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.

Using subsidiary-level data of 3,863 Chinese nonfinancial listed firms, we find their capital expenditures increase with foreign sales, and the difference arises from the investments of the firms’ foreign subsidiaries. We show that the foreign sales-foreign investment association becomes more sensitive when the economic policy uncertainty (EPU) increases in the domestic market. However, foreign EPU does not play such a significant role. We provide one possible explanation that due to global diversification, MNEs can hedge foreign EPU using their international subsidiary network, resulting in the overall investments unchanged. However, given China’s tight regulatory capital controls, the MNEs may be less able to hedge the domestic EPU, so that they reallocate investments from the domestic markets to the foreign markets, consistent with the transaction cost assumption underlying the real options theory. Robust tests show that access to foreign capital, profitability and institutional factors have little explanatory power over the MNEs’ foreign investment.

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.

This study investigates attributes of local firms that determine investment biases using mutual funds holding data across 48 markets. Controlling for variations in market level environments, we find that firm characteristics related to transaction cost, corporate governance, information asymmetry and local familiarity create significant barriers to foreign investments. The extent to which information asymmetry and familiarity constrain investment allocation is more observable for foreign than for domestic investors, even in developed and liberalized markets. However, in emerging and restricted markets, variations in foreign investment bias are mainly driven by market level cross-border investment barriers. Overall, the well-documented “home bias” phenomenon may be a joint effect of both firm and market level investment barriers.

Using block trades data on the Shanghai Stock Exchange (SSE) from 2003 – 2009, we study the pricing mechanisms of block buys and sells. We show that block trades are priced at discount (premium) for sells (buys). The discount/ premium varies depending on the characteristics of the stocks traded, the complexity of the trades, and also on whether the trades are internalized. We also study permanent and temporary price impact of the trades. As expected, seller-initiated trades do not seem to be information related as there is no significant information content. On the contrary, the prices decline after buyer-initiated trades, suggesting that buyers do not possess private information which leads to a permanent shift in prices. Temporary price impacts of all trades are large in magnitude and statistically significant, reflecting compensation for locating counterparties and the cost of negotiating terms. This suggests that the information platform on SSE for locating counterparties is yet to be fully developed to help reduce the transaction cost of block trades.

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.