Zero leverage puzzle: Do labour laws matter?

Exploiting the staggered passage of labour protection laws in the United States, we find that higher labour adjustment costs increased the likelihood of observing zero leverage firms by 22%. This effect is significantly larger in states with stronger unionization, in industries with higher volatility and concentration, and in firms with higher labour intensity. Both within-firm changes in debt policies and higher propensity of newer firms to be debt-free are important in explaining these patterns. Overall, our work contributes to the literature on the relation between financial and labour markets by highlighting the role of labour laws in explaining the zero-leverage puzzle.     

The EU self-surplus puzzle: an indication of VAT fraud?

International Tax and Public Finance - The world runs a trade surplus with itself: the reported values of exports exceed the reported values of imports. This is logically impossible but a...     

Culture and capital structure: What else to the puzzle?

This paper investigates four of Hofstede's cultural dimensions –individualism, masculinity, uncertainty avoidance, and long-term orientation– influence on firms' choices of short-term and long-term capital structures. Cultures influence on corporate risk-taking may drive their debt-to-equity mix based on the higher of their equity book or market value. We empirically test culture influence with a sample of 5968 firms from five industry sectors, across 33 countries, over 2009–2017. We find firms national culture influencing their choices of short-term and long-term debt to book and market value of equity. The influence is more significant on the short-term than the long-term capital structures. Furthermore, it is more significant on the short-term debt to market value of equity and on the long-term debt to book value of equity. Our robustness checks at the firm-level, country-level and sample-level confirm and reinforce our main results. These findings would provide financial analysts, investors, and creditors an in-depth understanding when comparing international firms' capital structures.     

FinTech and capital allocation efficiency: Another equity-efficiency dilemma?

The emergence of financial technology (FinTech) introduces new tools and solutions to lending and equity markets, and has significantly increased the accessibility of marginal firms to funds, a property known as financial inclusion. However, little is known about whether such inclusion is acquired at the cost of efficiency. This paper provides evidence on how FinTech affects the efficiency of capital allocation using firm-level data for China over the period of 2010–2020. The development of FinTech is measured as the number of FinTech firms in a city. We find interesting evidence that the growth of FinTech reduces the capital available to more efficient firms. The evidence stands robust at both the firm- and industry-level. We identify two mechanisms that can explain the inefficiencies of FinTech in capital allocation. The first mechanism involves FinTech-induced competition in the lending market, and the second explanation relates to the property of equity-efficiency dilemma of FinTech. Finally, we show that the inefficiencies of FinTech in capital allocation arise mainly from its impact on debt financing.     

Halloween or January? Yet another puzzle

Recent works suggest a potentially exploitable effect in US markets, the ‘Halloween Indicator’. This suggests that the greater part of changes in equity markets arises over the November-April period, with little change over the summer months, simultaneous with no evident changes in the risk profiles of the two six-month periods. We re-examine this and find contradictory evidence. Over the 1926-2002 period we find rather that the effect demonstrated may well be a reflection of the well-known January anomaly. Our conclusion therefore is that the jury remains out on the existence of a semi-annual seasonality.     

Do security breaches matter? The shareholder puzzle

This article analyzes the effect of computer breaches on publicly traded equities from 2005 to 2017. An event study is performed and breaches analyzed conditioned on whether the breach announcement has been made in the mainstream media or through other channels. We find that in the period prior to the announcement date in the media, the mean abnormal return is negative, reflecting a likely leakage of information. In the period following the announcement date, the mean abnormal return is positive, often more than offsetting the previous declines. The findings have important implications for analysts, portfolio managers, institutional investors, and regulators.     

Leverage vs. feedback: Which Effect drives the oil market?

This article brings new insights on the role played by (implied) volatility on the WTI crude oil price. An increase in the volatility subsequent to an increase in the oil price (i.e. inverse leverage effect) remains the dominant effect as it might reflect the fear of oil consumers to face rising oil prices. However, this effect is amplified by an increase in the oil price subsequent to an increase in the volatility (i.e. inverse feedback effect) with a two-day delayed effect. This lead-lag relation between the oil price and its volatility is central to any type of trading strategy based on futures and options on the OVX implied volatility index. It is of interest to traders, risk- and fund-managers.     

Does the forward premium puzzle disappear over the horizon?

