# Research articles for the 2019-04-14

arXiv

This work presents a methodology for forward electricity contract price projection based on market equilibrium and social welfare optimization. In the methodology supply and demand for forward contracts are produced in such a way that each agent (generator/load/trader) optimizes a risk adjusted expected value of its revenue/cost. When uncertainties are represented by a discrete number of scenarios, a key result in the paper is that contract price corresponds to the dual variable of the equilibrium constraints in the linear programming problem associated to the optimization of total agents' welfare. Besides computing an equilibrium contract price for a given year, the methodology can also be used to compute the evolution of the probability distribution associated to a contract price with a future delivery period; this an import issue in quantifying forward contract risks. Examples of the methodology application are presented and discussed

arXiv

Several algorithms have been proposed to filter information on a complete graph of correlations across stocks to build a stock-correlation network. Among them the planar maximally filtered graph (PMFG) algorithm uses $3n-6$ edges to build a graph whose features include a high frequency of small cliques and a good clustering of stocks. We propose a new algorithm which we call proportional degree (PD) to filter information on the complete graph of normalised mutual information (NMI) across stocks. Our results show that the PD algorithm produces a network showing better homogeneity with respect to cliques, as compared to economic sectoral classification than its PMFG counterpart. We also show that the partition of the PD network obtained through normalised spectral clustering (NSC) agrees better with the NSC of the complete graph than the corresponding one obtained from PMFG. Finally, we show that the clusters in the PD network are more robust with respect to the removal of random sets of edges than those in the PMFG network.

arXiv

We propose a new data-driven method to select the optimal number of relevant components in Principal Component Analysis (PCA). This new method applies to correlation matrices whose time autocorrelation function decays more slowly than an exponential, giving rise to long memory effects. In comparison with other available methods present in the literature, our procedure does not rely on subjective evaluations and is computationally inexpensive. The underlying basic idea is to use a suitable factor model to analyse the residual memory after sequentially removing more and more components, and stopping the process when the maximum amount of memory has been accounted for by the retained components. We validate our methodology on both synthetic and real financial data, and find in all cases a clear and computationally superior answer entirely compatible with available heuristic criteria, such as cumulative variance and cross-validation.

arXiv

Solar Renewable Energy Certificate Markets (SREC) markets are a relatively novel market-based system to incentivize the production of energy from solar means. A regulator imposes a floor on the amount of energy each regulated firm must generate from solar power in a given period, providing them with certificates for each generated MWh. Firms offset these certificates against the floor, paying a penalty for any lacking certificates. Certificates are tradable assets, allowing firms to purchase/sell them freely. In this work, we formulate a stochastic control problem for generating and trading in SREC markets for a regulated firm's perspective accounting for generation and trading costs, and the impact both have on prices. We provide a characterisation of the optimal strategy using the stochastic maximum principle and develop a numerical algorithm to solve this control problem, Based on this numerical solution, we provide detail and intuition for the optimal strategy for a regulated firm.

arXiv

As is known, an option price is a solution to a certain partial differential equation (PDE) with terminal conditions (payoff functions). There is a close association between the solution of PDE and the solution of a backward stochastic differential equation (BSDE). We can either solve the PDE to obtain option prices or solve its associated BSDE. Recently a deep learning technique has been applied to solve option prices using the BSDE approach. In this approach, deep learning is used to learn some deterministic functions, which are used in solving the BSDE with terminal conditions. In this paper, we extend the deep-learning technique to solve a PDE with both terminal and boundary conditions. In particular, we will employ the technique to solve barrier options using Brownian motion bridges.

SSRN

We employ a dynamic adjustment model (Flannery and Rangan, 2006) to investigate the determinants of capital structure and speed of adjustment (Drobetz and Wanzenried, 2006) in a panel of 85 U.S. ICT firms over the years 1990 to 2013. We estimate the capital structure using a wide range of factors commonly used in the empirical literature (growth and investment opportunities, profitability, firm size, default risk, and industry median capital structure). We expand on this literature to include two additional determinants: asset turnover, an inverse measure of firm agency costs (Morellec, Nikolov, and Schurhoff, 2012; Ang, Cole, and Lin, 2000), and R&D activity (Aghion, Bond, Klemm, and Marinescu, 2004). We find that the speed of adjustment increases with firm size, growth opportunities, and distance from the target capital structure, and decreases with default risk and agency costs. We also find that R&D expenditures and agency costs cause firms to maintain lower levels of debt. We employ four recently developed estimators in dynamic panel-data econometrics: the double-censored fractional estimator (Elsas and Florysiak, 2011), the bias-corrected least-squares dummy-variable estimator (Bruno, 2005), the iterative bootstrap-based bias correction for the fixed-effects estimator (Everaert and Pozzi, 2007), and the fixed-effects quasi-maximum-likelihood estimator (Kripfganz, 2016; Hsiao, Pesaran, and Tahmiscioglu, 2002). In addition, our panel-data regression results show that in the ICT sector, the leverage ratio exhibits high persistence. Moreover, it positively relates to growth and investment opportunities, firm size, capital investment, and industry median capital structure, and negatively relates to profitability and default risk.

