The Value Spread as a Predictor of Returns

Recent studies have used the value spread to predict aggregate stock returns to construct cash-flow betas that appear to explain the size and value anomalies. We show that two related variables, the book-to-market spread (the book-to-market of value stocks minus that of growth stocks) and the market-to-book spread (the market-to-book of growth stocks minus that of value stocks) predict returns in different directions and exhibit opposite cyclical variations. Most important, the value spread mixes information on the book-to-market and market-to-book spreads, and appears much less useful in predicting returns.

Is the value spread a useful predictor of returns?

No. Two related variables, the book-to-market spread (the book-to-market of value stocks minus the book-to-market of growth stocks), and the market-to-book spread (the market-to-book of growth stocks minus the market-to-book of value stocks) predict returns but with opposite signs. The value spread mixes the cyclical variations of the book-to-market and market-to-book spreads, and appears much less useful in predicting returns. Our evidence casts doubt on Campbell and Vuolteenaho [2004. Bad beta, good beta. American Economic Review 94(5), 1249-1275] because their conclusion relies critically on using the value spread as a predictor of aggregate stock returns.

The Impact of Carbon Disclosure on Financial Performance under Low Carbon Constraints

In the context of low-carbon constrained development, in order to avoid the risk brought by climate change, more and more companies choose to disclose carbon information, respond to the national policy of carbon emission reduction and focus on the sustainable development of enterprises. This paper will investigate the impact of carbon disclosure on financial performance based on the 2011–2018 CDP report, taking the Fortune 500 companies as a sample. The study finds that for carbon-intensive industries, carbon disclosure cannot significantly contribute to the improvement of financial performance in the current period, but for carbon-non-intensive industries, carbon disclosure can significantly contribute to the improvement of financial performance in the current period, and the positive impact of carbon disclosure on financial performance in the current period can be extended to the next period. Finally, based on the findings of the empirical study, this paper puts forward policy recommendations for the construction of China’s carbon disclosure system

The destructive time of temperature stratification during human movement and the recovery time after the human movement in the displacement ventilation system

Many factors such as human movement can weaken indoor temperature stratification and lead to the advantages of the displacement ventilation (DV) disappearing. Yet the investigation for the stratified destructive time during the human movement and the recovery time after human movement stops are still meagre so far. The concepts of dynamic steady-state and static steady-state are proposed to compute the stratified destructive time(Tb). The isothermal surface of 295K is engaged to explain the variation of Tb. At last, the stratified restoring time(Tc) after human movement stops are inquiries in our paper. The conclusion is as follows: Firstly, the value of Tb reaches a maximum value at the moving velocity of 1m/s, since the isothermal surface of 295K starts to be broken when the moving human velocity reaches 1.0m/s. However, the values of Tb remains about 50s for the human moving velocities between 2.4m/s to 4.0m/s. The reason is that the temperature stratification are disappear completely when the human moving velocity exceeds 2.4m/s. Finally, The Tc keeps in the 250s when the human moving velocity is between 1.2m/s to 2.0m/s. The current study provides new insights into the design of the DV system

Boundary layer wind tunnel tests of outdoor airflow field around urban buildings: A review of methods and status

Outdoor airflow fields have received increasing attention in the building aerodynamics community due to that the airflow distributions around outdoor buildings are closely related to issues such as thermal comfort, building ventilation, and pollutant dispersion. The focus of this paper is on the airflow distributions around buildings obtained through wind tunnel tests, and such studies are mostly conducted in boundary layer wind tunnel with long test section. This paper reviews current techniques for boundary layer wind tunnel tests of airflow distributions in urban outdoor environments. Then, the characteristics of airflow distributions around buildings in three typical configurations from previous studies (i.e. isolated building, street canyon, and building complexes) are reviewed. This review highlights that the proposed building models should be carefully assessed in combination with wind tunnel tests at the design stage. In addition, it is important to obtain wind tunnel test data for buildings with thermal effects, and the importance of arranging the underlying surfaces during the test is also emphasized

Fast Fluid Dynamics Simulation of the Airflow Around a Single Bluff Body

Fast and accurate simulation of the outdoor airflow distribution is important for studying urban microclimate. In this paper, two pressure-correction schemes (i.e., NIPC and NSPF) for solving the N-S equation item by item are implemented in OpenFOAM and their differences from the PISO algorithm in simulating the airflow around a single 1:1:2 bluff body are analyzed. The RNG k-ε turbulence model is chosen to study the airflow disturbance, while the second-order discretization scheme of Gauss limitedLinear is used to solve the advection term in the N-S equation. The results show that the NIPC can accurately predict the main airflow characteristics around the bluff body, while the NSPF cannot predict the recirculation region on its top. The two pressure-correction schemes underestimate the TKE distribution on the top and leeward sides of the bluff body when applying the RNG k-ε turbulence model, and the maximum relative error is about 30%. However, they are consistent with the results of the PISO algorithm under the same conditions. The two schemes are about 2.5-3.0 times faster than the PSIO algorithm when run on a CPU, and the NSPF is about 12% faster than the NIPC scheme