PERSEPSI AKUNTAN PUBLIK DAN AKUNTAN PENDIDIK TERHADAP KONSEP DASAR, PENGUKURAN DAN PENGUNGKAPAN AKUNTANSI LINGKUNGAN

This study aims to examine empirically the differences in auditors and lecturers' perceptions of basic concepts, measurements and environmental accounting disclosures.This research was conducted at Public Accountants registered at the Indonesian Institute of Public Accountants (IAPI) in Padang City and Accounting Educators who hold accountants at State and Private Universities in Padang City. The sample selection was based on convenience sampling method and finally obtained 31 auditors and 31 lecturers who had met the criteria for sampling. The data analysis technique used to test the hypothesis is the Independent Sample t-Test using SPSS version 16 software. The results of this study indicate that there are differences in perceptions between public accountants and educating accountants on the basic concepts of accounting, But according to public accountants and educating accountants, there are no differences in the measurement of environmental accounting and environmental accounting disclosures.

Transfer Learning and Textual Analysis of Accounting Disclosures: Applying Big Data Methods to Small(er) Data Sets

We introduce and apply machine transfer learning methods to analyze accounting disclosures. We use the examples of the new BERT language model and sentiment analysis of quarterly earnings disclosures to demonstrate the key transfer learning concepts of: (i) pre-training on generic "Big Data", (ii) fine-tuning on small accounting datasets, and (iii) using a language model that captures context rather than stand-alone words. Overall, we show that this new approach is easy to implement, uses widely-available and low-cost computing resources, and has superior performance relative to existing textual analysis tools in accounting. We conclude with suggestions for opportunities to apply transfer learning to address important accounting research questions.

Dark Trading and Post-Earnings-Announcement Drift

Both theory and evidence are mixed regarding the impact on prices of trading on “dark” venues partially exempt from National Market System requirements. Theory predicts that price discovery improves as dark venues siphon noisy uninformed trades, but increased adverse selection reduces liquidity. Empirical studies, which focus on intraday inefficiency, also find contradictory results. We extend that literature to investigate the impact of dark trading on a long-standing inefficiency based on under-reaction to quarterly earnings. We study a randomized controlled trial created by the “trade-at” rule of the Securities and Exchange Commission’s Tick Size Pilot Program that exogenously shocks dark trading. We supplement that with ordinary least squares and two-stage least squares regressions on a more representative Compustat/Center for Research in Security Prices sample. All our results suggest that under-reaction increases with dark trading, consistent with reduced liquidity limiting arbitrage. We contribute to the literature on dark trading and inefficient processing of accounting disclosures, highlighting the role of advances in trading technology. This paper was accepted by Brian Bushee, accounting.

PENGARUH PENGUNGKAPAN AKUNTANSI SUMBER DAYA MANUSIA DALAM LAPORAN KEUANGAN TERHADAP KINERJA KEUANGAN PERUSAHAAN (Studi Kasus Pada PG. Modjopanggoong Tulungagung)

The influence of human resource accounting disclosures in the financial statements of company’s financial performance (case study at the Modjopanggoong Tulungagung Sugar Factory) aims to determine the effect of human resource accounting disclosures in the financial statements of the financial performance of the company. The data analysis technique quantitative and analysis tool used are (HRCA) Human Resource Cost Accounting, (ROA) Return On Asset, (ROE) Return On Equity, (ATO) Asset Turn Over. The results showed by implementing human resources accounting will affect the financial statements with the increase in total assets, total equityand total profit and decrease in losses. There is an increase in the percentage of RO and ROE, but there is a decrease for ATO. Pengaruh Pengungkapan Akuntansi Sumber Daya Manusia Dalam Laporan Keuangan Terhadap Kinerja Keuangan Perusahaan (Studi Kasus pada PG. Modjopanggoong Tulungagung) bertujuan untuk mengetahui pengaruh pengungkapan akuntansi sumber daya manusia dalam laporan keuangan terhadap kinerja keuangan perusahaan. Tehnik analisis data yang digunakan adalah deskriptif kuantitatif dan alat analisis yang digunakan adalah analisis HRCA (Human Resource Cost Accounting), ROA (Return On Asset),ROE (Return On Equity), dan ATO (Asset Turn Over). Hasil penelitian menunjukkan dengan melakukan penerapan akuntansi sumber daya manusia maka akan berpengaruh pada laporan keuangan dengan meningkatnya total aset, total ekuitas dan jumlah laba serta penurunan rugi. Terdapat kenaikan prosentase ROA dan ROE, tetapi terjadi penurunan untuk ATO.

Tax Specific versus Generic Accounting-Based Textual Analysis and the Relationship with Effective Tax Rates: Building “Context”

A growing literature, typically using “bags of words” dictionaries, examines the information content of text in financial accounting disclosures. We generate context for our text analysis to help predict effective tax rates using two approaches. First, we create tax-specific, expert-derived, dictionaries and sSecond, we generate the counts for those bags of words using text taken from tax-related discussions of the Form 10K, as opposed to its entirety. We find that using expertise provides more information than simply using general accounting and finance dictionaries. In addition, we find that generating general accounting text variable values from tax-related content in the Form 10K provides statistically significant improvement in model fit. Contrary to more generic accounting and finance word-based text analysis, we find that the signs on our positive and negative tax event dictionaries are different and are consistent with theoretical expectations through each of our modeled time periods.