What is he best known for?
The fields of study he is best known for:
- Finance
- Accounting
- Macroeconomics
His main research concerns Accounting, Cost of capital, Finance, Equity and Corporate social responsibility.
His Accounting study often links to related topics such as Quality.
His Quality research is multidisciplinary, relying on both Investment behavior and Investment efficiency.
The various areas that he examines in his Cost of capital study include Equity value, Monetary economics and Equity capital markets.
His research in Equity focuses on subjects like Cost of equity, which are connected to Customer base, Microeconomics and Instrumental variable.
His Voluntary disclosure research includes themes of Institutional investor and Equity capital.
His most cited work include:
- Voluntary Nonfinancial Disclosure and the Cost of Equity Capital: The Initiation of Corporate Social Responsibility Reporting (1293 citations)
- Nonfinancial Disclosure and Analyst Forecast Accuracy: International Evidence on Corporate Social Responsibility Disclosure (659 citations)
- Is comprehensive income superior to net income as a measure of firm performance (367 citations)
What are the main themes of his work throughout his whole career to date?
Dan S. Dhaliwal focuses on Accounting, Monetary economics, Finance, Cost of capital and Earnings.
His studies deal with areas such as Quality and Information asymmetry as well as Accounting.
His Monetary economics study integrates concerns from other disciplines, such as Dividend tax, Indirect tax, Corporate tax, Financial system and Value-added tax.
In general Finance study, his work on Debt, Debt-to-GDP ratio and Transparency often relates to the realm of Sample, thereby connecting several areas of interest.
His Cost of capital research includes elements of Weighted average cost of capital, Equity and Economic capital.
He has researched Earnings in several fields, including Taxable income, Accounting method and Econometrics.
He most often published in these fields:
- Accounting (45.91%)
- Monetary economics (31.45%)
- Finance (25.79%)
What were the highlights of his more recent work (between 2013-2020)?
- Accounting (45.91%)
- Audit (17.61%)
- Monetary economics (31.45%)
In recent papers he was focusing on the following fields of study:
Dan S. Dhaliwal mainly investigates Accounting, Audit, Monetary economics, Cost of capital and Quality.
His studies in Accounting integrate themes in fields like Information asymmetry and Tax avoidance.
The concepts of his Audit study are interwoven with issues in Mergers and acquisitions and Investment.
His Monetary economics research incorporates elements of Corporate tax, Tax deduction and Debt.
His Cost of capital research incorporates themes from Earnings, Finance, Equity risk and Financial system.
He has researched Cost of equity in several fields, including Endogeneity, Microeconomics, Customer base and Equity.
Between 2013 and 2020, his most popular works were:
- Corporate social responsibility disclosure and the cost of equity capital: The roles of stakeholder orientation and financial transparency (225 citations)
- Corporate social responsibility disclosure and the cost of equity capital: The roles of stakeholder orientation and financial transparency (225 citations)
- The information content of mandatory risk factor disclosures in corporate filings (215 citations)
In his most recent research, the most cited papers focused on:
- Finance
- Accounting
- Macroeconomics
The scientist’s investigation covers issues in Accounting, Cost of capital, Audit, Information asymmetry and Corporate finance.
His Accounting research integrates issues from Marketing and Ex-ante.
His Cost of capital research is multidisciplinary, relying on both Transparency and Finance.
His Audit research includes themes of Incentive and Investment.
His Corporate finance research is multidisciplinary, incorporating elements of Market structure, Financial economics, Public finance and Product market.
His Public finance study incorporates themes from Institutional investor, Corporate governance and Earnings guidance.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
