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Artificial intelligence (AI)-based applications contribute to monitoring financial transactions and detect fraudulent activity in real-time by analyzing transaction patterns, consumer behavior, and other statistics, making them essential for quickly addressing potential threats in the fight against financial crime dynamics. Leveraging financial crime systems with intelligent supervised neuro-structures exploiting nonlinear autoregressive exogenous networks integrating damped least square (NARX-DLS) optimization methods to achieve an appropriate degree of accuracy and adaptability for the estimation of complex nonlinear financial crime differential systems (NFCDSs). The representative NFCDS for financial crime indicators is expressed as susceptible individuals, financial criminals, individuals under prosecution, imprisoned individuals, and honest individuals. The Adams numerical solver accomplishes the acquisition of synthetic data for the layer structure NARX-DLS algorithm execution to solve NFCDSs for various financial crime parameters, such as recruitment rate, influence rate, conversion rate to honest people, financial criminal prosecution rate per capita, discharge and acquittal rate from prosecutions, percentage of discharge rate from prosecution, transition rate to prison, and freedom rate. A sturdy overlap between the solutions of NARX-DLSs and the reference numerical results of NFCDSs implies that the error value is close to a desirable value of zero. The effectiveness of the NARX-DLSs is evidenced by including a variety of assessment metrics that carefully examine the model’s correctness and efficacy, including mean square error-based convergence arches, adaptive regulating parameters, error distribution, and input-error/cross-correlation analyses.
Artificial intelligence (AI)-based applications contribute to monitoring financial transactions and detect fraudulent activity in real-time by analyzing transaction patterns, consumer behavior, and other statistics, making them essential for quickly addressing potential threats in the fight against financial crime dynamics. Leveraging financial crime systems with intelligent supervised neuro-structures exploiting nonlinear autoregressive exogenous networks integrating damped least square (NARX-DLS) optimization methods to achieve an appropriate degree of accuracy and adaptability for the estimation of complex nonlinear financial crime differential systems (NFCDSs). The representative NFCDS for financial crime indicators is expressed as susceptible individuals, financial criminals, individuals under prosecution, imprisoned individuals, and honest individuals. The Adams numerical solver accomplishes the acquisition of synthetic data for the layer structure NARX-DLS algorithm execution to solve NFCDSs for various financial crime parameters, such as recruitment rate, influence rate, conversion rate to honest people, financial criminal prosecution rate per capita, discharge and acquittal rate from prosecutions, percentage of discharge rate from prosecution, transition rate to prison, and freedom rate. A sturdy overlap between the solutions of NARX-DLSs and the reference numerical results of NFCDSs implies that the error value is close to a desirable value of zero. The effectiveness of the NARX-DLSs is evidenced by including a variety of assessment metrics that carefully examine the model’s correctness and efficacy, including mean square error-based convergence arches, adaptive regulating parameters, error distribution, and input-error/cross-correlation analyses.
This study investigates the association between equity pledges and classification shifting earnings management in Chinese listed firms, spanning the period from 2016 to 2022. Additionally, it explores the moderating influence of product market competition (PMC) and analyst attention on this relationship. By analyzing a sample comprising 12,583 firm-year observations, several notable findings are observed. The regression results reveal a positive and statistically significant relationship between equity pledges and classification shifting earnings management (coefficient = 0.00234, p < 0.01). Moreover, this positive impact is further magnified when specifically considering downward classification shifting (coefficient = 0.00368, p < 0.01). Regarding the moderating factors, the results demonstrate a positive moderating effect of PMC on the relationship between equity pledges and classification shifting, with an interaction coefficient of 0.0165 (p < 0.01). This moderating effect is particularly pronounced in the context of downward classification shifting, with an interaction coefficient of 0.0142 (p < 0.01). Similarly, analyst attention also positively moderates the relationship, as indicated by an interaction coefficient of 0.00144 (p < 0.05), with a stronger effect observed in the case of downward classification shifting, with an interaction coefficient of 0.00329 (p < 0.01). Furthermore, additional tests reveal that leverage strengthens the aforementioned moderating effects. The three-way interaction involving debt, PMC, and equity pledges significantly influences classification shifting, with a coefficient of 0.0415 (p < 0.05). Specifically, debt exacerbates the moderating impact of competition on highly leveraged firms that engage in downward classification shifting, as evidenced by a coefficient of 0.0599 (p < 0.05). Similarly, debt reinforces the moderating role of analyst attention (coefficient = 0.00820, p < 0.05), especially for downward classification shifting (coefficient = 0.00902, p < 0.1). Propensity score matching and robustness tests validate the findings. Therefore, this research contributes to the understanding of the economic implications of equity pledge by focusing on earnings manipulation through classification shifting. It also examines this relationship within different competitive environments and external regulatory frameworks, aiming to promote the long-term viability of companies.
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