We study whether firm-specific climate change exposure affects environmental disclosure and how CEO traits influence this relationship. Using data from STOXX Europe 600 firms, we find that higher climate risk leads to higher-quality disclosure. A one-standard-deviation increase in climate change exposure is associated with up to a 28% increase in the disclosure score, relative to the sample average. This effect is stronger for firms with longer-tenured or younger CEOs. Our findings highlight that firm-level climate risk and CEO experience drive environmental transparency, even under Europe’s rigid reporting standards.

We investigate how language complexity of firm-specific news affects financial markets. We assess complexity in a new way using dependency distance, a widely-used psycholinguistic measure of cognitive load based on average syntactic distance between related words. An increase in language complexity is associated with significant shifts in volatility and trading volume. Using theories of disagreement, we argue that complex language fosters opposing views among investors, who trade more. Positive news written in complex language is incorporated into prices more slowly, producing short-run return continuation and significant risk-adjusted returns. Our paper shows that linguistic complexity reflects information density, not deliberate obfuscation.

We propose a market-based decision-support framework to guide the adoption of ESG-aligned blockchain technologies. This treats the cost of financing such technologies as a function of investor sentiment and applies sentiment forecasts to determine when adoption is economically optimal. We implement our framework using a novel set of blockchain ESG scores, data from the cryptocurrency markets and an investor sentiment proxy to identify periods when investors favour ESG features in blockchain technology. Our analysis reveals that blockchains with high ESG scores generate higher cryptocurrency returns than their lower-rated counterparts in times of optimistic market sentiment. However, they underperform in challenging market times. We also find that higher ESG scores are associated with higher market volatility. Governance and environmental factors have the strongest effect on investor preferences. On the basis of these findings, we model the adoption decision as a real-options timing problem and compute the sentiment level above which adoption is optimal. We find that delaying adoption until sentiment improves yields substantially greater value than adopting immediately. Overall, our work provides operational insights into how decision-makers can strategically integrate ESG features when introducing emerging technologies.

What is the relation between the aesthetic value of art and its market price? We address this question in the context of digital art markets by employing data from the popular CryptoPunks NFT art collection. We quantify the visual attractiveness of NFTs using four aesthetic measures that are associated with emotional effects in the cultural economics literature. Using a hedonic pricing model, we identify aesthetics as a driver of prices in digital art markets. Our results indicate that investors prefer NFTs with higher levels of colorfulness and texture complexity and lower levels of saturation and brightness.

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We shed light on the drivers and consequences of turnover in human resources for the U.K. football industry. We employ an event study using daily panel data of player transfers for a group of listed U.K. football clubs. Our results suggest asymmetric wealth effects: the acquisition of players is associated with negative abnormal club stock returns while player sales have an opposite effect. According to our findings, shareholders perceive that football managers overpay to acquire human resources. Our discussion draws possible links to the corporate finance literature which deals with the purchase and sale of firms and assets.

We seek the best skewness models for portfolio choice decisions. To this end, we compare the predictive ability and portfolio performance of several prominent skewness models in a sample of 10 international equity market indices. Overall, models that employ information from the option markets outperform models that only rely on stock returns. We propose an option-based skewness estimator that accounts for the skewness risk premium. This estimator offers the most informative forecasts of future skewness, the lowest prediction errors, and the best portfolio performance in most of our tests.

This paper uses investment portfolio theory to determine budget allocation in paid online search advertising. The approach focuses on risk-adjusted performance and favors diversified portfolios of unrelated or negatively correlated keywords. An empirical investigation employs averages, variances and covariances for keyword popularities, which are estimated using growth rates for 15 major sectors taken from the Google Trends database. In line with portfolio theory, the results show that the average keyword popularity growth is strongly related to the standard deviation of growth for each keyword in the sample (R 2 = 74% ). Hypothesis testing of differences in Sharpe ratios documents a significantly better performance of the proposed approach compared to that of other strategies currently used by practitioners.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Investor sentiment and attention are often linked to the same non-economic events making it difficult to understand why and how asset prices are affected. We disentangle these two potential drivers of investment behaviour by analysing a new data-set of medals for the major participating countries and sponsor firms over four Summer Olympic Games. Our results show that trading volume and volatility are substantially reduced following Olympic success although returns appear to be largely unaffected. Analysis of data from online search volumes and surveys measuring investor sentiment also suggests that the market impact of the Olympics is linked to changes in attention.

