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# Quantitative Techniques for Managers_Stock Market Data Analysis_Assingment.pptx

Quantitative Techniques for Managers
Stock Market Data Analysis
Assingment

Quantitative Techniques for Managers
Stock Market Data Analysis
Assingment

## More Related Content

### Quantitative Techniques for Managers_Stock Market Data Analysis_Assingment.pptx

1. 1. (2022 24)
3. 3. transmission company. Currently, it is one of the largest private sector power transmission companies operating in India, it was founded by Gautam Adani in December 2015 after separating the decade-old transmission business from Adani Enterprises.
4. 4. In finance, a stock index, or stock market index, is an index that measures a stock market, or a subset of the stock market, that helps investors compare current stock price levels with past prices to calculate market performance.
5. 5. represents the next rung of liquid securities after the NIFTY 50. It consists of 50 companies representing approximately 10% of the traded value of all stocks on the National Stock Exchange of India. The NIFTY Next 50 is owned and operated by India Index Services and Products Ltd.
6. 6. Please find below the comparison between Nifty Next Fifty’s Closing price and Adani Transmission Ltd.’s Closing price for the time period of 3rd Jan, 2022 to 30th Aug, 2022.
7. 7. Descriptives:
8. 8. that 50% closing price lie below 40615.4250 (Median) and 50% lie above 40615.4250 for Nifty Next 50. For ADANITRANS, 50% closing price lie below 2309.1000 (Median) and 50% lie above 2309.1000.
9. 9. understand that in 95% of the cases the closing value will be between 40073.8274 to 40744.5402 for Nifty Next 50 and 2379.0546 to 2541.0394 for ADANITRANS. This is a huge range because of the standard deviation being 2174.92343 and 525.26885 for Nifty Next 50 and ADANITRANS.
10. 10. By 5% Trimmed Mean, we can understand that this is not normal data as there is a huge difference between Mean and 5% Trimmed Mean for both Nifty Next 50 and ADANITRANS.
11. 11. Standard Deviation explains us the spread of the data/score from the mean score and Variance is square of Std Deviation, Variance gives us the picture of the variability of the data.
12. 12. between 1st Quartile and 3rd Quartile. Interquartile rang for Nifty Next 50 is very high (3741.40) in comparison to that of ADANITRANS (704.43). Larger Interquartile range in a group indicates greater spread of scores indicating higher variability in data.
13. 13. The Sig (Significance) value of Kolmogorov- Smirnov and Shapiro-Wilk is less than 0.05, which means that the data is not normal, we will have to reject the assumption of Null Hypothesis.
14. 14. (present below), we can observe that the data point is not falling on 45-degree line of Q-Q plot and in the detrended Q-Q plot we can observe that the data point is not falling on the horizontal line. This signals the departure from normality.
15. 15. Interquartile Range 3741.40 Skewness -.333 .190 Kurtosis -.951 .377ADANITRANS Mean 2460.0470 41.01661 Closing Price 95% Confidence Lower Bound 2379.0546 Interval for Mean Upper Bound 2541.0394 5% Trimmed Mean 2426.5486 Median 2309.1000 Variance 275907.365 Std. Deviation 525.26885 Minimum 1731.10 Maximum 3960.70
16. 16. 4250 4750 5 ADANITRANS 1900. 1958. 2040.6 2309.1 2745.0 3428.8 3599.0 Closing Price 7375 0000 250 000 500 000 750 Tukey's Nifty Next Fifty 38667. 40615. 42392. Hinges Closing Price 9250 4250 5250 ADANITRANS 2041.5 2309.1 2740.3 Closing Price 000 000 000
17. 17. 115 35405.40 2 114 35668.20 3 117 35716.25 4 118 36141.10 5 113 36268.70 ADANITRANS Closing Price Highest 1 164 3960.70 2 163 3855.55 3 162 3751.20 4 161 3715.80 5 160 3700.40 Lowest 1 1 1731.10 2 2 1754.95 3 4 1760.05 4 3 1761.05 5 5 1776.00
18. 18. Tests of Normality Kolmogorov-Smirnov Shapiro-Wilk Statistic df Sig. Statistic df Sig. Nifty Next Fifty Closing Price .085 164 .005.956164 .000ADANITRANS Closing
19. 19. Price .140 164.000 .890 164.000 a. Lilliefors Significance Correction
20. 20. Correlations
21. 21. Correlations is the extent to which two variables are related to each other.
22. 22. From the best fit line present in the scatter plot below we can conclude that there is a positive correlation between Nifty Next Fifty Closing Price and ADANITRANS Closing Price.
23. 23. Although the data is not normal but since the sample size is large, so we have conducted the Pearson Correlation test.
24. 24. Bootstrapping has been done here as the data is not normal. We can observe that our correlation coefficient is ranging from 0.201 to 0.430
