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The discount control mechanisms that closed-end funds often choose to adopt before IPO are supposedly implemented to narrow the difference between share price and net asset value. We find evidence that non-discretionary discount control mechanisms such as mandatory continuation votes serve as costly signals of information to reveal higher fund quality to investors. Rents of the skill signaled through the announcement of such policies accrue to managers rather than investors as differences in skill are revealed through growing assets under management rather than risk- adjusted performance.
Armstrong et al. (2022) review the empirical methods used in the accounting literature to draw causal inferences. They document a growing number of studies using quasi-experimental methods and provide a critical perspective on this trend as well as the use of these methods in the accounting literature. In this discussion, I complement their review by broadening the perspective. I argue for a design-based approach to accounting research that shifts attention from methods to the entire research design. I also discuss why studies that aim to draw causal inferences are important, how these studies fit into the scientific process, and why assessing the strength of the research design is important when evaluating studies and aggregating research findings.
Retained earnings and foreign portfolio ownership: implications for the current account debate
(2023)
In some countries, a sizable fraction of savings is derived from corporate savings. Although larger, traded corporations are often co-owned by foreign portfolio investors, current international accounting standards allocate all corporate savings to the host country. This paper suggests a framework to correct for this misleading attribution and applies this concept to Germany. For the years 2012 to 2020, our corrections retrospectively reduce German savings and consequently the German current account surplus by, on average, €11.5bn annually. This amounts to approximately five percent of Germany’s average official current account surplus (€226.6bn) across these years.
We find that high macroeconomic uncertainty is associated with greater accumulation of physical capital, despite a reduction in investment and valuations. To reconcile this puzzling evidence, we show that uncertainty predicts lower depreciation and utilization of existing capital, which dominates the investment slowdown. Motivated by these dynamics, we develop a quantitative production-based model in which firms implement precautionary savings through reducing utilization rather than raising invest-ment. Through this novel intensive-margin mechanism, uncertainty shocks command a quarter of the equity premium in general equilibrium, while flexibility in utilization adjustments helps explain uncertainty risk exposures in the cross-section of industry returns.
We assemble a data set of more than eight million German Twitter posts related to the war in Ukraine. Based on state-of-the-art methods of text analysis, we construct a daily index of uncertainty about the war as perceived by German Twitter. The approach also allows us to separate this index into uncertainty about sanctions against Russia, energy policy and other dimensions. We then estimate a VAR model with daily financial and macroeconomic data and identify an exogenous uncertainty shock. The increase in uncertainty has strong effects on financial markets and causes a significant decline in economic activity as well as an increase in expected inflation. We find the effects of uncertainty to be particularly strong in the first months of the war.
Flows of funds run by banks or by firms that belong to the same financial group as a bank are less volatile and less sensitive to bad past performance. This enables bank-affiliated funds to better weather distress and to hold lower precautionary cash buffers in comparison with their unaffiliated peers. Banks provide liquidity support to distressed affiliated funds by buying shares of those funds that are experiencing large outflows. This, in turn, diminishes the severity of strategic complementarities in investors’ redemptions. Liquidity support and other benefits of bank affiliation are conditional on the financial health of the parent company. Distress in the banking system spills over to the mutual fund sector via ownership links. Our research high-lights substantial dependencies between the banking system and the asset management industry, and identifies an important channel via which financial stability risks depend on the organisational structure of the financial sector.
Fund companies regularly send shareholder letters to their investors. We use textual analysis to investigate whether these letters’ writing style influences fund flows and whether it predicts performance and investment styles. Fund investors react to the tone and content of shareholder letters: A less negative tone leads to higher net flows. Thus, fund companies can use shareholder letters as a tactical instrument to influence flows. However, at the same time, a dishonest communication that is not consistent with the fund’s actual performance decreases flows. A positive writing style predicts higher idiosyncratic risk as well as more style bets, while there is no consistent predictive power for future performance.
Art-related non-fungible tokens (NFTs) took the digital art space by storm in 2021, generating massive amounts of volume and attracting a large number of users to a previously obscure part of blockchain technology. Still, very little is known about the attributes that influence the price of these digital assets. This paper attempts to evaluate the level of speculation associated with art NFTs, comprehend the characteristics that confer value on them and design a profitable trading strategy based on our findings. We analyze 860,067 art NFTs that have been deployed on the Ethereum blockchain and have been involved in 317,950 sales using machine learning methods to forecast the probability of sale, the trade frequency and the average price. We find that NFTs are highly speculative assets and that their price and recurrence of sale are heavily determined by the floor and the last sale prices, independent of any fundamental value.
Mamma mia! Revealing hidden heterogeneity by PCA-biplot : MPC puzzle for Italy's elderly poor
(2023)
I investigate consumption patterns in Italy and use a PCA-biplot to discover a consumption puzzle for the elderly poor. Data from the third wave (2017) of the Eurosystem’s Household Finance and Consumption Survey (HFCS) indicate that Italian poor old-aged households boast lower levels of the marginal propensity to consume (MPC) than suggested by the dominant consumption models. A customized regression analysis exhibits group differences with richer peers to be only half as large as prescribed by a traditional linear regression model. This analysis has benefited from a visualization technique for high-dimensional matrices related to the unsupervised machine learning literature. I demonstrate that PCA-biplots are a useful tool to reveal hidden relations and to help researchers to formulate simple research questions. The method is presented in detail and suggestions on incorporating it in the econometric modeling pipeline are given.
We investigate consumption patterns in Europe with supervised machine learning methods and reveal differences in age and wealth impact across countries. Using data from the third wave (2017) of the Eurosystem’s Household Finance and Consumption Survey (HFCS), we assess how age and (liquid) wealth affect the marginal propensity to consume (MPC) in the Netherlands, Germany, France, and Italy. Our regression analysis takes the specification by Christelis et al. (2019) as a starting point. Decision trees are used to suggest alternative variable splits to create categorical variables for customized regression specifications. The results suggest an impact of differing wealth distributions and retirement systems across the studied Eurozone members and are relevant to European policy makers due to joint Eurozone monetary policy and increasing supranational fiscal authority of the EU. The analysis is further substantiated by a supervised machine learning analysis using a random forest and XGBoost algorithm.