Research articles for the 2019-07-21
SSRN
We introduce the Realized moMents of Disjoint Increments (ReMeDI) paradigm to measure microstructure noise (the deviation of the observed asset prices from the fundamental values caused by market imperfections). We propose consistent estimators of arbitrary finite moments of a microstructure noise process, which could be serially dependent and nonstationary, based on high-frequency data. We characterize the limit distributions of the proposed estimators and construct robust confidence intervals under infill asymptotics. We further demonstrate that the ReMeDI approach also works on low-frequency, non-infill data. It thus can be applied to many asset pricing and macroeconomic models, in which the time series have a permanent and a transitory component.We propose two liquidity measures that gauge the instantaneous and average bid-ask spread with potentially autocorrelated order flows. They can be consistently estimated within our framework. We provide an economic model to justify such measures as an intermediary's inventory risk measure when meeting serially dependent liquidity demand. Empirically we find our new liquidity measures are very effective to identify liquidity drains during the Flash Crash, when the market experienced extreme selling pressures.
arXiv
Introduction: Food insecurity has been associated with increased risk for several health conditions and poor management of chronic disease. Key determinants for household food insecurity are income and food costs. Whereas short-term household incomes are likely to remain static, increased food prices would be a significant driver of food insecurity.
Design: Structured expert judgement elicitation, a well-established method for quantifying uncertainty, using experts. Each expert estimated the median, 5% and 95% quantiles of changes in ten food categories under Brexit deal and no-deal scenarios. These were aggregated based on the accuracy and informativeness of the experts on calibration questions.
Results Expected changes in food costs varied between categories. When combined in proportions used to calculate CPI, median food price changes for Brexit with a deal are expected to be +6.1% [90% credible interval:-3%, +17%] and with no deal +22.5% [+1%, +52%]. Conclusions: The number of households experiencing food insecurity and the severity of food insecurity is likely to increase since the median food cost increases expected after Brexit are significant. The increasing burden on healthcare services is likely to increase sharply. Moreover, the uncertainty in food costs is skewed, making higher increases more likely than lower rises. The plausible worst case would entail severe impacts. The demand for health services in both the short and longer term is likely to increase due to the effects of food insecurity on disease incidence, management of chronic conditions, amplifying the involvement of physicians in referral to emergency food relief.
SSRN
There is little evidence on how the large market for credit score improvement products affects consumers or credit market efficiency. A randomized encouragement design on a standard credit builder loan (CBL) identifies null average effects on whether consumers have a credit score and the score itself, with important heterogeneity: those with loans outstanding at baseline fare worse, those without fare better. Selection, treatment effect, and prediction models indicate the CBL reveals valuable information to markets, inducing positive selection and making credit histories more precise while keeping credit scores’ predictive power intact. With modest targeting changes, CBLs could work as intended.
SSRN
We examine the effect of economic policy uncertainty (EPU) on the financial reporting quality of US firms over 1999-2015. Using accruals-based earnings management as a proxy for financial reporting quality and the index of Baker et al. (2016) as an EPU measure we show that they exhibit a positive and significant association in accordance with the “lean against the wind†hypothesis. We also find a causal effect by employing the recently developed index of partisan conflict in the US Congress of Azzimonti (2018) as an instrument for EPU. In a cross-sectional analysis, we further show that the positive relationship between EPU and earnings management strengthens for riskier firms and firms belonging to politically sensitive industries. Overall, these results indicate that managers aim to provide outsiders with an improved financial picture of the company when EPU is high. These findings suggest that investors and regulators should be wary of firms’ financial reporting quality in periods of high economic policy uncertainty.
SSRN
This paper explores whether and how political connections affect the market for corporate bonds issued by privately owned enterprises (POEs) in China. We test two competing theories â€" the zero-default myth and the borrower channel theory â€" that predict how political connections affect the likelihood of bond issuance, refinancing costs, the market reaction to a bond issue announcement, and firms’ post-issue performance. Using a sample of Chinese POEs from 2007 to 2016, we show that â€" in line with the zero-default myth theory â€" politically connected POEs are more likely to issue corporate bonds as a debt financing instrument than their non-connected counterparts. They also achieve lower coupon rates (i.e., lower refinancing costs), despite exhibiting lower overall performance after bond issuance. We find that investors react positively to corporate bond-issuing announcements if the issuing firm is politically connected. At the same time, our research indicates that politically connected bond-issuing POEs in China have weaker corporate governance and a surprisingly higher default probability than non-connected issuers.
