Applying LSTM to Predict Firm Performance Based on Annual Reports: An Empirical Study from the Vietnam Stock Market

Le, Tri D. B.; Ngo, Man M.; Tran, Long Binh; Dương, Vũ

doi:10.1007/978-3-030-48853-6_41

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…To ensure a fair comparison, we adopted the same experimental protocol followed in LSTM evaluation. Furthermore, it is worth mentioning that we used model parameters similar to the previous works (Dastile et al, 2020;Le et al, 2021). The experimental results in Figure 5 were calculated based on denormalized input and predicted output data and the proposed method outperforms all other methods achieving improvements up to 26.62, 312.61, 0.011 and 0.78 in RMSE values for EBITDA, ROA, ROCE and ROE respectively.…”

Section: Resultsmentioning

confidence: 93%

“…However, few scientific papers focused on the application of deep learning methods such as LSTM model in predicting corporate profitability. Furthermore, Le et al (2021) investigated the performance of the LSTM model in predicting the sign (positive or negative) of Return on Assets in Vietnam. Furthermore, they provided evidence that LTSM model provided better prediction performance compared to logistic regression and RF.…”

Section: Intangible Assetsmentioning

confidence: 99%

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Can intangible assets predict future performance? A deep learning approach

Pechlivanidis

Ginoglou

Barmpoutis

2021

IJAIM

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Purpose The aim of this study is to evaluate of the predictive ability of goodwill and other intangible assets on forecasting corporate profitability. Subsequently, this study compares the efficiency of deep learning model to that of other machine learning models such as random forest (RF) and support vector machine (SVM) as well as traditional statistical methods such as the linear regression model. Design/methodology/approach Studies confirm that goodwill and intangibles are valuable assets that give companies a competitive advantage to increase profitability and shareholders’ returns. Thus, by using as sample Greek-listed financial data, this study investigates whether or not the inclusion of goodwill and intangible assets as input variables in this modified deep learning models contribute to the corporate profitability prediction accuracy. Subsequently, this study compares the modified long-short-term model with other machine learning models such as SVMs and RF as well as the traditional panel regression model. Findings The findings of this paper confirm that goodwill and intangible assets clearly improve the performance of a deep learning corporate profitability prediction model. Furthermore, this study provides evidence that the modified long short-term memory model outperforms other machine learning models such as SVMs and RF , as well as traditional statistical panel regression model, in predicting corporate profitability. Research limitations/implications Limitation of this study includes the relatively small amount of data available. Furthermore, the aim is to challenge the authors’ modified long short-term memory by using listed corporate data of Greece, a code-law country that suffered severely during the recent fiscal crisis. However, this study proposes that future research may apply deep learning corporate profitability models on a bigger pool of data such as STOXX Europe 600 companies. Practical implications Subsequently, the authors believe that their paper is of interest to different professional groups, such as financial analysts and banks, which the authors’ paper can support in their corporate profitability evaluation procedure. Furthermore, as well as shareholders are concerned, this paper could be of benefit in forecasting management’s potential to create future returns. Finally, management may incorporate this model in the evaluation process of potential acquisitions of other companies. Originality/value The contributions of this work can be summarized in the following aspects. This study provides evidence that by including goodwill and other intangible assets in the authors’ input portfolio, prediction errors represented by root mean squared error are reduced. A modified long short-term memory model is proposed to predict the numerical value of the profitability (or the profitability ratio) in contrast to other studies which deal with trend predictions, i.e. the binomial output result of positive or negative earnings. Finally, posing an extra challenge to the authors’ deep learning model, the authors’ used financial statements according to International Financial Reporting Standard data of listed companies in Greece, a code-law country that suffered during the recent fiscal debt crisis, heavily influenced by tax legislation and characterized by its lower investors’ protection compared to common-law countries.

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Section: Resultsmentioning

confidence: 93%

Section: Intangible Assetsmentioning

confidence: 99%

Can intangible assets predict future performance? A deep learning approach

Pechlivanidis

Ginoglou

Barmpoutis

2021

IJAIM

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Forecasting corporate financial performance with deep learning and interpretable ALE method: Evidence from China

Liang,

Liu,

et al. 2024

Journal of Forecasting

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Forecasting and analyzing corporate financial performance are of significant value to investors, managers, and regulators. In this paper, we constructed the one‐dimensional convolutional neural networks (1D‐CNN) and long short‐term memory (LSTM) deep learning models to investigate the feasibility of forecasting corporate financial performance with deep learning models, using the corporate financial features and environment, social and governance (ESG) rating index of Chinese A‐share listed corporation data from 2015 to 2021. Five evaluation metrics were employed to measure models' forecasting effects, and four competing machine learning models were built to verify the improvement in forecasting accuracy brought by the deep learning models. Furthermore, we also introduced the Accumulated Local Effects method to explore the forecasting processes of the deep learning models. The empirical results show the following: (1) Deep learning models can effectively extract the time‐series information in corporate data, thereby solving the task of predicting corporate financial performance with high accuracy. (2) The introduction of ESG information significantly contributes to the forecasting accuracy of corporate financial performance. For both 1D‐CNN and LSTM models, the ESG rating index can provide additional useful information for forecasting. (3) The interpretable results reveal the preference and emphasis of the two deep learning models for the different features. This further proves the robustness and reliability of deep learning models in forecasting corporate financial performance.

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