Annals of Financial Economics

A necessary condition for a dominance of prospect $F$ over prospect $G$ by all commonly employed investment criteria is $E_F(x)\ge E_G(x)$. Because of this requirement, optimal investment rules fail to distinguish between two alternative prospects where in practice one prospect is preferred over the other by all investors — a paradoxical result. We suggest the general almost second-degree stochastic dominance rule (GASSD) that relaxes this necessary condition, resolves the existing paradoxes and distinguishes between mathematically and economically relevant preferences. In the comparison of stocks and bonds for various horizons, the GASSD rule does not support the idiom “stocks for the long run”.

Utilizing a mixed data sampling (MIDAS) approach, we show that a daily newspaper-based index of uncertainty associated with infectious diseases can be used to predict, both in- and out-of-samples, low-frequency movements of output growth for the United States (US). The predictability of monthly industrial production growth and quarterly real Gross Domestic Product (GDP) growth during the current period of heightened economic uncertainty due to the COVID-19 pandemic is likely to be of tremendous value to policymakers.

Let $p$, $p(I)$, $\varrho$ and $p(M)$ denote, respectively, the current stock price, the future stock price that is conditioned on information, the minimum stock market tick size and the realized future stock price. Formal theoretical proofs in this study show modeling of stock returns in continuous time induces stock returns that have parameterization as gambles over lotteries. Stock returns have parameterization as gambles because in the presence of fairness of formation of $p<[p+\varrho ]<p(I)$, regardless arrival of liquidity and speculative trades feasibly induces $p(M)<p<p(I)$. Evolution of ‘(conditional) trade imbalances’ as random walks is shown to be a necessary and sufficient condition for parameterization of stock returns as gambles over lotteries. Suppose, on the contrary, a resort to modeling of stock returns in discrete time. The formal theory arrives at two dichotomous sufficiency conditions, which predict directionality and sizes of price changes, and facilitate evolution of stock returns as random walks. In presence of the two dichotomous conditions, fairness of formation of $p<[p+\varrho ]<p(I)$ necessarily induces, regardless of arrival of liquidity and speculative trades, $p(M)\ge [p+\varrho ]$. Risk is parameterized by $p_t(M)>p_t(I)>[p_t+\varrho ]$, because all else constant, an inversion of the perturbing conditionally positive trade imbalance induces $p_{t+1}(M)<p_t(M)$. Whereas then, $p_{t+1}(M)<p_t(M)$ has parameterization as ‘materialization of risk’, always, it is $p_t(M)>p_t(I)$ that is statistic for risk; risk, as such is well parameterized, that is, does not coincide with its materialization (note that whereas volatility is statistic for risk, it is not a statistic for materialization of risk; a statistic for risk necessarily is robust to non-materialization of risk). Given modeling in continuous time does not facilitate either of the two sufficiency conditions, always, risk has parameterization as the probability that $p_{t+1}(M)<p_t(M)$. Since risk, as such coincides qualitatively with its materialization, it is not well parameterized. Given study findings parameterize general equilibrium, formal theoretical predictions have characterization as axiomatic statements, as opposed to propositional (parameter-dependent) statements.

An increase in globalization and financial integration has induced countries to depend on each other to survive. This has facilitated trade and investments among economies across the globe. It is expected that countries’ international economic activities would contribute to the rate of exchange between the countries. However, uncertainties may alter the dynamics of exchange rate movements, thereby minimizing the contribution of its economic activities at various market conditions. In this regard, we examine the influence of variations in prices of commodities on nominal exchange rate in Ghana. Hence, we employ quantile regression with monthly data spanning from September 2007 to December 2020 for the variables — nominal exchange rate, cocoa, gold and Brent crude oil prices. We find a significant connection among the variables for most quantiles. Also, the study reveals that an upsurge in the price of crude oil corresponds to an appreciation of the Ghana Cedi during turbulent conditions. Conversely, cocoa price tends to appreciate the Ghana Cedi for both normal and extreme market conditions. We recommend that for the country to enjoy favorable exchange rate at all market conditions, there is a need for the producers of these exported commodities to be incentivized by the government in the form of subsidies, new technological equipment and education in order for the producers to increase their efficiency and add value to these commodities while effective inflation targeting policies are deployed. Implications for the study are further provided.

This paper addresses a deficiency in the existing literature by examining the time–frequency domain association between economic risk (ER) and financial risk (FR) for the Mexico, Indonesia, Nigeria and Turkey (MINT) nations using dataset from 1984 to 2018. To the authors’ awareness, the relationship between economics and finance has not been thoroughly investigated in the context of risk for the MINT nations. As a result, the outcomes of this research are anticipated to provide an insight on and initiate a fresh discussion regarding the financial-economic nexus. The Breitung and Candelon (BC) causality and the wavelet coherence (WTC) techniques are used to inspect the combined time–frequency causal interrelationship between FR and ER, in accordance with the study goals. The findings from the wavelet revealed the following: (i) a one-way causality exists from ER to FR in Mexico; (ii) a one-way causality exists from FR to ER in Indonesia; (iii) a unidirectional causality exists from ER to FR in Nigeria and (iv) a one-way causality exists from FR to ER in Turkey. Furthermore, the BC causality outcomes validate the WTC outcomes. The study findings are critical for both researchers and macroeconomic policymakers and can be utilized to make appropriate measures, if necessary, by adopting alternative or more appropriate financial and economic decisions.

This study examines the moderating role of the cash conversion cycle (CCC) while investigating the effects of working capital finance (WCF) on firm performance. Using more than 18000 observations from Chinese manufacturing firms, we computed several proxies for each variable of the study and merged these proxies via Principal Component Analysis (PCA) to create one master proxy for each variable. These master proxies contain all the essential information of individual proxies. Hence, they are more useful in producing reliable results than individual proxies. We also compared the predicting power of 15 econometric and machine learning estimators to select the best estimator. Based on the highest $R^2$ value, we used two machine learning estimators, K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN) for subsequent analysis. To strengthen the empirical analysis, we employed another machine learning technique, i.e., the Bagging method, which is an ensembling technique that uses multiple estimators simultaneously to improve the accuracy and generalization of results. We used the Bagging method with 50KNN estimators. The findings unfold that the sensitivity level of firm performance to short-term debts shifts when the CCC period of firms fluctuates. More precisely, the WCF–performance relationship in firms with extended CCC is more sensitive compared with this relationship in the full sample. On segregating the three elements of CCC, we observe that the WCF–performance relationship in firms carrying extended account receivable (AR) days or extended Inventory days is more sensitive than the full sample. These findings are useful for firms’ management for revising the optimal level of short-term debts according to CCC fluctuation. Also, the lending agencies can use these results for the assessment of firms’ risk levels and adjustment of the interest rate.