# Research articles for the 2020-06-26

Arbitrage with Financial Constraints and Market Power

SSRN

I study how financial constraints affect liquidity provision and welfare under different structures of the arbitrage industry. In competitive markets, financial constraints may impair arbitrageursâ€™ ability to provide liquidity, thereby reducing other investorsâ€™ welfare. Instead, in imperfectly competitive markets, I characterize situations in which imposing constraints on arbitrageurs leads to a Pareto improvement relative to a noconstraint case. Further, unlike the competitive case, a drop in arbitrage capital does not always lead to a reduction in market liquidity. A subtle interaction between financial constraints and arbitrageursâ€™ market power generates these Pareto improvmentand novel comparative statics.

SSRN

I study how financial constraints affect liquidity provision and welfare under different structures of the arbitrage industry. In competitive markets, financial constraints may impair arbitrageursâ€™ ability to provide liquidity, thereby reducing other investorsâ€™ welfare. Instead, in imperfectly competitive markets, I characterize situations in which imposing constraints on arbitrageurs leads to a Pareto improvement relative to a noconstraint case. Further, unlike the competitive case, a drop in arbitrage capital does not always lead to a reduction in market liquidity. A subtle interaction between financial constraints and arbitrageursâ€™ market power generates these Pareto improvmentand novel comparative statics.

Bank Lending in the Knowledge Economy

SSRN

We study the composition of bank loan portfolios during the transition of the real sector to a knowledge economy where firms increasingly use intangible capital. Exploiting heterogeneity in bank exposure to the compositional shift from tangible to intangible capital, we show that exposed banks curtail commercial lending and reallocate lending to other assets, such as mortgages. We estimate that the substantial growth in intangible capital since the mid-1980s explains around 30% of the secular decline in the share of commercial lending in banks' loan portfolios. We provide suggestive evidence that this reallocation increased the riskiness of banks' mortgage lending.

SSRN

We study the composition of bank loan portfolios during the transition of the real sector to a knowledge economy where firms increasingly use intangible capital. Exploiting heterogeneity in bank exposure to the compositional shift from tangible to intangible capital, we show that exposed banks curtail commercial lending and reallocate lending to other assets, such as mortgages. We estimate that the substantial growth in intangible capital since the mid-1980s explains around 30% of the secular decline in the share of commercial lending in banks' loan portfolios. We provide suggestive evidence that this reallocation increased the riskiness of banks' mortgage lending.

Corporate Policies and the Term Structure of Risk

SSRN

We build a dynamic corporate finance model with heterogeneity in the pricing and in the firm's exposure to aggregate risks. All else equal, we show that if long-term (persistent) shocks have a higher market price than short-term (temporary) shocks, firms shorten the horizon of corporate policies, favoring payouts over investment. In the cross section, this effect is stronger for firms more exposed to long-term shocks, but can be reversed for firms more exposed to short-term shocks. Our analysis is extended to embed time variation in risk prices over the business cycle, motivated by recent evidence on the term structure of equity.

SSRN

We build a dynamic corporate finance model with heterogeneity in the pricing and in the firm's exposure to aggregate risks. All else equal, we show that if long-term (persistent) shocks have a higher market price than short-term (temporary) shocks, firms shorten the horizon of corporate policies, favoring payouts over investment. In the cross section, this effect is stronger for firms more exposed to long-term shocks, but can be reversed for firms more exposed to short-term shocks. Our analysis is extended to embed time variation in risk prices over the business cycle, motivated by recent evidence on the term structure of equity.

Is COVID-19 a Threat to Financial Stability in Europe?

SSRN

The severe economic impact of the COVID-19 pandemic could threaten financial stability. However, assessing the gravity of this threat is challenging, since banksâ€™ accounting-based loan loss provisions are sluggish. We use a Merton contingent claims model to provide a real-time, market valuation-based assessment of the impact of COVID-19 on euro area banksâ€™ corporate loan portfolios. We calibrate the model based on observed stock price responses and use different scenarios for future volatility and incurred losses in case of default. Based on stock prices as of April 20, 2020, we estimate that the market-implied losses for euro area banks could reach over â‚¬1 trillion, or 4 to 25% of corporate creditsâ€™ book value (7 to 43% of available capital and reserves). Our analysis can be viewed as an early warning indicator of potential accounting losses to follow.

