FINANCIAL MANAGEMENT C A I I B MODULE A - PowerPoint PPT Presentation

About This Presentation
Title:

FINANCIAL MANAGEMENT C A I I B MODULE A

Description:

THIS IS BASED ON THE CONCEPT OF EROSION IN VALUE OF MONEY DUE TO INFLATION ... P( 90 97.5 x - 104-97.5 ) 2.97 sx 2.97 -2.52 Z 2.19. USE Z TABLE ... – PowerPoint PPT presentation

Number of Views:201
Avg rating:3.0/5.0
Slides: 61
Provided by: A54
Category:

less

Transcript and Presenter's Notes

Title: FINANCIAL MANAGEMENT C A I I B MODULE A


1
FINANCIAL MANAGEMENTC A I I B MODULE A
2
TIME VALUE OF MONEY
  • MONEY HAS TIME VALUE
  • THIS IS BASED ON THE CONCEPT OF EROSION IN VALUE
    OF MONEY 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

3
TIME VALUE OF MONEY
  • EXTENTOF EROSION IN THE VALUE OF MONEY IS AN
    UNKNOWN FACTOR. HENCE A WELL THOUGHT OUT DISCOUNT
    RATE HELPS TO BRING THE FUTURE CASH FLOWS 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
    WITH THE VALUE OF MONEY i.e. THE GREATER THE
    FACTOR THE LOWER IS THE VALUE OF MONEY

4
TIME VALUE OF MONEY
  • IF DESIRE FOR CURRENT CONSUMPTION ISGREATER THEN
    WE NEED TO OFFER INCENTIVES TO DEFER THE
    CONSUMPTION.
  • THE MONEY THUS SAVED IS THEN PROFITABLY OR
    GAINFULLY EMPLOYED . HENCE THE DISCOUNT RATE WILL
    BE LOWER.
  • INVESTMENT IN GOVERNMENT BONDS / SECURITIES IS
    LESS RISKY THAN IN THE PRIVATE SECTOR SIMPLY
    BECAUSE NOT ALL CASH FLOWS ARE EQUALLY
    PREDICTABLE AND WHERE THERE IS SOVEREIGN
    GUARANTEE THE RISK IS LESS.
  • IF THE RISK OF RETURN IS LOWER AS IN GOVT.
    SECURITIES THEN THE RATE OF RETURN IS ALSO
    LOWER.

5
TIME VALUE OF MONEY
  • THE PROCESS BY WHICH FUTURE FLOWS ARE ADJUSTED
    TO REFLECT THESE FACTORS IS CALLED DISCOUNTING
    THE MAGNITUDE IS REFLECTED IN THE DISCOUNT RATE.
  • THE DISCOUNT VARIES DIRECTLY WITH EACH OF THESE
    FACTORS.
  • THE DISCOUNT OF FUTURE FLOWS TO THE PRESENT IS
    DONE WITH THE NEED TO KNOW THE EFFICACY OF THE
    INVESTMENT.

6
TIME VALUE OF MONEY
  • THE DISCOUNTING BRING THE FLOWS TO A NET PRESENT
    VALUE OR N P V.
  • N P V IS THE NET OF THE PRESENT VALUE OF FUTURE
    CASH FLOWS 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 THE
    INVESTMENT WITH HIGHER N P V IS SELECTED. THE
    DISCOUNTED RATES FOR EACH ARE THE RISK RATES
    ASSOCIATED WITH INVESTMENTS.

7
TIME VALUE OF MONEY
  • REAL CASH FLOWS ARE NOMINAL CASH FLOWS ADJUSTED
    TO INFLATION.
  • NOMINAL CASH FLOWS ARE AS RECEIVED WHILE REAL
    CASH FLOWS ARE NOTIONAL FIGURES
  • REAL CASH FLOWS NOMINAL CASH FLOWS
  • 1
    INFLATION RATE

8
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 THE
    PRESENT BY A FACTOR KNOWN DISCOUNT
  • THE DISCOUNT RATE adjusted for inflation IS REAL
    RATE
  • THIS REAL RATE IS AN INFLATION ADJUSTED RATE

