• JOSEPH R
• PRAMOD DANG

Stock exchange price prediction is one of the most researched topics, attracting interest from both academics and industry. Various algorithms have been developed since the introduction of Artificial Intelligence (AI) and have been used to forecast equities market movement. Experienced quantitative traders understand that it's often straightforward to create a strategy with excellent predictive capabilities during a backtest. However, such backtest may obscure the risk of an overfit model, potentially resulting in significant underperformance when the strategy is implemented. less attention has been paid to the use of cross validation (CV) approaches for better stock price prediction. In this article we will attempt to find a partial remedy to the problem of an overfit machine learning model using a technique known as cross-validation.

"Stock Price Prediction: Machine Learning Models with K-fold Cross Validation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.k194-k198, March-2024, Available :http://www.jetir.org/papers/JETIR2403A24.pdf

"Stock Price Prediction: Machine Learning Models with K-fold Cross Validation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppk194-k198, March-2024, Available at : http://www.jetir.org/papers/JETIR2403A24.pdf