Research articles for the 2020-08-24

A Blockchain Transaction Graph based Machine Learning Method for Bitcoin Price Prediction
Xiao Li,Weili Wu

Bitcoin, as one of the most popular cryptocurrency, is recently attracting much attention of investors. Bitcoin price prediction task is consequently a rising academic topic for providing valuable insights and suggestions. Existing bitcoin prediction works mostly base on trivial feature engineering, that manually designs features or factors from multiple areas, including Bticoin Blockchain information, finance and social media sentiments. The feature engineering not only requires much human effort, but the effectiveness of the intuitively designed features can not be guaranteed. In this paper, we aim to mining the abundant patterns encoded in bitcoin transactions, and propose k-order transaction graph to reveal patterns under different scope. We propose the transaction graph based feature to automatically encode the patterns. A novel prediction method is proposed to accept the features and make price prediction, which can take advantage from particular patterns from different history period. The results of comparison experiments demonstrate that the proposed method outperforms the most recent state-of-art methods.

A Computational Approach to Hedging Credit Valuation Adjustment in a Jump-Diffusion Setting
T. van der Zwaard,L.A. Grzelak,C.W. Oosterlee

This study contributes to understanding Valuation Adjustments (xVA) by focussing on the dynamic hedging of Credit Valuation Adjustment (CVA), corresponding Profit & Loss (P&L) and the P&L explain. This is done in a Monte Carlo simulation setting, based on a theoretical hedging framework discussed in existing literature. We look at hedging CVA market risk for a portfolio with European options on a stock, first in a Black-Scholes setting, then in a Merton jump-diffusion setting. Furthermore, we analyze the trading business at a bank after including xVAs in pricing. We provide insights into the hedging of derivatives and their xVAs by analyzing and visualizing the cash-flows of a portfolio from a desk structure perspective. The case study shows that not charging CVA at trade inception results in an expected loss. Furthermore, hedging CVA market risk is crucial to end up with a stable trading strategy. In the Black-Scholes setting this can be done using the underlying stock, whereas in the Merton jump-diffusion setting we need to add extra options to the hedge portfolio to properly hedge the jump risk. In addition to the simulation, we derive analytical results that explain our observations from the numerical experiments. Understanding the hedging of CVA helps to deal with xVAs in a practical setting.

Binary Funding Impacts in Derivative Valuation
Junbeom Lee,Chao Zhou

We discuss the binary nature of funding impact in derivative valuation. Under some conditions, funding is either a cost or a benefit, i.e., one of the lending/borrowing rates does not play a role in pricing derivatives. When derivatives are priced, considering different lending/borrowing rates leads to semi-linear BSDEs and PDEs, and thus it is necessary to solve the equations numerically. However, once it can be guaranteed that only one of the rates affects pricing, linear equations can be recovered and analytical formulae can be derived. Moreover, as a byproduct, our results explain how debt value adjustment (DVA) and funding benefits are dissimilar. It is often believed that considering both DVA and funding benefits results in a double-counting issue but it will be shown that the two components are affected by different mathematical structures of derivative transactions. We find that funding benefit is related to the decreasing property of the payoff function, but this relationship decreases as the funding choices of underlying assets are transferred to repo markets.

Can Technical Analysis Generate Superior Returns in Securitized Property Markets? Evidence From East Asia Markets
Alhashel, Bader,Almudhaf, Fahad,Hansz, J. Andrew
This study applies various popular technical trading rules to Asian property market indices from 1995 to 2015 to investigate the profitability of these rules. The results validate the predictive and profitability power of technical indicators in the markets of Indonesia, Malaysia, Taiwan, and Thailand but not in the markets of China, Hong Kong, Japan, the Philippines, and Singapore. For markets in which technical indicators are predictive, this ability is proven useful in generating returns in excess of buy-and-hold returns using four different trading strategies. The study's results hold even after including transaction costs, adjustments for risk, and data snooping. The results for the markets in which technical analysis is fruitful contradict weak-form market efficiency, whereas markets in which technical analysis is not successful are at least weakly efficient.