9
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(1r) 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
    ON SEMI-ANNUAL OR MONTHLY BASIS THE THE EFFECTIVE
    RATE OF INTEREST CHANGES

  • n
  • EFFECTIVE INTEREST RATE (1 r) - 1

  • N
  • WHERE N NO. OF COMPOUNDING PERIODS

10
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 PERIOD

  • n
  • a) P V OF AN ANNUITY(A) A 1-- 1 (1
    r) r

  • n
  • b) F V OF AN ANNUITY(A) A(1 r) -- 1
    r
  • a) IS THE FORMULA OF EQUATED MONTHLY
  • INSTALMENT(EMI).

11
TIME VALUE MONEY
  • B) BEGINNING OF THE PERIOD

  • n-1
  • - a) P V OF ANNUITY(A) A A1- 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.

12
TIME VALUE OF MONEY
  • A PERPETUITY IS A CONSTANT CASH FLOW AT REGULAR
    INTERVALS 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.

13
SAMPLING METHODS
  • A SAMPLE IS A REPRESENTATIVE PORTION OF THE
    POPULATION
  • TWO TYPES OF SAMPLING
  • --- RANDOM OR PROBABILITY SAMPLING
  • --- NON-RANDOM OR JUDGEMENT SAMPLING
  • IN JUDGEMENT SAMPLING KNOWLEDGE OPINIONS ARE
    USED. IN THIS KIND OF SAMPLING BIASEDNESS CAN
    CREEP IN, FOR EX. IN INTERVIEWING TEACHERS
    ASKING THEIR OPINION ABOUT THEIR PAY RISE.

14
SAMPLING METHODS
  • FOUR METHODS OF SAMPLING
  • a) SIMPLE RANDOM
  • -- USE A RANDOM TABLE
  • -- ASSIGN DIGITS TO EACH ELEMENT OF THE
  • POPULATION(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
    SAME FOR ALL.

15
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 .
  • .

16
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
  • BUT CONSIDERABLE DIFFERENCE WITHOUT

17
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
    WITHIN
  • BUT SIMILAR WITHOUT
  • IMP WHETHER WE USE PROBABILITY OR JUDGEMENT
  • SAMPLING THE PROCESS IS BASED ON SIMPLE
    RANDOM
  • SAMPLING .

18
SAMPLING METHODS
  • EXAMPLES OF TYPES OF SAMPLING
  • SYSTEMATIC SAMPLING A SCHOOL WHERE ONE PICKS
    EVERY 15TH STUDENT.
  • STRATIFIED SAMPLING IN A LARGE ORGANISATION
    PEOPLE ARE GROUPED ACCORDING TO RANGE OF
    SALARIES.
  • CLUSTER SAMPLING A CITY IS DIVIDED INTO
    LOCALITIES.

19
SAMPLING METHODS
  • SINCE WE WOULD USING THE CONCEPT OF STANDARD
    DEVIATION LET US UNDERSTAND ITS SIGNIFICANCE
  • IT IS A MEASURE OF DISPERSION.
  • GENERAL FORMULA FOR STD. DEV. IS v?(X - µ)²

  • v N
  • WHERE X OBSERVATION
  • µ POPULATION MEAN
  • N ELEMENTS IN POPULATION

20
SAMPLING METHODS
  • DESPITE ALL THE COMPLEXITIES IN THE FORMULA THE
  • STD. DEV. IS THE SAME IN STATE AS SUMMATION
    OF DIFFERENCES 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

21
SAMPLING METHODS
  • FORMULA FOR STANDARD DEVIATION
  • -- FOR POPULATION S v(?fx2 N) - ?f2x2
    N
  • THIS IS FOR GROUPED DATA, WHERE f IS THE
    FREQUENCY
  • OF ELEMENTS IN EACH GROUP AND N IS THE SIZE OF
  • POPULATION

22
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.

