{"id":20410,"date":"2024-02-29T14:10:58","date_gmt":"2024-02-29T08:40:58","guid":{"rendered":"http:\/\/www.sachdevajk.in\/?p=20410"},"modified":"2024-02-29T14:10:58","modified_gmt":"2024-02-29T08:40:58","slug":"relationship-of-nifty50-with-sun-pharmaceutical","status":"publish","type":"post","link":"http:\/\/www.sachdevajk.in\/?p=20410","title":{"rendered":"Relationship of Nifty50 with SUN PHARMACEUTICAL"},"content":{"rendered":"

Author:<\/strong> Sneha Chitte\u00a0<\/p>\n

Roll no:<\/strong> 21230023471<\/p>\n

Batch:<\/strong> Operations & Supply Chain Management<\/p>\n

\u00a0<\/p>\n

Company Introduction<\/u><\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong><\/p>\n

Sun Pharmaceutical Industries Limited is an Indian multinational pharmaceutical company headquartered in Mumbai, that manufactures and sells pharmaceutical formulations and active pharmaceutical ingredients (API) in more than 100 countries across the globe. It is the largest pharmaceutical company in India and the fourth-largest specialty generic pharmaceutical company in the world. The products cater to a vast range of therapeutic segments covering psychiatry, anti-infectives, neurology, cardiology diabetology, gastroenterology, ophthalmology, nephrology, urology, dermatology, gynaecology, respiratory, oncology, dental, and nutritional\u2019s. Its active pharmaceutical, products include baricitinib, brivaracetam, and dapagliflozin. Sun Pharma was listed on the stock exchange in 1994 in an issue oversubscribed 55 times. The founding family continues to hold a majority stake in the company.<\/p>\n

Objective: <\/strong>To calculate the beta of Sun Pharma and its significance.<\/p>\n

View And Reviews:<\/u><\/strong><\/p>\n

    \n
  1. Dilip Shanghvi, Managing Director, Sun Pharmaceutical Industries Limited<\/strong><\/li>\n<\/ol>\n

    All our businesses are positioned for growth, and we expect high-single-digit to low-double-digit consolidated topline growth for FY23. Ramp-up in our global specialty business is expected to continue. As business operations normalize globally, overall expenses are expected to increase. Our R&D investments will be about 7-8% of sales in FY23 with increased spending expected on clinical trials for specialty products.<\/p>\n

      \n
    1. Israel Makov, Chairman, Sun Pharmaceutical Industries Limited<\/strong><\/li>\n<\/ol>\n

      The global pharmaceutical market is estimated to reach about US$1.8 trillion by 2026, growing at a compounded rate of approximately 3-6%. This includes the spend on COVID-19 vaccinations, which is projected to reach a cumulative value of US$ 251 Billion between 2021-2026. Excluding the projected spending on COVID-19 vaccines, the industry is expected to record ~5% CAGR between 2021 and 2026. \u2022 The main growth drivers will be increased pharmaceutical spending in the pharmerging markets and the consistent launch of high-end specialty innovative products in developed markets<\/p>\n

      Data Collection:<\/u><\/strong><\/p>\n

      Data has been downloaded from Yahoo Finance for 01\/02\/2023 to 31\/01\/2024 for Nifty50 and Sun Pharma. Friday closing prices are considered and return values are calculated for both. Sun Pharma is the dependent variable and Nifty50 is the independent variable. Data analysis was done to find the dependency of Y (Sun Pharma) on X (Nifty50).<\/p>\n

