Managers of public companies communicate with investors through channels such as conference calls and press releases. We develop a linear optimization model that predicts the optimal allocation of positive and negative information across channels, accounting for investors' limited processing capacity and channel-specific cognitive costs. The model demonstrates how managers can enhance comprehension of favorable messages while deflecting attention from unfavorable ones. Using textual features of positivity, readability, and message length, the model predicts up to 79% of real-world channel choices across over 24,000 earnings announcements. Firms with lower ESG and Social scores are more likely to engage in selective channeling, suggesting self-serving motives. Regression analyses using cosine similarity and Jaccard coefficients support the model's mechanics. These results offer practical insights: investors should be cautious with firms exhibiting low stakeholder orientation, while companies can optimize multi-channel strategies to account for stakeholders' cognitive limitations. Future research could extend the model to richer communication strategies, the persuadee's perspective, and microeconomic signaling frameworks integrating bounded rationality.

We present and expand existing theories about why individuals may assess positive outcomes differently from negative outcomes in intertemporal choices. All of our theories—based on utility or cost considerations – predict a conventional magnitude effect for positive outcomes, that is, a negative relation between outcome size and subjective discount rates. For negative outcomes, however, implications are different for utility- and cost-based approaches. We argue that the relevance of utility-based aspects is strengthened in a money frame, leading to a conventional magnitude effect even for negative outcomes, whereas cost-based considerations gain in importance in an interest rate frame, implying, in contrast, a “reverse” magnitude effect, that is, higher discount rates for (absolutely) higher outcome size. A web-based experiment with 676 participants confirms our theoretical findings: the conventional magnitude effect prevails for positive outcomes in the money and the interest rate frame and negative outcomes in the money frame. However, there is a reverse magnitude effect for negative outcomes in the interest rate frame. Our results might help to better understand prevailing magnitude effects in practical applications and might also be apt to derive suggestions for better designing of intertemporal decision problems.

Corporate disclosures convey crucial information to financial market participants. While machine learning algorithms are commonly used to extract this information, they often overlook the use of idiosyncratic terminology and industry-specific vocabulary within documents. This study uses an unsupervised machine learning algorithm, the Structural Topic Model, to overcome these issues. Our findings illustrate the link between machine-extracted risk factors discussed in corporate disclosures (10-Ks) and the corresponding pricing behavior by investors, focusing on a previously unexplored US REIT sample from 2005 to 2019. Surprisingly, when disclosed, most risk factors counterintuitively lead to a decrease in return volatility. This resolution of uncertainties surrounding known risk factors or the provision of additional facts about these factors contributes valuable insights to the financial market.

Different industries exhibit significantly different leverage; companies in the real estate investment trust (REIT) and technology/hardware sectors are extreme examples. In the United States, the leverage ratio is twice as high for REITs (50%) as compared to non-real-estate firms (around 25%), and the technology/hardware sector has the lowest ratio (around 17%). We theoretically and empirically analyze their differences. By decomposing the difference into three channels, we find that the industry-specific channel explains around 67% for REITs and 68% for technology/hardware firms; the value-based channel is mostly responsible for the remaining portion. Taking the nonlinear influences of extreme values into account, the relevance of the industry-specific channel is considerably reduced.

Tokens, the digital form of assets, are an innovation that has the potential to disrupt how to transfer and own financial instruments. We hand-collected data on 173 real estate tokens in the USA between 2019 and 2021 and trace back 238,433 blockchain transactions. We find that tokens provide broad real estate ownership to many small investors through digital fractional ownership and low entry barriers, while investors do not yet hold well-diversified real estate token portfolios. We analyze the determinants of the success of security token offerings (STOs), secondary market trading, and daily aggregated capital flows. In addition to some property-specific determinants, we find that crypto-market-specific determinants, such as transaction costs and the related sentiment, are relevant both to the STO and capital flows.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

This study analyzes the return-risk metrics of real estate security tokens as digital representatives of fractional ownership in physical properties. The author uses approximately 40,000 pricing data points for 180 tokenized properties in the United States between 2019 and 2022 to construct a monthly index. This index is used in various analyses to see whether the tokens ' returns follow the performance of the underlying markets for housing, securitized real estate, stock, and cryptocurrency. The token index shows no clear pattern of similarity to other asset classes and has its own return-risk pattern. The principal component analysis shows that debt and macroeconomic factors are the major drivers and that the crypto market and housing market are of minor importance in explaining variation in returns. This absence of a clear linear relationship with other assets makes real estate tokens attractive as diversifiers in a multiasset portfolio. However, investors looking for an alternative investment vehicle for the real estate asset class cannot rely on tokenized real estate.