This paper investigates US Treasury market volatility and develops new ways of dealing with the underlying interest rate volatility risk. We adopt an innovative approach which is based on a class of model-free interest rate volatility (VXI) indices we derive from options traded on the CBOE. The empirical analysis indicates substantial interest rate volatility risk for medium-term instruments which declines to the levels of the equity market only as the tenor increases to 30 years. We show that this risk appears to be priced in the market and has a significant time-varying relationship with equity volatility risk. US Treasury market volatility is found to be appealing from an investment diversification perspective since the VXI indices are negatively correlated with the levels of interest rates and of equity market implied volatility indices, respectively. Although VXI indices are affected by macroeconomic and monetary news, they are only partially spanned by information contained in the yield curve.

We compare the performance of popular covariance forecasting models in the context of a portfolio of major European equity indices. We find that models based on high-frequency data offer a clear advantage in terms of statistical accuracy. They also yield more theoretically consistent predictions from an empirical asset pricing perspective, and, lead to superior out-of-sample portfolio performance. Overall, a parsimonious Vector Heterogeneous Autoregressive (VHAR) model that involves lagged daily, weekly and monthly realised covariances achieves the best performance out of the competing models. A promising new simple hybrid covariance estimator is developed that exploits option-implied information and high-frequency data while adjusting for the volatility riskpremium. Relative model performance does not change during the global financial crisis, or, if a different forecast horizon, or, intraday sampling frequency is employed. Finally, our evidence remains robust when we consider an alternative sample of U.S. stocks.

We investigate investor's correlated attention as a determinant of excess stock market comovement. We propose a novel proxy, "co-attention", that measures the correlation in demand for market-wide information across stock markets approximated by the Google Search Volume Index (SVI). Our results reveal significant co-attention driven to some extent by correlated news and fundamentals. Most importantly, we find a positive association between co-attention and excess correlation. This effect is more pronounced in developed economies and during recessions. We fail to document significant effects of correlated news supply on stock markets, lending support to the idea that information demand governs investing decisions. Co-attention is not only induced through international investors but domestic investors as well. Our results provide evidence of attention-induced financial contagion in unrelated economies. International investors' co-attention appears to facilitate volatility transmission indirectly across markets.

Investors may accept financial returns from environmental assets that are lower than those justified by their risk, if additional benefits are present. We show this using a novel consumption-based asset pricing model that allows for risk-free assets and equities along with environmental assets. We assume that the later produce non-pecuniary utility dividends in addition to financial returns. Under realistic parameter assumptions, our model predicts discount rates for the environmental assets that are close those of the risk-free asset although their risk profile resembles that of equities. The results lend support to the much debated practice of adopting low discount rates in the cost-benefit analysis of long-term environmental damages. JEL classification: G12; H43; Q51; Q57

This paper studies the impact of weather on 31 countries stock index trading volumes, through the influence on investors attention. First, in our panel analysis we regress trading volumes on four weather variables (temperature, sky cloud cover, precipitation and snow). We find that precipitation and temperature are positively linked to trading volumes while snow has an opposite effect. And this relationship is also found to be nonlinear. We find that the trading volumes increase with low temperature and comfortable conditions whereas decrease with adverse weather conditions. For example, with 1 inch increase of snow leads to 2.82% decrease in trading volume of S&P 500. Second, we directly link weather effect to the measure of attention and sentiment. We find that the attention to the markets decreases with the increase of the temperature whereas weather appears has no impact on the weekly sentiment index of U.S. We propose attention as an alternative channel of weather effect entering the stock markets in addition to the weather sentiment. Lastly, we are able to explore the implications of weather effect and develop the economic application. The economic magnitude of the empirical results show an exploitable aggregate effect when the trading signals are based on 7 developed countries weather influence on U.S. market.