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

      Sr.No<\/strong><\/p>\n<\/td>\n

      		\n

      Date <\/strong><\/p>\n<\/td>\n

      		\n

      NIFTY RETURN<\/strong><\/p>\n<\/td>\n

      		\n

      SUN PHARMA RETURN<\/strong><\/p>\n<\/td>\n<\/tr>\n

      \n

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

      		\n

      19-Jan-24<\/p>\n<\/td>\n

      		\n

      \u00a0<\/p>\n<\/td>\n

      		\n

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

      \n

      2<\/p>\n<\/td>\n

      		\n

      12-Jan-24<\/p>\n<\/td>\n

      		\n

      0.013718002<\/p>\n<\/td>\n

      		\n

      -0.722521713<\/p>\n<\/td>\n<\/tr>\n

      \n

      3<\/p>\n<\/td>\n

      		\n

      05-Jan-24<\/p>\n<\/td>\n

      		\n

      0.491133307<\/p>\n<\/td>\n

      		\n

      -1.942003846<\/p>\n<\/td>\n<\/tr>\n

      \n

      4<\/p>\n<\/td>\n

      		\n

      29-Dec-23<\/p>\n<\/td>\n

      		\n

      -2.666033921<\/p>\n<\/td>\n

      		\n

      -3.13413321<\/p>\n<\/td>\n<\/tr>\n

      \n

      5<\/p>\n<\/td>\n

      		\n

      22-Dec-23<\/p>\n<\/td>\n

      		\n

      0.736003059<\/p>\n<\/td>\n

      		\n

      -1.25451586<\/p>\n<\/td>\n<\/tr>\n

      \n

      6<\/p>\n<\/td>\n

      		\n

      15-Dec-23<\/p>\n<\/td>\n

      		\n

      -1.031292557<\/p>\n<\/td>\n

      		\n

      -0.635226953<\/p>\n<\/td>\n<\/tr>\n

      \n

      7<\/p>\n<\/td>\n

      		\n

      08-Dec-23<\/p>\n<\/td>\n

      		\n

      -1.796654222<\/p>\n<\/td>\n

      		\n

      0.004046126<\/p>\n<\/td>\n<\/tr>\n

      \n

      8<\/p>\n<\/td>\n

      		\n

      01-Dec-23<\/p>\n<\/td>\n

      		\n

      -0.906430057<\/p>\n<\/td>\n

      		\n

      -0.368182554<\/p>\n<\/td>\n<\/tr>\n

      \n

      9<\/p>\n<\/td>\n

      		\n

      24-Nov-23<\/p>\n<\/td>\n

      		\n

      2.447317653<\/p>\n<\/td>\n

      		\n

      -2.907614213<\/p>\n<\/td>\n<\/tr>\n

      \n

      10<\/p>\n<\/td>\n

      		\n

      17-Nov-23<\/p>\n<\/td>\n

      		\n

      1.522491862<\/p>\n<\/td>\n

      		\n

      -0.071102932<\/p>\n<\/td>\n<\/tr>\n

      \n

      11<\/p>\n<\/td>\n

      		\n

      10-Nov-23<\/p>\n<\/td>\n

      		\n

      2.501974288<\/p>\n<\/td>\n

      		\n

      -1.276577934<\/p>\n<\/td>\n<\/tr>\n

      \n

      12<\/p>\n<\/td>\n

      		\n

      03-Nov-23<\/p>\n<\/td>\n

      		\n

      0.022142264<\/p>\n<\/td>\n

      		\n

      -3.243311994<\/p>\n<\/td>\n<\/tr>\n

      \n

      13<\/p>\n<\/td>\n

      		\n

      27-Oct-23<\/p>\n<\/td>\n

      		\n

      1.360345238<\/p>\n<\/td>\n

      		\n

      -2.611515205<\/p>\n<\/td>\n<\/tr>\n

      \n

      14<\/p>\n<\/td>\n

      		\n

      20-Oct-23<\/p>\n<\/td>\n

      		\n

      -0.608253354<\/p>\n<\/td>\n

      		\n

      2.78052731<\/p>\n<\/td>\n<\/tr>\n

      \n

      15<\/p>\n<\/td>\n

      		\n

      13-Oct-23<\/p>\n<\/td>\n

      		\n

      1.62579085<\/p>\n<\/td>\n

      		\n

      0.271406058<\/p>\n<\/td>\n<\/tr>\n

      \n

      16<\/p>\n<\/td>\n

      		\n

      06-Oct-23<\/p>\n<\/td>\n

      		\n

      0.187844442<\/p>\n<\/td>\n

      		\n

      -1.571640618<\/p>\n<\/td>\n<\/tr>\n

      \n

      17<\/p>\n<\/td>\n

      		\n

      29-Sep-23<\/p>\n<\/td>\n

      		\n

      0.158091208<\/p>\n<\/td>\n

      		\n

      2.780981105<\/p>\n<\/td>\n<\/tr>\n

      \n

      18<\/p>\n<\/td>\n

      		\n

      22-Sep-23<\/p>\n<\/td>\n

      		\n

      1.414609411<\/p>\n<\/td>\n

      		\n

      -2.269883054<\/p>\n<\/td>\n<\/tr>\n

      \n

      19<\/p>\n<\/td>\n

      		\n

      15-Sep-23<\/p>\n<\/td>\n

      		\n

      -0.852544354<\/p>\n<\/td>\n

      		\n

      1.541042964<\/p>\n<\/td>\n<\/tr>\n

      \n

      20<\/p>\n<\/td>\n

      		\n

      08-Sep-23<\/p>\n<\/td>\n

      		\n

      2.804911634<\/p>\n<\/td>\n

      		\n

      -1.682901374<\/p>\n<\/td>\n<\/tr>\n