Competitive ride-sourcing market with a third-party integrator
Yaqian Zhou,Hai Yang,Jintao Ke,Hai Wang,Xinwei Li

Recently, some transportation service providers attempt to integrate the ride services offered by multiple independent ride-sourcing platforms, and passengers are able to request ride through such third-party integrators or connectors and receive service from any one of the platforms. This novel business model, termed as third-party platform-integration in this paper, has potentials to alleviate the cost of market fragmentation due to the demand splitting among multiple platforms. While most existing studies focus on the operation strategies for one single monopolist platform, much less is known about the competition and platform-integration as well as the implications on operation strategy and system efficiency. In this paper, we propose mathematical models to describe the ride-sourcing market with multiple competing platforms and compare system performance metrics between two market scenarios, i.e., with and without platform-integration, at Nash equilibrium as well as social optimum. We find that platform-integration can increase total realized demand and social welfare at both Nash equilibrium and social optimum, but may not necessarily generate a greater profit when vehicle supply is sufficiently large or/and market is too fragmented. We show that the market with platform-integration generally achieves greater social welfare. On one hand, the integrator in platform-integration is able to generate a thicker market and reduce matching frictions; on the other hand, multiple platforms are still competing by independently setting their prices, which help to mitigate monopoly mark-up as in the monopoly market.

Discounted optimal stopping of a Brownian bridge, with application to American options under pinning
Bernardo D'Auria,Eduardo García-Portugués,Abel Guada

Mathematically, the execution of an American-style financial derivative is commonly reduced to solving an optimal stopping problem. Breaking the general assumption that the knowledge of the holder is restricted to the price history of the underlying asset, we allow for the disclosure of future information about the terminal price of the asset by modeling it as a Brownian bridge. This model may be used under special market conditions, in particular we focus on what in the literature is known as the "pinning effect", that is, when the price of the asset approaches the strike price of a highly-traded option close to its expiration date. Our main mathematical contribution is in characterizing the solution to the optimal stopping problem when the gain function includes the discount factor. We show how to numerically compute the solution and we analyze the effect of the volatility estimation on the strategy by computing the confidence curves around the optimal stopping boundary. Finally, we compare our method with the optimal exercise time based on a geometric Brownian motion by using real data exhibiting pinning.

Does Global Outsourcing Affect Corporate Customers’ Voluntary Disclosures?
Dai, Lili,Dai, Rui,Ng, Lilian K.,Peng, Zihang Ryan
Global outsourcing has become an economic imperative for many major corporations worldwide, but at the same time, it has brought substantial risks and uncertainties to these firms. In this study, we examine whether global sourcing of goods or services shapes U.S. corporate disclosure policies. Our main results indicate a negative relation between global outsourcing and voluntary disclosure. Further, identification tests show that managers reduce voluntary disclosures when they first outsource their inputs globally and when suppliers’ countries experience spikes in trade uncertainty and political risks. The global outsourcing effect on disclosure is stronger when corporate customers face a higher litigation risk, when there is less information transfer between customers and suppliers, and when customers are in more competitive product markets. Collectively, these results suggest that corporate customers decrease voluntary disclosures due to increased uncertainties along the global supply chain.

Even in Emerging Markets, Technical Trading is Hazardous to Your Wealth
Alhashel, Bader,Almudhaf, Fahad
This paper tests for the ability of a variety of technical indicators to generate excess returns at the individual stock level in the seven emerging and frontier markets of the Gulf region. While technical indicators show some early profitability promise, after controlling for the data snooping bias using the False Discovery Rate (FDR) methodology and non-synchronous trading, we fail to find any predictive ability or profitability for technical analysis. We arrive at a similar finding when assessing the risk-adjusted performance of a portfolio composed of stocks chosen based on technical indicators. The findings go to show the failure of technical analysis on the stock level. The findings are also evidence of the Gulf region markets being at least weak-form efficient and carry implications for investors choice of investment tools.

Geometric Arbitrage Theory and Market Dynamics Reloaded
Simone Farinelli

We have embedded the classical theory of stochastic finance into a differential geometric framework called Geometric Arbitrage Theory and show that it is possible to:

--Write arbitrage as curvature of a principal fibre bundle.

--Parameterize arbitrage strategies by its holonomy.

--Give the Fundamental Theorem of Asset Pricing a differential homotopic characterization.

--Characterize Geometric Arbitrage Theory by five principles and show they they are consistent with the classical theory of stochastic finance.

--Derive for a closed market the equilibrium solution for market portfolio and dynamics in the cases where:

-->Arbitrage is allowed but minimized.

-->Arbitrage is not allowed.

--Prove that the no-free-lunch-with-vanishing-risk condition implies the zero curvature condition. The converse is in general not true and additionally requires the Novikov condition for the instantaneous Sharpe Ratio Dynamics to be satisfied.

