Date of submission: March 6, 2023; date of acceptance: August 23, 2023. * Contact information: karina.harjanto@umn.ac.id, UMN Campus, Scientia Boule- vard, Banten, Indonesia, phone: +62 831 1704 9115; ORCID ID: https://orcid.org/0000- 0001-5595-5486. Copernican Journal of Finance & Accounting e-ISSN 2300-3065 p-ISSN 2300-12402023, volume 12, issue 3 Harjanto, K. (2023). The Analysis of Financial Reporting Quality and Firm Value. Copernican Journal of Finance & Accounting, 12(3), 27–41. http://dx.doi.org/10.12775/CJFA.2023.014 Karina Harjanto* Universitas Multimedia Nusantara tHe analysis of financial reporting quality and firm value Keywords: accrual quality, earnings management, financial reporting quality, firm value. J E L Classification: M41. Abstract: The objective of this research is to obtain empirical evidence about the ef- fect of financial reporting quality on firm value. Financial reporting quality is meas- ured using two proxies, accrual quality through Ball-Shivakumar Model (AQ) and earn- ings management through Modified Jones Model (EM). Firm value is measured using share price, Price to Book Value, Tobin’s Q, and Market Value-Added. Control variables used are leverage (Debt to Equity Ratio), profitability (Return on Asset), and firm size (ln total assets). This study focuses on manufacturing businesses that were listed on the Indonesia Stock Exchange during the years 2018 and 2021. The sample was chosen through a purposive sampling technique, specifically targeting companies listed on the Indonesia Stock Exchange that operate in the manufacturing sector and provide audit- ed financial statements. Analyzed the secondary data utilizing descriptive statistics, normality test, classical assumption tests, and hypothesis testing. The findings of this investigation are (1) AQ has no effect on share price, PBV, Tobin’s Q and MVA (2) EM has positive and significant effect on all proxies of firm value, except MVA. The quality of fi- nancial reporting, the level of leverage, the size of the firm, and its profitability all have a simultaneous and considerable impact on the value of the firm. Karina Harjanto2828  Introduction Introduction The manufacturing industry is an industry that plays a major role in the Indo- nesian economy. This can be seen from the data on the large contribution of the manufacturing sector to Gross Domestic Product according to the Central Statistics Agency during the 2018-2021 period which always occupies the first position, with an average contribution of 19% to GDP. Manufacturing compa- nies listed on the Indonesia Stock Exchange are required to submit audited fi- nancial statements no later than the end of the third month after closing the books. With the financial statements, interested parties such as investors and creditors can use them as consideration for making decisions. In preparing fi- nancial statements, issuers are required to comply with the Statement of Fi- nancial Accounting Standards (PSAK) prepared by the Indonesian Institute of Accountants (IAI). Since 2012, Indonesia has agreed to converge with the Inter- national Financial Reporting Standard (IFRS), which means the rules in PSAK will be in accordance with IFRS standards. Adoption of IFRS is significantly associated with an increase in measured reporting quality (Isidro, Nanada & Wysocki, 2020). High-quality financial reporting is crucial in user’s decision- making process (Herath & Albarqi, 2017). In the last 10 years, standards for preparing financial statements in Indonesia have been continuously updated, with the aim of improving the quality of financial reports. IFRS convergence in Indonesia often leads to an increase in financial reporting quality, a decrease in information asymmetry, a sharing of risk, and a reduction in the cost of capi- tal (Wahyuni, Puspitasari & Puspitasari, 2020). However, implementing inter- national accounting standard isn’t without limitations and challenges, such as corporate governance and environmental concerns, need for training, and dif- ference with national accounting standard or other law and regulation (Uzma, 2016). Therefore, this research was carried out to obtain scientific evidence about the benefit of the quality of financial statements for investors, which will be illustrated by the investor’s assessment of the value of the company. This research will be among the first that specifically examine the relationship be- tween financial reporting quality and firm value. Financial reporting is believed to narrow information asymmetry and mar- ket uncertainty for external users and investors (Lin, Jiang, Tang & He, 2014). Financial reporting quality include not only financial information but also oth- er pertinent non-financial information that aids in decision-making. Financial thE analysis of finanCial rEPorting quality and firm valuE 2929 reporting quality, as defined by the International Accounting Standards Board (IASB), Financial Accounting Standards Board (FASB), the Australia Account- ing Standard Board (AASB), and the Accounting Standard Board in the Unit- ed Kingdom (ASB) [UK] refers to the provision of precise and unbiased infor- mation in financial statements regarding the underlying financial position and economic performance of an entity (Herath & Albarqi, 2017). (Ball & Shivaku- mar, 2005) defined financial reporting quality as “the usefulness of financial statements to investors, creditors, managers and all other parties contract- ing with the firm”. Accounting information can impact investment decisions by mitigating information asymmetry between managers and shareholders and modifying moral hazard costs resulting from agency conflicts among different stakeholders in the company (Roychowdhury, Shroff & Verdi, 2019). Previous research used multiple proxies in measuring financial reporting quality such as accruals quality, accounting conservatism, the likelihood of misstatements, the likelihood of material weaknesses in internal control, or audit fees. In this paper, two proxies of financial reporting quality are used, quality of accruals through the Ball-Shivakumar model and earnings management through Modi- fied Jones model. The Ball-Shivakumar model proposes that nonlinear accrual models, which include the prompt identification of losses, outperform linear ones. Thus, this model incorporated a dummy variable representing the cash flow of the cur- rent year and its interaction with the magnitude of cash flows (Martínez-Fer- rero, Garcia-Sanchez & Cuadrado-Ballesteros, 2015). Ball-Shivakumar mod- el measures timeliness in financial statement recognition of economic losses. “Loss recognition timeliness is a summary indicator of the speed with which adverse economic events are