SSRN

We study bankruptcy outcomes of 275 firms and find that hiring CEOs with golden parachutes (GPs) during financial distress is associated with a lower probability of liquidation. In contrast, firms led by incumbent CEOs with GPs are more likely to be liquidated, as are firms led by new CEOs without GPs. Since GPs are nullified during bankruptcy, the observed relationship cannot be attributed to an explicit incentive effect. Rather, we contend that during financial distress GPs help recruit reputable CEOs who, even without explicit incentives, continue to maximize shareholder value due to implicit reputational and career concerns.

arXiv

Modelling all possible life cycles of a company in a highly competitive economic environment gives a significant advantage to the owner in his business investment activities. This article proposes and analyses a dynamic model of a company's life cycle with known action costs and transition probabilities, that can be affected by an outside influence. For this task, the Markov model was utilized. The proposed model is illustrated on a task of determining an advertising policy for a car dealership, that would increase the stock equity of a company. The result demonstrates the usefulness of a model for use in determining future actions of a company. We also review multiple models of the influence of outside factors on a company's total capitalization.

SSRN

Motivation â€" Dyreng, Hanlon, Maydew, and Thornock (2017) find that US domestic corporations achieve greater levels of tax avoidance than their multinational counterparts (MNEs). Motivated by recent research, this study examines if the authorsâ€™ unexpected findings are associated with the research design choices used when the cash effective tax rate (Cash ETR) estimates corporate tax avoidance. Objectives â€" The objectives of this study are to develop and test an alternative corporate tax avoidance measure that is not subject to the problematic research design choices that exist when estimating the Cash ETR. Specifically, we investigate alternatives to the researcher-selected cutoffs, the methods used to control for influential observations, and the discarding of loss years. Methods â€" Building on the linear corporate tax function used by Edwards, Kubata, and Shevlin (2018) this study develops an alternative corporate tax avoidance measure. The tax avoidance measure developed in this study is the marginal propensity to tax (MPT) which represents the incremental tax payment in the current period due to an additional dollar of current period pretax income.Results â€" When testing the MPT measure on the Dyreng et al. (2017) dataset, this study finds that MNEs engage in significantly more tax avoidance than domestic corporations. Specifically, the findings indicate that the MPT for MNEs is 3 percentage points lower than domestic corporations (26 percent and 29 percent, respectively). The results suggest that the unexpected results reported by Dyreng et al. (2017) reflect the undue influence of a few observations combined with the ordinary least squares (OLS) estimation procedure. To our knowledge, this is the first study to provide empirical evidence to suggest that the treatment of influential observations is an important factor to consider when estimating corporate tax avoidance. Contribution â€" The MPT measure provides an empirically viable corporate tax avoidance estimator that offers several distinct advantages. The MPT 1) provides an easily interpretable measure of corporate tax avoidance that is meaningful for all firm year observations, 2) controls for changes in pretax income by using a linear corporate tax function, 3) utilizes robust regression to control for influential observations â€" eliminating the need for a priori choices by researchers, and 4) uses piecewise linear regressio n to accommodate the inclusion of loss years â€" after documenting the nonlinearity that exists when including loss years in the sample. Additionally, using the MPT measure, this study provides an alternative explanation for Dyreng et al.â€™s (2017) unexpected finding that US domestic corporations achieve greater levels of tax avoidance than MNEs.

