Expectations of Returns and Expected Returns

We analyze time series of investor expectations of future stock market returns from six data sources between 1963 and 2011. The six measures of expectations are highly positively correlated with each other, as well as with past stock returns and with the level of the stock market. However, investor expectations are strongly negatively correlated with model-based expected returns. The evidence is not consistent with rational expectations representative investor models of returns.Economic

Issuer Quality and Corporate Bond Returns

We show that the credit quality of corporate debt issuers deteriorates during credit booms, and that this deterioration forecasts low excess returns to corporate bondholders. The key insight is that changes in the pricing of credit risk disproportionately affect the financing costs faced by low quality firms, so the debt issuance of low quality firms is particularly useful for forecasting bond returns. We show that a significant decline in issuer quality is a more reliable signal of credit market overheating than rapid aggregate credit growth. We use these findings to investigate the forces driving time-variation in expected corporate bond returns

X-CAPM: An Extrapolative Capital Asset Pricing Model

Survey evidence suggests that many investors form beliefs about future stock market returns by extrapolating past returns. Such beliefs are hard to reconcile with existing models of the aggregate stock market. We study a consumption-based asset pricing model in which some investors form beliefs about future price changes in the stock market by extrapolating past price changes, while other investors hold fully rational beliefs. We find that the model captures many features of actual prices and returns; importantly, however, it is also consistent with the survey evidence on investor expectations.Economic

RamStart

According to the 2015 Bureau of Labor Statistics, nationally, students with disabilities are less likely to graduate with a bachelor’s degree and more likely to be unemployed.1 At VCU, these students are served by the Student Accessibility and Educational Opportunity Office (SAEO), which currently has only two case managers for 1500 registered students while the Association for Higher Education and Disabilities (AHEAD) recommends an individual case load of 350 or fewer students. While these students attend New Student Orientation, there are currently no programs or sessions specifically designed to address their needs. RamStart is a model for presemester transition workshops for new students who have been granted accommodations through SAEO for disabilities and their families which is designed to provide them with tools for self-advocacy and independence. The goal of these workshops is to help ease the students’ transition to VCU by educating them and their parents about SAEO’s services, their rights and responsibilities, Family Educational Rights and Privacy Act (FERPA), campus resources, and University policies and procedures to improve their chances of success

Rainy Day Stocks

We study the good- and bad-times performance of equity portfolios formed on characteristics. Many characteristics associated with good performance during bad times – value, profitability, small size, safety, and total volatility – also perform well during good times. Stocks with characteristics signifying high liquidity, such as high turnover and low bid ask spreads, perform well during bad times but otherwise underperform. We develop a simple but flexible procedure to recover a “risk neutral alpha” that recognizes a 1% return experienced during bad times as being more valuable than a 1% return generated during good times. We also show how an investor can build a “rainy day” portfolio that minimizes underperformance during bad times

A Bottom-Up Approach to Lithium-Ion Battery Cost Modeling with a Focus on Cathode Active Materials

In this study, we develop a method for calculating electric vehicle lithium-ion battery pack performance and cost. To begin, we construct a model allowing for calculation of cell performance and material cost using a bottom-up approach starting with real-world material costs. It thus provides a supplement to existing models, which often begin with fixed cathode active material (CAM) prices that do not reflect raw metal price fluctuations. We collect and display data from the London Metal Exchange to show that such metal prices, in this case specifically cobalt and nickel, do indeed fluctuate and cannot be assumed to remain static or decrease consistently. We input this data into our model, which allows for a visualization of the effects of these metal price fluctuations on the prices of the CAMs. CAMs analyzed include various lithium transition metal oxide-type layered oxide (NMC and NCA) technologies, as well as cubic spinel oxide (LMO), high voltage spinel oxide (LNMO), and lithium metal phosphate (LFP). The calculated CAM costs are combined with additional cell component costs in order to calculate full cell costs, which are in turn scaled up to full battery pack costs. Economies of scale are accounted for separately for each cost fraction. Document type: Articl

Artificial intelligence-ready skin cancer alchemy:transforming routine teledermatology data into metadata-embedded DICOM files

Most skin artificial intelligence (AI) classifiers are trained only on images with diagnostic labels. However, the addition of clinical information can improve predictive accuracy. Recent interest has been stimulated in incorporating clinical data into image files, using the well-established international Digital Imaging and Communication in Medicine (DICOM) standards (Caffery L, Weber J, Kurtansky N et al. DICOM in dermoscopic research: experience report and a way forward. J Digit Imaging 2021; 34: 967–73). We have developed an automated process of creating metadata-embedded DICOM files, directly from a live teledermatology system, described below. Through our Community and Locality Imaging Centre (CLIC) model, patients referred from primary care are triaged to CLIC for high-quality image capture. There, trained health professionals use a mobile application to capture standardized DICOM information for each lesion. Each lesion dataset contains images (macroscopic, dermoscopic) and clinical metadata (patient and lesion information). Datasets are transferred to an image management system, for teledermatology and verification of ground-truth diagnoses by a consultant dermatologist. On completion of diagnoses, datasets are flagged for conversion into DICOM format, where metadata are embedded in the image files. Flagged datasets are cleaned and clinical metadata are mapped to DICOM attributes. Datasets are converted into metadata-embedded DICOM files, and reviewed for conformance to the DICOM standard using the open-source fo-dicom library (v5). These files are further tested for conformance to DICOM standard using the dciodvfy validator tool. Compliant DICOM files are then transferred to a trusted research environment for research. To test whether these DICOM files are usable for AI research, they are examined using the DICOM viewing software 3D Slicer (https://www.slicer.org/), ensuring images are usable and metadata are correctly translated. Image pixel data and clinical metadata are extracted using pydicom, into a format suitable for AI algorithm development. In our pilot work, 658 lesion datasets have been converted into metadata-embedded DICOM files. Conversion on existing hardware [virtual Intel central processing units with 2.60 GHz (two processors) and 8 GB of memory] took < 1 s per image. Metadata-embedded DICOM files were approximately 0.2 kB bigger than the original JPEG files. For 3-MB images, this represented a negligible 0.003% increase in storage requirement. Testing has shown that these files can be successfully handled by algorithms within an AI research environment. In summary, we have demonstrated the feasibility of automating the conversion of routine teledermatology data into AI-ready image files encoded with clinical metadata. Future work is planned to evaluate the utility of this output on the performance of AI classifiers

A Model of Credit Market Sentiment

We present a model of credit market sentiment in which investors form beliefs about future creditworthiness by extrapolating past defaults. Our key contribution is to model the endogenous two-way feedback between credit market sentiment and credit market outcomes. This feedback arises because investors’ beliefs depend on past defaults, but beliefs also drive future defaults through investors’ willingness to refinance debt at low interest rates. Our model is able to capture many documented features of credit booms and busts, including the link between credit growth and future returns, and the “calm before the storm” periods in which fundamentals have deteriorated but the credit market has not yet turned

A Comparative-Advantage Approach to Government Debt Maturity

We study optimal government debt maturity in a model where investors derive monetary services from holding riskless short-term securities. In a setting where the government is the only issuer of such riskless paper, it trades off the monetary premium associated with short-term debt against the refinancing risk implied by the need to roll over its debt more often. We then extend the model to allow private financial intermediaries to compete with the government in the provision of short-term, money-like claims. We argue that if there are negative externalities associated with private money creation, the government should tilt its issuance more towards short maturities. The idea is that the government may have a comparative advantage relative to the private sector in bearing refinancing risk and, hence, should aim to partially crowd out the private sector's use of short-term debt