SSRN
Psychophysicists have well-established that for simple visual stimuli, what we hear can influence what we see. Here we show in a series of laboratory experiments that this is also true for the perception of price volatility. We provide evidence that pairing visual exposure to high (low) price volatility with high (low) ambient sound sharpens volatility perception during volatility regimes and, as a direct consequence of that, worsens volatility perception during the transition phase between regimes. We further find evidence that such influence of ambient sound on volatility perception reflects some direct impact of sound on perceived volatility rather than other potential mechanisms such as associative learning, increased attention, and increased arousal. We propose that this originates from the evolutionary advantage to using both visual and auditory cues to establish the relative predictability of events in natural environments. These findings provide novel insights into exchange, bank, and proprietary trading floor designs, and offer important implications for the decision-making of the active traders on those floors.
arXiv
Workers who earn at or below the minimum wage in the United States are mostly either less educated, young, or female. Little is known, however, concerning the extent to which the minimum wage influences wage differentials among workers with different observed characteristics and among workers with the same observed characteristics. This paper shows that changes in the real value of the minimum wage over recent decades have affected the relationship of hourly wages with education, experience, and gender. The results suggest that changes in the real value of the minimum wage account in part for the patterns of changes in education, experience, and gender wage differentials and mostly for the patterns of changes in within-group wage differentials among female workers with lower levels of experience.
SSRN
In October 2014, Estimize began providing management guidance on its platform for firms that provide guidance. We exploit this exogenous shock to salience of management guidance as a quasi-natural experiment to study how salience of management guidance affects forecasting behavior. Using a difference-in-differences design, we show that an increase in salience increases the number of Estimize contributors, increases contributors’ forecast accuracy, lowers contributors’ forecast dispersion, and increases herding by contributors towards the guidance forecast. Our results imply that, absent endogeneity, the increased salience of management guidance increases attention to the guidance information by market participants with attentional constraints, thereby increasing the likelihood that they incorporate guidance information when forming expectations about a firm’s future performance.
SSRN
Using login data from an online trading brokerage, we test whether investors have a greater propensity to sell assets when they have made a gain rather than a loss relative to the price at their latest login to their account. This disposition effect on returns since latest login exists alongside the widely-documented disposition effect on returns since purchase. We also show a strong interaction effect: investors tend to hold on to stocks that have made either a negative return since latest login or a negative return since purchase. Even a small loss since latest login annuls the disposition effect of a much larger gain since purchase. We interpret these findings in a Prospect Theory inspired model of realization utility with enhanced loss aversion.
arXiv
We propose a class of financial models in which the prices of assets are L\'evy-Ito processes driven by Brownian motion and a dynamic Poisson random measure. Each such model consists of a pricing kernel, a money market account, and one or more risky assets. The Poisson random measure is associated with an $n$-dimensional L\'evy process. We show that the excess rate of return of a risky asset in a pure-jump model is given by an integral of the product of a term representing the riskiness of the asset and a term representing the level of market risk aversion. The integral is over the state space of the Poisson random measure and is taken with respect to the L\'evy measure associated with the $n$-dimensional L\'evy process. The resulting framework is applied to the theory of interest rates and foreign exchange, allowing one to construct new models as well as various generalizations of familiar models.
SSRN
Semi-analytical methods are presented for computing joint loss distributions of credit portfolios in which obligors may occur more than once. A single-factor Gaussian copula model is used. It is demonstrated how to compute loss distributions if (1) some portfolio positions are tranches, (2) varying seniority bonds of the same obligor are present, and (3) obligors may be repeated across positions. The algorithm makes use of an exhaustive enumeration of scenarios and is exponential in time complexity. However, it is demonstrated that certain groups of conditional loss distributions can be combined using convolution which is faster then enumeration of scenarios. Monte Carlo simulations confirm the correctness of the proposed set of algorithms. The loss distribution of a portfolio of ten 10-name tranches with high overlap of names between tranches can be computed in approximately five seconds.
arXiv
We introduce a computational framework for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the replicating martingale of a portfolio from a finite sample of its terminal cumulative cash flow. The learned replicating martingale is given in closed form thanks to a suitable choice of the kernel. We develop an asymptotic theory and prove convergence and a central limit theorem. We also derive finite sample error bounds and concentration inequalities. Numerical examples show good results for a relatively small training sample size.