reflected in the both income statements and bal- ance sheets, and thus is an important attribute of earnings quality” (Ball & Shi- vakumar, 2005). Earnings management refers to deliberate actions undertaken by firm man- agers, driven by opportunistic motives and/or the need for information, to pro- vide financial outcomes that do not accurately reflect the actual performance. The discretionary element of accruals adjustment can serve as an indicator of managerial discretion, and hence of accounting fraud. Not all accruals are op- tional; therefore, it is important to distinguish between the optional and non- optional parts in order to assess the occurrence and degree of earnings ma- nipulation. The calculation of the discretionary accruals adjustment (DAA) involves deducting the non-discretionary accruals adjustment (NDAA) from Karina Harjanto3030 the total accruals adjustment (TAA). The DAA represents the atypical accru- als that make up the variable used to measure earnings management (Martín- ez-Ferrero et al., 2015). Discretionary accruals represent managerial interven- tions into financial reporting process (Islam, Ali & Ahmad, 2011). The signal theory approach highlights that organizations can enhance their corporate value by transmitting signals to investors through the disclosure of information pertaining to company performance, thereby offering a glimpse into future business potential (Antonius & Harjanto, 2022). Signal theory elu- cidates how corporations might utilize information to convey favorable or un- favorable signals to users (Regina & Harjanto, 2022). Financial statements are important tools for investors in making investing decisions. Investors use data in financial statements to analyze firm’s value. In this research, four proxies are used for firm value. The first proxy is share price. (Husna & Satria, 2019) stated that the firm value is defined as the price at which a company is deemed viable for potential investors. The primary goal of the company’s management is to maximize stockholder wealth by increasing the company’s stock price. To optimize the company’s stock price, one must enhance the enterprise val- ue or firm worth. The second proxy is Price to Book Value (PBV). (Radja & Ar- tini, 2020) stated that PBV shows how much value company can create rela- tive to the amount of invested capital. Manufacturing companies’ PBV during 2018-2021 keeps declining, from 2.94 in 2018, 2.84 in 2019, 2.36 in 2020, and 1.82 in 2021. This data shows that investors interests in buying manufactur- ing companies’ shares also deteriorating during this period. The third proxy is Tobin’s Q. (Cahan, Villiers, Jeter, Naiker & Van Staden, 2016) stated that Tobin’s Q quantified the anticipated long-term worth of a company rather than its pre- sent economic performance. Tobin’s Q incorporates the market’s evaluation of a company’s prospective cash flows and the level of risk associated with those cash flows. The fourth proxy is market value added (MVA). Young and O’Byrne (2001) mentioned that “MVA is the difference between the market value of the firm and the total capital invested in the firm. If MVA is positive, it shows that a company is a value creator, because market value exceeds invested capital.” Based on the theory above, the hypotheses tested in this research are: Ha1: Financial reporting quality (AQ) is positively associated with firm val- ue (share price). Ha2: Financial reporting quality (EM) is positively associated with firm val- ue (share price). thE analysis of finanCial rEPorting quality and firm valuE 3131 Ha3: Financial reporting quality (AQ) is positively associated with firm val- ue (PBV). Ha4: Financial reporting quality (EM) is positively associated with firm val- ue (PBV). Ha5: Financial reporting quality (AQ) is positively associated with firm val- ue (Tobin’s Q). Ha6: Financial reporting quality (EM) is positively associated with firm val- ue (Tobin’s Q). Ha7: Financial reporting quality (AQ) is positively associated with firm val- ue (MVA). Ha8: Financial reporting quality (EM) is positively associated with firm val- ue (MVA). Figure 1. Research Model Ha8: Financial reporting quality (EM) is positively associated with firm value (MVA). Figure 1. Research Model Source: own study. RESEARCH METHODOLOGY Research object The object utilized in this study comprises the manufacturing firms that are listed on the Indonesia Stock Exchange (IDX) within the period of 2018-2021. The research methodology employed in this work is a causal study. The study utilizes secondary data, specifically the financial statements of selected firms. The research sample will be chosen by a purposive sampling technique. Dependent Variable Firm value refers to the evaluation or appraisal made by investors regarding the company's proficiency in effectively utilizing its resources. In this study, firm value is measured using four proxies. The first proxy is share price. According to (Regina & Harjanto, 2022), share price is calculated as the average of daily closing price of a company in a year The second proxy is Price to Book Value (PBV). According to Hery (2016) PBV can be formulated as follows: 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑡𝑡𝑡𝑡 𝐵𝐵𝑡𝑡𝑡𝑡𝐵𝐵 ����𝑃𝑃 � ����� ����� ���� ����� ��� ����� (1) Share price: the average of daily closing price of a company in a year Book value per share: total shareholders’ equity divided by number of outstanding shares. The third proxy is Tobin’s Q. Following (Latif, Bhatti & Raheman, 2017), Tobin’s Q is measured as follows: ACCRUAL QUALITY (AQ) EARNINGS MANAGEMENT (EM) FIRM VALUE (SHARE PRICE) SIZE (SIZE) (PBV) (TOBIN'S Q) PROFITABILITY (ROA) (MVA) DEBT STRUCTURE (DER) S o u r c e : own study. Research methodologyResearch methodology Research objectResearch object The object utilized in this study comprises the manufacturing firms that are listed on the Indonesia Stock Exchange (IDX) within the period of 2018-2021. The research methodology employed in this work is a causal study. The study utilizes secondary data, specifically the financial statements of selected firms. The research sample will be chosen by a purposive sampling technique. Karina Harjanto3232 Dependent Variable Dependent Variable Firm value refers to the evaluation or appraisal made by investors regarding the company’s proficiency in effectively utilizing its resources. In this study, firm value is measured using four proxies. The first proxy is share price. Ac- cording to (Regina & Harjanto, 2022), share price is calculated as the average of daily closing price of a company in a year. The second proxy is Price to Book Value (PBV). According to Hery (2016) PBV can be formulated as follows: Ha8: Financial reporting quality (EM) is positively associated with firm value (MVA). Figure 1. Research Model Source: own study. RESEARCH METHODOLOGY Research object The object utilized in this study comprises the manufacturing firms that are listed on the Indonesia Stock Exchange (IDX) within the period of 2018-2021. The research methodology employed in this work is a causal study. The study utilizes secondary data, specifically the financial statements of selected firms. The research sample will be chosen by a purposive sampling technique. Dependent Variable Firm value refers to the evaluation or appraisal made by investors regarding the company's proficiency in effectively utilizing its resources. In this study, firm value is measured using four proxies. The first proxy is share price. According to (Regina & Harjanto, 2022), share price is calculated as the average of daily closing price of a company in a year The second proxy is Price to Book Value (PBV). According to Hery (2016) PBV can be formulated as follows: 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑡𝑡𝑡𝑡 𝐵𝐵𝑡𝑡𝑡𝑡𝐵𝐵 ����𝑃𝑃 � ����� ����� ���� ����� ��� ����� (1) Share price: the average of daily closing price of a company in a year Book value per share: total shareholders’ equity divided by number of outstanding shares. The third proxy is Tobin’s Q. Following (Latif, Bhatti & Raheman, 2017), Tobin’s Q is measured as follows: ACCRUAL QUALITY (AQ) EARNINGS MANAGEMENT (EM) FIRM VALUE (SHARE PRICE) SIZE (SIZE) (PBV) (TOBIN'S Q) PROFITABILITY (ROA) (MVA) DEBT STRUCTURE (DER) (1) Share price: the average of daily closing price of a company in a year Book value per share: total shareholders’ equity divided by number of out- standing shares. The third proxy is Tobin’s Q. Following (Latif, Bhatti & Raheman, 2017), Tobin’s Q is measured as follows: 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇�𝑠𝑠 � � 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑀𝑀�𝑣𝑣𝑇𝑇𝑀𝑀� � 𝑇𝑇𝑇𝑇𝑇𝑇𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑣𝑣𝑇𝑇𝑀𝑀𝑇𝑇𝑇𝑇𝑣𝑣𝑇𝑇𝑀𝑀𝑇𝑇𝑀𝑀𝑠𝑠 𝑇𝑇𝑇𝑇𝑇𝑇𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑀𝑀𝑠𝑠𝑠𝑠𝑀𝑀𝑀𝑀𝑠𝑠 �2� Market value of equity: the average of daily closing price times number of outstanding shares Book value of liabilities: total liabilities Book value of assets: total assets The fourth proxy is market value added. Following (Young & O’Byrne, 2001), MVA is calculated as follows: 𝑀𝑀𝑅𝑅𝐷𝐷 � 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑐𝑐𝑀𝑀𝑐𝑐𝑇𝑇𝑀𝑀𝑀𝑀𝑣𝑣 � 𝑇𝑇𝑇𝑇𝑣𝑣𝑀𝑀𝑠𝑠𝑀𝑀𝑀𝑀� 𝑐𝑐𝑀𝑀𝑐𝑐𝑇𝑇𝑀𝑀𝑀𝑀𝑣𝑣 �3� Market value of capital : the average of daily closing price times number of outstanding shares Invested capital: total equity Independent Variable Financial Reporting Quality is measured using two proxies. The first proxy is quality of accruals through the Ball-Shivakumar Model (AQ) (Martínez-Ferrero et al., 2015): ∆𝑊𝑊𝑊𝑊�� � �� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,��� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,��� � ��∆𝑅𝑅𝑅𝑅𝑅𝑅�,� � ��𝑃𝑃𝑃𝑃𝑅𝑅�,� � ��𝐷𝐷𝑂𝑂𝑊𝑊𝑂𝑂�,� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,� ∗ 𝐷𝐷𝑂𝑂𝑊𝑊𝑂𝑂�,� � � �4� WCit:  Account Receivable +  Inventory -  Account Payable -  Taxes Payable +  Other Assets CFO: Operating cash flows Revenue: Changes in revenue PPE: Property, Plant, and Equipment DOCF: indicator variable for negative cash flows i indicates the company and t refers to the time period. All variable in equation is scaled with total assets, except for DOCF. The absolute value of residuals from this model is used as proxy for AQ. The lower degree of this proxy, the higher degree of AQ, which means higher FRQ. The second proxy is earnings management through accruals (EM). Low values of EM represents low level of earnings management activities that is associated with higher FRQ. Modified Jones model is used in calculating EM (Dechow, Sloan & Sweeney, 1995): 𝐷𝐷𝐷𝐷�� � 𝑇𝑇𝐷𝐷𝑊𝑊�,� 𝑇𝑇𝐷𝐷�,��� � �𝐷𝐷𝐷𝐷�,� �5� (2) Market value of equity: the average of daily closing price times number of outstanding shares Book value of liabilities: total liabilities Book value of assets: total assets The fourth proxy is market value added. Following (Young & O’Byrne, 2001), MVA is calculated as follows: 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇�𝑠𝑠 � � 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑀𝑀�𝑣𝑣𝑇𝑇𝑀𝑀� � 𝑇𝑇𝑇𝑇𝑇𝑇𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑣𝑣𝑇𝑇𝑀𝑀𝑇𝑇𝑇𝑇𝑣𝑣𝑇𝑇𝑀𝑀𝑇𝑇𝑀𝑀𝑠𝑠 𝑇𝑇𝑇𝑇𝑇𝑇𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑀𝑀𝑠𝑠𝑠𝑠𝑀𝑀𝑀𝑀𝑠𝑠 �2� Market value of equity: the average of daily closing price times number of outstanding shares Book value of liabilities: total liabilities Book value of assets: total assets The fourth proxy is market value added. Following (Young & O’Byrne, 2001), MVA is calculated as follows: 𝑀𝑀𝑅𝑅𝐷𝐷 � 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑐𝑐𝑀𝑀𝑐𝑐𝑇𝑇𝑀𝑀𝑀𝑀𝑣𝑣 � 𝑇𝑇𝑇𝑇𝑣𝑣𝑀𝑀𝑠𝑠𝑀𝑀𝑀𝑀� 𝑐𝑐𝑀𝑀𝑐𝑐𝑇𝑇𝑀𝑀𝑀𝑀𝑣𝑣 �3� Market value of capital : the average of daily closing price times number of outstanding shares Invested capital: total equity Independent Variable Financial Reporting Quality is measured using two proxies. The first proxy is quality of accruals through the Ball-Shivakumar Model (AQ) (Martínez-Ferrero et al., 2015): ∆𝑊𝑊𝑊𝑊�� � �� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,��� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,��� � ��∆𝑅𝑅𝑅𝑅𝑅𝑅�,� � ��𝑃𝑃𝑃𝑃𝑅𝑅�,� � ��𝐷𝐷𝑂𝑂𝑊𝑊𝑂𝑂�,� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,� ∗ 𝐷𝐷𝑂𝑂𝑊𝑊𝑂𝑂�,� � � �4� WCit:  Account Receivable +  Inventory -  Account Payable -  Taxes Payable +  Other Assets CFO: Operating cash flows Revenue: Changes in revenue PPE: Property, Plant, and Equipment DOCF: indicator variable for negative cash flows i indicates the company and t refers to the time period. All variable in equation is scaled with total assets, except for DOCF. The absolute value of residuals from this model is used as proxy for AQ. The lower degree of this proxy, the higher degree of AQ, which means higher FRQ. The second proxy is earnings management through accruals (EM). Low values of EM represents low level of earnings management activities that is associated with higher FRQ. Modified Jones model is used in calculating EM (Dechow, Sloan & Sweeney, 1995): 𝐷𝐷𝐷𝐷�� � 𝑇𝑇𝐷𝐷𝑊𝑊�,� 𝑇𝑇𝐷𝐷�,��� � �𝐷𝐷𝐷𝐷�,� �5� (3) Market value of capital: the average of daily closing price times number of outstanding shares Invested capital: total equity thE analysis of finanCial rEPorting quality and firm valuE 3333 Independent VariableIndependent Variable Financial Reporting Quality is measured using two proxies. The first proxy is quality of accruals through the Ball-Shivakumar Model (AQ) (Martínez-Ferrero et al., 2015): 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇�𝑠𝑠 � � 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑀𝑀�𝑣𝑣𝑇𝑇𝑀𝑀� � 