The technical indicators are highly uncertain therefore possess greater influence on the stock market prediction. Among different techniques developed for effective prediction of the financial market the AI techniques show better prediction efficiency. In this paper, a hybrid model combined with autoencoder (AE) and kernel extreme learning machine (KELM) is proposed for further improvement in the quality of financial market prediction. This study mainly emphasizes on a precise prediction of the financial market, the main motive behind stock price prediction is minimizing the substantial losses faced by investors, and analysing the profitability with the help of buying and selling amount. The prime advantage of the proposed technique over the conventional SAE is robust prediction of different financial market with reduction in error. To authenticate the performance of the proposed deep learning (DL) technique (KELM-AE), high-frequency data of different financial market like Yes Bank, SBI, ASHR, and DJI are taken into consideration and the performance of the proposed technique is investigated in MATLAB based simulation in accordance with MAPE (Mean Absolute Percentage Error), MAE (Mean Absolute Error) and RMSE (Root Mean Square Error).The application of SAE is new in the field of predicting different bank data. The validation of the model is performed by comparing it with other traditional methods based on different performance indexes. The simulation result indicates that the proposed DL based technique (KELM-AE) outperforms other models with a MAPE value of less than 2%for future prediction, irrespective of the financial market. For example the MAPE value for KELM-AE is observed to be1.074 %, 0.888%, 1.021% for YES, SBI and BOI respectively which is much lower as compared to other model like ELM that shows a MAPE value of 1.714%, 1.473% and 1.550% for the above mentioned bank.

技术指标具有高度不确定性，因此对股市预测具有更大的影响。在为有效预测金融市场而开发的不同技术中，人工智能技术表现出更好的预测效率。本文提出了一种结合自动编码器（AE）和内核极限学习机（KELM）的混合模型，以进一步提高金融市场预测的质量。这项研究主要强调对金融市场的精确预测，股价预测的主要动机是最大程度地减少投资者面临的重大损失，并借助买卖量来分析盈利能力。与传统的SAE相比，所提出的技术的主要优势在于可以可靠地预测不同的金融市场，并减少误差。为了验证所提出的深度学习（DL）技术（KELM-AE）的性能，考虑了不同金融市场（例如Yes Bank，SBI，ASHR和DJI）的高频数据，并研究了所提出技术的性能在基于MATLAB的仿真中，按照MAPE（均值绝对百分比误差），MAE（均值绝对误差）和RMSE（均方根误差）进行计算.SAE在预测不同银行数据领域中是新的应用。该模型的验证是通过将其与其他基于不同性能指标的传统方法进行比较来进行的。仿真结果表明，所提出的基于DL的技术（KELM-AE）优于其他模型，其MAPE值在未来预测中均小于2％，而与金融市场无关。