330 Wirtschaft
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Recently, the Bank of Japan outlined a “two perspectives” approach to the conduct of monetary policy that focuses on risks to price stability over different time horizons. Interpreting this as pertaining to different frequency bands, we use band spectrum regression to study the determination of inflation in Japan. We find that inflation is related to money growth and real output growth at low frequencies and the output gap at higher frequencies. Moreover, this relationship reflects Granger causality from money growth and the output gap to inflation in the relevant frequency bands. Keywords: spectral regression, frequency domain, Phillips curve, quantity theory. JEL Numbers: C22, E3, E5
The complexities of geopolitical events, financial and fiscal crises, and the ebb and flow of personal life circumstances can weigh heavily on individuals’ minds as they make critical economic decisions. To investigate the impact of cognitive load on such decisions, the authors conducted an incentivized online experiment involving a representative sample of 2,000 French households. The results revealed that exposure to a taxing and persistent cognitive load significantly reduced consumption, particularly for individuals under the threat of furlough, while simultaneously increasing their account balances, particularly for those not facing such employment uncertainty. These effects were not driven by supply constraints or a worsening of credit constraints. Instead, cognitive load primarily affected the optimality of the chosen policy rules and impaired the ability of the standard economic model to accurately predict consumption patterns, although this effect was less pronounced among college-educated subjects
Abundant studies show that individuals often struggle and frequently fail to form a correct perception of how much they are worth in terms of income or net wealth, both in absolute terms and relative to others. The authors find that wealth misperception arises even in a frictionless environment. They show that this wealth misperception is related to low cognitive abilities and inattention, and that subjects who misperceive wealth have a greater tendency to borrow and spend out of gains. A standard optimal consumption choice model, enriched with a rational but inattentive agent à la Gabaix aligns the key experimental findings.
We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous real assets. We assemble a database of 1.1 million paintings auctioned between 2008 and 2015. We use a popular machine-learning technique—neural networks—to develop a pricing algorithm based on both non-visual and visual artwork characteristics. Our out-of-sample valuations predict auction prices dramatically better than valuations based on a standard hedonic pricing model. Moreover, they help explaining price levels and sale probabilities even after conditioning on auctioneers’ pre-sale estimates. Machine learning is particularly helpful for assets that are associated with high price uncertainty. It can also correct human experts’ systematic biases in expectations formation—and identify ex ante situations in which such biases are likely to arise.
Biased auctioneers
(2022)
We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and non-visual object characteristics. We find that higher automated valuations relative to auction house pre-sale estimates are associated with substantially higher price-to-estimate ratios and lower buy-in rates, pointing to estimates’ informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers’ prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.
How do insiders trade?
(2016)
We characterize how informed investors trade in the options market ahead of corporate news when they receive private, but noisy, information about (i) the timing of the announcement and (ii) its impact on stock prices. Our theoretical framework generates a rich set of predictions about the insiders’ behavior and their maximum expected returns. Three different analyses offer empirical support for our approach. First, predicted trades resemble illegal insider trades documented in SEC litigation cases with insiders being more likely to trade in options that offer higher expected returns. Second, pre-announcement patterns in unusual activity in the options market ahead of significant corporate news are consistent with the predictions of our framework. We employ our approach to characterize informed trading ahead of twelve different types of news including the announcement of earnings, corporate guidance, M&As, product innovations, management changes, and analyst recommendations. Third, to address concerns that pre-announcement patterns are driven by speculation, we show that measures capturing trading activity in call (put) options with high expected returns predict significant positive (negative) corporate news in the aggregate cross-section.
Cryptocurrencies provide a unique opportunity to identify how derivatives impact spot markets. They are fully fungible, trade across multiple spot exchanges at different prices, and futures contracts were selectively introduced on bitcoin (BTC) exchange rates against the USD in December 2017. Following the futures introduction, we find a significantly greater increase in cross-exchange price synchronicity for BTC--USD relative to other exchange rate pairs, as demonstrated by an increase in price correlations and a reduction in arbitrage opportunities and volatility. We also find support for an increase in price efficiency, market quality, and liquidity. The evidence suggests that futures contracts allowed investors to circumvent trading frictions associated with short sale constraints, arbitrage risk associated with block confirmation time, and market segmentation. Overall, our analysis supports the view that the introduction of BTC--USD futures was beneficial to the bitcoin spot market by making the underlying prices more informative.
We provide a comprehensive analysis of the determinants of trading in the sovereign credit default swaps (CDS) market, using weekly data for single-name sovereign CDS from October 2008 to September 2015. We describe the anatomy of the sovereign CDS market, derive a law of motion for gross positions and their components, and identify the key factors that drive the cross-sectional and time-series properties of trading volume and net notional amounts outstanding. While a single principal component accounts for 54 percent of the variation in sovereign CDS spreads, the largest common factor explains only 7 percent of the variation in sovereign CDS net notional amounts outstanding. Moreover, unlike for CDS spreads, common global factors explain very little of the variation in sovereign CDS trading and net notional amounts outstanding, suggesting that it is driven primarily by idiosyncratic country risk. We analyze several local and regional channels that may explain the trading in sovereign CDS: (a) country-specific credit risk shocks, including changes in a country's credit rating and related outlook changes, (b) the announcement and issuance of domestic and international debt, (c) macroeconomic sentiment derived from conventional and unconventional monetary policy, macro-economic news and shocks, and (d) regulatory channels, such as changes in bank capital adequacy requirements. All our findings suggest that sovereign CDS are more likely used for hedging than for speculative purposes.
Looking beyond ESG preferences: The role of sustainable finance literacy in sustainable investing
(2024)
We assess how sustainable finance literacy affects people’s sustainable investment behavior, using a pre-registered experiment. We find that an increase in sustainable finance literacy leads to a 4 to 5% increase in the probability of investing sustainably. This effect is moderated by sustainability preferences. In the absence of moderate sustainability preferences, any additional increase in sustainable finance literacy is at minimum irrelevant, and we find some evidence that it might even reduce sustainable investments. Our findings underscore the role of knowledge in shaping sustainable investment decisions, highlighting the importance of factors beyond sustainability preferences.
The right to ask questions and voice their opinions at annual general meetings (AGMs) represents one of the few avenues for shareholders to communicate directly and publicly with the firm’s management. Examining AGM transcripts of U.S. companies between 2007 and 2021, we find that shareholders actively express their concerns about environmental, social and governance (ESG) issues in accordance with their specific relationship with the company. Further, they are also demonstrably more vocal about ESG issues at AGMs of firms with poor sustainability performance. What is more, we show that this soft engagement translates into a more negative tone which, in turn, results in lower approval rates for management proposals. Shareholders' soft engagement at AGMs is hence an effective way to "walk the talk".