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FINANCIAL MANAGEMENT C A I I B MODULE A

Transcript of Caiib Fm Moda Cont

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FINANCIAL MANAGEMENT

C A I I B

MODULE A

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TIME VALUE OF MONEY

MONEY HAS TIME VALUE

THIS IS BASED ON THE CONCEPT OF EROSION IN VALUE OFMONEY DUE TO INFLATION

HENCE THE NEED TO CONVERT TO A PRESENT VALUE

OTHER REASONS FOR NEED TO REACH PRESENT VALUE IS

-- DESIRE FOR IMMEDIATE CONSUMPTION RATHER THAN

WAIT FOR THE FUTURE

-- THE GREATER THE RISK IN FUTURE THE GREATER THE

EROSION

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TIME VALUE OF MONEY

EXTENTOF EROSION IN THE VALUE OF MONEY IS ANUNKNOWN FACTOR. HENCE A WELL THOUGHT OUTDISCOUNT RATE HELPS TO BRING THE FUTURE CASHFLOWS TO THE PRESENT.

THIS HELPS TO DECIDE ON THE TYPE OF INVESTMENT,EXTENT OF RETURN & SO ON.

 ALL THREE FACTORS THAT CONTRIBUTE TO THE EROSION

IN VALUE OF MONEY HAVE AN INVERSE RELATIONSHIP WITHTHE VALUE OF MONEY i.e. THE GREATER THE FACTOR THELOWER IS THE VALUE OF MONEY

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TIME VALUE OF MONEY

IF DESIRE FOR CURRENT CONSUMPTION ISGREATER THENWE NEED TO OFFER INCENTIVES TO DEFER THECONSUMPTION.

THE MONEY THUS SAVED IS THEN PROFITABLY ORGAINFULLY EMPLOYED . HENCE THE DISCOUNT RATE WILLBE LOWER.

INVESTMENT IN GOVERNMENT BONDS / SECURITIES IS LESSRISKY THAN IN THE PRIVATE SECTOR SIMPLY BECAUSE NOT

 ALL CASH FLOWS ARE EQUALLY PREDICTABLE AND WHERETHERE IS SOVEREIGN GUARANTEE THE RISK IS LESS.

IF THE RISK OF RETURN IS LOWER AS IN GOVT. SECURITIESTHEN THE RATE OF RETURN IS ALSO LOWER.

 

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TIME VALUE OF MONEY

 THE PROCESS BY WHICH FUTURE FLOWS ARE ADJUSTEDTO REFLECT THESE FACTORS IS CALLED DISCOUNTING &THE MAGNITUDE IS REFLECTED IN THE DISCOUNT RATE.

THE DISCOUNT VARIES DIRECTLY WITH EACH OF THESEFACTORS.

THE DISCOUNT OF FUTURE FLOWS TO THE PRESENT IS

DONE WITH THE NEED TO KNOW THE EFFICACY OF THEINVESTMENT.

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TIME VALUE OF MONEY

THE DISCOUNTING BRING THE FLOWS TO A NET PRESENTVALUE OR N P V.

N P V IS THE NET OF THE PRESENT VALUE OF FUTURE CASHFLOWS AND THE INITIAL INVESTMENT.

IF N P V IS POSITIVE THEN WE ACCEPT THE INVESTMENT AND VICE VERSA.

IF 2 INVESTMENTS ARE TO BE COMPARED THEN THEINVESTMENT WITH HIGHER N P V IS SELECTED. THEDISCOUNTED RATES FOR EACH ARE THE RISK RATES ASSOCIATED WITH INVESTMENTS.

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TIME VALUE OF MONEY

REAL CASH FLOWS ARE NOMINAL CASH FLOWS ADJUSTEDTO INFLATION.