𝑇𝑇𝑇𝑇𝑇𝑇𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑣𝑣𝑇𝑇𝑀𝑀𝑇𝑇𝑇𝑇𝑣𝑣𝑇𝑇𝑀𝑀𝑇𝑇𝑀𝑀𝑠𝑠 𝑇𝑇𝑇𝑇𝑇𝑇𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑀𝑀𝑠𝑠𝑠𝑠𝑀𝑀𝑀𝑀𝑠𝑠 �2� Market value of equity: the average of daily closing price times number of outstanding shares Book value of liabilities: total liabilities Book value of assets: total assets The fourth proxy is market value added. Following (Young & O’Byrne, 2001), MVA is calculated as follows: 𝑀𝑀𝑅𝑅𝐷𝐷 � 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑐𝑐𝑀𝑀𝑐𝑐𝑇𝑇𝑀𝑀𝑀𝑀𝑣𝑣 � 𝑇𝑇𝑇𝑇𝑣𝑣𝑀𝑀𝑠𝑠𝑀𝑀𝑀𝑀� 𝑐𝑐𝑀𝑀𝑐𝑐𝑇𝑇𝑀𝑀𝑀𝑀𝑣𝑣 �3� Market value of capital : the average of daily closing price times number of outstanding shares Invested capital: total equity Independent Variable Financial Reporting Quality is measured using two proxies. The first proxy is quality of accruals through the Ball-Shivakumar Model (AQ) (Martínez-Ferrero et al., 2015): ∆𝑊𝑊𝑊𝑊�� � �� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,��� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,��� � ��∆𝑅𝑅𝑅𝑅𝑅𝑅�,� � ��𝑃𝑃𝑃𝑃𝑅𝑅�,� � ��𝐷𝐷𝑂𝑂𝑊𝑊𝑂𝑂�,� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,� ∗ 𝐷𝐷𝑂𝑂𝑊𝑊𝑂𝑂�,� � � �4� WCit:  Account Receivable +  Inventory -  Account Payable -  Taxes Payable +  Other Assets CFO: Operating cash flows Revenue: Changes in revenue PPE: Property, Plant, and Equipment DOCF: indicator variable for negative cash flows i indicates the company and t refers to the time period. All variable in equation is scaled with total assets, except for DOCF. The absolute value of residuals from this model is used as proxy for AQ. The lower degree of this proxy, the higher degree of AQ, which means higher FRQ. The second proxy is earnings management through accruals (EM). Low values of EM represents low level of earnings management activities that is associated with higher FRQ. Modified Jones model is used in calculating EM (Dechow, Sloan & Sweeney, 1995): 𝐷𝐷𝐷𝐷�� � 𝑇𝑇𝐷𝐷𝑊𝑊�,� 𝑇𝑇𝐷𝐷�,��� � �𝐷𝐷𝐷𝐷�,� �5� 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇�𝑠𝑠 � � 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑀𝑀�𝑣𝑣𝑇𝑇𝑀𝑀� � 𝑇𝑇𝑇𝑇𝑇𝑇𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑣𝑣𝑇𝑇𝑀𝑀𝑇𝑇𝑇𝑇𝑣𝑣𝑇𝑇𝑀𝑀𝑇𝑇𝑀𝑀𝑠𝑠 𝑇𝑇𝑇𝑇𝑇𝑇𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑀𝑀𝑠𝑠𝑠𝑠𝑀𝑀𝑀𝑀𝑠𝑠 �2� Market value of equity: the average of daily closing price times number of outstanding shares Book value of liabilities: total liabilities Book value of assets: total assets The fourth proxy is market value added. Following (Young & O’Byrne, 2001), MVA is calculated as follows: 𝑀𝑀𝑅𝑅𝐷𝐷 � 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑐𝑐𝑀𝑀𝑐𝑐𝑇𝑇𝑀𝑀𝑀𝑀𝑣𝑣 � 𝑇𝑇𝑇𝑇𝑣𝑣𝑀𝑀𝑠𝑠𝑀𝑀𝑀𝑀� 𝑐𝑐𝑀𝑀𝑐𝑐𝑇𝑇𝑀𝑀𝑀𝑀𝑣𝑣 �3� Market value of capital : the average of daily closing price times number of outstanding shares Invested capital: total equity Independent Variable Financial Reporting Quality is measured using two proxies. The first proxy is quality of accruals through the Ball-Shivakumar Model (AQ) (Martínez-Ferrero et al., 2015): ∆𝑊𝑊𝑊𝑊�� � �� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,��� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,��� � ��∆𝑅𝑅𝑅𝑅𝑅𝑅�,� � ��𝑃𝑃𝑃𝑃𝑅𝑅�,� � ��𝐷𝐷𝑂𝑂𝑊𝑊𝑂𝑂�,� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,� ∗ 𝐷𝐷𝑂𝑂𝑊𝑊𝑂𝑂�,� � � �4� WCit:  Account Receivable +  Inventory -  Account Payable -  Taxes Payable +  Other Assets CFO: Operating cash flows Revenue: Changes in revenue PPE: Property, Plant, and Equipment DOCF: indicator variable for negative cash flows i indicates the company and t refers to the time period. All variable in equation is scaled with total assets, except for DOCF. The absolute value of residuals from this model is used as proxy for AQ. The lower degree of this proxy, the higher degree of AQ, which means higher FRQ. The second proxy is earnings management through accruals (EM). Low values of EM represents low level of earnings management activities that is associated with higher FRQ. Modified Jones model is used in calculating EM (Dechow, Sloan & Sweeney, 1995): 𝐷𝐷𝐷𝐷�� � 𝑇𝑇𝐷𝐷𝑊𝑊�,� 𝑇𝑇𝐷𝐷�,��� � �𝐷𝐷𝐷𝐷�,� �5� 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇�𝑠𝑠 � � 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑀𝑀�𝑣𝑣𝑇𝑇𝑀𝑀� � 𝑇𝑇𝑇𝑇𝑇𝑇𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑣𝑣𝑇𝑇𝑀𝑀𝑇𝑇𝑇𝑇𝑣𝑣𝑇𝑇𝑀𝑀𝑇𝑇𝑀𝑀𝑠𝑠 𝑇𝑇𝑇𝑇𝑇𝑇𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑀𝑀𝑠𝑠𝑠𝑠𝑀𝑀𝑀𝑀𝑠𝑠 �2� Market value of equity: the average of daily closing price times number of outstanding shares Book value of liabilities: total liabilities Book value of assets: total assets The fourth proxy is market value added. Following (Young & O’Byrne, 2001), MVA is calculated as follows: 𝑀𝑀𝑅𝑅𝐷𝐷 � 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑐𝑐𝑀𝑀𝑐𝑐𝑇𝑇𝑀𝑀𝑀𝑀𝑣𝑣 � 𝑇𝑇𝑇𝑇𝑣𝑣𝑀𝑀𝑠𝑠𝑀𝑀𝑀𝑀� 𝑐𝑐𝑀𝑀𝑐𝑐𝑇𝑇𝑀𝑀𝑀𝑀𝑣𝑣 �3� Market value of capital : the average of daily closing price times number of outstanding shares Invested capital: total equity Independent Variable Financial Reporting Quality is measured using two proxies. The first proxy is quality of accruals through the Ball-Shivakumar Model (AQ) (Martínez-Ferrero et al., 2015): ∆𝑊𝑊𝑊𝑊�� � �� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,��� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,��� � ��∆𝑅𝑅𝑅𝑅𝑅𝑅�,� � ��𝑃𝑃𝑃𝑃𝑅𝑅�,� � ��𝐷𝐷𝑂𝑂𝑊𝑊𝑂𝑂�,� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,� ∗ 𝐷𝐷𝑂𝑂𝑊𝑊𝑂𝑂�,� � � �4� WCit:  Account Receivable +  Inventory -  Account Payable -  Taxes Payable +  Other Assets CFO: Operating cash flows Revenue: Changes in revenue PPE: Property, Plant, and Equipment DOCF: indicator variable for negative cash flows i indicates the company and t refers to the time period. All variable in equation is scaled with total assets, except for DOCF. The absolute value of residuals from this model is used as proxy for AQ. The lower degree of this proxy, the higher degree of AQ, which means higher FRQ. The second proxy is earnings management through accruals (EM). Low values of EM represents low level of earnings management activities that is associated with higher FRQ. Modified Jones model is used in calculating EM (Dechow, Sloan & Sweeney, 1995): 𝐷𝐷𝐷𝐷�� � 𝑇𝑇𝐷𝐷𝑊𝑊�,� 𝑇𝑇𝐷𝐷�,��� � �𝐷𝐷𝐷𝐷�,� �5� (4) ∆WCit: ∆ Account Receivable + ∆ Inventory - ∆ Account Payable - ∆ Taxes Pay- able + ∆ Other Assets CFO: Operating cash flows ∆Revenue: Changes in revenue PPE: Property, Plant, and Equipment DOCF: indicator variable for negative cash flows i indicates the company and t refers to the time period. All variable in equation is scaled with total assets, except for DOCF. The abso- lute value of residuals from this model is used as proxy for AQ. The lower de- gree of this proxy, the higher degree of AQ, which means higher FRQ. The second proxy is earnings management through accruals (EM). Low val- ues of EM represents low level of earnings management activities that is associ- ated with higher FRQ. Modified Jones model is