SSRN
This paper intends to show that potential hold-up problems with government spur greater policy uncertainty, thus discourage investment. We employ the enactment of Property Law as an external shock and find that after the legislation, the investment level significantly goes up in firms which are more likely to be affected by this hold-up problem.
SSRN
This paper introduces a model for rating a firm's default risk based on fuzzy logic and expert system and an associated model of sensitivity analysis (SA) for managerial purposes. The rating model automatically replicates the evaluation process of default risk performed by human experts. It makes use of a modular approach based on rules blocks and conditional implications. The SA model investigates the change in the firm's default risk under changes in the model inputs and employs recent results in the engineering literature of Sensitivity Analysis. In particular, it (i) allows the decomposition of the historical variation of default risk, (ii) identifies the most relevant parameters for the risk variation, and (iii) suggests managerial actions to be undertaken for improving the firm's rating.
arXiv
In this study, we applied a stochastic spread pairs trading strategy on the Indian commodity market. The complete set of commodities were taken whose spot price was available for the period of January 1st 2010 to December 31st 2018 including energy, metals and the agricultural commodity sector. Spot data was taken from the MCX pooled spot prices for 17 commodities. The data was split into training period (January 1st 2010 to 14th March 2017) and testing period(15th Match 2017 to 31st December 2018). The splitting was done using a 80:20 split.Johanssen Cointegration tests were done on training data for pairs of commodities to check for long-run relationship and the cointegrated commodities were selected for formation of the trading process. We found a total of 12 cointegrated pairs out of 136 possible pairs. Cointegration was assumed for the testing period. A single-factor stochastic trading approach was applied on the logarithmic spread of the cointegrated pairs for both the training and testing period.The parameters of stochastic spread model were estimated using differential evolution algorithm. Also parameters for the trading rule were optimized by backtesting on the training period and assumed for the testing period. The results show a sharpe ratio of above 1.4 for all the commodity cointegrated pairs in the backtesing period.
SSRN
A systemically important firm could be too-connected-to-fail and/or too-important-to-fail, two properties which centrality measures and community detection methods can capture respectively. This paper examines the performance of these measures in a variance decomposition global financial network. Too-connected-to-fail risk and vulnerability rankings are quite robust to the choice of centrality measure. The PageRank centrality measure, however, does not seem as suitable for assessing vulnerabilities. Two community identification methods, edge betweenness and the map equation (Infomap) were used to identify systemic communities, which in turn capture the too-important-tofail dimension of systemic risk. The first method appears more robust to different weighting schemes but tends to isolate too many firms. The second method exhibits the opposite characteristics. Overall,the analysis suggests that centrality measures and community identification methods complement each other for assessing systemic risk in financial networks.
SSRN
This paper exploits a unique natural experiment in which a regulator limited voluntary disclosure of oil and gas firms. We examine the implications of this disclosure rule on unexplained trading volume and market liquidity. Relying on the theoretical framework of Kim and Verrecchia (1994), the analysis assumes that the rule is an exogenous shock that increased the precision of disclosed information. Based on a sample of current filings, we find indications that on average, after the new regulation came into effect, filings of oil and gas firms generated less unexplained trading volume than they had prior to the regulation. A possible interpretation of these findings is a decline in investor disagreement following the rule. We also find that liquidity around current filings of oil and gas firms increased following the disclosure rule. Moreover, some results indicate that differences in unexplained trading volume associated with the characteristics of the filing firm or of the filing itself prior to the rule were moderated after it came into effect.
SSRN
Dealers, who strategically supply liquidity to traders, are subject to both liquidity and adverse selection costs. While liquidity costs can be mitigated through inter-dealer trading, individual dealers’ private motives to acquire information compromise inter-dealer market liquidity. Post-trade information disclosure can improve market liquidity by counteracting dealers’ incentives to become better informed through their market-making activities. Asymmetric disclosure, however, exacerbates the adverse selection problem in inter-dealer markets, in turn decreasing equilibrium liquidity provision. A non-monotonic relationship may arise between the partial release of post-trade information and market liquidity. This points to a practical concern: a strategic post-trade platform has incentives to maximize adverse selection and may choose to release information in a way that minimizes equilibrium liquidity provision.