This paper provides the first comprehensive study of the horizon effect in tests of the forward rate unbiasedness hypothesis. It estimates Fama regressions employing 1-month through to 10-year horizon data for the five most heavily traded US dollar currency pairs pre-crisis 1980–2006. In contrast with extant studies, it fully deals with the econometric problems of long horizon regressions by means of a novel heteroskedastic- and autocorrelation-consistent bootstrap. The regression results confirm a clear horizon effect in that the slope coefficient approaches unity as the forward contract maturity is extended. The puzzle disappears at the 3-year horizon and beyond for all currencies.     

Do oil futures prices predict stock returns?

This paper explores stock return predictability by exploiting the cross-section of oil futures prices. Motivated by the principal component analysis, we find the curvature factor of the oil futures curve predicts monthly stock returns: a 1% per month increase in the curvature factor predicts 0.4% per month decrease in stock market index return. This predictive pattern is prevailing in non-oil industry portfolios, but is absent for oil-related portfolios. The in- and out-of-sample predictive power of the curvature factor for non-oil stocks is robust and outperforms many other predictors, including oil spot prices. The predictive power of the curvature factor comes from its ability to forecast supply-side oil shocks, which only affect non-oil stocks and are hedged by oil-related stocks.     

How should firms selectively hedge? Resolving the selective hedging puzzle

We provide a model of intertemporal hedging consistent with selective hedging, a widespread practice corroborated by recent empirical studies. We argue that the optimal hedge is a value hedge involving total current value of future earnings. More importantly, the hedging decision is independent of risk preferences of the firm or agent. Our closed-form solutions imply several implications for the risk management policy in a firm. In order to lock in profits a hedge increase is recommended in favorable states of nature, while in bad states the firm should decrease the hedge and wait. Our main new empirical implication is that selective hedging should be more prevalent in industries where managers are exposed to convex cash flow structures and are more likely to “value hedge” their exposures.     

How well does nonlinear mean reversion solve the PPP puzzle?

This paper addresses the degree to which models which exhibit nonlinear mean reversion (NMR) present a resolution to the Purchasing Power Parity Puzzle. This paper develops a method of estimating a representative distribution of half lives which is based upon the observed distribution of shocks in a given time series rather than choosing shock sizes arbitrarily which is the current practice in the literature. This approach is implemented with data on five real exchange rates. The empirical analysis shows that half lives shorter than the consensus are observed frequently enough to support the proposition that NMR is a solution to the PPP puzzle.     

Are real options a missing piece in the diversification-value puzzle?

This paper examines the impact of corporate diversification on a firm's market value in terms of changes in its mix of value sources between growth options and assets-in-place. We argue that the traditionally assumed replicability of corporate diversification benefits by individual investors might not be as feasible when diversification implies acquiring new growth options as when it only involves assets-in-place investments. We further explain why a different effect of diversification on a firm's mix of value sources can occur, therefore leading to a mediating role of growth options between diversification and market value. Using a panel sample of U.S. firms from 1998 to 2010, we find that a firm's growth options portfolio helps explain the effect of diversification strategy on its market value.     

Do precious metals hedge crude oil volatility jumps?

In this study, we investigate the impacts of contemporaneous and lagged implied oil volatility (OVX) jumps on precious metals (gold, palladium, platinum, and silver) with the focus on hedging property of precious metals. Additionally, the impacts of OVX jumps on precious metals is investigated in returns and volatility. The results show that gold returns are relatively less responsive against contemporaneous and lagged OVX jumps, thus, gold acts as a weak hedge against OVX jumps. However, other metals (copper, palladium, platinum, and silver) do not serve as a hedge against contemporaneous OVX jumps. Nevertheless, these metals takeover the traditional hedging favourite ‘gold’ in the case of lagged OVX jumps and offers a strong hedge. It suggests that ignoring past information could severely undermine the investigation of OVX and the precious metal relationship. OVX jumps result in an increased volatility in precious metals, thereby indicating that all precious metals behave as a single asset class in terms of volatility transmissions. Moreover, gold is the contemporaneous metal of choice when risk perception is high or investors are averse to risk. Nevertheless, as information is diffused, other metals yield better performance as a hedge.     

Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role?

This study examines whether geopolitical risk (GPR) exhibits an ability to forecast crude oil volatility from a time-varying transitional dynamics perspective. Unlike previous studies that assume an oversimplification of the fixed transition probabilities for crude oil volatility, we develop an asymmetric time-varying transition probability Markov regime switching (AS-TVTP-MS) GARCH model. In-sample estimated results show that GPR yields strong evidence of regime switching behavior on crude oil volatility and that the negative shocks of GPR result in greater effects on switching probabilities than positive shocks. Out-of-sample results indicate that the AS-TVTP-MS GARCH model containing the GPR index outperforms other models, suggesting that the consideration of GPR information and time-varying regime switching together results in superior predictive performance. Moreover, the predictability of oil volatility is further verified to be economically significant in the framework of portfolio allocation. In addition, our results are robust to various settings.     

