Volatility forecast with artificial neural networks as univariate time series, with examples from stock market indexes

Uysal, Fahriye; Erturan, Mahmut Burak

doi:10.18844/gjbem.v7i3.2965

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…This method outperforms other methods in 87% of comparisons using probability parameters [4]. It can be seen that stock forecasting is very important for both investment activities and market monitoring [5][6].…”

Section: Introductionmentioning

confidence: 93%

Prediction Method and Optimization of Stock Trend Based on Neural Network

2021

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The stock market is characterized by the coexistence of high risks. In order to seek benefits and avoid risks, people do not hesitate to explore its laws and seek the best forecasting methods and methods. The purpose of this paper is to study the stock trend prediction method and optimization based on neural network. The predictability of the securities market is expounded. On the basis of the prediction model based on the historical stock transaction data, a stock prediction model integrating news and investor sentiment is constructed. The advantage of linear and complex time series forecasting problems, the stock training data is used as the input data of the improved CLSTM and BiLSTM deep neural network, and the stock trend forecasting model is trained. Continuously optimize and improve the model, select appropriate parameters to obtain a good stock trend prediction model. By continuously revising the LSTM level, the accuracy of the stock trend prediction model is significantly improved.

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Section: Introductionmentioning

confidence: 93%

Prediction Method and Optimization of Stock Trend Based on Neural Network

2021

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Evaluation of multimodal data-driven financial risk prediction methods for corporate green credit

Wang,

Gu,

2024

IFS

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With the rapid economic development and increasingly serious environmental problems, many regions have launched green credit policies. Green credit can reduce the loan interest rate of the environmental protection industry and lower the financing threshold. Traditional risk prediction methods cannot comprehensively evaluate the green credit risk of the enterprise based on the degree of green environmental protection and the industry environment in which the enterprise is located, resulting in the inconsistency between the credit financial risk prediction and the actual results, which increases the bank credit risk. In order to strengthen the management level of green credit and reduce the probability of non-performing loans, a scientific risk assessment method was constructed by using a combination of automatic encoding network and bidirectional long short-term memory neural network model to predict the financial risks of green credit, driven by multi-modal data. Through the study of multimodal data, this paper took green credit financial risk as the research object, aggregated the information of various enterprises to improve the bank’s capital utilization rate, and also promoted enterprises to take the initiative to transform into the direction of green environmental protection. Finally, the experiment proved that multimodal data fusion model was more superior than random forest in risk prediction, reducing the bank’s non-performing loan rate by 3.1% and improving the bank’s risk control level.

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Volatility forecast with artificial neural networks as univariate time series, with examples from stock market indexes

Cited by 2 publications

References 26 publications

Prediction Method and Optimization of Stock Trend Based on Neural Network

Prediction Method and Optimization of Stock Trend Based on Neural Network

Evaluation of multimodal data-driven financial risk prediction methods for corporate green credit

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