This study analyzes the return–risk metrics of real estate security tokens as digital representatives of fractional ownership in physical properties. The author uses approximately 40,000 pricing data points for 180 tokenized properties in the United States between 2019 and 2022 to construct a monthly index. This index is used in various analyses to see whether the tokens’ returns follow the performance of the underlying markets for housing, securitized real estate, stock, and cryptocurrency. The token index shows no clear pattern of similarity to other asset classes and has its own return–risk pattern. The principal component analysis shows that debt and macroeconomic factors are the major drivers and that the crypto market and housing market are of minor importance in explaining variation in returns. This absence of a clear linear relationship with other assets makes real estate tokens attractive as diversifiers in a multiasset portfolio. However, investors looking for an alternative investment vehicle for the real estate asset class cannot rely on tokenized real estate.

Previous research has shown that real estate serves as a diversifier in mixed-asset portfolios. However, this empirical finding is beset with some drawbacks associated with direct real estate investment. In order to overcome some of these drawbacks, we use theoretical real estate exchange traded notes (ETNs) in a mean-shortfall setting to optimize an international mixed-asset portfolio. In addition, the typical long-only strategy is abandoned in favor of a 130/30 long-short and an exchange rate hedge strategy. Not only in-sample but also out-of-sample portfolios yield significant diversification benefits by means of real estate ETNs in different portfolio strategies.

Purpose: We investigate causes for the cost overrun and delay of the railway project Stuttgart 21. Besides, we try to forecast the actual costs and completion date at an early stage. Design/methodology/approach: The results of exploratory research show the causes for the cost overrun and delay of Stuttgart 21; we compare our findings with other railway projects. To estimate the costs at an early stage, the reference class forecasting (RCF) model is applied; to estimate the time, we apply an OLS regression. Findings: We find that the following causes are relevant for the cost overrun and delay of Stuttgart 21: scope changes, geological conditions, high risk-taking propensity, extended implementation, price overshoot, conflict of interests and lack of citizens' participation. The current estimated costs are within our 95% confidence interval based on RCF; our time forecast underestimates or substantially overestimates the duration actually required. Research limitations/implications: A limitation of our approach is the low number of comparable projects which are available. Practical implications: The use of hyperbolic function or stepwise exponential discount function can help to give a clearer picture of the costs and benefits. The straightforward use of the RFC for costs and OLS for time should motivate more decision-makers to estimate the actual costs and time which are necessary in the light of the rising demand for democratic participation amongst citizens. Social implications: More realistic estimates can help to reduce the significant distortion at the beginning of infrastructure projects. Originality/value: We are among the first who use the RCF to estimate the costs in Germany. Furthermore, the hyperbolic discounting function is added as a further theoretical explanation for cost underestimation.

We analyze whether lower rents for energy-inefficient apartments reflect tenants’ willingness to pay due to a higher green awareness, purchasing power, or energy consumption costs. Based on a German rental apartment dataset from Q1 2007 to Q1 2019, we use interaction terms for socioeconomic characteristics in a hedonic regression model. We find that rents are lower for apartments with higher energy consumption, even in neighborhoods with lower levels of green awareness. This relationship is stronger in neighborhoods with higher purchasing power, such that communities with low levels of green awareness and high purchasing power show the steepest negative slope for increasing energy consumption (−8.6% from the highest to lowest rating). Thus, the rent-decreasing effect of purchasing power is higher than that of green awareness. Splitting the entire period into smaller windows, we find that the interaction effect of green awareness has emerged in the most recent years (2017–2019). This may be driven by changes in regulation, which have made it easier for tenants to assess the energy consumption before they rent, or by a general increase in green awareness over this period.