Corruption and the shadow economy: like oil and vinegar, like water and fire?

From a theoretical point of view, the relationship between corruption and the shadow economy is ambiguous: They can either be substitutes or complements. This paper contributes to this debate by using a structural equation model with two latent variables to extract information on various dimensions of corruption and the shadow economy. Analyzing a sample of 51 countries around the world over the period 2000 to 2005, we present empirical evidence for a complementary (positive) relationship of corruption and the shadow economy.     

The historic oil price fluctuation during the Covid-19 pandemic: What are the causes?

On 20 April 2020, the West Texas Intermediate (WTI) crude oil price dropped to negative levels for the first time in history. This study examines the factors underlying the historic oil price fluctuation during the Covid-19 pandemic. The autoregressive distributed lag (ARDL) bounds testing approach incorporating a structural break is applied to the daily series from 17 January to 14 September 2020 to analyze long-run relationships and short-run dynamics. The results reveal that increases in Covid-19 pandemic cases, US economic policy uncertainty, and expected stock market volatility contributed to the fall in the WTI crude oil price, whereas the fall in the global stock markets appears to significantly reduce the fall. Furthermore, the Russia–Saudi Arabia oil price war and speculation on oil futures are shown to play a critical part in the collapse of the oil markets. The findings are consistent with our expectations. Although it is reasonable to assume that the solution to this oil crisis is a pick-up in global oil demand, which will occur only when the novel coronavirus is defeated, this study proposes policy recommendations to cope with the current oil price crash.     

Investor flow-chasing and price–performance puzzle: Evidence from global infrastructure funds

Despite the importance and peculiarity of the infrastructure fund, determinants of infrastructure fund flow and the relation between fees and fund performance were poorly understood. This paper documents two new findings using a unique dataset for global infrastructure funds from January 2005 to June 2019. First, investor flow-chasing exists at the level of infrastructure fund companies, which is intensified by the opacity of information and uncertainty of returns. Second, infrastructure funds charge higher fees even when their before-fee performance is worse, which is explained by fund characteristics and year effects. Based on these findings, we put forward countermeasures from the perspectives of investors, regulators, investor protection managers, and fund managers, with incentive mechanism reforms to alleviate the price-performance puzzle, thereby improving the efficiency of infrastructure fund portfolios.     

Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?

A reliable crude oil price forecast is important for market pricing. Despite the widespread use of ensemble empirical mode decomposition (EEMD) in financial time series forecasting, the one-time decomposition on the entire time series leads the in-sample data to be affected by the out-of-sample data. Consequently, the forecasting accuracy is overstated. This study incorporates a rolling window into two prevalent EEMD-based modeling paradigms, namely decomposition-ensemble and denoising, to ensure that only in-sample time series is processed by EEMD and used for model training. Given the time-consuming process of stepwise preprocessing and model fitting, two non-iterative machine learning algorithms, random vector functional link (RVFL) neural network and extreme learning machine (ELM), are used as predictors. Hence, we develop the rolling decomposition-ensemble and rolling denoising paradigms, respectively. Contrary to the majority of prior studies, empirical results based on monthly spot price time series for the Brent and West Texas Intermediate (WTI) markets indicate that EEMD plays a weak role in improving crude oil price forecasts when only the in-sample set is preprocessed. This is compatible with the weak form of the efficient market hypothesis (EMH). Nevertheless, the suggested rolling EEMD-denoising model has an advantage over other employed models for long-term forecasting.     

Volatility forecasting of crude oil futures market: Which structural change-based HAR models have better performance?

The paper focuses on the smooth and sharp structural changes in crude oil futures volatility and singles out the flexible Fourier form (FFF) and the modified ICSS algorithm to detect them, respectively, so as to explore whether different structural change-based HAR models exhibit significantly better performance for crude oil return volatility forecasting than traditional HAR-type models. The empirical results indicate that, on the one hand, crude oil market displays a strong evidence of breaks, and the incorporation of trigonometric terms can account for the structural changes in crude oil return volatility. On the other hand, the flexible Fourier form (FFF) based HAR-type models and the Structural Breakpoints (SB) based HAR-type models yield superior forecasting performance than traditional HAR-type models. Meanwhile, the forecasting results and economic performance of the former usually outperform the latter, particularly for the short- and medium-term forecasts.