This study examines whether capital markets respond to firm-level corruption by lowering access to external financing. Using a global panel of non-financial listed firms from 77 countries over 1998-2018, we analyze how corruption - measured using Refinitiv's firm-level "Policy, Bribery and Corruption Score" - affects the intensity of equity and debt financing. We classify firms into financially constrained and unconstrained groups based on multiple criteria and estimate panel regressions with extensive firm-, industry-, year-, and country-fixed effects. Our results show that corruption is associated with higher financing frictions, with the effect concentrated in debt markets. Equity issuance is largely insensitive to corruption, whereas debt financing falls significantly as corruption increases, consistent with creditors' heightened concerns about enforcement and information risk. These effects remain robust across alternative definitions of financing constraints, nonlinear and rescaled corruption measures, and additional institutional and macro-risk controls. We also find that corruption increases the external-cash flow sensitivity of firms, suggesting that corrupt firms face more market frictions. These patterns are especially pronounced among financially unconstrained firms. Overall, the findings highlight that capital markets - particularly debt markets - discipline firms with higher corruption exposure, revealing an important channel through which institutional weaknesses translate into financing frictions.
In this paper, we examine the driving forces of CEO to median employee pay ratios from a macro-perspective using a sample of large corporations included in the DJIA index from 1949 to 2022. We find that CEO-employee pay ratios increase with the Industry Production Index and inflation rate, but decrease with GDP per capita growth rate, unemployment rate, stock market, and industry trends. Our results also show that CEO pay structure changes from salary and bonus-dominated to option and stock-dominated pay enable CEOs to enjoy a much better pay package compared to lower-level employees, while executive pay-related regulations in the 2000s have lowered CEO-employee pay ratios. Our findings provide new insights to various stakeholders, regulators, the public and media on this long-debated social and economic issue.
We demonstrate that fintech and digitalization adoption is positively associated with employee-based efficiency, proxied by the natural logarithm of revenue per employee. Additionally, we utilize two machine learning-based feature selection approaches, F-regression, and random forest, to identify the most significant variables within the fintech categories as potential candidates for the prediction process. Furthermore, we employ a machine learning model selection process based on the root mean squared error (RMSE) standard. Our findings indicate that the Extra Trees Regressor yields the lowest RMSE in the tested sample, making it the most effective model for predicting employee-based performance efficiency in our sample. Overall, our findings suggest that the use of fintech and digitalization significantly boosts firm efficiency from the perspective of employee productivity.
Asset redeployability reflects firm ability to reallocate or sell capital assets in secondary markets. Using data from publicly listed firms, we show that asset redeployability is negatively related to managerial ability, suggesting capable managers maintain lower redeployability levels. We reveal labor efficiency as one channel through which managerial ability influences asset redeployability. Managerial ability's negative effect on asset redeployability is stronger under low political risk, consistent with it serving as a costly form of insurance against uncertainty. Our findings imply that while asset redeployability is commonly viewed as a source of corporate flexibility, it may also reflect inefficient asset allocation.
We study financial returns on videogame attributes as digital alternative investments, using CS:GO skins as an example. Using an extensive dataset of monthly returns on 4,565 skins for 2013-2024, we find that these niche investments outperform most traditional and alternative assets with the average return about 40% pa and provide diversification benefits. The time-series analysis indicates independence of their returns from equity market risk factors, as well as other financial markets, however, the returns exhibit strong seasonality patterns with peaks observed in January and April. The cross-sectional analysis reveals that items from direct drops and of better quality sell for higher prices and generate higher returns, whereas the presence of premium features and higher rarity are associated with significantly higher prices, but lower average returns. We observe a strong "penny stock effect" that cheaper items yield higher percentage returns. Lower financial returns on expensive skins are likely compensated by the "emotional dividend" of gamers.
This paper assesses both statistical significance and economic relevance of distributional timing in out-of-sample density forecasts. A comprehensive set of model specifications incorporating dynamic higher moments is estimated, and a wide range of statistical tests is conducted. The results consistently indicate that modeling time-varying skewness and kurtosis significantly enhances the adequacy and accuracy of density forecasts. In utility-based evaluations, transitioning from portfolios based on constant higher moments to those incorporating time-varying skewness and kurtosis generates average annual gains of 265 and 439 basis points, respectively. Furthermore, an investor would be willing to pay approximately 170 basis points per year to obtain information on skewness and kurtosis beyond volatility. Among the competing models, the unfolded GARCH model-which decomposes returns into the product of their absolute values and signs-not only demonstrates the strongest statistical significance for capturing the left tail of financial returns, but also yields the highest average gains of approximately 9.4% and 11.9% for investors who unfold skewness and kurtosis timing in their decision-making.