NOMINAL CASH FLOWS ARE AS RECEIVED WHILE REAL CASHFLOWS ARE NOTIONAL FIGURES

REAL CASH FLOWS = NOMINAL CASH FLOWS

  1 – INFLATION RATE

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TIME VALUE OF MONEY

THERE ARE 5 TYPES OF CASH FLOWS: -- SIMPLE CASH FLOWS -- ANNUITY -- INCREASING ANNUITY -- PERPETUITY -- GROWING PERPETUITY

THE FUTURE CASH FLOWS ARE CONVERTED TO THEPRESENT BY A FACTOR KNOWN DISCOUNT

THE DISCOUNT RATE adjusted for inflation IS REAL RATE

THIS REAL RATE IS AN INFLATION ADJUSTED RATE

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TIME VALUE OF MONEY

DISCOUNTING IS THE INVERSE OF COMPOUNDING

FINAL AMOUNT = A PRINCIPAL = P

RATE OF INT. = r PERIOD = n

n n

 A = P(1+r) WHERE (1 + r) = COMPOUNDING FACTOR   n n

P = A__

  (1+ r) WHERE 1 ÷ (1 + r) = DISCOUNTING FACTOR

IF INSTEAD OF COMPOUNDING ON ANNUAL BASIS IT IS ONSEMI-ANNUAL OR MONTHLY BASIS THE THE EFFECTIVE RATEOF INTEREST CHANGES

  n

EFFECTIVE INTEREST RATE = (1 + r) - 1

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TIME VALUE OF MONEY

 ANNUITY IS A CONSTANT CASH FLOW AT REGULAR INTERVALS FOR A FIXED PERIOD

THERE 4 TYPES OF ANNUITIES

 A) END OF THE PERIODn

  a) P V OF AN ANNUITY(A) = A [1-- {1÷ (1 + r)} ]÷ rn

  b) F V OF AN ANNUITY(A) = A{(1 + r) -- 1} ÷ r

a) IS THE FORMULA OF EQUATED MONTHLY

INSTALMENT(EMI).

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TIME VALUE MONEY

B) BEGINNING OF THE PERIOD

n-1

 - a) P V OF ANNUITY(A) = A + A[1- {1÷ (1 + r) }] ÷ r

  n

 - b) F V OF ANNUITY(A) = A(1+ r){(1 + r) - 1} ÷ r

IF g IS THE RATE AT WHICH THE ANNUITY GROWS THEN

  n n

P V OF ANNUITY(A) = A(1 + g ){1 – [(1 + g) ÷ (1 + r)] } ÷ (r + g)

IMP: IN BANKS , TERM LOANS MADE AT X% REPAYABLE AT

REGULAR INTERVALS GIVE A YIELD 1.85X%.

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TIME VALUE OF MONEY

 A PERPETUITY IS A CONSTANT CASH FLOW AT REGULARINTERVALS FOREVER. IT IS ANNUITY OF INFINITE DURATION.

P V PERPETUITY(A) = A ÷ r

P V PERPETUITY(A) = A ÷ (r – g) IF PERPETUITY IS GROWING AT g.

RULE OF 72: DIVIDING 72 BY THE INTEREST RATE GIVES

THE NUMBER OF YEARS IN WHICH THE

PRINCIPAL DOUBLES.

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SAMPLING METHODS

 A SAMPLE IS A REPRESENTATIVE PORTION OF THEPOPULATION

TWO TYPES OF SAMPLING:

 --- RANDOM OR PROBABILITY SAMPLING

 --- NON-RANDOM OR JUDGEMENT SAMPLING

IN JUDGEMENT SAMPLING KNOWLEDGE & OPINIONS AREUSED. IN THIS KIND OF SAMPLING BIASEDNESS CAN CREEPIN, FOR EX. IN INTERVIEWING TEACHERS ASKING THEIROPINION ABOUT THEIR PAY RISE.

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SAMPLING METHODS

FOUR METHODS OF SAMPLING:

a) SIMPLE RANDOM

-- USE A RANDOM TABLE

-- ASSIGN DIGITS TO EACH ELEMENT OF THEPOPULATION(SAY 2)

-- USE A METHOD OF SELECTING THE DIGITS (SAY FIRST 2

  OR LAST 2) FROM THE TABLE TO SELECT A SAMPLE

THE CHANCE OF ANY NUMBER APPEARING IS THE SAMEFOR ALL.

