• B. Mohana Sowmya
• A. Nagamanikanta
• B. Lalitha Priyanka
• Dr. S. Narayana

The stock market is where shares of publicly traded companies are exchanged. The Exchange enables dealers to trade shares in companies and other securities. Forecasting the price of shares is one of the most extensive studies and the most difficult problems, attracting researchers from many fields, including economics, history, finance, mathematics, and computing. The volatile nature of the stock market makes the application of simple time series or regression techniques challenging. Our project attempts to forecast the fluctuation of the stock market index using deep learning techniques. Moreover, we optimize the topology of the CNN network to improve the performance of the models. This study proposes a method to systematically optimize the parameters of the CNN model using the genetic algorithm (GA). The experimental results show that GA-CNN surpasses comparative models and demonstrates the effectiveness of the GA-CNN hybrid approach.

STOCK MARKET PREDICTION USING GENETIC ALGORITHM

"STOCK MARKET PREDICTION USING GENETIC ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.f403-f407, June-2021, Available :http://www.jetir.org/papers/JETIR2106759.pdf

"STOCK MARKET PREDICTION USING GENETIC ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppf403-f407, June-2021, Available at : http://www.jetir.org/papers/JETIR2106759.pdf