used in calculating EM (Dechow, Sloan & Sweeney, 1995): 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇�𝑠𝑠 � � 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑀𝑀�𝑣𝑣𝑇𝑇𝑀𝑀� � 𝑇𝑇𝑇𝑇𝑇𝑇𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑣𝑣𝑇𝑇𝑀𝑀𝑇𝑇𝑇𝑇𝑣𝑣𝑇𝑇𝑀𝑀𝑇𝑇𝑀𝑀𝑠𝑠 𝑇𝑇𝑇𝑇𝑇𝑇𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑀𝑀𝑠𝑠𝑠𝑠𝑀𝑀𝑀𝑀𝑠𝑠 �2� Market value of equity: the average of daily closing price times number of outstanding shares Book value of liabilities: total liabilities Book value of assets: total assets The fourth proxy is market value added. Following (Young & O’Byrne, 2001), MVA is calculated as follows: 𝑀𝑀𝑅𝑅𝐷𝐷 � 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑣𝑣𝑀𝑀𝑣𝑣𝑣𝑣𝑀𝑀 𝑇𝑇𝑜𝑜 𝑐𝑐𝑀𝑀𝑐𝑐𝑇𝑇𝑀𝑀𝑀𝑀𝑣𝑣 � 𝑇𝑇𝑇𝑇𝑣𝑣𝑀𝑀𝑠𝑠𝑀𝑀𝑀𝑀� 𝑐𝑐𝑀𝑀𝑐𝑐𝑇𝑇𝑀𝑀𝑀𝑀𝑣𝑣 �3� Market value of capital : the average of daily closing price times number of outstanding shares Invested capital: total equity Independent Variable Financial Reporting Quality is measured using two proxies. The first proxy is quality of accruals through the Ball-Shivakumar Model (AQ) (Martínez-Ferrero et al., 2015): ∆𝑊𝑊𝑊𝑊�� � �� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,��� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,��� � ��∆𝑅𝑅𝑅𝑅𝑅𝑅�,� � ��𝑃𝑃𝑃𝑃𝑅𝑅�,� � ��𝐷𝐷𝑂𝑂𝑊𝑊𝑂𝑂�,� � ��𝑂𝑂𝑊𝑊𝑂𝑂�,� ∗ 𝐷𝐷𝑂𝑂𝑊𝑊𝑂𝑂�,� � � �4� WCit:  Account Receivable +  Inventory -  Account Payable -  Taxes Payable +  Other Assets CFO: Operating cash flows Revenue: Changes in revenue PPE: Property, Plant, and Equipment DOCF: indicator variable for negative cash flows i indicates the company and t refers to the time period. All variable in equation is scaled with total assets, except for DOCF. The absolute value of residuals from this model is used as proxy for AQ. The lower degree of this proxy, the higher degree of AQ, which means higher FRQ. The second proxy is earnings management through accruals (EM). Low values of EM represents low level of earnings management activities that is associated with higher FRQ. Modified Jones model is used in calculating EM (Dechow, Sloan & Sweeney, 1995): 𝐷𝐷𝐷𝐷�� � 𝑇𝑇𝐷𝐷𝑊𝑊�,� 𝑇𝑇𝐷𝐷�,��� � �𝐷𝐷𝐷𝐷�,� �5� (5) DAit: Discretionary accrual company i at year t TACit: Total accrual company i at year t TAit-1: Total asset company i at year t-1 NDAit: Non discretionary accrual company i at year t Karina Harjanto3434 Control VariablesControl Variables There are three control variables used, namely firm size (natural logarithm of total assets), Profitability (Return on Assets), and leverage (Debt to Equity). Data analysisData analysis This research employs multiple regression analysis to examine the data. The formulae for multiple regression are as follows: DAit: Discretionary accrual company i at year t TACit: Total accrual company i at year t TAit-1: Total asset company i at year t-1 NDAit: Non discretionary accrual company i at year t Control Variables There are three control variables used, namely firm size (natural logarithm of total assets), Profitability (Return on Assets), and leverage (Debt to Equity). Data analysis This research employs multiple regression analysis to examine the data. The formulae for multiple regression are as follows: PRICE AQ EM FS ROA DER e 6 PBV AQ EM FS ROA DER e 7 TBQ AQ EM FS ROA DER e 8 MVA AQ EM FS ROA DER e 9 RESULTS AND DISCUSSION The data utilized in this study include secondary data obtained from manufacturing companies that were listed on the IDX (Indonesia Stock Exchange) during the years 2018 and 2021. The sample is chosen using the purposive sampling technique in the following manner: Table 1. Sample Selection (6) DAit: Discretionary accrual company i at year t TACit: Total accrual company i at year t TAit-1: Total asset company i at year t-1 NDAit: Non discretionary accrual company i at year t Control Variables There are three control variables used, namely firm size (natural logarithm of total assets), Profitability (Return on Assets), and leverage (Debt to Equity). Data analysis This research employs multiple regression analysis to examine the data. The formulae for multiple regression are as follows: PRICE AQ EM FS ROA DER e 6 PBV AQ EM FS ROA DER e 7 TBQ AQ EM FS ROA DER e 8 MVA AQ EM FS ROA DER e 9 RESULTS AND DISCUSSION The data utilized in this study include secondary data obtained from manufacturing companies that were listed on the IDX (Indonesia Stock Exchange) during the years 2018 and 2021. The sample is chosen using the purposive sampling technique in the following manner: Table 1. Sample Selection (7) DAit: Discretionary accrual company i at year t TACit: Total accrual company i at year t TAit-1: Total asset company i at year t-1 NDAit: Non discretionary accrual company i at year t Control Variables There are three control variables used, namely firm size (natural logarithm of total assets), Profitability (Return on Assets), and leverage (Debt to Equity). Data analysis This research employs multiple regression analysis to examine the data. The formulae for multiple regression are as follows: PRICE AQ EM FS ROA DER e 6 PBV AQ EM FS ROA DER e 7 TBQ AQ EM FS ROA DER e 8 MVA AQ EM FS ROA DER e 9 RESULTS AND DISCUSSION The data utilized in this study include secondary data obtained from manufacturing companies that were listed on the IDX (Indonesia Stock Exchange) during the years 2018 and 2021. The sample is chosen using the purposive sampling technique in the following manner: Table 1. Sample Selection (8) DAit: Discretionary accrual company i at year t TACit: Total accrual company i at year t TAit-1: Total asset company i at year t-1 NDAit: Non discretionary accrual company i at year t Control Variables There are three control variables used, namely firm size (natural logarithm of total assets), Profitability (Return on Assets), and leverage (Debt to Equity). Data analysis This research employs multiple regression analysis to examine the data. The formulae for multiple regression are as follows: PRICE AQ EM FS ROA DER e 6 PBV AQ EM FS ROA DER e 7 TBQ AQ EM FS ROA DER e 8 MVA AQ EM FS ROA DER e 9 RESULTS AND DISCUSSION The data utilized in this study include secondary data obtained from manufacturing companies that were listed on the IDX (Indonesia Stock Exchange) during the years 2018 and 2021. The sample is chosen using the purposive sampling technique in the following manner: Table 1. Sample Selection (9) Results and discussionResults and discussion The data utilized in this study include secondary data obtained from manufac- turing companies that were listed on the IDX (Indonesia Stock Exchange) dur- ing the years 2018 and 2021. The sample is chosen using the purposive sam- pling technique in the following manner: thE analysis of finanCial rEPorting quality and firm valuE 3535 Table 1. Sample Selection Criterias Companies