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SAMPLING METHODS

b) SYSTEMATIC SAMPLING

-- ELEMENTS OF THE SAMPLE ARE SELECTED AT A UNIFORM

 

INTERVAL MEASURED IN TERMS OF TIME, SPACE OR

ORDER.

-- AN ERROR MAY TAKE PLACE IF THE ELEMENTS IN THE

POPULATION ARE SEQUENTIAL OR THERE IS A CERTAINITY

OF CERTAIN HAPPENINGS .

.

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SAMPLING METHODS

c) STRATIFIED SAMPLING-- DIVIDE POPULATION INTO HOMOGENOUS GROUPS

-- FROM EACH GROUP SELECT AN EQUAL NO. OF ELEMENTS

 AND GIVE WEIGHTS TO THE GROUP/STRATA ACCORDING

PROPORTION TO THE SAMPLE OR

--SELECT AT RANDOM A SPECIFIED NO. OF ELEMENTS FROM

EACH STRATA CORRESPONDING TO ITS PROPORTION

TO THE POPULATION

-- EACH STRATUM HAS VERY LITTLE DIFFERENCE WITHIN

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SAMPLING METHODS

  d) CLUSTER SAMPLING

-- DIVIDE THE POPULATION INTO GROUPS WHICH ARE

CLUSTERS

-- PICK A RANDOM SAMPLE FROM EACH CLUSTER

-- EACH CLUSTER HAS CONSIDERABLE DIFFERENCE WITHINBUT SIMILAR WITHOUT

IMP: WHETHER WE USE PROBABILITY OR JUDGEMENT

SAMPLING THE PROCESS IS BASED ON SIMPLE RANDOM

SAMPLING .

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SAMPLING METHODS

EXAMPLES OF TYPES OF SAMPLING:

SYSTEMATIC SAMPLING : A SCHOOL WHERE ONE PICKSEVERY 15TH STUDENT.

STRATIFIED SAMPLING: IN A LARGE ORGANISATION PEOPLE ARE GROUPED ACCORDING TO RANGE OF SALARIES.

CLUSTER SAMPLING: A CITY IS DIVIDED INTO LOCALITIES.

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SAMPLING METHODS

SINCE WE WOULD USING THE CONCEPT OF STANDARDDEVIATION LET US UNDERSTAND ITS SIGNIFICANCE

IT IS A MEASURE OF DISPERSION.

GENERAL FORMULA FOR STD. DEV. IS √∑(X - µ)²√ N 

WHERE X = OBSERVATION

µ = POPULATION MEANN = ELEMENTS IN POPULATION

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SAMPLING METHODS

DESPITE ALL THE COMPLEXITIES IN THE FORMULA THE

STD. DEV. IS THE SAME IN STATE AS SUMMATION OFDIFFERENCES BETWEEN THE ELEMENTS AND THEIR MEAN.

. --- IT IS THE RELIABLE MEASURE OF VARIABILITY .

. --- IT IS USED WHEN THERE IS NEED TO MEASURE

CORRELATION COEFFICIENT, SIGNIFICANCE OF

DIFFERENCE BETWEEN MEANS.

--- IT IS USED WHEN MEAN VALUE IS AVAILABLE.

--- IT IS USED WHEN THE DISTRIBUTION IS NORMAL OR NEAR

NORMAL

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  SAMPLING METHODS

FORMULA FOR STANDARD DEVIATION:

 

-- FOR POPULATION S   = √{(∑fx2÷ N) - ∑f 2x2÷ N}

THIS IS FOR GROUPED DATA, WHERE f IS THE FREQUENCY

OF ELEMENTS IN EACH GROUP AND N IS THE SIZE OF

 POPULATION

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SAMPLING METHODS

 IT IS IMPORTANT TO REMEMBER THAT EACH SAMPLE HAS

 A DIFFERENT MEAN AND HENCE DIFFERENT STD.

DEVIATION. A PROBABILITY DISTRIBUTION OF THE

SAMPLE MEANS IS CALLED THE SAMPLING

DISTRIBUTION OF THE MEANS. THE SAME PRINCIPLE

 APPLIES TO A SAMPLE OF PROPORTIONS.

