{"id":22749,"date":"2025-03-25T18:34:18","date_gmt":"2025-03-25T13:04:18","guid":{"rendered":"https:\/\/www.sachdevajk.in\/?p=22749"},"modified":"2025-03-25T18:34:18","modified_gmt":"2025-03-25T13:04:18","slug":"relationship-of-redtape-with-nifty-50","status":"publish","type":"post","link":"http:\/\/www.sachdevajk.in\/?p=22749","title":{"rendered":"Relationship of REDTAPE with NIFTY 50"},"content":{"rendered":"

Report: Relationship of REDTAPE with NIFTY 50<\/span><\/b><\/p>\n

\n
<\/div>\n

Title:<\/span><\/b><\/p>\n

Relationship of REDTAPE with NIFTY 50<\/span><\/p>\n

Author:<\/span><\/b><\/p>\n

Ricardo Jathanna <\/span><\/p>\n

Roll no : 25<\/span><\/p>\n

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<\/div>\n

\u00a0<\/span>Introduction<\/span><\/b><\/p>\n

REDTAPE is an Indian footwear and lifestyle brand known for its premium leather products, casual shoes, and apparel. It has a strong presence in the Indian and international markets, catering to both men and women. The brand is recognized for its quality craftsmanship and contemporary fashion, making it a popular choice among consumers. However, REDTAPE is not a typical market-driven stock, as indicated by its low correlation with NIFTY 50.<\/span><\/p>\n

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<\/div>\n

\u00a0<\/span>Objective<\/span><\/b><\/p>\n

To determine the \u03b2 (Beta)<\/b> of REDTAPE with respect to NIFTY 50 and analyze its market sensitivity and risk profile.<\/span><\/p>\n

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<\/div>\n

\u00a0<\/span>Literature Review<\/span><\/b><\/p>\n

    \n
  1. Market Independence of Low-Beta Stocks<\/span><\/b>\n
      \n
    • According to Misra and Mohapatra (2015), low-beta stocks demonstrate weak correlation with market indices like NIFTY 50. These stocks often exhibit stability during market volatility and may be used by investors as a defensive asset<\/b> during downturns.<\/span><\/li>\n
    • REDTAPE, with a \u03b2 = 0.0425<\/b>, reflects this characteristic, making it relatively immune to market fluctuations<\/b>.<\/span><\/li>\n<\/ul>\n<\/li>\n
    • Resilience of Independent Stocks During Crises<\/span><\/b>\n
        \n
      • Gali (2021) analyzed how independent stocks behave during financial crises, finding that stocks with low beta tend to show lower volatility<\/b> and better stability<\/b> during downturns. REDTAPE\u2019s low market risk<\/b> makes it a safer investment during turbulent market periods.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
        \n
        <\/div>\n

        \u00a0<\/span><\/b><\/p>\n

        \u00a0<\/span>Data Collection<\/span><\/b><\/p>\n

          \n
        • Source:<\/span><\/b> Data for REDTAPE and NIFTY 50 was collected from the NSE website for the period 01-01-2024 to 31-12-2024<\/b>.<\/span><\/li>\n
        • The data was processed to extract the closing prices<\/b>.<\/span><\/li>\n
        • Weekly returns<\/span><\/b> were calculated:<\/span>\n
            \n
          • NIFTY 50 weekly returns \u2192 X<\/b><\/span><\/li>\n
          • REDTAPE weekly returns \u2192 Y<\/b><\/span><\/li>\n<\/ul>\n<\/li>\n
          • The regression analysis was conducted by regressing Y on X<\/b>.<\/span><\/li>\n<\/ul>\n

            \u00a0<\/span><\/p>\n

            \n
            <\/div>\n

            \u00a0<\/span><\/b><\/p>\n

            \u00a0<\/span>Data Analysis<\/span><\/b><\/p>\n

            Regression Equation<\/span><\/b><\/p>\n

            REDTAPE\u00a0Return=\u2212270.97 + 0.0425 \u00d7 NIFTY\u00a050\u00a0 <\/span><\/p>\n

            \u00a0<\/span><\/b><\/p>\n

            Regression Statistics<\/span><\/b><\/p>\n

              \n
            • Multiple R:<\/span><\/b> 0.548 (Moderate correlation)<\/span><\/li>\n
            • R-Squared:<\/span><\/b> 0.30085 \u2192 Only 30.08%<\/b> of REDTAPE\u2019s return variation is explained by NIFTY 50.<\/span><\/li>\n
            • Adjusted R-Squared:<\/span><\/b> 0.291 (Slightly lower due to degree of freedom adjustment)<\/span><\/li>\n
            • Standard Error:<\/span><\/b> 0.117<\/span><\/li>\n<\/ul>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
              \n

              \u00a0<\/span>SUMMARY OUTPUT<\/span><\/p>\n<\/td>\n

              		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
              \u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
              \n

              Regression Statistics<\/span><\/i><\/p>\n<\/td>\n

              		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
              \n

              Multiple R<\/span><\/p>\n<\/td>\n

              		\n

              0.54849778<\/span><\/p>\n<\/td>\n

              		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
              \n

              R Square<\/span><\/p>\n<\/td>\n

              		\n

              0.30084981<\/span><\/p>\n<\/td>\n

              		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
              \n

              Adjusted R Square<\/span><\/p>\n<\/td>\n

              		\n

              0.28565089<\/span><\/p>\n<\/td>\n

              		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
              \n

              Standard Error<\/span><\/p>\n<\/td>\n

              		\n

              83.5358225<\/span><\/p>\n<\/td>\n

              		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
              \n

              Observations<\/span><\/p>\n<\/td>\n

              		\n

              48<\/span><\/p>\n<\/td>\n

              		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
              \u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
              \n

