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@article{168657,
        author = {Dr. Raghu C},
        title = {An economic analysis of Predictive Modelling of D-mart’s stock prices},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {5},
        number = {11},
        pages = {767-771},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168657},
        abstract = {Predictive  modeling  of  stock  prices  is  a  sophisticated  analytical  technique  that  combines statistical  methods,  machine  learning  algorithms,  and  historical  market  data  to  forecast  future price  movements.  This  process  plays  a  vital  role  in  financial  markets,  as  it  enables  investors, traders  and  analysts  to  make  more  informed  decisions  regarding  their  investment  strategies. Given  the  complexity  of  stock  price  movements,  predictive  modeling  helps  in  understanding underlying trends, minimizing risks, and maximizing returns. At  the  core  of  predictive  modeling  is  the  collection  and  analysis  of  historical  stock  price  data. This  data  typically  includes  various  metrics,  such  as  opening,  closing,  high  and  low  prices, along with trading volumes over a specified period. The analysis of this historical data allows for the identification of trends, seasonality and cyclic patterns that may influence future stock performance. Predictive modeling employs various statistical techniques, such as time series analysis, regression analysis, and volatility modeling, to forecast future prices. Time series analysis,  for instance,  helps  in  understanding  temporal  patterns  and  auto-correlations  in  stock  prices,  while regression  analysis can identify relationships between stock  prices  and  independent  variables, such as economic indicators, interest rates and inflation rates. In recent years, machine learning algorithms have gained prominence in predictive modeling due to their ability to analyze large datasets and uncover complex patterns that traditional statistical methods may overlook. Techniques like  decision  trees, support vector  machines,  neural networks,  and  ensemble  methods  can  be  utilized  to  create  predictive  models  that  adapt  and improve  over  time  based  on  new  data.  These  models  can  be  trained  on  historical  data  to recognize  patterns  and  relationships,  enabling  them  to  make  predictions  about  future  stock movements. This paper analyses a macro perspective of predictive modelling so as to create a more  comprehensive  model  that  can  better  reflect  the  complexities  of  the  stock  market  and improve prediction accuracy.},
        keywords = {Forecasting, Predictive Analysis, Stock prices, Volatality, Trends.},
        month = {October},
        }