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SAMPLING METHODS

 A STD. DEVIATION OF THE DISTRIBUTION OF THE SAMPLE

MEANS IS CALLED THE STD. ERROR OF THE MEAN. THE

STD. ERROR INDICATES THE SIZE OF THE CHANCE

ERROR BUT ALSO THE ACCURACY IF WE USE THE

SAMPLE STATISTIC TO ESTIMATE THE POPULATION STATISTIC

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SAMPLING METHODS

TERMINOLGY :\

µ = MEAN OF THE POPULATION DISTRIBUTION

µx¯   = MEAN OF THE SAMPLING DITRIBUTION OF THE MEANS

x¯ = MEAN OF A SAMPLE

σ  = STD. DEVIATION OF THE POPULATION DISTRIBUTION

σx¯   = STD. ERROR OF THE MEAN

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SAMPLING METHODS

σx¯= σ  WHERE n IS THE SAMPLE SIZE. THIS FORMULA IS√n 

TRUE FOR INFINITE POPULATION OR FINITE

POPULATION WITH REPLACEMENT.

Z = x¯ - µ WHERE Z HELPS TO DETERMINE THE DISTANCE

σx¯

OF THE SAMPLE MEAN FROM THE POPULATION

MEAN.

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SAMPLING METHODS

STD. ERROR FOR FINITE POPULATION:

σx ¯ = σ  √ [N-n] WHERE N IS THE POPULATION SIZE

√n √ [N-1]

 AND √ [N-n] IS THE FINITE POPULATION MULTIPLIER

√ [N-1]

THE VARIABILITY IN SAMPLING STATISTICS RESULTS FROMSAMPLING ERROR DUE TO CHANCE. THUS THE DIFFERENCEBETWEEN SAMPLES AND BETWEEN SAMPLE ANDPOPULATION MEANS IS DUE TO CHOICE OF SAMPLES.

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SAMPLING METHODS

CENTRAL LIMIT THEOREM

THE RELATIONSHIP BETWEEN THE SHAPE OF POPULATIONDISTRIBUTION AND THE SAMPLNG DIST. IS CALLED CENTRALLIMIT THEOREM.

 AS SAMPLE SIZE INCREASES THE SAMPLING DIST. OF THEMEN WILL APPROACH NORMALITY REGARDLESS OF THEPOPULATION DIST.

SAMPLE SIZE NEED NOT BE LARGE FOR THE MEAN TO APPROACH NORMAL

WE CAN MAKE INFERENCES ABOUT THE POPULATIONPARAMETERS WITHOUT KNOWING ANYTHING ABOUT THESHAPE OF THE FREQUENCY DIST. OF THE POPULATION

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SAMPLING METHODS

EXAMPLE: n = 30, µ = 97.5, σ = 16.3 a) WHAT IS THE PROB. OF X LYING BETWEEN 90 & 104  ANS) σx¯= σ  , = 2.97   √n   

  P( 90 – 97.5 < x¯ - µ < 104-97.5 )   2.97 σx¯ 2.97

  -2.52 < Z < 2.19

  USE Z TABLE

  P = 0.4941 + 0.4857 = 0.98

 b) FOR MEAN X LYING BELOW 100   P( Z< 100 – 104 )   2.97

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REGRESSION AND CORRELATION

REGRESSION & CORRELATION ANALYSES HELP TO

DETERMINE THE NATURE AND STRENGTH OF RELATIONSHIP

BETWEEN 2 VARIABLES. THE KNOWN VARIABLE IS CALLED

THE INDEPENDENT VARIABLE WHEREAS THE VARIABLE WE

 ARE TRYING TO PREDICT IS CALLED THE DEPENDENT

VARIABLE. THIS ATTEMPT AT PREDICTION IS CALLED

REGRESSION ANALYSES WHEREAS CORRELATION TELLS

THE EXTENT OF THE RELATIONSHIP.

