Intelligent Systems in Accounting, Finance and Management最新文献

The objective of our study is to demonstrate the feasibility of predicting chief executive officer (CEO) compensation by exploring various machine learning methods. In our analysis, we examine six models: k-nearest neighbors, random forest, decision tree, extra trees, extreme gradient boosting, and support vector machines regressors. We find that XGBoost, random forest, and extra trees regressors exhibit the highest predictive power with the lowest error. Decision tree feature importance analyses identify firm size, CEO age, tangibility, cash holding, and Tobin's Q as key factors in predicting CEO compensation. We also conduct ordinary least squares regressions and find that the significance levels of the coefficients are comparable to the feature importances from the machine learning analysis. Our feature-grouping analysis shows that firms' financial performance and economic characteristics play the most significant role in determining CEO compensation, followed by board characteristics. The analysis further indicates that the predictive power of random forest and extra trees is stronger for forecasting next year's compensation than for predicting the current year's. These findings are valuable for compensation consultants and stakeholders involved in benchmarking decisions.

With the widespread adoption of Internet-based AI technologies, addressing financial fraud has become increasingly critical, particularly within the realm of machine learning. In this case, deep learning and natural language processing (NLP) techniques offer powerful means of detecting fraudulent activity by analyzing financial documents, thereby enhancing both the efficiency and precision of such assessments and supporting financial security. In this study, we introduce deep representation learning-based approaches relying mainly on large language models (LLMs) for identifying fraud in financial statements by examining temporal changes in the Management Discussion and Analysis (MD&A) sections of corporate disclosures. Departing from conventional techniques that rely only on word frequency analysis, we propose DeepFraud that combines time-evolving financial LLM embeddings, such as FinBERT, FinLlama, and FinGPT embeddings, of paragraphs and uses long short-term memory (LSTM) to predict frauds via historical textual embeddings. In addition to LLM embeddings, we also integrate (1) time-evolving word frequencies of words relevant to fraud detection, such as those expressing sentiment or uncertainty, and (2) time-evolving financial ratios. Trajectories of paragraph-level embeddings, frequencies, and ratios are used to construct a fraud detection model, which we evaluate against machine learning methods and deep time-series models. Using 30 years of financial report data (from 1995 to 2024), our experiments demonstrate that DeepFraud on average enhances fraud detection performance across a number of scenarios and on average outperforms the competing approaches as well as conventional word frequency approaches. Our framework introduces a novel direction for deep feature engineering in the field of financial statement fraud detection.

Machine learning dominates automated property valuation, yet comprehensive comparisons of predictive models remain scarce. This study compares 28 rent prediction models using 79,735 Belgian residential rental properties from 2022. Predictive performance is evaluated with traditional and alternative metrics for train data, test data, and across deciles. The results confirm that tree-based ensemble models outperform others, with stacking and averaging yielding superior results at a higher computational cost. Furthermore, middle-range rents show better predictive accuracy than extremes. Traditional and alternative metrics provide consistent findings. These insights aid real estate stakeholders seeking to enhance their expert systems for real estate price modeling.

This article examines the decision-making processes in open innovation labs (OI-labs) in government. Through a qualitative single case study, we explore how the use of causal and effectual reasoning, as dichotomous logics, evolves over time and is manifested in the form of organizational practices to tackle temporal, relational, and cultural complexity. The findings reveal three episodes: the conceptualizing of the lab (predominantly causation), the building of the lab (predominantly effectuation), and the sustaining of the lab (hybrid causation–effectuation). Moreover, shifts in the logic are aimed at addressing different types of complexity, and over time, a hybrid logic emerges.

There is an increasing interest in financial text mining tasks. Significant progress has been made by using deep learning-based models on a generic corpus, which also shows reasonable results on financial text mining tasks such as financial sentiment analysis. However, financial sentiment analysis is still demanding work because of the insufficiency of labeled data for the financial domain and its specialized language. General-purpose deep learning methods are not as effective mainly due to specialized language used in the financial context. In this study, we focus on enhancing the performance of financial text mining tasks by improving the existing pretrained language models via NLP transfer learning. Pretrained language models demand a small quantity of labeled samples, and they could be enhanced to a greater extent by training them on domain-specific corpora instead. We propose an enhanced model FinSentiment, which incorporates enhanced versions of a number of recently proposed pretrained models, such as BERT, XLNet, RoBERTa, GPT, Llama, and T5, to better perform across NLP tasks in financial domain by training these models on financial domain corpora. The corresponding finance-specific models in FinSentiment are called Fin-BERT, Fin-XLNet, Fin-RoBERTa, Fin-GPT, Fin-Llama, and Fin-T5, respectively. We also propose variants of these models jointly trained over financial domain and general corpora. Our finance-specific FinSentiment models, in general, show the best performance across three financial sentiment analysis datasets, even when only a subpart of these models is fine-tuned with a smaller training set. Our results exhibit enhancement for each tested performance criteria on the existing results for these datasets. Extensive experimental results demonstrate the effectiveness and robustness of especially RoBERTa pretrained on financial corpora. Overall, we show that NLP transfer learning techniques are favorable solutions to financial sentiment analysis tasks. Our source code has been deposited at https://github.com/seferlab/finsentiment.

This paper presents an innovative approach to comprehensively and systematically evaluate manual journal entries (MJEs) and enhance the control procedures in auditing. The proposed approach combines quantitative and qualitative information to develop various Key Risk Indicators (KRIs) that provide insights into potential risks associated with MJEs. The approach incorporates textual analytics into traditional quantitative measures. Using the data obtained from a multinational company, the application of the proposed testing approach demonstrates its effectiveness in identifying potential high-risk MJEs and improving the company's journal entry testing and monitoring procedures. The findings contribute to current audit practices by offering a more efficient and comprehensive method for evaluating MJEs.

Predicting volatility is very important for the financial markets as it helps to determine risk and decision-making. Predicting volatilities for such stock indices, which include the Nifty 50, is important for traders, investors, and policymakers. In this study, advanced hybrid models are used to predict the volatility of the Nifty 50 index over intervals of 1, 7, 14, and 21 days. The GJR-GARCH-LSTM and the GARCH-LSTM are two hybrid models that forecast the volatility of the Nifty 50. The effect of including the cash reserve ratio (CRR) in the analysis is also looked at. As the forecast horizon grows, the results show decreased prediction accuracy. The mean squared error (MSE) increased by 0.78% from the 1-day to the 7-day forecast, decreased by 2.63% between the 1-day and 7-day projections, rose by about 55% from the 7-day to the 14-day forecast, and grew by 56% between the 14-day and 21-day projections. The GJR-GARCH-LSTM model had better results compared to the simple GARCH-LSTM hybrid model. The novelty of this study is in building and validating hybrid models, specifically the GJR-GARCH-LSTM, to predict Nifty 50 index volatility and using the CRR as a macroeconomic explanatory variable. Different from current literature, which tends to use hybrid models in a generic sense, this paper adapts the model to the Indian financial environment and shows the additional predictive power of monetary policy determinants such as CRR.