This paper proposes the concepts of ESG information flows and a predictable framework of ESG flows based on AR process, and studies how ESG information flows are incorporated into and affect a dynamic portfolio with transaction costs. Two methods, called the ESG factor model and the ESG preference model, are considered to embed ESG information flows into a dynamic mean-variance model. The dynamic optimal portfolio can be expressed as a traditional optimal portfolio without ESG information and a dynamic ESG preference portfolio, and the impact of ESG information on optimal trading is explicitly analyzed. The rich numerical results show that ESG information can improve the out-of-sample performance, and ESG preference portfolio has the best out-of-sample performance including the net returns, Sharpe ratio and cumulative return of portfolios, and contribute to reducing risk and transaction costs. Our dynamic trading strategy provides valuable insights for sustainable investment both in theory and practice.

In this paper, we show that only the systematic component of exchange rate expectations of professional investors is a strong predictor of the cross-section of currency returns. The predictability is strong in short and long horizons. The strategy offers significant Sharpe ratios for holding periods of 1 to 12 months, and it is unrelated to existing currency investment strategies, including risk-based currency momentum. The results hold for forecast horizons of 3, 12, and 24 months, and they are robust after accounting for transaction costs. The idiosyncratic component of currency expectations does not contain important information for the cross-section of currency returns. Our strategy is more significant for currencies with low sentiment and it is not driven by volatility and illiquidity. The results are robust when we extract the systematic component of the forecasts using a larger number of predictors.

The implementation of prefabricated housing (PH) has become prevalent in China recently because of its advantages in improving production efficiency and saving energy. However, the benefits of adopting PH cannot always be accrued by the stakeholders because of the arising transaction costs (TCs) in the projects’ development process. This study investigates the strategies for developers to make rational choices for minimizing the TCs of the PH project considering their own attributes and external constraints. A Bayesian Belief Network model was applied as the analytical method, based on the surveys in China. The single sensitive analysis indicated that developers influence the TCs of PH through the three most impactful factors: Prefabrication rate, PH experience, and Contract payment method. Furthermore, combined strategies were recommended for developers in various situations based on the multiple sensitivity analysis. Developers facing high prefabrication rate challenges are suggested to reduce the risks by procuring high-qualified general contractors and adopting unit-price contracts type. For developers with limited PH experience, adopting the Engineering-Procurement-Construction procurement method is the most efficient in reducing their TCs in the context of China’s PH market. This study contributes to the current body of knowledge concerning the effect of traders’ attributes and choices on TCs, expanding the application of TCs theory and fulfilling the study on the determinants of TCs in construction management.

This paper proposes a new forecasting framework for the stock market that combines machine learning algorithms with several technical analyses. The paper considers three different algorithms: the Random Forests (RF), the Gradient-boosted Trees (GBT), and the Deep Neural Networks (DNN), and performs forecasting tasks and statistical arbitrage strategies. The portfolio weight optimization strategy is also proposed to capture the model's return and risk information from output probabilities. The paper then uses the stock data in the Chinese A-share market from January 1, 2011, to December 31, 2020, and observes that all three machine learning models achieve significant returns in the Chinese stock market. The DNN achieves an average daily return of 0.78% before transaction costs, outperforming the 0.58% of the RF and 0.48% of the GBT, far exceeding the general market level. The performance of the weighted portfolio based on the ESG score is also improved in all three machine learning strategies compared to the equally weighted portfolio. These results help bridge the gap between academic research and professional investments and offer practical implications for financial asset pricing modelling and corporate investment decisions.

This study provides a comprehensive analysis of the effects of Computer-based Trad-ing (CBT) on Treasury bond expected returns. We document a strong relationship between bond expected returns and the overall intensity at which CBT takes place in the Treasury market. Investing in bonds with the largest beta to the aggregate CBT intensity and shorting those with the smallest generates large and significant returns. Those returns are not due to compensation for facing conventional sources of risk or to transaction costs. Our results are consistent with capital-flow based explanations implied by asset pricing models with institutional investors.

This study examines the effect of transaction costs and information asymmetry on firms’ capital-structure decisions in 40 countries. The findings indicate that transaction costs affect both capital-market timing and capital-structure rebalancing. Past market-timing activity has a significantly negative impact on the current debt ratio, and this impact is stronger for firms facing lower transaction costs of external financing, as defined by legal origin, capital-market development, and securities rules in their home countries. Further analysis indicates that firms in countries with lower transaction costs also rebalance their capital structure more quickly after a deviation from the target, but the rebalancing does not eliminate the market timing effect on capital structure completely.

We examine the execution quality of electronic stock traders who are geographically dispersed throughout the U.S. Traders who are located near market central computers in the New York City area experience faster order execution. Moreover, the time to execute orders rises as a trader’s actual distance (mileage) to NYC widens. In electronic market settings, data transfer limitations and transmission slowdowns result in geographically dispersed electronic traders having different access to trading speed. We find that speed advantaged traders experience lower transaction costs and engage in strategies that are more conducive to speed.

This paper examines the impact of idiosyncratic risk on the cross-section of weekly stock returns from 1963 to 2006. I use an exponential GARCH model to forecast expected idiosyncratic volatility and employ a combination of the size e§ect, value premium, return momentum and short-term reversal to measure relative mispricing. I ?nd that stock returns monotonically increase in idiosyncratic risk for relatively undervalued stocks and monotonically decrease in idiosyncratic risk for relatively overvalued stocks. This phenomenon is robust to various subsamples and industries, and cannot be explained by risk factors or ?rm characteristics. Further, transaction costs, short-sale constraints and information uncertainty cannot account for the role of idiosyncratic risk. Overall, these ?ndings are consistent with the limits of arbitrage arguments and demonstrate the importance of idiosyncratic risk as an arbitrage cost.

This paper examines the impact of idiosyncratic risk on the cross-section of weekly stock returns from 1963 to 2006. I use an exponential GARCH model to forecast expected idiosyncratic volatility and employ a combination of the size effect, value premium, return momentum and short-term reversal to measure relative mispricing. I ?find that stock returns monotonically increase in idiosyncratic risk for relatively undervalued stocks and monotonically decrease in idiosyncratic risk for relatively overvalued stocks. This phenomenon is robust to various subsamples and industries, and cannot be explained by risk factors or ?rm characteristics. Further, transaction costs, short-sale constraints and information uncertainty cannot account for the role of idiosyncratic risk. Overall, these ?findings are consistent with the limits of arbitrage arguments and demonstrate the importance of idiosyncratic risk as an arbitrage cost.

We investigate the information cost of stock trading during the 2000 presidential election. We find that the uncertainty of the election induces information asymmetry of politically sensitive firms under the Bush/Gore platforms. The unusual delay in election results in a significant increase in the adverse selection component of trading cost of politically sensitive stocks. Cross-sectional variations in bid-ask spreads are significantly and positively related to changes in information cost, controlling for the effects of liquidity cost and stock characteristics. This empirical evidence is robust to different estimation methods.