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REGRESSION AND CORRELATION

THE VALUES OF THE 2 VARIABLES ARE PLOTTED ON A

GRAPH WITH X AS THE INDEPENDENT VARIABLE. THE

POINTS WOULD BE SCATTERED . DRAW A LINE BETWEEN

POINTS SUCH THAT AN EQUAL NUMBER LIE ON EITHER SIDE

OF THE LINE. FIND THE EQN. SAY Y= a +b X ; PLOT THE

 POINTS ON THE LINE.

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REGRESSION AND CORRELATION

ONE CAN DRAW ANY NUMBER OF LINES BETWEEN THEPOINTS. THE LINE WITH BEST ’ FIT’ IS THE THAT WITH LEASTSQUARE DIFFERENCE BETWEEN THE ACTUAL ANDESTIMATED POINTS.

IN THE EQN. Y = a + b X b = SLOPE = ∑ XY – n X¯ Y¯

∑ X¯ 2  – n X¯ 2

SLOPE OF THE LINE INDICATES THE EXTENT OF CHANGE INY DUE TO CHANGE IN X.

. a = Y¯ - b X¯

WHERE X¯ , Y¯ ARE MEAN VALUES

.

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REGRESSION AND CORRELATION

STD ERROR OF ESTIMATE

Se = √{∑(Y – Ye ) ÷ (n -2)} or   = √{√ Y² -a √Y – b √ (XY)}

√(n-2) 

. WHERE Ye = ESTIMATES OF Y

n – 2 IS USED BECAUSE WE LOSE 2 DEGREES OF FREEDOM

IN ESTIMATING THE REGRESSION LINE.

IF SAMPLE IS n THE DEG OF FREEDOM = n-1 i.e. WE CANFREELY GIVE VALUES TO n-1 VARIABLES. 

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REGRESSION AND CORRELATION

THERE ARE 3 MEASURES OF CORRELATION

- COEFFICIENT OF DETERMINATION. IT MEASURES THE

STRENGTH OF A LINEAR RELATIONSHIP

COEFF. OF DET. = r 2 = ∑(Y – Ye )2

1- ----------------∑( Y - Y¯ )2

COEF. OF DETERMINATION IS r ²COEFF. OF CORRELATION IS r√ r² = + r, HENCE FROM r 2 TO r WE KNOW THE STRENGTH

BUT NOT THE DIRECTION.

.

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REGRESSION AND CORRELATION

-COVARIANCE. IT MEASURES THE STRENGTH &

DIRECTION OF THE RELATIONSHIP.

COVARIANCE = ∑( X - X¯ )(Y - Y¯ )

n

- -COEFFICIENT OF CORRELATION. IT MEASURES THE

DIMENSIONLESS STRENGTH & DIRECTION OF THE

RELATIONSHIP

COEFF.OF CORR. = COVARIANCE

σxσy

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TREND ANALYSIS

4 TYPES OF TIME SERIES VARIATIONS: -- a) SECULAR TREND IN WHICH THERE IS FLUCTUATION BUT   STEADY INCREASE IN TREND OVER A LARGE PERIOD OF   TIME.

-- b) CYCLICAL FLUCTUATION IS A BUSINESS CYCLE THAT   SEES UP & DOWN OVER A PERIOD OF A FEW YEARS.   THERE MAY NOT BE A REGULAR PATTERN.

-- c) SEASONAL VARIATION WHICH SEE REGULAR CHANGES

  DURING A YEAR.

-- d) IRREGULAR VARIATION DUE TO UNFORESEEN   CIRCUMSTANCES.

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TREND ANALYSIS

IN TREND ANALYSIS WE HAVE TO FIT A LINEAR TREND BY

 LEAST SQUARES METHOD. TO EASE THE COMPUTATION WE

USE CODING METHOD WHERE WE ASSIGN NUMBERS TO THE

YEARS FOR EXAMPLE. THEN WE CALCULATE THE VALUES OF

 CONSTANTS a & b IN THE EQN. Y = a + b X AND THEN USE

THE EQN. FOR FORECASTING.

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TREND ANALYSIS

STUDY OF SECULAR TRENDS HELPS TO DESCRIBE A

HISTORICAL PATTERN;

USE PAST TRENDS TO PREDICT THE FUTURE;

 AND ELIMINATE TREND COMPONENT WHICH

MAKES IT EASIER TO STUDY THE OTHER 3 COMPONENTS.

