مطالب

The accurate imputation of missing values in time series data is paramount for maintaining the integrity and reliability of analyses and predictions. This article investigates the effica-cy of various missing values imputation methods, encom-passing well-known machine learning and statistical tech-niques. Moreover, for a better understanding, they imple-mented two financial data time series: S&P 500 and Bitcoin markets spanning from 2016 to 2023 on a daily frequency. Initially utilizing complete datasets, controlled missingness was introduced by randomly removing 45 data points. Then, these methods applied multiple imputation strategies for estimating and substituting these missing values. Experi-mental evaluation yielded insightful findings regarding the performance of the different methods. The examined ma-chine learning methods, including k-Nearest Neighbors (k-NN), Random Forest, Deep Learning, and Decision Trees, consistently outperformed their statistical counterparts, such as Mean Imputation, Regression Imputation, Hot-Deck Im-putation, and Expectation-Maximization Imputation. Nota-bly, Random Forest emerged as the most effective method, showcasing superior performance in terms of accuracy and robustness. Conversely, the Mean Imputation method exhibited com-paratively inferior outcomes, suggesting its limited suitabil-ity for financial time series data. This research contributes to the ongoing discourse on data integrity within finance ana-lytics and serves as a comprehensive guide for practitioners seeking optimal missing values imputation methods. The empirical evidence provided herein advances the under-standing of imputation techniques' relative performance and their application in financial data, facilitating enhanced de-cision-making processes and yielding more reliable predic-tions.

The flow of information in the capital market is strategically important because it determines the path of investors' decisions. In this decision-making process, the managers of the companies can disclose timely and reliable information based on their cognitive and perceptual characteristics of capital market situations. This article aims to contribute to the capital market knowledge literature by presenting the framework of managers' inertia drivers in response to reliable disclosure of information. This study adopted mixed, both inductive and deductive approaches to develop an integrated framework, validate its practicability, and verify its effectiveness in selected firms listed on the Tehran Stock Exchange, respectively. In developing the framework and implementation procedure, the study employed a systematic screening data collection (qualitative) approach to review the managers' inertia drivers. Then, in this study's second phase, the Interpretive Rating Process (IRP) and Fuzzy Reference System are used to develop the framework of managers' inertia drivers in response to reliable disclosure of information. The study's results in the qualitative part indicate the determination of 8 driving areas of managers' inertia in the reliable disclosure of information. On the other hand, the quantitative section results showed that managers' overconfidence and excitability are the most influential fields in stimulating managers' inertia in the timely disclosure of information. Based on the results, it was determined that the excitability of managers' overconfidence in creating inertia causes managers' subjective estimates to cause exclusivity in information disclosure.