23
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

24
SAMPLING METHODS
  • TERMINOLGY \
  • µ MEAN OF THE POPULATION DISTRIBUTION
  • µx MEAN OF THE SAMPLING DITRIBUTION OF THE
    MEANS
  • x MEAN OF A SAMPLE
  • s STD. DEVIATION OF THE POPULATION
    DISTRIBUTION
  • sx STD. ERROR OF THE MEAN

25
SAMPLING METHODS
  • sx s WHERE n IS THE SAMPLE SIZE. THIS
    FORMULA IS
  • vn
  • TRUE FOR INFINITE POPULATION
    OR FINITE
  • POPULATION WITH REPLACEMENT.
  • Z x - µ WHERE Z HELPS TO DETERMINE THE
    DISTANCE
  • sx
  • OF THE SAMPLE MEAN FROM
    THE POPULATION
  • MEAN.

26
SAMPLING METHODS
  • STD. ERROR FOR FINITE POPULATION
  • sx s v N-n WHERE N IS THE POPULATION
    SIZE
  • vn v N-1
  • AND v N-n IS THE FINITE POPULATION MULTIPLIER
  • v N-1
  • THE VARIABILITY IN SAMPLING STATISTICS RESULTS
    FROM SAMPLING ERROR DUE TO CHANCE. THUS THE
    DIFFERENCE BETWEEN SAMPLES AND BETWEEN SAMPLE AND
    POPULATION MEANS IS DUE TO CHOICE OF SAMPLES.

27
SAMPLING METHODS
  • CENTRAL LIMIT THEOREM
  • THE RELATIONSHIP BETWEEN THE SHAPE OF POPULATION
    DISTRIBUTION AND THE SAMPLNG DIST. IS CALLED
    CENTRAL LIMIT THEOREM.
  • AS SAMPLE SIZE INCREASES THE SAMPLING DIST. OF
    THE MEN WILL APPROACH NORMALITY REGARDLESS OF THE
    POPULATION DIST.
  • SAMPLE SIZE NEED NOT BE LARGE FOR THE MEAN TO
    APPROACH NORMAL
  • WE CAN MAKE INFERENCES ABOUT THE POPULATION
    PARAMETERS WITHOUT KNOWING ANYTHING ABOUT THE
    SHAPE OF THE FREQUENCY DIST. OF THE POPULATION

28
SAMPLING METHODS
  • EXAMPLE n 30, µ 97.5, s 16.3
  • a) WHAT IS THE PROB. OF X LYING BETWEEN 90 104
  • ANS) sx s , 2.97
  • vn
  • P( 90 97.5 lt x - µ lt 104-97.5 )
  • 2.97 sx
    2.97
  • -2.52 lt Z lt 2.19
  • USE Z TABLE
  • P 0.4941 0.4857 0.98
  • b) FOR MEAN X LYING BELOW 100
  • P( Zlt 100 104 )
  • 2.97
  • 0.50 0.4115 0.0885

29
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.

30
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.

31
REGRESSION AND CORRELATION
  • ONE CAN DRAW ANY NUMBER OF LINES BETWEEN THE
    POINTS. THE LINE WITH BEST FIT IS THE THAT
    WITH LEAST SQUARE DIFFERENCE BETWEEN THE ACTUAL
    AND ESTIMATED POINTS.
  • IN THE EQN. Y a b X
  • b SLOPE ? XY n X Y
  • ? X2 n X2
  • SLOPE OF THE LINE INDICATES THE EXTENT OF CHANGE
    IN Y DUE TO CHANGE IN X.
  • . a Y - b X
  • WHERE X , Y ARE MEAN VALUES
  • .

32
REGRESSION AND CORRELATION
  • STD ERROR OF ESTIMATE
  • Se v?(Y Ye ) (n -2) or vv Y²
    -a vY b v (XY)


  • v(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 CAN FREELY GIVE VALUES TO n-1 VARIABLES.

33
REGRESSION AND CORRELATION
  • THERE ARE 3 MEASURES OF CORRELATION
  • - COEFFICIENT OF DETERMINATION. IT MEASURES THE
  • STRENGTH OF A LINEAR RELATIONSHIP
  • COEFF. OF DET. r2 ?(Y Ye )2
  • 1-
    ----------------

  • ?( Y - Y )2
  • COEF. OF DETERMINATION IS r²
  • COEFF. OF CORRELATION IS r
  • v r² r, HENCE FROM r2 TO r WE KNOW
    THE STRENGTH
  • BUT NOT THE DIRECTION.
  • .