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TREND ANALYSIS

ONCE THE SECULAR TREND LINE IS FITTED THE CYCLICAL &

IRREGULAR VARIATIONS ARE TACKLED SINCE SEASONAL

VARIATIONS MAKE A COMPLETE CYCLE WITHIN A YEAR AND

DO NOT AFFECT THE ANALYSIS.

THE ACTUAL DATA IS DIVIDED BY THE PREDICTED DATA

 A RELATIVE CYCLICAL RESIDUAL IS OBTAINED

 A PERCENTAGE DEVIATION FROM TREND FOR EACH VALUE

IS FOUND

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TREND ANALYSIS

SEASONAL VARIATION IS ELIMINATED BY MOVING AVERAGE

 METHOD

 

.  a) FIND AVERAGE OF 4 QTRS. BY PROCESS OF SLIDING

b) DIVIDE EACH VALUE BY 4

c) FIND AVERAGE OF SUCH VALUES IN b) FOR 2 QTRS BY

SLIDING METHOD

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TREND ANALYSIS

d) CALCULATE THE PERCENTAGE OF ACTUAL VALUE TO

MOVING AVERAGE VALUE

e) MODIFY THE TABLE ON QTR. BASIS AND AFTER

DISCARDING THE HIGHEST AND LOWEST VALUE FOR EACH

QTR FIND THE MEANS QTR. WISE.

f) ADJUST THE MODIFIED MEANS TO BASE 100 AND OBTAIN A

 SEASONAL INDEX

g) USE THE INDEX TO GET DESEASONALISED VALUES.

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PROBABILITY DISTRIBUTION

THIS CHAPTER IS ON METHODS TO ESTIMATE POPULATION

PROPORTION AND MEAN:

THERE ARE 2 TYPES OF ESTIMATES:

POINT ESTIMATE: WHICH IS A SINGLE NUMBER TO ESTIMATE

 AN UNKNOWN POPULATION PARAMETER. IT IS INSUFFICIENT

IN THE SENSE IT DOES NOT KNOW THE EXTENT OF WRONG.

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PROBABILITY DISTRIBUTION

INTERVAL ESTIMATE: IT IS A RANGE OF VALUES

USED TO ESTIMATE A POPULATION PARAMETER;

 ERROR IS INDICATED BY EXTENT OF ITS RANGE

 AND BY THE PROBABILITY OF THE TRUE

POPULATION LYING WITHIN THAT RANGE.

ESTIMATOR IS A SAMPLE STATISTIC USED TO ESTIMATE A

POPULATION PARAMETER.

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PROBABILITY DISTRIBUTION

CRITERIA FOR A GOOD ESTIMATOR

a) UNBIASEDNESS: MEAN OF SAMPLING DISTRIBUTION OF

SAMPLE MEANS ~ POPULATION MEANS. THE STATISTIC

 ASSUMES OR TENDS TO ASSUME AS MANY VALUES

 ABOVE AS BELOW THE POP. MEAN

b) EFFICIENCY: THE SMALLER THE STANDARD ERROR, THE

MORE EFFICIENT THE ESTIMATOR OR BETTER THE

CHANCE OF PRODUCING AN ESTIMATOR NEARER TO THE

POP.PARAMETER .

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PROBABILITY DISTRIBUTION

c) CONSISTENCY: AS THE SAMPLE SIZE INCREASES, THE

SAMPLE STASTISTIC COMES CLOSER TO THE POPULATION

PARAMETER.

d) SUFFICIENCY: MAKE BEST USE OF THE EXISTING SAMPLE.

PROBABILITY Of 0.955 MEANS THAT 95.5 OF ALL SAMPLE

MEANS ARE WITHIN + 2 STD ERROR OF MEAN

POPULATION µ.

  SIMILARLY, 0.683 MEANS + 1 STD ERROR.

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PROBABILITY DISTRIBUTION

CONFIDENCE INTERVAL IS THE RANGE OF THE

ESTIMATE WHILE CONFIDENCE LEVEL IS THE

PROBABILITY THAT WE ASSOCIATE WITH INTERVAL

ESTIMATE THAT THE POPULATION PARAMETER IS IN IT

.

