Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability

Lee, Joonhyuck; Jang, Dong-Sik; Park, Sangsung

doi:10.3390/su9060899

Cited by 35 publications

(14 citation statements)

References 40 publications

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“…Recently, with the outstanding performance in various classification problems [27,28], there have been attempts to apply deep learning techniques to stock market prediction. Deep learning techniques have achieved remarkable success in numerous prediction tasks, since they can extract useful features automatically during the learning process [29,30].…”

Section: Stock Market Predictionmentioning

confidence: 99%

Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

2018

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With recent advances in computing technology, massive amounts of data and information are being constantly accumulated. Especially in the field of finance, we have great opportunities to create useful insights by analyzing that information, because the financial market produces a tremendous amount of real-time data, including transaction records. Accordingly, this study intends to develop a novel stock market prediction model using the available financial data. We adopt deep learning technique because of its excellent learning ability from the massive dataset. In this study, we propose a hybrid approach integrating long short-term memory (LSTM) network and genetic algorithm (GA). Heretofore, trial and error based on heuristics is commonly used to estimate the time window size and architectural factors of LSTM network. This research investigates the temporal property of stock market data by suggesting a systematic method to determine the time window size and topology for the LSTM network using GA. To evaluate the proposed hybrid approach, we have chosen daily Korea Stock Price Index (KOSPI) data. The experimental result demonstrates that the hybrid model of LSTM network and GA outperforms the benchmark model.

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Section: Stock Market Predictionmentioning

confidence: 99%

Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

2018

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“…They found that business performance predictions are related to the industries. Lee et al (2017) proposed a deep neural network-based performance prediction model using financial and patent indicators.…”

Section: Business Performance Measurement and Prediction -Related Workmentioning

confidence: 99%

Composite Financial Performance Index Prediction – A Neural Networks Approach

Popa

Bogdan

et al. 2021

Journal of Business Economics and Management

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Financial indicators are the most used variables in measuring the business performance of companies, signaling about the financial position, comprehensive income, and other significant reporting aspects. In a competitive environment, the performance measurement model allows performing comparative analysis in the same industry and between industries. This paper aims to design a composite financial index to determine the financial performance of listed companies, further used in predicting business performance through neural networks. Principal components analysis was used to build a composite financial index, employing four traditional accounting indicators and four value-based indicators for the period 2011–2018. Five experiments were conducted to predict business performance through the composite financial index. The results showed that observations from two years, of the first three experiments, indicate a better predictive behavior than the same experiments using observations from one year. Therefore, we concluded that observations from more than one year are necessary to predict the value of the financial performance index. Findings led us to the conclusion that recurrent neural networks model predicted better financial performance composite index when taken into consideration more real data for the financial performance index (2012–2018) instead of just for one year (2018).

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“…Furthermore, it proves that DBN outperforms the SVM. Lee et al (2017) tackled the bankruptcy prediction problem based on DBN which use RBM. The built model includes two phases: (1) unsupervised learning phase and (2) a finetuning phase.…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE

Smiti

Soui

2020

Inf Syst Front

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Imbalanced classification on bankruptcy prediction is considered as one of the most important topics in financial institutions. In this context, various statistical and artificial intelligence methods have been proposed. Recently, deep learning algorithms are experiencing a resurgence of interest, and are widely used to build a prediction and classification models. To this end, we propose a novel deep learning-based approach called BSM-SAES. This approach combines Borderline Synthetic Minority oversampling technique (BSM) and Stacked AutoEncoder (SAE) based on the Softmax classifier. The aim is to develop an accurate and reliable bankruptcy prediction model which includes the features extraction process. To assess the classification performance of our proposed model, k-nearest neighbor, decision tree, support vector machine, and artificial neural network, C5.0 that are machine learning methods, are applied. We evaluate our proposed approach on the Polish imbalanced datasets. The obtained results confirm the efficiency of our proposed model compared to other machine learning models regarding predicting and classifying the financial status of a firm.

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Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability

Cited by 35 publications

References 40 publications

Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

Composite Financial Performance Index Prediction – A Neural Networks Approach

Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE

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