Manufacturing companies listed in IDX during 2018-2021 153 Less: Companies that did not publish audited financial statements, closed their books at the end of the year, used rupiah currency for the period January 1, 2018 to Decem- ber 31, 2021 34 Less: Companies that did stock splits, reverse stock splits, rights issues, or buybacks during the period January 1, 2018 to December 31, 2021 12 Less: Companies that are suspended during the period January 1, 2018 to December 31, 2021 14 Less: Companies that did not have positive net income during the period January 1, 2018 to December 31, 2021 38 Companies selected as sample 55 S o u r c e : data analyzed. The final data used in this research is 220 firm-years observations. Statistic de- scriptive shows that average manufacturing companies’ share price is Rp3,259. PBV has a mean of 1.99 which means investors are willing to pay 1.99 times of company’s book value for sample companies in this study. Average Tobin’s Q is 1.56 which shows that companies are able to manage its assets to increase funding through investors and creditors, while average MVA is Rp11,409 bil- lion. Positive MVA indicates that companies are able to create value for its shareholders. Average EM is 0.29 which shows that the companies did earn- ings management by earnings maximization. Average AQ is 0.04. The low score of AQ shows that financial reporting quality is actually high. Average DER is 0.71, which means companies used more internal funding that external debt. Average size is 28.91, which means all sample companies are categorized as big companies. Average ROA is 8.44%, which means companies is capable to gener- ate 8.44% net profit by using its assets. The data utilized in this study has suc- cessfully undergone testing to confirm its adherence to normality and classic assumptions. Karina Harjanto3636 Table 2. Coefficient of Determination Test Equations R R square Adjusted R square Std. Error of the Estimate F Sig. F 1 0.715 0.511 0.498 0.42937 40.695 0.000 2 0.694 0.481 0.451 0.35329 16.143 0.000 3 0.644 0.415 0.381 0.21742 12.072 0.000 4 0.879 0.773 0.745 0.33615 27.915 0.000 S o u r c e : data analyzed. The coefficient of determination (R2) test aims to measure how far the mod- el’s ability to explain dependent variation (Ghozali, 2012). Based on the equa- tions in Table 2, equation 4 has the highest adjusted R square of 74.5%, which means EM, AQ, DER, FS, and ROA can explain 74.5% of MVA. It also shows that the equation 4 is the most able to explain variances in firm value as measured by MVA. All equations also have F significance value of 0.0000 (less than 0.05) which indicates that the independent variables, namely EM, AQ, DER, FS, and ROA simultaneously have a significant influence on firm value measured by share price, PBV, TBQ, and MVA. Table 3. T-test Results Variable Equation 1 (PRICE) Equation 2 (PBV) Equation 3 (TBQ) Equation 4 (MVA) AQ 0.064 -0.028 0.025 0.021 EM -0.154** -0.304** -0.243** -0.118 DER -0.093 -0.261* -0.139 -0.053 SIZE 0.628** 0.325** 0.338** 0.775** ROA 0.246** 0.352** 0.372** 0.216* ** Significant at 1% level * Significant at 5% level S o u r c e : data analyzed. thE analysis of finanCial rEPorting quality and firm valuE 3737 AQ has positive but insignificant effect on PRICE, TBQ, and MVA, and negative but insignificant effect on PBV. It shows that Ha1, Ha3, Ha5, and Ha7 are reject- ed, so that FRQ measured with accrual quality through Ball-Shivakumar Mod- el does not have any impact on the firm’s value, which is represented by share price, PBV, Tobin’s Q, and MVA. From 220 data in this study, 164 has AQ lower than average, which suggest high FRQ. However, out of 164, 107 reported poor financial performance, identified by low ROA with average of 3.86%. 62 obser- vation from 107 reported a decrease of 13% in revenue and 8% in net income. 55 out of 107 observations reporting decrease in operating cash flows with av- erage 130%. High FRQ is observed in AQ score, which means companies are re- porting reduction in revenue and net income timely. Even though, the quality of financial reporting is high, investors immediately reacted to the negative sig- nal of lower revenue, net income, and operating cash flows. This poor financial performance caused investors to sell their investment, indicated by decreasing in share price of 4%. In turn, PBV also decline by 2.5% with average PBV only 1.11, and Tobin’s Q average only 1.06, which shows that companies barely able to use assets to raise funding from creditors and investors. This result is in line with (Fambudi & Fitriani, 2020), but differ from the result of (Latif et al., 2017) that stated accrual quality improves firm value on Pakistan companies. The re- sults in Table 3 shows that AQ consistently has no effect on multiple proxies of firm value. This result is in accordance with (Ball et al., 2003) who suggested that financial reporting in Hong Kong, Malaysia, Singapore, and Thailand does not include the identification of economic income, especially economic losses, in a timely manner. Based on table 4, EM shows negative significant effect on share price, PBV, and Tobin’s Q. Low EM values associated with high financial reporting quality (FRQ), therefore FRQ has positive and significant effect on firm value, so Ha2, Ha4, and Ha6 are accepted. This result is in line with (Leung, 2016), whose re- search on Canadian firms found that accounting quality and firm value has in- creased after IFRS adoption, and (Fernandes & Ferreira, 2011) who found nega- tive relation between earnings management and firm valuation based on data from 43 countries. DER consistently show negative effect on firm value on all equation, with significant negative effect only on PBV. On the other hand, both SIZE and ROA consistently have positive and significant effect on all proxies of firm value, which shows that higher firm value is caused by company’s assets and net income. Karina Harjanto3838 The regression equation was derived based on the results of the t-statistical test presented in Table 3: 107 observations reporting decrease in operating cash flows with average 130%. High FRQ is observed in AQ score, which means companies are reporting reduction in revenue and net income timely. Even though, the quality of financial reporting is high, investors immediately reacted to the negative signal of lower revenue, net income, and operating cash flows. This poor financial performance caused investors to sell their investment, indicated by decreasing in share price of 4%. In turn, PBV also decline by 2.5% with average PBV only 1.11, and Tobin’s Q average only 1.06, which shows that companies barely able to use assets to raise funding from creditors and investors. This result