 AS THE CONFIDENCE INTERVAL GROWS SMALLER, THE

CONFIDENCE LEVEL FALLS.

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PROBABILITY DISTRIBUTION

FORMULA:

 

ESTIMATE OF POPULATION : σ^= √ (x - x¯ )²

STD. DEVIATION √(n – 1)

ESTIMATE OF STD. ERROR : σ^x¯  = σ^  OR = σ^ √(N - n)

√ n √ n √(N - 1)

STANDARD ERROR OF THE : σp¯

= √p q PROPORTION √n 

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BOND VALUATION

BONDS ARE LONG TERM LOANS WITH A PROMISE OF SERIES

OF FIXED INTEREST PAYMENTS AND REPAYMENT OF

PRINCIPAL

THE INTEREST PAYMENT ON BOND IS CALLED COUPON RATE

IS COUPON RATE.

THEY ARE ISSUED AT A DISCOUNT AND REPAID AT PAR.

GOVT. BONDS ARE FOR LARGE PERIODS

 BONDS HAVE A MARKET AND PRICES ARE QUOTED ON

NSE/BSE.

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BOND VALUATION

BOND PRICES ARE LINKED WITH INTEREST RATES IN THE

MARKET.

IF THE INTEREST RATES RISE, THE BOND PRICES FALL AND

VICE VERSA.

PRESENT VALUE OF BONDS CAN ALSO BE CALCULATED

USING THE DISCOUNT FACTOR FOR THE COUPONS AS WELL

 AS THE FINAL PAYMENT OF THE FACE VALUE

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BOND VALUATION

SOME IMPORTANT STANDARD MEASURES:

CURRENT YIELD: IT IS THE RETURN ON THE PRESENT

MARKET PRICE OF A BOND = (COUPON INCOME)*100

CURRENT PRICE

RATE OF RETURN: IT IS THE RATE OF RETURN ON YOUR

INVESTMENT

.RATE OF RETURN = (COUPON INCOME+ PRICE CHANGE)

INVESTMENT PRICE.

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BOND VALUATION

YIELD TO MATURITY: THIS MEASURE TAKES INTO ACCOUNT

CURRENT YIELD AND CHANGE IN BOND VALUE OVER ITS

LIFE . IT IS THE DISCOUNT RATE AT WHICH THE PRESENT

VALUE (PV) OF COUPON INCOME & THE FINAL PAYMENT AT

FACE VALUE = CURRENT PRICE.n

. PRICE = ∑ C i + C n + F V WHERE C i = COUPONi =1 (1 + r) n-1 (1 + r) n INCOME

F V = FACEVALUE 

n = LIFE OF

BOND 

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BOND VALUATION

IF THE YIELD TO MATURITY (YTM) REMAINS UNCHANGED,

THEN THE RATE OF RETURN = YTM

.

EVEN IF INTEREST RATES DO NOT CHANGE, THE BOND

PRICES CHANGE WITH TIME;

 AS WE NEAR THE MATURITY PERIOD, THE BOND PRICES

TEND TO THE PAR/FACE VALUE.

.

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BOND VALUATION

THERE ARE 2 RISKS IN BOND’S INVESTMENT 

 a) INTEREST RATE RISK: WHERE THE BOND PRICES CHANGE

 INVERSELY WITH INTEREST RATE. ALSO THE LARGER THE

 MATURITY PERIOD OF A BOND, THE GREATER THESENSITIVITY TO

PRICE.

DEFAULT RISK: WHICH IS TRUE WITH PRIVATE BONDS

RATHER THAN GOVT. BONDS( GILT EDGED SECURITIES)

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BOND VALUATION

DIFFERENT TYPES OF BONDS:

ZERO COUPON BOND: NO COUPON INCOME.

FLOATING RATE BOND: INTEREST RATES CHANGE ACCORDING TO THE MARKET.

CONVERTIBLE BOND: BONDS CONVERTED TO SHARES AT ALATER DATE.

