Stock market prediction is one of the critical issues in fiscal market. It is important issue for the traders and investors. Artificial Neural Networks (ANNs) associated with nature inspired algorithms are playing an increasingly vital role in many areas including medical field, security systems and stock market. Several prediction models have been developed by researchers to forecast stock market trend. However, few studies have focused on improving stock market prediction accuracy especially when utilizing artificial neural networks to perform the analysis. This paper proposed nine new integrated models for forecasting intraday stock price based on the potential of three ANNs, Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Time Delay Neural Network (TDNN) and nature inspired algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC).The developed models were named as GA-BPNN, PSO-BPNN, ABC-BPNN, GA-RBFNN, PSO-RBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN. Nature inspired algorithms are employed for optimizing the parameters of ANNs. Technical indicators calculated from historical data are fed as input to developed models. Proposed hybrid models validated on four datasets representing different sectors in NSE. Four statistical metrics, Root Mean Square Error (RMSE), Hit Rate (HR), Error Rate (ER) and prediction accuracy were utilized to gauge the performance of the developed models. Results proved that the PSO-BPNN model yielded the highest prediction accuracy in estimating intraday stock price. The other models, GA-BPNN, ABC-BPNN, GA-RBFNN, PSO-RBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN produced lower performance with mean prediction accuracy of 97.24%, 98.37%, 84.01%, 85.15%, 84.01%, 83.87%, 89.95% and 78.61% respectively.

股市预测是财政市场的关键问题之一。对于交易者和投资者来说，这是重要的问题。与自然启发算法相关的人工神经网络（ANN）在医疗领域，安全系统和股票市场等许多领域中发挥着越来越重要的作用。研究人员已经开发了几种预测模型来预测股市趋势。但是，很少有研究集中在提高股票市场预测的准确性上，特别是在利用人工神经网络进行分析时。本文基于三种神经网络的潜力，提出了九种新的预测日内股价的集成模型，即反向传播神经网络（BPNN），径向基函数神经网络（RBFNN），时延神经网络（TDNN）和自然启发算法，例如遗传算法（GA），粒子群优化（PSO）和人工蜂群（ABC）。开发的模型分别命名为GA-BPNN，PSO-BPNN，ABC-BPNN ，GA-RBFNN，PSO-RBFNN，ABC-RBFNN，GA-TDNN，PSO-TDNN和ABC-TDNN。自然启发算法被用于优化人工神经网络的参数。根据历史数据计算得出的技术指标将作为已开发模型的输入。拟议的混合模型在代表NSE不同部门的四个数据集上得到了验证。利用四个统计指标，均方根误差（RMSE），命中率（HR），错误率（ER）和预测精度来评估开发模型的性能。结果证明，在估计盘中股价时，PSO-BPNN模型具有最高的预测准确性。其他型号，