34
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

  • sxsy

35
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.

36
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.

37
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.

38
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
  • THE PAST CYCLICAL VARIATION IS ANALYSED

39
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

40
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.

41
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.

42
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.

43
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 .

44
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.

45
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.


46
PROBABILITY DISTRIBUTION
  • FORMULA

  • ESTIMATE OF POPULATION s v (x - x )²
  • STD. DEVIATION
    v(n 1)
  • ESTIMATE OF STD. ERROR sx s OR
    s v(N - n)

  • v n v n v(N - 1)
  • STANDARD ERROR OF THE sp vp q
  • PROPORTION
    vn

47
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.

48
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

49
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.

50
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 COUPON
  • i 1 (1 r) n-1 (1 r) n

    INCOME


  • F V FACE VALUE


  • n LIFE OF

  • BOND

51
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.
  • .

52
BOND VALUATION
  • THERE ARE 2 RISKS IN BONDS 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 THE
    SENSITIVITY TO
  • PRICE.
  • DEFAULT RISK WHICH IS TRUE WITH PRIVATE BONDS
  • RATHER THAN GOVT. BONDS( GILT EDGED
    SECURITIES)

53
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 A
    LATER DATE.
  • BONDS ON CALL THE ISSUER RESERVES THE RIGHT TO
    CALL BACK THE BOND AT ANY POINT IN TIME GENERALLY
    OVER PAR.

54
BOND VALUATION
  • SOME THOUGHTS ON BONDS
  • THE INTEREST IS CALLED COUPON INCOME AS COUPONS
    ARE ATTACHED TO THE BONDS FOR INTEREST PAYMENTS
    OVER 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 SINCE
    TOWARDS THE END OF THE BOND PERIOD THE PRICE
    MOVES NEARER THE FACE VALUE. i.e. PREMIUM BOND ?
    AND DISCOUNT BOND ?.
  • IF BOND IS PURCHASED AT FACE VALUE THEN Y T M IS
    THE COUPON RATE.

55
LINEAR PROGRAMMING
  • EVERY ORGANISATION USES RESOURCES SUCH AS
    MEN(WOMEN), MACHINES MATERIALS AND MONEY.
  • THESE ARE CALLED RESOURCES
  • THE OPTIMUM USE OF RESOURCES TO PRODUCE THE
    MAXIMUM POSSIBLE PROFIT IS THE ESSENCE OF LINEAR
    PROGRAMMING
  • EACH RESOURCE WOULD HAVE CONSTRAINTS
  • HENCE WORKING WITHIN THE CONSTRAINTS MINIMIZING
    COST MAXIMIZING PROFIT SHOULD BE THE CORPORATE
    PHILOSOPHY.

56
LINEAR PROGRAMMING
  • IN LINEAR PROGRAMMING PROBLEMS, THE CONSTRAINTS
    ARE IN THE FORM OF INEQUALITIES
  • LABOUR AVAILABLE FOR UPTO 200 HRS. lt
    200
  • MAXIMUM FUNDS AVAILABLE IS RS. 30,000/- lt
    30,000
  • MINIMUM MATERIAL TO BE USED IS 300 KGS gt
    300
  • SOLUTION TO THESE EQUATIONS ARE BY GRAPHICAL
  • METHOD OR THE SIMPLEX METHOD

57
SIMULATION
  • SIMULATION IS A TECHNIQUE WHERE MODEL OF THE
    PROBLEM, WITHOUT GETTING TO REALITY, IS MADE TO
    KNOW THE END RESULTS
  • SIMULATION IS IDEAL FOR SITUATIONS WHERE SIZE OR
    COMPLEXITY 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
  • .

58
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.

59
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 THE SUBJECT THAT
    MAY NOT BEEN TOUCHED UPON.

60
END
  • ANY QUERIES MAY BE ADDRESSED TO
  • chitturb_at_rediffmail.com
Write a Comment
User Comments (0)
About PowerShow.com