SSRN

This paper empirically investigates firm-specific determinants of agency costs, a relatively new and unexplored area in corporate finance. We estimate dynamic agency costs models, linking debt, firm size, and R&D activity to agency costs for a panel of U.S. information and communication technology (ICT) firms over 1990-2013. We adopt the Blundell and Bond (1998) two-step system GMM technique, which explicitly accounts for persistence, endogeneity, and unobservable firm heterogeneity. We provide the first evidence that our inverse proxy for agency costs, namely asset turnover (Ang, et al., 2000), exhibits an inverted U-shaped relationship with debt and a U-shaped relationship with firm size and R&D activity. These findings imply that agency costs experience a minimum value (in case of debt) and a maximum value (in case of firm size and R&D activity) and, therefore, that agency costs are higher at both low and high levels of debt, and lower at both low and high levels of firm size and R&D activity. We find that the level of debt of the average firm in the sample falls below the level that minimizes agency costs. We also document that, consistent with the agency literature, short-term debt provides an additional effective monitoring mechanism to alleviate agency costs. Our findings reveal that agency costs are dynamic in nature, mean-reverting, and persistent over time. This notion confirms the Florackis and Ozkan (2009) conjecture that managers behave as though an optimal level of agency costs exist that they pursue. Finally, we find a positive association between firm profitability and agency costs and a negative association between agency costs and firm growth. Extensive additional analysis confirms the robustness of our results.

SSRN

Using positions data on bond futures, I document that speculators' spread trades contain private information about future economic activities and asset prices. Strong steepening trades are associated with negative payroll surprises in subsequent months and can predict asset markets' reaction to future payroll releases, suggesting that speculators hold superior information about future payrolls. Steepening trades can also predict the rise of stock prices within a few hours before subsequent FOMC announcements, implying that the pre-FOMC stock drift is driven by informed speculation. Overall, evidence highlights spread traders' superior information and its important role in explaining announcement returns and pre-announcement drifts.

SSRN

We analyze participation by investment banks and other nonbank lenders in syndicated loan financings. We find that investment banks are more likely than commercial banks to lead syndicates to riskier borrowers and they participate more often than commercial banks in the riskier tranches of multi-facility loans. Though non-bank entities such as insurance companies and mutual funds rarely play lead roles in syndications, they also participate more frequently in riskier, multi-facility syndicated credits. Maskara (2010) argues that multi-facility syndicated loans derive economic value from the participation of lender groups with varying levels of risk aversion. We find empirical support for his theoretical arguments.

arXiv

Let $X$ and $Y$ be domains of $\mathbb{R}^n$ equipped with probability measures $\mu$ and $ \nu$, respectively. We consider the problem of transporting $\mu$ to $\nu$ in a way that minimizes a cost $c: X \times Y \to \mathbb{R}$ of the form $c(x,y)= \Psi(x-y)$ for some convex function $\Psi$. For this problem, we find an associated K\"ahler manifold whose orthogonal holomorphic bisectional curvature is proportional to the so-called MTW tensor, which was introduced by Ma, Trudinger, and Wang and plays an essential role in the regularity theory of optimal transport. We also show that relative $c$-convexity geometrically corresponds to geodesic convexity with respect to a dual affine connection. Taken together, these results provide a geometric framework for optimal transport which is complementary to the pseudo-Riemannian theory of Kim and McCann.

We provide several applications of this work. In particular, we find a complete K\"ahler surface with non-negative orthogonal bisectional curvature that is not a Hermitian symmetric space or biholomorphic to $\mathbb{C}^2$. We also address a question in mathematical finance raised by Pal and Wong on the regularity of \textit{pseudo-arbitrages}, or investment strategies which outperform the market.

SSRN

We show that small firms using syndicated loans for their mid- and long-term financial needs have significantly higher leverage than firms that do not borrow in this market. This difference cannot be attributed to firm characteristics like the availability of growth opportunities, asset tangibility, R&D spending, profitability and net sales that are known to influence capital structure. We also find that the capital structure of other firms that borrow in the syndicated loan market is not different from those that do not. We show that already highly leveraged small firms are more likely to borrow in the syndicated loan market than other firms. The higher debt in the capital structure of small firms that rely on syndicated loans consequently can be attributed to the availability of capital rather than demand for capital, as shown more generally by Faulkender and Petersen (2006).

SSRN

Firms in Latin America could differentiate themselves by adopting better information disclosure practices. In this paper, we construct an Information Disclosure Index (IDI) for a sample of 454 firms in the six largest Latin America countries. We look at 3.191 company reports and show that firms with better disclosure practices have better market valuation (Tobinâ€™s Q) and operating performance (ROE). We then measure the tone of the information disclosed using word content analysis, and find that uncertainty in tone is negatively associated with higher firm valuation (Tobinâ€™s Q) and better financial performance (ROE).