Determinants of the Value Relevance of Accounting Information in Emerging Economies: Evidence from Iraq

AL-JAWAHERI, Bushra Abdul Wahhab; Najm, Karar Jasim; Machi, Ahmed Hussein

doi:10.1145/3503491.3503501

Cited by 1 publication

(2 citation statements)

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“…If we make the actual value to be predicted y, then the loss function at this time can be expressed by (6), and X represents the input data sequence. Considering that the surplus data is negative or positive and the characteristics of LSTM itself, we choose the sigmoid function as the σ activation function (7), the tanh function as the φ θ function (8), and the SoftMax function as the π θ function (9).…”

Section: Improved Seq2seqmentioning

confidence: 99%

“…From the beginning of being concerned to today's research, many scholars have been exploring the correlation between accounting earnings and earnings information based on the company's stock price. In these studies [1,[8][9][10][11], a large number of theoretical and empirical studies show that the relationship between accounting earnings and stock returns (usually expressed by earnings response coefficient) changes alternately. Many studies also found that the stock price will fluctuate in the window period of accounting earnings information announcement such as annual report.…”

Section: Introductionmentioning

confidence: 99%

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Design of Accounting Earnings Forecasting Model Based on Artificial Intelligence

Liu¹,

Shi²,

Pham

2022

Scientific Programming

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The increasing complexity of the international situation intensifies the changes of the economic environment. People’s demand for information represented by accounting earnings, such as judging the profitability and risk coefficient of the company, is becoming more and more urgent. This study puts forward the theory of predicting accounting earnings through accounting earnings factors in a nonlinear way and designs an accounting earnings forecasting model based on artificial intelligence. Integrating LSTM, seq2seq, and reinforcement learning and combining with self-attention like mechanism, a complex multifactor time series forecasting model is established, and reinforcement learning is used to stabilize the model to prevent overfitting, which puts forward a new solution to the multifactor time series forecasting problem of complex relationship. The experimental results and comparative analysis show the effectiveness of the enhanced recurrent neural network accounting earnings prediction model designed in this study.

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Section: Improved Seq2seqmentioning

confidence: 99%