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FINANCIAL MANAGEMENT C A I I B MODULE A

FINANCIAL MANAGEMENT C A I I B MODULE A. 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

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FINANCIAL MANAGEMENT C A I I B MODULE A

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  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(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 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 + 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%.

  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 √∑(X - µ)² √ 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 = √{(∑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 • σ = STD. DEVIATION OF THE POPULATION DISTRIBUTION • σx¯ = STD. ERROR OF THE MEAN

  25. SAMPLING METHODS σx¯=σWHERE 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.

  26. 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 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, 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 • 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¯ ∑ X¯2 – n X¯2 • 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 = √{∑(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 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 √ 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 σxσy

  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 : 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

  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

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