Implications of the Tradeoff between Inside and Outside Social Status in Group Choice
Takaaki Hamada

We investigate a group choice problem of agents pursuing social status. We assume heterogeneous agents want to signal their private information (ability, income, patience, altruism, etc.) to others, facing tradeoff between "outside status" (desire to be perceived in prestigious group from outside observers) and "inside status" (desire to be perceived talented from peers inside their group). To analyze the tradeoff, we develop two stage signaling model in which each agent firstly chooses her group and secondly chooses her action in the group she chose. They face binary choice problems both in group and action choices. Using cutoff strategy, we construct an partially separating equilibrium such that there are four populations: (i) choosing high group with strong incentive for action in the group, (ii) high group with weak incentive, (iii) low group with strong incentive, and (iv) low group with weak incentive. By comparative statics results, we find some spillover effects from a certain group to another, on how four populations change, when a policy is taken in each group. These results have rich implications for group choice problems like school, firm or residential preference.

Is culture related to strong science? An empirical investigation
Mahmood Khosrowjerdi,Lutz Bornmann

National culture is among those societal factors which could influence research and innovation activities. In this study, we investigated the associations of two national culture models with citation impact of nations (measured by the proportion of papers belonging to the 10% and 1% most cited papers in the corresponding fields, PPtop 10% and PPtop 1%). Bivariate statistical analyses showed that of six Hofstede's national culture dimensions (HNCD), three dimensions of power distance, individualism, and uncertainty avoidance had statistically significant associations with citation impact of nations. The study also revealed that of two Inglehart-Welzel cultural values (IWCV), the value survival versus self-expression is statistically significantly related to citation impact indicators. We additionally calculated multiple regression analyses controlling for the possible effects of confounding factors including international migrant stock, investments in research and development, number of researchers, international co-authorships, and national self-citations. The results revealed that the statistically significant associations of HNCD with citation impact indicators disappeared. But the statistically significant relationship between survivals versus self-expression values and citation impact indicators remained stable even after controlling for the confounding variables. Thus, the freedom of expression and trust in society might contribute to better scholarly communication systems, higher level of international collaborations, and further quality research.

Optimizing tail risks using an importance sampling based extrapolation for heavy-tailed objectives
Anand Deo,Karthyek Murthy

Motivated by the prominence of Conditional Value-at-Risk (CVaR) as a measure for tail risk in settings affected by uncertainty, we develop a new formula for approximating CVaR based optimization objectives and their gradients from limited samples. A key difficulty that limits the widespread practical use of these optimization formulations is the large amount of data required by the state-of-the-art sample average approximation schemes to approximate the CVaR objective with high fidelity. Unlike the state-of-the-art sample average approximations which require impractically large amounts of data in tail probability regions, the proposed approximation scheme exploits the self-similarity of heavy-tailed distributions to extrapolate data from suitable lower quantiles. The resulting approximations are shown to be statistically consistent and are amenable for optimization by means of conventional gradient descent. The approximation is guided by means of a systematic importance-sampling scheme whose asymptotic variance reduction properties are rigorously examined. Numerical experiments demonstrate the superiority of the proposed approximations and the ease of implementation points to the versatility of settings to which the approximation scheme can be applied.

Paying Too Much? Price Dispersion in the U.S. Mortgage Market
Bhutta, Neil,Fuster, Andreas,Hizmo, Aurel
We document wide dispersion in the mortgage rates that households pay on identical loans, and show that borrowers' financial sophistication is an important determinant of the rates obtained. We estimate a gap between the 10th and 90th percentile mortgage rate that borrowers with the same characteristics obtain for identical loans, in the same market, on the same day, of 54 basis points|equivalent to about $6,500 in upfront costs (points) for the average loan. Time-invariant lender attributes explain little of this rate dispersion, and considerable dispersion remains even within loan officer. Comparing the rates borrowers obtain to the real-time distribution of rates that lenders could offer for the same loan and borrower type, we find that borrowers who are likely to be the least financially savvy tend to substantially overpay relative to the rates available in the market. In the time series, the average overpayment decreases when overall market interest rates rise, suggesting that a rising level of borrowing costs encourages more search and negotiation. Furthermore, new survey data provide direct evidence that financial knowledge and shopping both affect the mortgage rates borrowers get, and that shopping activity increases with the level of market rates.