is in line with (Fambudi & Fitriani, 2020), but differ from the result of (Latif et al., 2017) that stated accrual quality improves firm value on Pakistan companies. The results in Table 3 shows that AQ consistently has no effect on multiple proxies of firm value. This result is in accordance with (Ball et al., 2003) who suggested that financial reporting in Hong Kong, Malaysia, Singapore, and Thailand does not include the identification of economic income, especially economic losses, in a timely manner. Based on table 4, EM shows negative significant effect on share price, PBV, and Tobin’s Q. Low EM values associated with high financial reporting quality (FRQ), therefore FRQ has positive and significant effect on firm value, so Ha2, Ha4, and Ha6 are accepted. This result is in line with (Leung, 2016), whose research on Canadian firms found that accounting quality and firm value has increased after IFRS adoption, and (Fernandes & Ferreira, 2011) who found negative relation between earnings management and firm valuation based on data from 43 countries. DER consistently show negative effect on firm value on all equation, with significant negative effect only on PBV. On the other hand, both SIZE and ROA consistently have positive and significant effect on all proxies of firm value, which shows that higher firm value is caused by company’s assets and net income. The regression equation was derived based on the results of the t-statistical test presented in Table 3: 0.064 AQ 0.154 EM 0.093 DER 0.628 SIZE 0.246 ROA 10 0.028 AQ 0.304 EM 0.261 DER 0.325 SIZE 0.352 ROA (11) 0.025 AQ 0.243 EM 0.139 DER 0.338 SIZE 0.372 ROA (12) 0.021 AQ 0.118 EM 0.053 DER 0.775 SIZE 0.216 ROA (13) Table 4. Bivariate Analysis (10) 107 observations reporting decrease in operating cash flows with average 130%. High FRQ is observed in AQ score, which means companies are reporting reduction in revenue and net income timely. Even though, the quality of financial reporting is high, investors immediately reacted to the negative signal of lower revenue, net income, and operating cash flows. This poor financial performance caused investors to sell their investment, indicated by decreasing in share price of 4%. In turn, PBV also decline by 2.5% with average PBV only 1.11, and Tobin’s Q average only 1.06, which shows that companies barely able to use assets to raise funding from creditors and investors. This result is in line with (Fambudi & Fitriani, 2020), but differ from the result of (Latif et al., 2017) that stated accrual quality improves firm value on Pakistan companies. The results in Table 3 shows that AQ consistently has no effect on multiple proxies of firm value. This result is in accordance with (Ball et al., 2003) who suggested that financial reporting in Hong Kong, Malaysia, Singapore, and Thailand does not include the identification of economic income, especially economic losses, in a timely manner. Based on table 4, EM shows negative significant effect on share price, PBV, and Tobin’s Q. Low EM values associated with high financial reporting quality (FRQ), therefore FRQ has positive and significant effect on firm value, so Ha2, Ha4, and Ha6 are accepted. This result is in line with (Leung, 2016), whose research on Canadian firms found that accounting quality and firm value has increased after IFRS adoption, and (Fernandes & Ferreira, 2011) who found negative relation between earnings management and firm valuation based on data from 43 countries. DER consistently show negative effect on firm value on all equation, with significant negative effect only on PBV. On the other hand, both SIZE and ROA consistently have positive and significant effect on all proxies of firm value, which shows that higher firm value is caused by company’s assets and net income. The regression equation was derived based on the results of the t-statistical test presented in Table 3: 0.064 AQ 0.154 EM 0.093 DER 0.628 SIZE 0.246 ROA 10 0.028 AQ 0.304 EM 0.261 DER 0.325 SIZE 0.352 ROA (11) 0.025 AQ 0.243 EM 0.139 DER 0.338 SIZE 0.372 ROA (12) 0.021 AQ 0.118 EM 0.053 DER 0.775 SIZE 0.216 ROA (13) Table 4. Bivariate Analysis (11) 107 observations reporting decrease in operating cash flows with average 130%. High FRQ is observed in AQ score, which means companies are reporting reduction in revenue and net income timely. Even though, the quality of financial reporting is high, investors immediately reacted to the negative signal of lower revenue, net income, and operating cash flows. This poor financial performance caused investors to sell their investment, indicated by decreasing in share price of 4%. In turn, PBV also decline by 2.5% with average PBV only 1.11, and Tobin’s Q average only 1.06, which shows that companies barely able to use assets to raise funding from creditors and investors. This result is in line with (Fambudi & Fitriani, 2020), but differ from the result of (Latif et al., 2017) that stated accrual quality improves firm value on Pakistan companies. The results in Table 3 shows that AQ consistently has no effect on multiple proxies of firm value. This result is in accordance with (Ball et al., 2003) who suggested that financial reporting in Hong Kong, Malaysia, Singapore, and Thailand does not include the identification of economic income, especially economic losses, in a timely manner. Based on table 4, EM shows negative significant effect on share price, PBV, and Tobin’s Q. Low EM values associated with high financial reporting quality (FRQ), therefore FRQ has positive and significant effect on firm value, so Ha2, Ha4, and Ha6 are accepted. This result is in line with (Leung, 2016), whose research on Canadian firms found that accounting quality and firm value has increased after IFRS adoption, and (Fernandes & Ferreira, 2011) who found negative relation between earnings management and firm valuation based on data from 43 countries. DER consistently show negative effect on firm value on all equation, with significant negative effect only on PBV. On the other hand, both SIZE and ROA consistently have positive and significant effect on all proxies of firm value, which shows that higher firm value is caused by company’s assets and net income. The regression equation was derived based on the results of the t-statistical test presented in Table 3: 0.064 AQ 0.154 EM 0.093 DER 0.628 SIZE 0.246 ROA 10 0.028 AQ 0.304 EM 0.261 DER 0.325 SIZE 0.352 ROA (11) 0.025 AQ 0.243 EM 0.139 DER 0.338 SIZE 0.372 ROA (12) 0.021 AQ 0.118 EM 0.053 DER 0.775 SIZE 0.216 ROA (13) Table 4. Bivariate Analysis (12) 107 observations reporting decrease in operating cash flows with average 130%. High FRQ is observed in AQ score, which means companies are reporting reduction in revenue and net income