25. 25. The value of correlation coefficient is 0.325 and p value is 0.000022 which means that there is a significant correlation between Nifty Next Fifty Closing Price and ADANITRANS Closing Price.
26. 26. Correlations Nifty Next
27. 27. Fifty Closing Price
29. 29. Pearson Correlation 1 .325** Sig. (2-tailed) 0.000022 N 164 164 Bootstrapc Bias 0 - .002 Std. Error 0 .060 BCa 95%
30. 30. Confidence Interval Lower . .201 Upper . .430 ADANITRANS
31. 31. Closing Price Pearson Correlation .325** 1 Sig. (2-tailed) 0.000022 N 164 164 Bootstrapc Bias -.002 0 Std. Error .0600 BCa 95%
32. 32. Confidence Interval Lower .201. Upper .430. **. Correlation is significant at the 0.01 level (2-tailed). c. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples
33. 33. Regression
34. 34. Regression helps us study the cause-and- effect relationship between the variables.
35. 35. Square is the measure of total influence of independent variable on the dependant variable. R square is the percentage of influence explained by the independent variable in the dependant variable.
36. 36. Independent Variable is Nifty Next Fifty and Dependant Variable is ADANITRANS.
37. 37. Durbin Watson test value is 0.17 which is in the range of -3.29 and +3.29, so we can conclude that the sample is independent.
38. 38. 10.5% of ADANITRANS closing value can be accounted by the Nifty Next 50 closing value. Std Error of Estimate is quite high as 498.36682, this indicates the error in prediction.
39. 39. we can say that the regression as a whole is significant up to 99% level so it is statistically significant and we have rejected the null hypothesis that all the variable is equal to zero and model is essentially explaining none of the variation in our dependant variable.
40. 40. We can conclude that Nifty Next Fifty closing price’s 1 unit has 0.078 unit of positive effect/influence on ADANITRANS closing price.
41. 41. 1 standard deviation change in Nifty Next Fifty closing price is going to cause 0.325 standard deviation change in ADANITRANS closing price.
42. 42. Model Summaryb
43. 43. Model
44. 44. R
45. 45. the Estimate Durbin- Watson 1 .325a .105 .100498.36682 .017a. Predictors: (Constant), Nifty Next Fifty Closing Price b. Dependent Variable: ADANITRANS Closing Price
46. 46. ANOVAa
47. 47. Model Sum of Squares
48. 48. df
49. 49. Mean Square
50. 50. F
51. 51. Sig. 1 Regression 4737042.8271 4737042.82719.073 .000b Residual 40235857.59
52. 52. 6 162248369.491 Total 44972900.42
53. 53. 3 163 a. Dependent Variable: ADANITRANS Closing Price b. Predictors: (Constant), Nifty Next Fifty Closing Price
54. 54. Coefficientsa Unstandardized
55. 55. Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant) -707.302726.299 -.974 .332 Nifty Next Fifty Closing
56. 56. Price .078.018.3254.367 .000a. Dependent Variable: ADANITRANS Closing Price
57. 57. Residuals Statisticsa
58. 58. Minimum
59. 59. Maximum
60. 60. Mean Std.
61. 61. Deviation
62. 62. 2460.0470 170.47467 164 Residual- 896.72473 1241.99536 .00000 496.83574 164 Std. Predicted Value -2.301 1.595 .0001.000 164 Std. Residual-1.799 2.492 .000.997164 a. Dependent Variable: ADANITRANS Closing Price
63. 63. 27-Jan-22 40299.3 2009.3 28-Jan-22 40633.2 1986.8 31-Jan-22 41097.25 1970.9 01-Feb-22 41810.6 1986.9 02- Feb-22 42249.1 2016 03-Feb-22 42030.15 2006.2 04-Feb-22 41998 2033.1 07- Feb-22 41716.9 2028.5 08-Feb-22 41522.1 1968.3 09-Feb-22 41991.9 1955.2 10- Feb-22 42165.35 2036.4 11-Feb-22 41553.05 2016.05 14-Feb-22 40164.8 1915.25 15-Feb-22 41138.8 1926.95 16- Feb-22 41058.2 1932.55 17-Feb-22 41145.65 2020.85 18-Feb-22 40709.15 1958.1 21-Feb-22 40177.45 1895.9 22-Feb-22
64. 64. 2443.85 06-Apr-22 42910.45 2484.05 07-Apr-22 42784.3 2449.65 08-Apr-22 43445.9 2540.25 11-Apr-22 43878.15 2756.5 12-Apr-22 43382.2 2680.55 13- Apr-22 43443.9 2690.75 18-Apr-22 43289.4 2728.3 19-Apr-22 42500.45 2598.05 20-Apr-22 42713 2687.55 21-Apr-22 43256.15 2699.6 22-Apr-22 42934.55 2658.55 25-Apr-22 42084.35 2615.9 26-Apr-22 43072.25 2812.55 27-Apr-22 42504.75 2712.2 28-Apr-22 43086.1 2796.65 29-Apr-22 42533.95 2789.5
65. 65. Jun-22 36955.45 2057.5 15-Jun-22 37036.1 2057.3 16-Jun-22 36268.7 2124.65 17-Jun-22 35668.2 2032.6 20- Jun-22 35405.4 2060.25 21-Jun-22 36381.05 2215.05 22-Jun-22 35716.25 2122.35 23-Jun-22 36141.1 2105 24-Jun-22 36616.3 2152.1 27-Jun-22 36882.45 2140.55 28-Jun-22 36937.25 2163.4 29-Jun-22 36679.25 2347.9 30-Jun-22 36505.4 2473.65 01-Jul-22 36901.2 2400.65 04-Jul-22 37273.9 2423.1 05- Jul-22 37306 2476.3 06-Jul-22 37937.65