Portfolio Selection under Median and Quantile Maximization
Xue Dong He,Zhaoli Jiang,Steven Kou

In this paper, we study a portfolio selection problem in which an agent trades a risk-free asset and multiple risky assets with deterministic mean return rates and volatility and wants to maximize the $\alpha$-quantile of her wealth at some terminal time. Because of the time inconsistency caused by quantiles, we consider intra-personal equilibrium strategies. We find that among the class of time-varying, affine portfolio strategies, the intra-personal equilibrium does not exist when $\alpha>1/2$, leads to zero investment in the risky assets when $\alpha<1/2$, and is a portfolio insurance strategy when $\alpha=1/2$. We then compare the intra-personal equilibrium strategy in the case of $\alpha=1/2$, namely under median maximization, to some other strategies and apply it to explain why more wealthy people invest more precentage of wealth in risky assets. Finally, we extend our model to account for multiple terminal time.

Power-type derivatives for rough volatility with jumps
Weixuan Xia

In this paper we propose an efficient pricing-hedging framework for volatility derivatives which simultaneously takes into account path roughness and jumps. Instead of dealing with log-volatility, we directly model the instantaneous variance of a risky asset in terms of a fractional Ornstein-Uhlenbeck process driven by an infinite-activity L\'{e}vy subordinator, which is shown to exhibit roughness under suitable conditions and also eludes the need for an independent Brownian component. This structure renders the characteristic function of forward variance obtainable at least in semi-closed form, subject to a generic integrable kernel. To analyze financial derivatives, primarily swaps and European-style options, on average forward volatility, we introduce a general class of power-type derivatives on the average forward variance, which also provide a way of adjusting the option investor's risk exposure. Pricing formulae are based on numerical inverse Fourier transform and, as illustrated by an empirical study on VIX options, permit stable and efficient model calibration once specified.

Pricing Efficiency of Saudi Exchange Traded Funds (ETFs)
Almudhaf, Fahad,Alhashel, Bader
This paper aims to investigate the pricing efficiency of Saudi Sharia-compliant (i.e. Islamic) exchange-traded funds (ETFs). The paper adheres to a positivist research philosophy with a deductive research approach where data is collected, analyzed and interpreted to examine a hypothesis. Ordinary least squares (OLS) regressions are applied to investigate pricing efficiency and persistence. The results show that Saudi ETFs do not currently offer proper diversification for investors, possibly due to their low trading volumes and the delays of market prices in reflecting net asset value (NAV). On average, ETFs trade at a premium to their NAVs. Moreover, the authors find that the deviations of ETF prices from their NAVs (i.e. premiums or discounts) do not disappear in one day. The results reveal a significant positive relationship between the trading volume of Saudi ETFs and volatility, a significant positive correlation between ETF returns and contemporaneous deviations and a significant negative relationship between returns and lagged deviations. These findings can be interpreted as evidence against the market efficiency of Saudi ETFs. Individual and institutional investors can use Saudi ETFs, especially as their efficiency improves with increased trading volume (liquidity). Saudi regulators must increase their efforts to educate market participants and expand the availability of information to enhance transparency and awareness of the benefits of investing in ETFs, which will positively affect liquidity and pricing efficiency in the future. This paper is the first to perform empirical tests on Saudi ETFs. Saudi Arabia deserves further attention because it is the most significant stock market in the Gulf Cooperation Council and only recently allowed foreigners to participate.

Stationarity of the detrended price return in stock markets
Karina Arias-Calluari,Morteza. N. Najafi,Michael S. Harré,Fernando Alonso-Marroquin

This paper proposes a governing equation for stock market indexes that accounts for non-stationary effects. This is a linear Fokker-Planck equation (FPE) that describes the time evolution of the probability distribution function (PDF) of the price return. By applying Ito's lemma, this FPE is associated with a stochastic differential equation (SDE) that models the time evolution of the price return in a fashion different from the classical Black-Scholes equation. Both FPE and SDE equations account for a deterministic part or trend, and a stationary, stochastic part as a q-Gaussian noise. The model is validated using the S\&P500 index's data. After removing the trend from the index, we show that the detrended part is stationary by evaluating the Hurst exponent of the multifractal time series, its power spectrum, and its autocorrelation.

