De berekening van deze IRR kan gebeuren via een iteratieve techniek steunend op lineaire interpolatie zoals beschreven als volgt. [br][br]Door te experimenteren met verschillende waarden van [math]r[/math] in [math]NPV(r)[/math] zoek je twee waarde [math]a[/math] en [math]a_1[/math] waarvoor respectievelijk [math]NPV(a)>0[/math] en [math]NPV(a_1)<0[/math]. [br][br]Neem hier [math]a=0.15[/math] en [math]a_1=0.16[/math]: [br][br] [math]NPV(0.15)\approx5.65[/math] en [math]NPV(0.16)\approx-17.71[/math][br][img width=441,height=240]data:image/png;base64,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[/img] [br][br]We benaderen de grafiek tussen [math](a,NPV(a))[/math] en [math](a_1,NPV(a_1))[/math] door het lijnstuk dat deze twee punten met elkaar verbindt. Het snijpunt [math](a_2,0)[/math] van deze rechte met de X-as wordt gevonden door lineaire interpolatie, d.w.z. door de richtingscoëfficiënt van deze rechte te berekenen als [math]\frac{\bigtriangleup y}{\bigtriangleup x}[/math] via de punten[br] [math](a,NPV(a))[/math] en [math](a_1,NPV(a_1))[/math] [br]en vervolgens via[br] [math](a,NPV(a))[/math] en [math](a_2,0)[/math]: [br][br] [math]\frac{NPV\left(a_1\right)-NPV\left(a\right)}{_{a_1-a}}=\frac{0-NPV\left(a\right)}{a_2-a}[/math][br]d.w.z.[br] [math]a_2-a=\frac{-NPV\left(a\right)\cdot\left(a_1-a\right)}{NPV\left(a_1\right)-NPV\left(a\right)}[/math] [br]i.e.[br] [math]a_2=a-\frac{NPV\left(a\right)\cdot\left(a_1-a\right)}{NPV\left(a_1\right)-NPV\left(a\right)}[/math][br][br]of numeriek [math]a_2\approx0.1524[/math]. [br][br]Indien we de berekeningen uitvoeren tot op 7 cijfers na het decimale punt, dan bekomen we [math]a_2\approx0.1524173[/math] terwijl het resultaat van NPV hiervoor tot op 6 cijfers nauwkeurig gelijk is aan [math]NPV(a_2)\approx-0.082497[/math] .[br][br]Door de bovenstaande procedure te hernemen met [math]a[/math] en [math]a_2[/math] i.p.v. met [math]a[/math] en [math]a_1[/math] levert dit de volgende benadering op voor een waarde [math]a_3[/math] :[br][br] [math]a_3\approx0.1523825[/math] waarbij [math]NPV(a_3)\approx-0.000378[/math] [br][br][img 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[/img][br]Door op een dergelijke wijze iteratief te werk te gaan en telkens twee waarden te nemen met een verschillend teken voor de bijhorende NPV-resultaten, bekom je achtereenvolgens [br][br] [math]a_4\approx0.1523824[/math][br] [math]a_5\approx0.1523824[/math] [br][br]De iteratie stopt zodra je twee waarden vindt die gelijk zijn (tot op een aantal decimalen), zodat een benadering voor IRR op deze wijze gegeven wordt door 0.152 382 4. [br][br]Als andere benaderingstechniek kan je de regel van Newton-Raphson hanteren, waarbij uitgegaan wordt van het snijpunt van de raaklijn in (a, NPV(a)) aan de grafiek van NPV met de X-as om “[math]a_2[/math]” te bepalen:[br] [math]a_2=a-\frac{f\left(a\right)}{f'\left(a\right)}[/math][br] [br]met [math]f=NPV[/math], waarna met deze [math]a_2[/math] wordt verder gewerkt (zoals in Excel tot op 7 decimalen):[br][br] IRR[math]\approx[/math] 0.152 382 4 [math]\approx[/math] 15.24%[br]