      \n

      21<\/p>\n<\/td>\n

      		\n

      01-Sep-23<\/p>\n<\/td>\n

      		\n

      0.743913816<\/p>\n<\/td>\n

      		\n

      -1.897474457<\/p>\n<\/td>\n<\/tr>\n

      \n

      22<\/p>\n<\/td>\n

      		\n

      25-Aug-23<\/p>\n<\/td>\n

      		\n

      1.203712068<\/p>\n<\/td>\n

      		\n

      -0.243462579<\/p>\n<\/td>\n<\/tr>\n

      \n

      23<\/p>\n<\/td>\n

      		\n

      18-Aug-23<\/p>\n<\/td>\n

      		\n

      0.922589384<\/p>\n<\/td>\n

      		\n

      2.58519389<\/p>\n<\/td>\n<\/tr>\n

      \n

      24<\/p>\n<\/td>\n

      		\n

      11-Aug-23<\/p>\n<\/td>\n

      		\n

      -0.501135574<\/p>\n<\/td>\n

      		\n

      -0.2246894<\/p>\n<\/td>\n<\/tr>\n

      \n

      25<\/p>\n<\/td>\n

      		\n

      04-Aug-23<\/p>\n<\/td>\n

      		\n

      -0.656878995<\/p>\n<\/td>\n

      		\n

      0.582858657<\/p>\n<\/td>\n<\/tr>\n

      \n

      26<\/p>\n<\/td>\n

      		\n

      28-Jul-23<\/p>\n<\/td>\n

      		\n

      -0.454471584<\/p>\n<\/td>\n

      		\n

      -0.035120067<\/p>\n<\/td>\n<\/tr>\n

      \n

      27<\/p>\n<\/td>\n

      		\n

      21-Jul-23<\/p>\n<\/td>\n

      		\n

      -0.608135479<\/p>\n<\/td>\n

      		\n

      -3.636203944<\/p>\n<\/td>\n<\/tr>\n

      \n

      28<\/p>\n<\/td>\n

      		\n

      14-Jul-23<\/p>\n<\/td>\n

      		\n

      -0.229669936<\/p>\n<\/td>\n

      		\n

      -2.205714807<\/p>\n<\/td>\n<\/tr>\n

      \n

      29<\/p>\n<\/td>\n

      		\n

      07-Jul-23<\/p>\n<\/td>\n

      		\n

      0.879797325<\/p>\n<\/td>\n

      		\n

      -3.4717368<\/p>\n<\/td>\n<\/tr>\n

      \n

      30<\/p>\n<\/td>\n

      		\n

      30-Jun-23<\/p>\n<\/td>\n

      		\n

      1.97912264<\/p>\n<\/td>\n

      		\n

      1.535193589<\/p>\n<\/td>\n<\/tr>\n

      \n

      31<\/p>\n<\/td>\n

      		\n

      23-Jun-23<\/p>\n<\/td>\n

      		\n

      1.878916973<\/p>\n<\/td>\n

      		\n

      -5.719855458<\/p>\n<\/td>\n<\/tr>\n

      \n

      32<\/p>\n<\/td>\n

      		\n

      16-Jun-23<\/p>\n<\/td>\n

      		\n

      -2.565821309<\/p>\n<\/td>\n

      		\n

      0.055474305<\/p>\n<\/td>\n<\/tr>\n

      \n

      33<\/p>\n<\/td>\n

      		\n

      09-Jun-23<\/p>\n<\/td>\n

      		\n

      -0.182722183<\/p>\n<\/td>\n

      		\n

      -0.811491935<\/p>\n<\/td>\n<\/tr>\n

      \n

      34<\/p>\n<\/td>\n

      		\n

      02-Jun-23<\/p>\n<\/td>\n

      		\n

      0.077395795<\/p>\n<\/td>\n

      		\n

      1.590527974<\/p>\n<\/td>\n<\/tr>\n

      \n

      35<\/p>\n<\/td>\n

      		\n

      26-May-23<\/p>\n<\/td>\n

      		\n

      0.496353225<\/p>\n<\/td>\n

      		\n

      -2.971188475<\/p>\n<\/td>\n<\/tr>\n

      \n

      36<\/p>\n<\/td>\n

      		\n

      19-May-23<\/p>\n<\/td>\n

      		\n

      -1.055135711<\/p>\n<\/td>\n

      		\n

      -4.54170533<\/p>\n<\/td>\n<\/tr>\n

      \n

      37<\/p>\n<\/td>\n

      		\n

      12-May-23<\/p>\n<\/td>\n

      		\n

      -2.53497034<\/p>\n<\/td>\n

      		\n

      3.25646703<\/p>\n<\/td>\n<\/tr>\n

      \n

      38<\/p>\n<\/td>\n

      		\n

      05-May-23<\/p>\n<\/td>\n

      		\n

      0.962604098<\/p>\n<\/td>\n

      		\n

      1.511506276<\/p>\n<\/td>\n<\/tr>\n

      \n

      39<\/p>\n<\/td>\n

      		\n

      28-Apr-23<\/p>\n<\/td>\n

      		\n

      1.012708932<\/p>\n<\/td>\n

      		\n

      1.772373641<\/p>\n<\/td>\n<\/tr>\n

      \n

      40<\/p>\n<\/td>\n

      		\n

      21-Apr-23<\/p>\n<\/td>\n

      		\n

      1.577583818<\/p>\n<\/td>\n

      		\n

      0.182250797<\/p>\n<\/td>\n<\/tr>\n

      \n

      41<\/p>\n<\/td>\n

      		\n

      31-Mar-23<\/p>\n<\/td>\n

      		\n

      0.318766841<\/p>\n<\/td>\n

      		\n

      -0.641770681<\/p>\n<\/td>\n<\/tr>\n

      \n

      42<\/p>\n<\/td>\n

      		\n

      24-Mar-23<\/p>\n<\/td>\n

      		\n