We compare the predictive ability and economic value of implied, realized, and GARCH volatility models for 13 equity indices from 10 countries. Model ranking is similar across countries, but varies with the forecast horizon. At the daily horizon, the Heterogeneous Autoregressive model offers the most accurate predictions, whereas an implied volatility model that corrects for the volatility risk premium is superior at the monthly horizon. Widely used GARCH models have inferior performance in almost all cases considered. All methods perform significantly worse over the 2008–09 crisis period. Finally, implied volatility offers significant improvements against historical methods for international portfolio diversification. © 2016 Wiley Periodicals, Inc. Jrl Fut Mark 36:1164–1193, 2016

We investigate the controversial role of the informal sector in the economy of 64 countries between 2003 and 2007 by focusing for the first time on the impact it has on sovereign debt markets. In addition to a standard ordered probit regression, we employ two nonparametric neural network modeling techniques in order to capture possible complex interactions between our variables. Results confirm our main hypothesis that the informal sector has significant adverse effects on credit ratings and lending costs. MLP neural networks offer the best fit to the data, followed by the RBF neural networks and probit regression, respectively. The results do not change with respect to the stage of economic development of a country and contradict views about the possibility of significant economic benefits arising from the informal sector. Our study has important implications, especially in the context of the ongoing sovereign debt crisis, since it suggests that a reduction in the informal sector of financially challenged countries is likely to help in relaxing credit risk concerns and cutting down lending costs. Finally, a decision tree analysis is used to exploit the inherent discreteness in the data and derive intuitive rules with respect to the level of the informal sector.

We investigate the role of Information and Communication Technologies (ICTs) as a possible determinant of credit risk ratings and cost of debt, using for the first time in such a context and as a comprehensive proxy of ICTs’ usage and diffusion, the Network Readiness Index. The empirical analysis of a panel of 65 countries between 2001-2010, by a modified random effects approach that allow us to distinguish between short and long run effects, confirms that ICTs are a significant long-run determinant of credit ratings and lending costs, especially for non-OECD countries.

We shed light on the drivers and consequences of turnover in human resources for the UK football industry. We employ an event study using daily panel data of player transfers for a group of listed UK football clubs. Our results suggest asymmetric wealth effects: the acquisition of players is associated with negative abnormal club stock returns while player sales have an opposite effect. According to our findings, shareholders perceive that football managers overpay to acquire human resources. Our discussion draws possible links to the corporate finance literature which deals with the purchase and sale of firms and assets.

We investigate the economic factors that drive electricity risk premia in the European emissions constrained economy. Our analysis is undertaken for monthly baseload electricity futures for delivery in the Nordic, French and British power markets. We find that electricity risk premia are significantly related to the volatility of electricity spot prices, demand and revenues, and the price volatility of the carbon dioxide (CO2) futures traded under the EU Emissions Trading Scheme (EU ETS). This finding has significant implications for the pricing of electricity futures since it highlights for the first time the role of carbon market uncertainties as a main determinant of the relationship between spot and futures electricity prices in Europe. Our results also suggest that for the electricity markets under scrutiny futures prices are determined rationally by risk-averse economic agents.

We develop a model of dynamic interactions between price variations in leasing and selling markets for automobiles. Our framework assumes a differential game between multiple Bertrand-type competing firms which offer differentiated products to forward-looking agents. Empirical analysis of our model using monthly US data from 2002 to 2011 shows that variations in selling (cash) market prices lead rapidly dissipating changes of leasing market prices in the opposite direction. We discuss the practical implications of these results by augmenting a standard leasing valuation formula. The additional terms represent the leased asset value changes that can be expected on the basis of past variations in automobile selling market prices.