timely. Even though, the quality of financial reporting is high, investors immediately reacted to the negative signal of lower revenue, net income, and operating cash flows. This poor financial performance caused investors to sell their investment, indicated by decreasing in share price of 4%. In turn, PBV also decline by 2.5% with average PBV only 1.11, and Tobin’s Q average only 1.06, which shows that companies barely able to use assets to raise funding from creditors and investors. This result is in line with (Fambudi & Fitriani, 2020), but differ from the result of (Latif et al., 2017) that stated accrual quality improves firm value on Pakistan companies. The results in Table 3 shows that AQ consistently has no effect on multiple proxies of firm value. This result is in accordance with (Ball et al., 2003) who suggested that financial reporting in Hong Kong, Malaysia, Singapore, and Thailand does not include the identification of economic income, especially economic losses, in a timely manner. Based on table 4, EM shows negative significant effect on share price, PBV, and Tobin’s Q. Low EM values associated with high financial reporting quality (FRQ), therefore FRQ has positive and significant effect on firm value, so Ha2, Ha4, and Ha6 are accepted. This result is in line with (Leung, 2016), whose research on Canadian firms found that accounting quality and firm value has increased after IFRS adoption, and (Fernandes & Ferreira, 2011) who found negative relation between earnings management and firm valuation based on data from 43 countries. DER consistently show negative effect on firm value on all equation, with significant negative effect only on PBV. On the other hand, both SIZE and ROA consistently have positive and significant effect on all proxies of firm value, which shows that higher firm value is caused by company’s assets and net income. The regression equation was derived based on the results of the t-statistical test presented in Table 3: 0.064 AQ 0.154 EM 0.093 DER 0.628 SIZE 0.246 ROA 10 0.028 AQ 0.304 EM 0.261 DER 0.325 SIZE 0.352 ROA (11) 0.025 AQ 0.243 EM 0.139 DER 0.338 SIZE 0.372 ROA (12) 0.021 AQ 0.118 EM 0.053 DER 0.775 SIZE 0.216 ROA (13) Table 4. Bivariate Analysis (13) Table 4. Bivariate Analysis PRICE PBV TBQ MVA AQ EM SIZE DER ROA PRICE 1 PBV 0.220** 1 TBQ 0.224** 0.962** 1 MVA 0.266** 0.388** 0.536** 1 AQ -0.026 0.178* 0.167* 0.069 1 EM 0.617** -0.018 -0.005 0.132 -0.066 1 SIZE 0.457** 0.165* 0.243** 0.483** -0.122 0.161* 1 DER -0.036 0.097 0.032 -0.057 0.324** -0.084 0.035 1 ROA 0.141* 0.473** 0.489** 0.278** 0.324* 0.031 0.075 -0.084 1 ** Correlation is significant at 1% level (2-tailed) * Correlation is significant at 5% level (2-tailed) S o u r c e : data analyzed. Based on table 4, all four proxies of firm value have positive and significant cor- relations with each other. This implies that any of the four proxies can be used interchangeably in measuring firm value. AQ has positive significant correla- tion with PBV and Tobin’s Q, positive but insignificant correlation with MVA, and negative insignificant correlation with share price. Meanwhile, EM has thE analysis of finanCial rEPorting quality and firm valuE 3939 negative and insignificant relationship with PBV and Tobin’s Q, positive and insignificant relationship with MVA, and only positive significant correlation with share price.  Conclusion Conclusion In this study, financial reporting quality (FRQ) is measured using two prox- ies, accrual quality (AQ) through Ball-Shivakumar Model and earnings man- agement (EM) through Modified Jones Model. AQ measured FRQ based on time- liness in reporting loss, which can affect management investing decisions and debt agreement. EM measured FRQ based on discretionary component of ac- cruals which indicated accounting manipulation. Based on the results, only EM has negative and significant effect on firm value. Low EM indicated high FRQ, or suggest that companies didn’t do accounting manipulation, and investors re- acted positively to it. On the other hand, data in this study shows that majority of companies reported low AQ, which mean most companies recognized loss timely. However, this causes a reduction in net income and negative reaction from investors, which lead to reduction in firm value. The results from this re- search shows that a different proxy in measuring FRQ will lead to a different conclusion whether FRQ affects firm value. Sample companies in this study re- ported increasing AQ from 2018-2021, indicating lower FRQ, while EM actually decreasing from 2018-2021, indicating higher FRQ. It shows that the develop- ment of Statement of Financial Accounting Standards (PSAK) actually lowers the earnings management practices done by managers, but have no impact in timeliness of loss recognition. However, the average share price, PBV, Tobin’s Q, and market value-added of sample companies show decreasing trend from 2018 to 2021, with sharp drop in 2020 data mainly due to the effect of Covid-19. (Joshi, 2022) report similar negative return of Indian stock returns during Cov- id-19 period. The main reason for this negative trend is that companies’ finan- cial performance measure by ROA also showing negative trend, from 10.46% in 2018 to 8.08% in 2020. This study has multiple constraints. Initially, this study solely employs two indicators of FRQ, while FRQ encompasses a wide-ranging notion that may be assessed using other elements. Furthermore, equation 4 exhibits the highest adjusted R2 value of 74.5%, while equation 3 has the lowest value of 38.1%. This indicates that at least 25.5% of the variation in firm value is attributed to Karina Harjanto4040 elements that were not investigated in this study. It is recommended that fu- ture research expands to include other sectors such as services, mining, and property in many nations and employs more proxies to measure FRQ.  Acknowledgements Acknowledgements This study is being undertaken with financial support from Universitas Multi- media Nusantara and is being facilitated by the teaching staff of the Bachelor of Accounting Program, Faculty of Business, Universitas Multimedia Nusantara.  References References Antonius, A., & Harjanto, K. (2022). Impact of Selected Factors Towards Shareholder Value Creation. https://doi.org/10.4108/eai.7-10-2021.2316221. Ball, R., Robin, A., & Wu, J.S. (2003). Incentives versus standards: Properties of accounting income in four East Asian countries. Journal of Accounting and Economics, 36(1-3 SPEC. ISS.), 235–270. https://doi.org/10.1016/j.jacceco.2003.10.003. Ball, R., & Shivakumar, L. (2005). Earnings quality in UK private firms: Comparative loss recognition timeliness. 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