Syahrbanu Aqilah Nainawa, Sudradjat Sudradjat

DETECTION OF FINANCIAL DISTRESS IN TECHNOLOGY COMPANIES LISTED ON THE INDONESIAN STOCK EXCHANGE

Introduction

Detection of financial distress in technology companies listed on the indonesian stock exchange. Detect financial distress in technology companies listed on the Indonesian Stock Exchange (IDX) from 2020-2022. This study analyzes the sensitivity of Ohlson and Grover models, highlighting Grover's higher accuracy in prediction.

Abstract

The purpose of this study is to determine and analyse the sensitivity of the results of financial distress analysis using the Ohlson and Grover models to predict financial distress in technology companies listed on the IDX for the 2020-2022 period. The study employs quantitative data extracted from the financial statements of technology companies listed on the Indonesia Stock Exchange (IDX) from 2020 to 2022. Utilizing the Ohlson and Grover models, the research aims to detect financial distress within these technology firms and determine the sensitivity level of the two models. This descriptive research design relies on secondary data sources, emphasizing the objectivity and popularity inherent in data-driven analyses. The research results of financial distress detection analysis, Ohlson model detects 10 distress zones and 8 safe zones, while Grover model detects 5 distress zones, 1 gray zone and 12 safe zones. From this analysis, the Grover model shows a higher level of sensitivity in recognizing financial distress with 12 samples consistent with the results of the Grover model analysis.

Review

This study provides a valuable initial assessment of the applicability and sensitivity of the Ohlson and Grover models for detecting financial distress among technology companies listed on the Indonesian Stock Exchange (IDX) between 2020 and 2022. Utilizing a quantitative, descriptive approach based on secondary financial statement data, the research aims to compare how these two established models classify the financial health of IDX tech firms. The key finding highlights a divergence in their classifications, with the Grover model ultimately identifying fewer distressed companies but being deemed more sensitive, consistent with 12 samples, compared to the Ohlson model. The research makes a pertinent contribution by focusing on the technology sector, a dynamic and often volatile industry, within the specific context of an emerging market like Indonesia. The comparative analysis of the Ohlson and Grover models is a strength, offering practical insights into the real-world utility and varying predictive powers of different financial distress assessment tools. Evaluating the sensitivity of these models is crucial for practitioners and researchers to select appropriate diagnostic instruments, especially when dealing with recent market data (2020-2022) that captures the post-pandemic economic landscape affecting technology firms. While the study offers useful preliminary findings, there are opportunities for further refinement and expansion. The descriptive nature of the research, while effective for identifying distress, does not delve into the underlying causes or drivers of financial distress within these technology companies, which could provide deeper practical implications. Future research could explore these causal factors, potentially through qualitative analysis or more advanced statistical methods. Additionally, the abstract refers to the Grover model showing "a higher level of sensitivity" with "12 samples consistent" without fully elaborating on the criteria for this consistency or the overall accuracy of the models against an independent measure of actual distress. A clearer articulation of how "sensitivity" was measured and validated, perhaps alongside a discussion of predictive accuracy or misclassification rates, would significantly strengthen the conclusions and provide a more comprehensive understanding of the challenges faced by technology firms.

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