SSRN

The severe economic impact of the COVID-19 pandemic could threaten financial stability. However, assessing the gravity of this threat is challenging, since banksâ€™ accounting-based loan loss provisions are sluggish. We use a Merton contingent claims model to provide a real-time, market valuation-based assessment of the impact of COVID-19 on euro area banksâ€™ corporate loan portfolios. We calibrate the model based on observed stock price responses and use different scenarios for future volatility and incurred losses in case of default. Based on stock prices as of April 20, 2020, we estimate that the market-implied losses for euro area banks could reach over â‚¬1 trillion, or 4 to 25% of corporate creditsâ€™ book value (7 to 43% of available capital and reserves). Our analysis can be viewed as an early warning indicator of potential accounting losses to follow.

MTSS-GAN: Multivariate Time Series Simulation Generative Adversarial Networks

SSRN

MTSS-GAN is a new generative adversarial network (GAN) developed to simulate diverse multivariate time series (MTS) data with finance applications in mind. The purpose of this synthesiser is two-fold, we both want to generate data that accurately represents the original data, while also having the flexibility to generate data with novel and unique relationships that could help with model testing and robustness checks. The method is inspired by stacked GANs originally designed for image generation. Stacked GANs have produced some of the best quality images, for that reason MTSS-GAN is expected to be a leading contender in multivariate time series generation.

SSRN

MTSS-GAN is a new generative adversarial network (GAN) developed to simulate diverse multivariate time series (MTS) data with finance applications in mind. The purpose of this synthesiser is two-fold, we both want to generate data that accurately represents the original data, while also having the flexibility to generate data with novel and unique relationships that could help with model testing and robustness checks. The method is inspired by stacked GANs originally designed for image generation. Stacked GANs have produced some of the best quality images, for that reason MTSS-GAN is expected to be a leading contender in multivariate time series generation.

Monitoring and Tax Planning â€" Evidence from State-Owned Enterprises

SSRN

In this paper, we provide new evidence on the association of state ownership and tax planning and show that shareholdersâ€™ monitoring incentives affect a firmâ€™s tax planning. Using the unique setting of the German corporate tax system, we distinguish between state owners that directly benefit from state-owned enterprisesâ€™ (SOEsâ€™) income tax payments and those that do not. Our results indicate that the negative association between state ownership and tax planning is concentrated in SOEs where the state owner directly benefits from the tax payments. These results are robust to various specifications and suggest that shareholdersâ€™ monitoring incentives are a determinant of firmsâ€™ tax planning activities.

SSRN

In this paper, we provide new evidence on the association of state ownership and tax planning and show that shareholdersâ€™ monitoring incentives affect a firmâ€™s tax planning. Using the unique setting of the German corporate tax system, we distinguish between state owners that directly benefit from state-owned enterprisesâ€™ (SOEsâ€™) income tax payments and those that do not. Our results indicate that the negative association between state ownership and tax planning is concentrated in SOEs where the state owner directly benefits from the tax payments. These results are robust to various specifications and suggest that shareholdersâ€™ monitoring incentives are a determinant of firmsâ€™ tax planning activities.

Mutual Funds' Competition for Investment Flows based on Relative Performance

SSRN

N mutual funds compete for fund flows based on relative performance over their average returns, by choosing between an idiosyncratic and a common risky investment opportunities. The unique constant equilibrium is derived in closed form, which imply that most funds decrease the investments in their idiosyncratic risky assets under competition, in order to lower the risk of the relative performance. It pushes all funds to herd and hurts their after-fee Sharp ratios. If a fund is sufficiently disadvantaged in the competition with poor investment opportunities, its investment can be excessively risky, and pushes all funds further away from their average. However, the performance of the disadvantaged funds can improve comparing to the case without competition and benefits the investors.

SSRN

N mutual funds compete for fund flows based on relative performance over their average returns, by choosing between an idiosyncratic and a common risky investment opportunities. The unique constant equilibrium is derived in closed form, which imply that most funds decrease the investments in their idiosyncratic risky assets under competition, in order to lower the risk of the relative performance. It pushes all funds to herd and hurts their after-fee Sharp ratios. If a fund is sufficiently disadvantaged in the competition with poor investment opportunities, its investment can be excessively risky, and pushes all funds further away from their average. However, the performance of the disadvantaged funds can improve comparing to the case without competition and benefits the investors.

