Banik Shipra, Khodadad Khan A F M, Anwer Mohammad
School of Engineering and Computer Science, Independent University, Dhaka 1229, Bangladesh.
Comput Intell Neurosci. 2014;2014:318524. doi: 10.1155/2014/318524. Epub 2014 Feb 19.
Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.
由于事实的性质非常嘈杂且随时间变化，预测股票市场对应用研究人员来说一直是一项艰巨的任务。然而，这一假设已在多项实证研究中得到体现，许多研究人员已有效地应用机器学习技术来预测股票市场。本文研究了供投资者使用的股票预测。投资者通常因投资目的不确定和资产盲目而遭受损失，这一点始终是事实。本文提出了一种粗糙集模型、一种神经网络模型以及一种混合神经网络和粗糙集模型，以找出达卡证券交易所股票的最佳买卖时机。调查结果表明，我们提出的混合模型比单一的粗糙集模型和神经网络模型具有更高的精度。我们相信本文的研究结果将有助于股票投资者在达卡证券交易所决定最佳的买卖时间。