As a fund-of-funds, Target Date Fund (TDFs) portfolios consist of other funds. Using 18,229 unique mutual funds and ETFs from 2011 to 2022, we analyze the performance of funds included in TDFs. We find TDFs lack skill, in general, in selecting underlying funds. Managers only outperform in selecting domestic income funds, while index domestic equity and other asset classes underperform. Increased popularity within TDF portfolios relates to lower underlying fund performance. These results suggest TDFs are unable to select outperforming investments, in general, likely due to either lack of skill and/or agency problems arising from lack of investor monitoring.
Using search traffic on the EDGAR system of the Securities and Exchange Commission (SEC), we examine investor demand for information and its impact on security prices. Focusing on the registration period for IPOs when information asymmetry between investors and the issuing firm is likely to be high, we document that viewership of peer firm filings significantly increases on IPO filing dates. We find that investor demand for information is positively related to the probability of IPO success and predicts both price revisions and initial returns. Our results indicate that information acquisition is reflected in the pricing of newly issued securities.
Potential conflicts of interests and immense time pressure involved with a SPAC merger with a private operating company ("de-SPAC") have raised concerns about the financial reporting quality of the merged entity. We empirically examine the financial reporting quality of de-SPACs in the merger year and the two subsequent years. Using several individual financial reporting quality proxies and a composite reporting quality measure, we find that de-SPACs exhibit lower financial reporting quality than propensity score-matched IPOs for the merger/IPO year. The lower financial reporting quality persists for at least two more years after the de-SPAC year. We find evidence that better corporate governance is associated with higher de-SPAC financial reporting quality. Finally, we find that poor financial reporting quality is associated with de-SPAC underperformance. Our results suggest that investors in SPACs/de-SPACs are likely to benefit from the SEC (2024)'s final rules aimed at enhancing the transparency of financial disclosures.
The Federal Reserve holds Federal Open Market Committee and Board Meetings, with six- and two-week cadence, respectively. The financial literature associates these meetings with stock market cycles of corresponding frequencies. These can be exploited through a portfolio strategy that invests in the market at alternate weeks. Since this strategy lacks theoretical foundations, we provide a rigorous framework for detecting market cycles and determining optimal portfolios that profit from them. We isolate uncorrelated components of stock returns associated with two- and six-week cycles, we replicate them and design an optimal portfolio that maximizes the investor's wealth by properly exploiting such cyclicality.
This study examines the impact of female CEOs and CFOs on working capital management. We find that firms led by female executives manage the working capital cycle more efficiently and aggressively, resulting in a shorter cash conversion cycle and lower working capital requirements. Female CEOs are associated with improvements in operational efficiency, while female CFOs influence the financing aspects of working capital, contributing to higher cash holdings.
Environment, Social, and Governance (ESG) criteria become a relevant factor in the investment universe. We develop an AI-based algorithm that uses public data, mainly Web-based information, to assign E, S, and G ratings to companies. Using our scoring procedure, we construct portfolios, comprising 50 firms each from the SnP 500 index, 50 firms with the highest scores and 50 with the lowest scores for 4 scoring categories: ESG, E, S, and G, for the years 2018-2021. We find that, except in 2021, high-ESG score portfolios consistently outperform low-ESG score portfolios. In particular, we observe that the shares of high G-score companies outperform low G-score portfolios, with the largest difference in performance between high and low-score portfolios. The data support the hypothesis that indicators of good corporate governance can identify better performing firms. We also note the outperformance of high S-rated portfolios in 2018-2020. We find that the E-portfolios behave differently from the S and G portfolios. Due to data constraints, we view this paper as exploratory only, and further research is due to validate our findings.
At the IRMC conference in Florence, Italy, in June 2023, we devoted a special session to mark the 50th anniversary of the publication of two seminal papers that had a major impact on the field of Finance: one by Black and Scholes and the other by Merton. Both papers proposed an analytical solution to the valuation of European options, both leading to the same formula, while providing two different methods to prove the model. The impact of the Option Pricing Model (OPM) of BSM has been vast, affecting the valuation of derivatives, influencing trading instruments and strategies, transforming over time the entire field of corporate finance, such that all claims on the corporation can be seen as derivatives on the firm's assets. In this note, we concentrate on the applications of the OPM to corporate finance. This "contingent claims" approach has ushered in a new era in corporate finance. Below, we highlight some of the seminal papers that have had a vast influence on the analysis of the way firms finance their investments and the methods used to determine the value of stakeholder claims.