Statistical Learning for Probability-Constrained Stochastic Optimal Control
Alessandro Balata,Michael Ludkovski,Aditya Maheshwari,Jan Palczewski

We investigate Monte Carlo based algorithms for solving stochastic control problems with probabilistic constraints. Our motivation comes from microgrid management, where the controller tries to optimally dispatch a diesel generator while maintaining low probability of blackouts. The key question we investigate are empirical simulation procedures for learning the admissible control set that is specified implicitly through a probability constraint on the system state. We propose a variety of relevant statistical tools including logistic regression, Gaussian process regression, quantile regression and support vector machines, which we then incorporate into an overall Regression Monte Carlo (RMC) framework for approximate dynamic programming. Our results indicate that using logistic or Gaussian process regression to estimate the admissibility probability outperforms the other options. Our algorithms offer an efficient and reliable extension of RMC to probability-constrained control. We illustrate our findings with two case studies for the microgrid problem.

Stock Buybacks: Value Creation or Destruction?
Gordon, Michael
Since a regulatory change in 1982, stock buybacks- the action of companies purchasing their own stock- have become an increasingly popular use of cash amongst publicly traded companies, eclipsing dividend payments and rivaling capital expenditure. The merits of conducting large-scale buyback programs are hotly debated and are the subject of research papers dating back decades. In this paper, we examine the topic of buybacks through several lenses. First, the regulatory history of buybacks is analyzed to understand the framework within which companies operate when repurchasing their own stock. Next, we analyze the negative impacts of buybacks on companies’ balance sheet strength and investment in real growth, with a particular emphasis on the impact of the post-2008 monetary policy landscape. Then, we will examine the positive effects of buybacks and the motivations companies have for conducting buybacks. Lastly, several recent legislative proposals will be detailed to understand the current environment. To ensure the continued sustainability of companies, regulators should carefully study all of the ramifications associated with buybacks and enact tougher limits on how much stock companies can repurchase.

Tail risk forecasting using Bayesian realized EGARCH models
Vica Tendenan,Richard Gerlach,Chao Wang

This paper develops a Bayesian framework for the realized exponential generalized autoregressive conditional heteroskedasticity (realized EGARCH) model, which can incorporate multiple realized volatility measures for the modelling of a return series. The realized EGARCH model is extended by adopting a standardized Student-t and a standardized skewed Student-t distribution for the return equation. Different types of realized measures, such as sub-sampled realized variance, sub-sampled realized range, and realized kernel, are considered in the paper. The Bayesian Markov chain Monte Carlo (MCMC) estimation employs the robust adaptive Metropolis algorithm (RAM) in the burn in period and the standard random walk Metropolis in the sample period. The Bayesian estimators show more favourable results than maximum likelihood estimators in a simulation study. We test the proposed models with several indices to forecast one-step-ahead Value at Risk (VaR) and Expected Shortfall (ES) over a period of 1000 days. Rigorous tail risk forecast evaluations show that the realized EGARCH models employing the standardized skewed Student-t distribution and incorporating sub-sampled realized range are favored, compared to a range of models.

The Analysis of Benefits in Consumer Protection Regulation
Jackson, Howell E.,Rothstein, Paul
Over the past decade, cost-benefit analysis in the field of financial regulation (“financial CBA”) has emerged as a topic of intense public interest. In reviewing rulemakings under the Administrative Procedure Act, courts have demanded greater rigor in the financial CBA that regulators provide in support of new regulations. Industry experts and other analysts have repeatedly questioned the adequacy of agency assessments of costs and benefits. And legal academics have engaged in a robust dialogue over the merits of financial CBA and the value of alternative institutional structures for overseeing financial CBA.This Article adds to the expanding literature on financial CBA by offering a detailed study of how regulatory agencies actually undertake benefit analysis in promulgating new regulations involving matters of consumer finance and other analogous areas of consumer protection. After a brief literature review, the Article proposes a taxonomy for categorizing benefit analysis in the area of consumer financial regulation. This taxonomy reflects traditional market failures, cognitive limitations of consumers, as well as several other beneficial outcomes commonly associated with regulations designed to protect consumers. Taking the taxonomy as a framework, the Article then reports on a detailed survey of seventy-two consumer protection regulations adopted in recent years, and presents an overview of the range and quality of benefit analysis that government officials actually undertook in the surveyed regulations. The Article next provides a more detailed discussion of twenty “exemplars” of benefit analysis drawn from regulations in the sample and focusing on the strengths and weaknesses of what might be considered state-of-the-art benefit analysis in consumer protection regulation in the years immediately following the enactment of the Dodd-Frank Act. The Article concludes with a discussion of potential lines of academic research and institutional reform that might assist financial regulators in conducting more complete benefit analysis for consumer protection regulation in the future.

