Comparison of News Impacts on Sectoral Stock Returns during the COVID-19 Pandemic in Turkey

This study examines how the volatility of the sectoral stock returns within Borsa İstanbul are affected during the COVID-19 pandemic. The analysis uses daily stock return data for four main sector indices: services, finance, industry, and technology. The sample period of the study covers 03.03.2015–11.03.2021, and 12.03.2020-03.04.2021 is separately analyzed for the COVID-19 period. When E-GARCH models and news impact curves are analyzed, it is found that the services sector stock returns volatility differs from other sectoral stock returns.

IPOs and Corporate Taxes

How does going public affect firms’ tax obligations and tax planning? Using a panel of U.S. corporate tax return data from 1994 to 2018, we compare tax payments for firms that completed an IPO with those that filed for an IPO but later withdrew and remained private. We find that in the years immediately following IPO completion, firms have a higher probability of paying taxes and pay more U.S. tax. The effects occur regardless of tax status in the pre-IPO period and are not explained by statutory limitations imposed on the use of pre-IPO losses. Higher income reported for financial reporting purposes, as well as lower interest deductions attributable to debt repayment, contribute to the increased tax payments. These increases are partially offset by higher tax deductions for post-IPO investment and employment spending. Furthermore, the IPO is associated with increased tax planning through foreign tax haven use. The evidence adds to the nascent literature examining corporate tax implications of the IPO decision.

Who Sells During A Crash? Evidence from Tax-Return Data on Daily Sales of Stock Short Title: Who Sells During A Crash?

Using U.S. tax-return data containing the universe of individual taxable stock sales from 2008 to 2009, we examine which individuals increased their sale of stocks following episodes of market tumult. We find that the increase was disproportionately concentrated among investors in the top 1 and top 0.1% of the overall income distribution, retired individuals, and individuals at the very top of the dividend income distribution. Our estimates suggest that, following the day when Lehman Brothers collapsed, taxpayers in the top 0.1% sold $1.7 billion more in stocks than individuals in the bottom 75%. This difference is equal to 89% of average daily sales by taxpayers in the top 0.1%.

US Individual Income Tax Return Simulated Data: A Methodology

This paper proposes and tests a methodology for the development of a simulation for individual tax returns in the United States, enabling students of taxation and interested parties to examine changes to the tax code, examine the effects of tax planning alternatives, and conduct repeated experimental testing on the tax return data. The simulation produced data for 147,000 tax returns, representing approximately 1% of the population of filed tax returns as noted by the IRS/SOI. We present the methodology on how we created the simulation and compare the tax returns of the simulation to the measures provided by the IRS. Our simulated return data very closely matched the number and combined dollar value of the IRS/SOI summary data at the adjusted gross income (AGI), state, and filing status levels.

Big Data Business Actual Analysis: Stock Price Prediction Based on Time Series Model

This paper selects the daily closing price data of the Shanghai Composite Index from January 1, 2016 to December 31, 2017, excluding holidays, and preprocesses the data. After taking the logarithm and converting it into the rate of return data, the first-order difference is performed to make it into a stable time series, and then the ARMA(p,q) model is constructed. Through parameter significance test, residual test and characteristic root test, according to the minimum principle of AIC, the optimal model is finally determined to be ARMA(2,5) of sparse coefficient, and the expression of the model is obtained. The GARCH(1,1) model is established for the residual of ARMA(2,5), and the model expression is obtained. In order to directly predict the return rate of the Shanghai Composite Index, the ARIMA(2,1,5) model of the sparse coefficient is constructed for the return rate of the Shanghai Composite Index, and the model expression is obtained. By predicting the Shanghai Composite Index return data on January 2, 2018, it is found that the prediction error of the model is small, and it can be used for subsequent predictions.

Does the Individual Mandate Affect Insurance Coverage? Evidence from Tax Returns

We estimate the effect of the ACA’s individual mandate on insurance coverage using regression discontinuity and regression kink designs with tax return data. We have four key results. First, the actual penalty paid per uninsured month is less than half the statutory amount. Second, nonetheless, we find visually clear and statistically signifi-cant responses to both extensive margin exposure to the mandate and to marginal increases in the mandate penalty. Third, we find substantial heterogeneity in who responds; men are especially responsive. Fourth, our estimates imply fairly small quantitative responses to the individual mandate, especially in the Health Insurance Exchanges. (JEL G22, H24, H51, I13, I18)

PENERAPAN BENFORD'S LAW UNTUK MENDETEKSI DUGAAN KETIDAKPATUHAN MATERIAL PADA SPT TAHUNAN PPH ORANG PRIBADI

This study aims to examine whether the Individual Income Tax Return data set conforms to the Benford's Law pattern and to examine whether there are indications of material noncompliance in that data set based on the application of Benford's Law. This research is a quantitative research. The data source in this study is the taxation database owned by the Directorate General of Taxes (DGT), Ministry of Finance. The results of this study indicate that most of the Individual Income Tax Return data set variables conform to Benford's Law pattern and there are indications of material noncompliance in that data set. Tax officer, in this case account representatives and tax auditors are expected to be able to use the results of this study to carry out further analysis of the numerical class in the Individual Income Tax Return data set that is not appropriate and deviates from Benford's Law pattern. DGT, as a tax institution, is expected to consider the use of Benford's Law to assist the taxpayer supervision and inspection process.

Entity Structure and Taxes: An Analysis of Embedded Pass-Through Entities

We examine the effect of pass-through entities embedded in corporate structures on tax avoidance, tax uncertainty, and tax noncompliance using unique, confidential tax return data that link corporations and pass-through entities together through Schedules K-1. We develop measures of the use of pass-through entities such as the number and "connectedness" of pass-throughs within the structure, the presence of loss pass-throughs or asymmetric allocations of such losses, and connections to entities external to the firm. We predict and find that these features are associated with lower effective tax rates, higher current year additions to tax reserves, and larger amounts of proposed IRS audit adjustments, controlling for probability of audit selection. This large-sample evidence could help the IRS understand how pass-throughs affect compliance and financial statements users anticipate the tax effects related to entity structure