Optimal Distributional Trading Gain: State Price Density Equilibrium and Bayesian Statistics

SSRN

This paper considers multiple market agents who have distinct distributional opinions about the state price density. We first determine the optimal trading positions of a utility maximizing market taker who trades Arrow-Debreu securities for prices set by the market maker. We use calculus of variations to determine the solution of this problem for a general utility function. The choice of the logarithmic utility function leads to a solution in terms of a likelihood ratio of the densities corresponding to the market taker and the market maker and the resulting optimal utility is the Kullback-Leibler divergence. In a market without the market maker, the distributional opinions of market takers reach an equilibrium in the form of the linear mixture of the distributions. We show that when the the result of the outcome is observed, the profit and loss from trading updates agents' bankrolls in a Bayesian fashion, which provides one to one correspondence for the logarithmic utility maximazers' profits and Bayesian statistics. We extend these results to exponential and power utility functions.

SSRN

This paper considers multiple market agents who have distinct distributional opinions about the state price density. We first determine the optimal trading positions of a utility maximizing market taker who trades Arrow-Debreu securities for prices set by the market maker. We use calculus of variations to determine the solution of this problem for a general utility function. The choice of the logarithmic utility function leads to a solution in terms of a likelihood ratio of the densities corresponding to the market taker and the market maker and the resulting optimal utility is the Kullback-Leibler divergence. In a market without the market maker, the distributional opinions of market takers reach an equilibrium in the form of the linear mixture of the distributions. We show that when the the result of the outcome is observed, the profit and loss from trading updates agents' bankrolls in a Bayesian fashion, which provides one to one correspondence for the logarithmic utility maximazers' profits and Bayesian statistics. We extend these results to exponential and power utility functions.

Rehabilitation of the Internal Rate of Return

SSRN

The Internal Rate of Return (IRR) is a widely used investment performance measure. However, due to well-known pitfalls and wrong assumptions rather attributed than inherent in the IRR technique some experts on capital budgeting recommend either using the IRR rule very carefully or avoiding it entirely. The paper discusses the most significant pitfalls concerning the IRR in some cases appear to be common misconceptions, while the actual pitfalls are related to the NPV rather than the IRR. The paper affirms that: (i) if a project is conventional, the IRR being the NPV function root, is a rate of return or an interest rate and much better than any other modified IRR; (ii) irrelevant IRRs are due to an imperfection of the NPV method that should not be used for evaluating nonconventional projects; (iii) if a project is nonconventional the GNPV method should be used that is an extension of the NPV method from one to two different discount rates for investment and loan; (iv) the GIRR and GERR are rate of return and rate of cost for nonconventional project.

SSRN

The Internal Rate of Return (IRR) is a widely used investment performance measure. However, due to well-known pitfalls and wrong assumptions rather attributed than inherent in the IRR technique some experts on capital budgeting recommend either using the IRR rule very carefully or avoiding it entirely. The paper discusses the most significant pitfalls concerning the IRR in some cases appear to be common misconceptions, while the actual pitfalls are related to the NPV rather than the IRR. The paper affirms that: (i) if a project is conventional, the IRR being the NPV function root, is a rate of return or an interest rate and much better than any other modified IRR; (ii) irrelevant IRRs are due to an imperfection of the NPV method that should not be used for evaluating nonconventional projects; (iii) if a project is nonconventional the GNPV method should be used that is an extension of the NPV method from one to two different discount rates for investment and loan; (iv) the GIRR and GERR are rate of return and rate of cost for nonconventional project.

Understanding Volatility-Managed Portfolios

SSRN

Contrary to the intuition that the standard risk-return tradeoff should lead to underperformance of a portfolio that scales down exposure during volatile periods a recent paper by Moreira and Muir (2017) actually shows that volatility-managed portfolios produce robust and significant alphas. The present paper investigates the mechanisms that lead to the outperformance of volatility management. By implementing timing regressions and relating returns of a volatility-managed portfolio to discount-rate, cash-flow and expected volatility news we provide evidence that volatility management outperforms by levering up good times without increasing downside exposure to fundamental risk drivers. On the contrary, during the most severe cumulative news shocks (either to cash flows, discount rates or expected volatility) the scaling strategy suffers less than the buy-and-hold portfolio and, thus, increases investor utility.

SSRN

Contrary to the intuition that the standard risk-return tradeoff should lead to underperformance of a portfolio that scales down exposure during volatile periods a recent paper by Moreira and Muir (2017) actually shows that volatility-managed portfolios produce robust and significant alphas. The present paper investigates the mechanisms that lead to the outperformance of volatility management. By implementing timing regressions and relating returns of a volatility-managed portfolio to discount-rate, cash-flow and expected volatility news we provide evidence that volatility management outperforms by levering up good times without increasing downside exposure to fundamental risk drivers. On the contrary, during the most severe cumulative news shocks (either to cash flows, discount rates or expected volatility) the scaling strategy suffers less than the buy-and-hold portfolio and, thus, increases investor utility.