BONDS ON CALL: THE ISSUER RESERVES THE RIGHT TOCALL BACK THE BOND AT ANY POINT IN TIME GENERALLYOVER PAR.

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BOND VALUATION

SOME THOUGHTS ON BONDS THE INTEREST IS CALLED COUPON INCOME AS COUPONS

 ARE ATTACHED TO THE BONDS FOR INTEREST PAYMENTSOVER THE LIFE OF THE BOND

BOND INTEREST REMAINS THE SAME IRRESPECTIVE OF THE

CHANGES IN THE INT. RATES IN THE MARKET BOND PRICES ARE USUALLY QUOTED AT %AGE OF THEIR

FACE VALUE i.e. 102.5.

CURRENT YIELD OVERSTATES RETURN ON PREMIUM BONDS& UNDERSTATES RETURN ON DISCOUNT BONDS; SINCETOWARDS THE END OF THE BOND PERIOD THE PRICEMOVES NEARER THE FACE VALUE. i.e. PREMIUM BOND  ANDDISCOUNT BOND .

IF BOND IS PURCHASED AT FACE VALUE THEN Y T M IS THECOUPON RATE.

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LINEAR PROGRAMMING

EVERY ORGANISATION USES RESOURCES SUCH ASMEN(WOMEN), MACHINES MATERIALS AND MONEY.

THESE ARE CALLED RESOURCES

THE OPTIMUM USE OF RESOURCES TO PRODUCE THEMAXIMUM POSSIBLE PROFIT IS THE ESSENCE OF LINEARPROGRAMMING

EACH RESOURCE WOULD HAVE CONSTRAINTS

HENCE WORKING WITHIN THE CONSTRAINTS; MINIMIZINGCOST; MAXIMIZING PROFIT SHOULD BE THE CORPORATEPHILOSOPHY.

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LINEAR PROGRAMMING

IN LINEAR PROGRAMMING PROBLEMS, THE CONSTRAINTS ARE IN THE FORM OF INEQUALITIES

LABOUR AVAILABLE FOR UPTO 200 HRS. < 200

MAXIMUM FUNDS AVAILABLE IS RS. 30,000/- < 30,000

MINIMUM MATERIAL TO BE USED IS 300 KGS > 300

SOLUTION TO THESE EQUATIONS ARE BY GRAPHICAL

METHOD OR THE SIMPLEX METHOD

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SIMULATION

SIMULATION IS A TECHNIQUE WHERE MODEL OF THEPROBLEM, WITHOUT GETTING TO REALITY, IS MADE TOKNOW THE END RESULTS

SIMULATION IS IDEAL FOR SITUATIONS WHERE SIZE ORCOMPLEXITY OF THE SITUATION DOES NOT PERMIT USE OF

 ANY OTHER METHOD

IN SHORT, SIMULATION IS A REPLICA OF REALITY.

EXAMPLES OF PROBLEM SITUATIONS FOR SIMULATION ARE

-- AIR TRAFFIC QUEUING -- RAIL OPERATIONS -- ASSEMBLY LINE SYSTEMS -- AND SO ON

.

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SIMULATION

 THEREFORE IT IS CLEAR THAT WHEN USE OF REAL SYSTEM

UPSETS THE WORKING SCHEDULE IN THE SYSTEM OR IS

IMPOSSIBLE TO EXPERIMENT REAL TIME, AND IT IS

TOO EXPENSIVE TO UNDERTAKE THE EXERCISE, THEN

SIMULATION IS IDEAL.

. HOWEVER SIMULATION CAN BE A COSTLY EXERCISE, TIME

CONSUMING AND WITH VERY FEW GUIDING PRINCIPLES.

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FINAL LEG

THANK YOU VERY MUCH FOR YOUR

PATIENCE; I TRUST IT WAS USEFUL.

BEFORE WE DISPERSE LET US GO

THRU’ A SET OF QUESTIONS WITH

MULTIPLE CHOICE ANSWERS,WHICH

WILL COVER THOSE ASPECTS OF THESUBJECT THAT MAY NOT BEEN

TOUCHED UPON.

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END

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