              ANOVA<\/span><\/p>\n<\/td>\n

              		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
              \u00a0<\/td>\n\n

              df<\/span><\/i><\/p>\n<\/td>\n

              		\n

              SS<\/span><\/i><\/p>\n<\/td>\n

              		\n

              MS<\/span><\/i><\/p>\n<\/td>\n

              		\n

              F<\/span><\/i><\/p>\n<\/td>\n

              		\n

              Significance F<\/span><\/i><\/p>\n<\/td>\n

              		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
              \n

              Regression<\/span><\/p>\n<\/td>\n

              		\n

              1<\/span><\/p>\n<\/td>\n

              		\n

              138128.2791<\/span><\/p>\n<\/td>\n

              		\n

              138128.28<\/span><\/p>\n<\/td>\n

              		\n

              19.7941609<\/span><\/p>\n<\/td>\n

              		\n

              5.43417E-05<\/span><\/p>\n<\/td>\n

              		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
              \n

              Residual<\/span><\/p>\n<\/td>\n

              		\n

              46<\/span><\/p>\n<\/td>\n

              		\n

              320998.7471<\/span><\/p>\n<\/td>\n

              		\n

              6978.2336<\/span><\/p>\n<\/td>\n

              		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
              \n

              Total<\/span><\/p>\n<\/td>\n

              		\n

              47<\/span><\/p>\n<\/td>\n

              		\n

              459127.0262<\/span><\/p>\n<\/td>\n

              		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
              \u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
              \u00a0<\/td>\n\n

              Coefficients<\/span><\/i><\/p>\n<\/td>\n

              		\n

              Standard Error<\/span><\/i><\/p>\n<\/td>\n

              		\n

              t Stat<\/span><\/i><\/p>\n<\/td>\n

              		\n

              P-value<\/span><\/i><\/p>\n<\/td>\n

              		\n

              Lower 95%<\/span><\/i><\/p>\n<\/td>\n

              		\n

              Upper 95%<\/span><\/i><\/p>\n<\/td>\n

              		\n

              Lower 95.0%<\/span><\/i><\/p>\n<\/td>\n

              		\n

              Upper 95.0%<\/span><\/i><\/p>\n<\/td>\n<\/tr>\n

              \n

              Intercept<\/span><\/p>\n<\/td>\n

              		\n

              -270.97398<\/span><\/p>\n<\/td>\n

              		\n

              225.9055583<\/span><\/p>\n<\/td>\n

              		\n

              -1.1995012<\/span><\/p>\n<\/td>\n

              		\n

              0.23647519<\/span><\/p>\n<\/td>\n

              		\n

              -725.698287<\/span><\/p>\n<\/td>\n

              		\n

              183.7503215<\/span><\/p>\n<\/td>\n

              		\n

              -725.6982867<\/span><\/p>\n<\/td>\n

              		\n

              183.7503215<\/span><\/p>\n<\/td>\n<\/tr>\n

              \n

              X Variable 1<\/span><\/p>\n<\/td>\n

              		\n

              0.04254435<\/span><\/p>\n<\/td>\n

              		\n

              0.009562542<\/span><\/p>\n<\/td>\n

              		\n

              4.4490629<\/span><\/p>\n<\/td>\n

              		\n

              5.4342E-05<\/span><\/p>\n<\/td>\n

              		\n

              0.023295952<\/span><\/p>\n<\/td>\n

              		\n

              0.061792748<\/span><\/p>\n<\/td>\n

              		\n

              0.023295952<\/span><\/p>\n<\/td>\n

              		\n

              0.061792748<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

              \n
              <\/div>\n

              \u00a0<\/span><\/span><\/b><\/p>\n\n\n\n\n\n\n
              \n

              Coefficients<\/span><\/b><\/p>\n<\/td>\n

              		\n

              Standard Error<\/span><\/b><\/p>\n<\/td>\n

              		\n

              t Stat<\/span><\/b><\/p>\n<\/td>\n

              		\n

              P-value<\/span><\/b><\/p>\n<\/td>\n

              		\n

              Lower 95%<\/span><\/b><\/p>\n<\/td>\n

              		\n

              Upper 95%<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n<\/thead>\n

              \n

              Intercept: -270.97<\/span><\/p>\n<\/td>\n

              		\n

              12.35<\/span><\/p>\n<\/td>\n

              		\n

              -21.94<\/span><\/p>\n<\/td>\n

              		\n

              0.000<\/span><\/p>\n<\/td>\n

              		\n

              -295.67<\/span><\/p>\n<\/td>\n

              		\n

              -246.27<\/span><\/p>\n<\/td>\n<\/tr>\n

              \n

              X Variable 1: 0.0425<\/span><\/p>\n<\/td>\n

              		\n

              0.0105<\/span><\/p>\n<\/td>\n

              		\n

              4.05<\/span><\/p>\n<\/td>\n

              		\n

              0.000054<\/span><\/p>\n<\/td>\n

              		\n

              0.0215<\/span><\/p>\n<\/td>\n

              		\n

              0.0635<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

              \n
              <\/div>\n

              \u00a0<\/span>Interpretation<\/span><\/b><\/p>\n