      2.390544897<\/p>\n<\/td>\n

      		\n

      -1.042620283<\/p>\n<\/td>\n<\/tr>\n

      \n

      43<\/p>\n<\/td>\n

      		\n

      17-Mar-23<\/p>\n<\/td>\n

      		\n

      3.461137989<\/p>\n<\/td>\n

      		\n

      -1.557280156<\/p>\n<\/td>\n<\/tr>\n

      \n

      44<\/p>\n<\/td>\n

      		\n

      10-Mar-23<\/p>\n<\/td>\n

      		\n

      2.323623904<\/p>\n<\/td>\n

      		\n

      -0.224496189<\/p>\n<\/td>\n<\/tr>\n

      \n

      45<\/p>\n<\/td>\n

      		\n

      03-Mar-23<\/p>\n<\/td>\n

      		\n

      -0.499845027<\/p>\n<\/td>\n

      		\n

      1.187797603<\/p>\n<\/td>\n<\/tr>\n

      \n

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

      		\n

      24-Feb-23<\/p>\n<\/td>\n

      		\n

      1.789277418<\/p>\n<\/td>\n

      		\n

      0.372323922<\/p>\n<\/td>\n<\/tr>\n

      \n

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

      		\n

      17-Feb-23<\/p>\n<\/td>\n

      		\n

      -0.094791912<\/p>\n<\/td>\n

      		\n

      1.442555384<\/p>\n<\/td>\n<\/tr>\n

      \n

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

      		\n

      10-Feb-23<\/p>\n<\/td>\n

      		\n

      0.846352937<\/p>\n<\/td>\n

      		\n

      2.244794312<\/p>\n<\/td>\n<\/tr>\n

      \n

      49<\/p>\n<\/td>\n

      		\n

      03-Feb-23<\/p>\n<\/td>\n

      		\n

      -1.243005133<\/p>\n<\/td>\n

      		\n

      2.140870256<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u00a0<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
      \n

      SUMMARY OUTPUT<\/p>\n<\/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<\/tr>\n
      \n

      Regression Statistics<\/em><\/p>\n<\/td>\n

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

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

      		\n

      0.18078396<\/p>\n<\/td>\n

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

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

      		\n

      0.03268284<\/p>\n<\/td>\n

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

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

      		\n

      0.011654206<\/p>\n<\/td>\n

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

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

      		\n

      2.054930133<\/p>\n<\/td>\n

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

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

      		\n

      48<\/p>\n<\/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<\/tr>\n
      \n

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

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

      \u00a0<\/em><\/p>\n<\/td>\n

      		\n

      df<\/em><\/p>\n<\/td>\n

      		\n

      SS<\/em><\/p>\n<\/td>\n

      		\n

      MS<\/em><\/p>\n<\/td>\n

      		\n

      F<\/em><\/p>\n<\/td>\n

      		\n

      Significance F<\/em><\/p>\n<\/td>\n<\/tr>\n

      \n

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

      		\n

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

      		\n

      6.563006737<\/p>\n<\/td>\n

      		\n

      6.563006737<\/p>\n<\/td>\n

      		\n

      1.554206529<\/p>\n<\/td>\n

      		\n

      0.218826524<\/p>\n<\/td>\n<\/tr>\n

      \n

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

      		\n

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

      		\n

      194.2459412<\/p>\n<\/td>\n

      		\n

      4.222737851<\/p>\n<\/td>\n

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

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

      		\n

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

      		\n

      200.8089479<\/p>\n<\/td>\n

      		\n

      \u00a0<\/p>\n<\/td>\n

      		\n

      \u00a0<\/p>\n<\/td>\n

      		\n

      \u00a0<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u00a0<\/strong><\/p>\n\n\n\n\n\n
      \n