The Economic Impacts of Direct Natural Disaster Exposure
Johar, Meliyanni,Johnston, David W.,Shields, Michael,Siminski, Peter,Stavrunova, Olena
This paper studies how having your home damaged or destroyed by a natural disaster impacts on economic and financial outcomes. Our context is Australia, where disasters are frequent. Estimates of regression models with individual, area and time fixed-effects, applied to 10 waves of data (2009-2018), indicate that residential destruction has no average impact on employment and income, but increases financial hardship and financial risk aversion. These impacts are generally short-lived, larger for renters than home owners, and greater for smaller isolated disasters. Using a Group Fixed Effects estimator, we find that around 20% of the population have low resilience to financial shocks, and for these individuals we find a substantive increase in financial hardships. The most vulnerable are the young, single parents, those in poor health, those of lower socioeconomic status, and those with little social support. These results can help target government aid after future natural disasters to those with the greatest need.

The Economics of Soft Dollars: A Review of the Literature and New Evidence from the Implementation of MiFID II
Jackson, Howell E.,Zhang, Jeffery
For nearly half a century, the bundling of research services into commissions that paid for the execution of securities trades has been the focus of both policy discussion and academic debate. The practice whereby asset management firms make use of investor funds to cover the costs of research, known as “soft dollar” payments in the United States, resembles a form of kickback or self-dealing. The payments allow asset managers to use investor funds to subsidize the cost of the asset managers’ own research efforts even though those managers charge investors a separate and explicit management fee for advisory services. So why does this form of kickback continue to exist? Over the years, defenders of the practice have argued that soft dollars mitigate principal-agent problems between the investment manager and the broker, improve fund performance, and provide a public good in terms of the increased production of research on public companies. This article evaluates these theoretical arguments through the lens of academic work done in the past as well as an emerging new body of empirical studies exploring the impact of MiFID II, a European Union Directive that severely restricted the use of soft dollar payments in European capital markets as of January 2018. The weight of empirical evidence, including recent evidence coming out of Europe, suggests that the theoretical arguments in favor of soft dollars are not robust. In particular, MiFID II’s unbundling of commissions appears to have, on balance, improved European market efficiency by eliminating redundancy and producing information that is of greater value to investors.

The Nature of the Fintech Firm and its Implications for Financial Regulation
Jackson, Howell E.
This chapter explores recent Fintech innovations through the lens of Ronald Coase’s classic article: The Nature of the Firm. Applying a transaction cost analysis, the chapter argues that developments in computer technology, data processing, and information networks are reshaping the manner in which financial services are produced, unsettling the boundaries separating regulated firms from outside vendors and open market transactions. These changes raise challenging questions as to the appropriate contours of regulatory perimeters as well as the structure of regulation and supervision in the many area of financial regulation. Fintech innovations also have the potential to be harnessed to serve public purposes, including expanding access to financial services and improving supervisory practices. At a minimum, Fintech innovations and most especially machine learning and artificial intelligence complicate the application of legal doctrines based on human intentionality. More broadly, the scale and scope of these technological developments may lead to a fundamentally rethinking of the appropriate goals of regulatory policy for financial firms and the economy more broadly, particularly with respect to privacy and the accumulation of personal information in private and public hands.

Voluntary Disclosure and Real Earnings Management in SEO Firms: Evidence from the 2005 Securities Offering Reform
Zhu, Yicheng
The Securities Offering Reform (SOR) in 2005 eased restrictions on firm disclosure prior to equity offerings. Prior literature find a better information environment with increased disclosure and reduced information asymmetry in seasoned equity offering (SEO) firms after the regulation. Building on these findings, our paper documents that SEO firms reduce real earnings management activities after the SOR became effective. Our results indicate a substitution effect between voluntary disclosure and real earnings management in SEO firms. Specifically, when restrictions on disclosure prior to equity offerings are removed, SEO firms opt for increased disclosure and reduced level of real activities manipulation. Our paper adds to the literature on earnings management activities around SEOs. It contributes to the studies on the impact of voluntary disclosure on earnings management by employing a difference-in-differences design to address the endogeneity issue in this line of research. It also has important implications for regulations on securities offerings.