The Weight of Evidence (WOE) variable transformation method is widely used in credit risk analysis. This paper introduces a two-staged, local regression-based binning method to estimate WOE. Using a dataset from the banking industry, the study demonstrates that selecting an appropriate number of bins and smoothing factor minimizes information loss and enhances prediction accuracy. The proposed method performs well on imbalanced datasets and can handle both monotonic and U-shaped relationships between the transformed WOE and the original variable, ensuring business soundness. This approach enables smoother credit score migration when financial ratios shift between bins. Given the advantages of the WOE method, such as handling missing values and maintaining model interpretability, the proposed method shows superior performance compared to existing variable transformation approaches, making it highly suitable for financial risk analysis, especially credit risk analysis.
The objective of this work is to study banks’ response to the Basel III capital regulation for global systemically important banks (G-SIBs). The buffer is revised annually. We cannot apply conventional event studies and treatment effect evaluation methodology, so we modify it to account for the multiperiod treatment and the ongoing changes in the composition of the pilot (treated) and control groups. We reveal an asymmetric reaction by G-SIBs to changes in the capital buffer. Banks tend to reduce their capital ratios and expand lending when the applicable capital buffer is officially decreased. On the contrary, banks do not increase their capital ratios and do not contract lending as expected by the regulator after the applicable capital buffer is raised. The capital cushion accumulated earlier allows banks to withstand such a rise in the prudential capital buffer. Moreover, a change in regulation stimulates more risk-taking by global banks. When tightened, it is accompanied by an increase in own funds, while it is not accompanied by such an increase when it is eased. Thus, we recommend that the list of G-SIBs and the applicable capital surcharges be revised less often to avoid such unintended responses to regulation.
Fifty years have passed since the Black-Scholes-Merton model was first published, revolutionizing the financial world. It has not only been the dominant pricing model for options and financial assets with embedded options, but it has radically changed the way we approach financial theory, financial institutions and corporate finance and operate in capital markets. Today, its use is ubiquitous in risk management across markets and institutions. It also serves as the basis for the Contingent Claims Approach (CCA) applied in corporate finance and across finance overall. The BSM model provides forward-looking information (variances, correlations) across financial asset classes, including equities, fixed income (FI), foreign exchange, commodities, and credit), enabling more timely and dynamic analyses of these respective markets.
In this paper, we investigate the predictive power of non-price indicators for short-term stock returns for prominent high-volume stocks and SPY using weekly options data. By analyzing open interest and volume distributions, we forecast weekly and monthly aggregated returns around option expirations. We show that these options trading dynamics are crucial predictors of stock returns, even amid market turbulence. Notably, the lagged open interest call and put, as well as call and put volume, retain statistical significance in predicting returns with proper controls. Both in-sample (2013-2022) and out-of-sample (2023) tests confirm the predictors’ robustness, consistently outperforming the S&P 500 and NASDAQ 100 indexes, and the aggregated active trading strategies of the key market movers. Our findings align with the role of options and informed trading on the price discovery of stocks, as demonstrated by Chakravarty et al. [2004, Informed Trading in Stock and Option Markets, Journal of Finance 59(3), 1235–1257]. Integrating traditional variables from Fama and French [2012, Size, Value, and Momentum in International Stock Returns, Journal of Financial Economics 105(3), 457–472; 2015, A Five-Factor Asset Pricing Model, Journal of Financial Economics 116(1), 1–22] and Amihud and Mendelson [1980, Dealership Market — Market-Making with Inventory, Journal of Financial Economics 8, 31–53] further enhances our models’ predictive efficacy. The non-price indicators exhibited significantly enhanced predictive power during the COVID-19 crisis, surpassing their effectiveness under regular market conditions. Additionally, we explore return volatility forecasting using our predictors through GARCH modeling, further highlighting their strategic importance in investment performance.
In this paper, we develop a measure of systemic risk based on an ensemble of early warning models that allows the simultaneous indication of both the timing and the level of the countercyclical capital buffer (CCyB), including its positive neutral level (CCyBPN). The risk measure is the reverse-engineered capital ratio that prevents crisis signals. It effectively predicts crisis episodes across most countries studied. We found that an adequate level of the CCyB often substantially surpasses the implicit limit of 2.5%. The CCyBPN was found to be in the range of 1.5-3%. This methodology is universally applicable to countries that have not experienced banking crises.