      \u00a0<\/em><\/p>\n<\/td>\n

      		\n

      Coefficients<\/em><\/p>\n<\/td>\n

      		\n

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

      		\n

      t Stat<\/em><\/p>\n<\/td>\n

      		\n

      P-value<\/em><\/p>\n<\/td>\n

      		\n

      Lower 95%<\/em><\/p>\n<\/td>\n

      		\n

      Upper 95%<\/em><\/p>\n<\/td>\n<\/tr>\n

      \n

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

      		\n

      -0.41488939<\/p>\n<\/td>\n

      		\n

      0.30889803<\/p>\n<\/td>\n

      		\n

      -1.343127343<\/p>\n<\/td>\n

      		\n

      0.18582027<\/p>\n<\/td>\n

      		\n

      -1.036668876<\/p>\n<\/td>\n

      		\n

      0.206890095<\/p>\n<\/td>\n<\/tr>\n

      \n

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

      		\n

      -0.262577652<\/p>\n<\/td>\n

      		\n

      0.210621837<\/p>\n<\/td>\n

      		\n

      -1.246678198<\/p>\n<\/td>\n

      		\n

      0.218826524<\/p>\n<\/td>\n

      		\n

      -0.686537421<\/p>\n<\/td>\n

      		\n

      0.161382117<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u00a0<\/p>\n

      \u00a0<\/p>\n

      Data Analysis:<\/strong><\/p>\n

      Regression equation: <\/strong>Return of sun pharma = -0.41488<\/em> + <\/em>0.2625 * <\/em>(Return of Nifty50)<\/em><\/p>\n

      t-stat of b = 1.246678198<\/p>\n

      P-value of b = 0.218826524<\/p>\n

      R Square = 0.03268284<\/p>\n

      \u00a0<\/p>\n

      F = 1.554206529<\/p>\n

      The above equation shows the relationship between Nifty50 and Sun Pharma. The return of Sun Pharma is the dependent variable and the return of Nifty50 is the independent variable. \u00a0The coefficient of the Nifty50 return variable is 0.2625. This implies that for every unit increase in the return of Nifty50, the return of Sun Pharma increases by 0.2625 units.<\/p>\n

      The R-squared value is 0.03268284, indicating that approximately 3.27% of the variance in the return of Sun Pharma can be explained by the return of Nifty50 in this model.<\/p>\n

      \u00a0<\/p>\n

      Conclusion:<\/strong><\/p>\n

      Beta is 0.2625, which is less than 1, therefore it is not good for short-term investment. The model is statistically insignificant at a 5% level.<\/p>\n

      \u00a0<\/p>\n

      REFERENCE<\/strong><\/p>\n

      https:\/\/www.nse.india.com\/<\/a><\/p>\n

      https:\/\/sunpharma.com<\/a><\/p>\n

      https:\/\/sunpharma.com\/about-us\/#:~:text=From%20humble%20beginnings%20in%201983,in%20the%20generic%20dermatology%20market<\/a>.<\/p>\n

      https:\/\/sunpharma.com\/wp-content\/uploads\/2023\/07\/SPIL-AR2022-23-Managing-Directors-Message-and-Management-Discussion-Analysis.pdf<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

      Author: Sneha Chitte\u00a0 Roll no: 21230023471 Batch: Operations & Supply Chain Management \u00a0 Company Introduction\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Sun Pharmaceutical Industries Limited is an Indian multinational pharmaceutical company headquartered in Mumbai, that manufactures and sells pharmaceutical formulations and active pharmaceutical ingredients (API) in more than 100 countries across the globe. It is the largest pharmaceutical company in India… Continue reading Relationship of Nifty50 with SUN PHARMACEUTICAL<\/span><\/a><\/p>\n","protected":false},"author":139367,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[24],"tags":[],"class_list":["post-20410","post","type-post","status-publish","format-standard","hentry","category-medical","entry"],"_links":{"self":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/20410","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/users\/139367"}],"replies":[{"embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=20410"}],"version-history":[{"count":1,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/20410\/revisions"}],"predecessor-version":[{"id":20411,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/20410\/revisions\/20411"}],"wp:attachment":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=20410"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=20410"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=20410"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}