Summary CFA Level 1 2023 - All-in-one Formula (with Graphs and CFA Calculator Instructions)
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Course
CFA
Institution
CFA (CFA)
CFA Level 1 All-in-one Formulas across all topics
- Full suite formulas across all topics
- With graphs for Economics
- With CFA Calculator instructions
Ordinary Annuity
Are [ ( I t ) / AT [I ]
N
FV = r -
]
I pv = -
n
N i N
l l T i r
P MT : A
CPT FV l CPT PV
3 .
Annuity Due
calculator
change setting of
-
2ND P MT
2ND ENTER
2ND CPT
, 4.
perpetuity
PV =
In
N 500 (
input a
very large
no .
)
5- Mean
i. Arithmetic (
population &
sample
: same )
I = ¥,
Xi
nii
Geometric
.
Rr)
T
( It RG )
=
C l t Ri ) C l t - -
-
C it RT )
iii.
Harmonic
I
In, ¥
E.
=
.
6 . Position
of percentile (
Py )
Ly = Cnt 1) ÷
ly Location
of penenti he
-
:
of
n :
total no . observations
y
:
% point (
e.g
.
3rd
quartile
=
75% )
9 b
if ly = -
,
96 9
X
Py
-
-
g =
Xi o
-
X
g
lo -
9
,7.
Dispersion
i. Mean Absolute Deviation )
( MAD
MAD =
¥ilx
n
population Variance
ii.
62
¥ = ,
( Xi -
u)
-
T
2ND 8 → I -
V
2ND 7
2ND CEI C
( No . ) ENTER N
2ND 8
iii.
Sample Variance
E,cxi-
S2 =
n
-
I
be
Degree offreedom
iv. Semi variance
-
=
( Xi -
I) I in -
i )
Senwranianre
v .
Target
FEB )
'
= ( Xi -
B) l Cn - i
, 8 .
Chebyshev 's
Inequality
with K d I I
proportion of observations
s -
.
1-
= I
K2
-
9 .
Coefficient of Variation
CV =
¥70 Sample s -
d .
skewness
Sample
10 n
(chin,] E.in/i--xj3s3
.
Sk =
Up samples - d .
11 Kurtosis
-tEnTn÷ Ei× E%i÷
.
ke
-
,
>
For normal distribution , kurtosis =3
-
'
. Excess kurtosis =
Kurtosis
-
3
, 12 .
Probability
i . P ( AB ) =
PLA l B ) PCB )
↳
joint probability probability of A
green
B
of A & B
ii.
p CA or B) =
PLA) t PCB ) -
P LAB)
iii. p ( s) t p ( Sc ) =
I
↳ event A
complement of
For exclusive & exhaustive events :
mutually
iv. n
P CA ) =
PLAS ) .
t
PLA Sr) t - -
-
t
p CAS n )
=p (
Al si ) pls ) , e - .
- t
PCAIsn ) pls )
n
Values
Expected
' 3 .
i.
Expected Value ELX ) =
FE PLX
,
; ) E.
hey
of
n
one
outcomes
possible
X
of
ii. Conditional
Expected Value
El x Is ) =
pcx , I s ) x ,
t - - - t Pl Xnl S) X n
iii. Total probability Rule
EU ) =
EL Xt si ) pls ) i t . . - t
Ecxlsn) pls ) n
be
exclusive &
mutually
exhaustive
, Pex y )
4
Joint probability Function
' ,
.
Cov ( RA IRB ) {§ Pl Rai RBJ ) ( Rai ERA) ( RB ERB
)
-
i -
=
,
-
,
PLX ) PLT)
independent if PLXT )
'
ii. x.
f
=
iii. x,
y uncorrelated if ELXT )
=
EL x ) ECT)
new
15
Bayes formulaPl BIA
→ '
.
seok
) PCA)
P ( A/B ) =
pl B) → new
16 .
Counting I
i. n !
= n ( n -
l
) Cn -
2) . . -
ii. n
objects labelled with K labels =
n.int#n .
iii. choose r
from n
, X order :
YC =
cn%
iv. choose r
from n , V order :
IP =
cn7
it Binomial Bln )
,
p
.
Mean
up
i. =
ii. Var
npcl p )
= -
↳ trials probability of success
of ! no -
n " ×
)
"
-
iii. PCX = x =
(n -
× , ! × ! P Cl -
p)
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