Prediction of stock market using artificial neural network

Budhani, Neelima; Jha, C. K.; Budhani, Sandeep Kumar

doi:10.1109/icsctet.2015.7371196

Cited by 27 publications

(9 citation statements)

References 1 publication

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“…The authors Neelima Budhani et al [3] worked on numerous techniques like specialized investigation, central examination, time series investigation and measurable investigation and so forth are totally used to endeavor to anticipate the cost in the offer market however none of these strategies is demonstrated as a reliably adequate forecast instrument. Fake Neural Network (ANN), a field of Artificial Intelligence (AI), is a famous method for distinguishing obscure and secret examples in information that is reasonable for share market forecast.…”

Section: Iiirelated Workmentioning

confidence: 99%

Prognosticate Engine to Invest Money

Sekhar¹

2022

IJSREM

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Stock market prediction facilitates in deciding the organization’s stock valuation trading on any exchange. One can gain profits by estimating the future price of stock. The market assumptions suggests that the current updated information is reflected by the stock prices, so we can attempt to predict the fore coming stock price by observing market trends by utilizing technology. This purportedly allows us to gain future price information using predictive analysis. This paper Prognosticate Engine to Invest Money using knowledge engineering techniques is an attempt to predict stock prices through the use of advanced technologies to find promising results for trend prediction. This paper provides investors with an investment system to navigate through the stock market. It laid a basis for democratizing knowledge engineering technologies to the stockers in the scenario of choosing investment opportunities. It also takes precedence for developing and evaluating advanced models and upgrading Auto-ML in the fiscal environment. Keywords—Stock Market, Investment, Profit, Prognosticate Engine.

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Section: Iiirelated Workmentioning

confidence: 99%

Prognosticate Engine to Invest Money

Sekhar¹

2022

IJSREM

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“…In terms of stock market funds, [24] used an LSTM neural network to analyze shortterm stock market funds. [25] proposed a financial warning model based on an SOM-BP neural network for listed companies in the stock market, while [26][27] used a neural network based on a metaheuristic algorithm to analyze stock market data. These practices show that the neural network could provide better results.…”

Section: Related Knowledgementioning

confidence: 99%

Research on Financial Risk Crisis Prediction of Listed Companies Based on IWOA-BP Neural Network

Li¹,

Li²

2022

Journal of Internet Technology

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<p>To avoid the risk brought by the financial crisis, the Improved Whale Optimization Algorithm-Back Propagation neural network (IWOA-BP) financial crisis early warning model is proposed. This paper selects the data from financial statements of some of the listed Chinese manufacturing companies from 2015-2019 as the research sample. First, the financial data of enterprises are screened by principal component analysis, and the early warning model is constructed from the financial and nonfinancial factors of six indicators: solvency, operating capacity, profitability, development capacity, cash flow and risk level factors. Second, the Whale Optimization Algorithm is optimized by the chaos strategy, as well as by the dynamic weight and sine cosine algorithm. Finally, the improved Whale Algorithm is optimized for BP neural network parameters. In the simulation experiments, the performance of the improved whale optimization algorithm is substantially improved. In addition, in the empirical analysis, compared to the prediction model with other algorithms, the prediction model of this paper has better results in terms of prediction accuracy.</p> <p> </p>

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“…There are not many installations, and the production cost is significantly higher than that of hard substrates. In recent years, with the rapid development of IC substrate technology, flexible CSP/BGA substrates have gradually been replaced [5][6][7].…”

Section: Literature Review 21 Ic Carrier Board Manufacturing Process and Application 211 Ic Carrier Boardmentioning

confidence: 99%

“…According to the four categories, multiple categories can be extended according to the four categories. The various data used in this research are the monthly requirements for IC carrier boards, among which the network training data is from January 2018 to December 2019 [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Intelligence in IC Substrate Production Predicting

Liu

2021

Preprint

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Today's technology products are changing with each day, the purpose is to bring more convenience to people, but also the competition among the technology industries is more competitive. In such environment, whether the company's decision-making is correct or not will directly affect the future development of an enterprise. Therefore, how an enterprise can formulate and construct a set of appropriate decision-making systems to accurately predict the future market will be the first important issue for enterprises. This research proposed an artificial intelligence predicting system to estimate manufacturing capacities and client demands, and providing it to manufacturing managers as a reference for inventory arrangements so that inventory can be adjusted appropriately to avoid excessive inventory levels. In recent years, neural networks have been widely and effectively applied to many predicting problems. The main reason is that most of the predicting problems are nonlinear models. And the backward neural network has the ability to construct nonlinear models. In this study, a predicting model combining grey correlation and neural network will be used to establish a high-accuracy predition system for the production predict of IC product. First, grey correlation analysis will be used to screen out the most relevant factors among many factors. And then put these factors into the neural network prediction model for training and prediction. The results show that the training prediction error and the empirical error value are about 14%. This value indicates that the prediction ability is better, so the proposed prediction model can be applied to the prediction of IC substrate production. It provided a predictive reference material and provide decision making with a more accurate, convenient and a fast tool to enhance the company’s competitiveness.

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Prediction of stock market using artificial neural network

Cited by 27 publications

References 1 publication

Prognosticate Engine to Invest Money

Prognosticate Engine to Invest Money

Research on Financial Risk Crisis Prediction of Listed Companies Based on IWOA-BP Neural Network

Using Artificial Intelligence in IC Substrate Production Predicting

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