Closing Price Prediction of Nifty Stock Using LSTM with Dense Network

Singh, Dilip; Kumar, Bhupendra

doi:10.1007/978-981-16-4807-6_37

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…The results show that the GRU-LSTM model outperforms the existing GRU and LSTM networks in stock time series forecasting model 8 . In 2021, Dilip Singh proposed a hierarchical LSTM with dense network layers that can effectively predict the closing price of certain stocks in NIFTY 9 . Although the experimental results show that combining neural network models will increase the "black box" 10 .…”

Section: Introductionmentioning

confidence: 99%

Research on stock prediction based on simulated annealing algorithm and ensemble neural learning

Sun¹,

Tian²

2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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By combining the relevant theoretical knowledge of simulated annealing algorithm, the parameters of the XGBoost model are optimized, and the LSTM-SA-XGBoost stock prediction model is designed. In order to verify the prediction performance of the combined model, the combined model and the single model are respectively evaluated by the prediction model performance evaluation indicators: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Accuracy rate (Accuracy) and the f1-score is analyzed and evaluated to verify that the two combined models proposed in this paper have good approximation ability and generalization ability in the prediction of stock fluctuations, and can improve the prediction performance of a single LSTM model or a single RNN model. The prediction performance of stock price rise and fall is verified, and the hybrid model based on LSTM neural network proposed in this paper has certain feasibility and stability in the prediction of stock price rise and fall.

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Section: Introductionmentioning

confidence: 99%

Research on stock prediction based on simulated annealing algorithm and ensemble neural learning

Sun¹,

Tian²

2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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China’s Stock Market Trend Prediction Model based on Adversarial Learning

Yang,

Zhang

2023

Applied Mathematics and Nonlinear Sciences

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There are numerous stock market theories as a result of the gradual usage of mathematical models by researchers to forecast equities during the past few decades. By quantifying the rise and fall range, the prediction problem can be changed into a multi-classification problem based on the related data. This paper describes an Adversarial Learning-based stock forecast model by building a three-tier LSTM training network using the Adversarial Learning concept, selecting 300 stocks to represent the overall performance of the Chinese stock market, increasing the proportion of large to small training datasets, and strengthening the model’s ability to obtain detailed information from a small amount of data.

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Stock Index Prediction with Financial News Sentiments and Technical Indicators

Maharjan¹,

Shakya²

2022

JISMAC

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The price of a stock in the market can be influenced by many factors, out of which the sentiment of the investors plays a vital role. Most often, the sentiment of investors depends on the sentiment of the news headlines. Therefore, news headlines also play an important role in the fluctuation of the stock index. This paper uses the combination of Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional Gated Recurrent Unit (BiGRU) algorithms for the prediction of news sentiment scores based on national news headlines and financial news data. Technical indicators like Relative Strength Index (RSI), Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Stochastic Oscillator along with normal stock indicators like ’Date’, ’Open’, ’Close’, ’High’, ’Low’ and ’Volume’ data can be used to predict the short-term momentum of the stock value. This paper uses the BiGRU algorithm to predict the stock index value (a) with technical indicators only and (b) with technical indicators and news sentiment scores. Keeping all the hyperparameters constant, the BiGRU algorithm provided better prediction results when news sentiment scores were added to the dataset along with technical parameters as an input.

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Closing Price Prediction of Nifty Stock Using LSTM with Dense Network

Cited by 3 publications

References 9 publications

Research on stock prediction based on simulated annealing algorithm and ensemble neural learning

Research on stock prediction based on simulated annealing algorithm and ensemble neural learning

China’s Stock Market Trend Prediction Model based on Adversarial Learning

Stock Index Prediction with Financial News Sentiments and Technical Indicators

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