The world is currently experiencing the environmental challenge of global warming, necessitating careful planning of carbon dioxide ( CO 2 ${\rm{CO}}_2$ ) emissions to deal with this problem. This study examines the environmental challenge posed by CO 2 ${\rm{CO}}_2$ emissions from both a long and short-term perspective. In the long term, despite efforts made by countries, our change-point detection analysis shows that there has been no structural change in CO 2 ${\rm{CO}}_2$ emissions since 1950. Without significant efforts, the carbon budget corresponding to the Paris Agreement's target will be exhausted by 2046. To achieve this target, a significant reduction in global CO 2 ${\rm{CO}}_2$ emissions of 3.22% per year is necessary. In the short term, COVID-19 is thought to have relieved pressure on CO 2 ${\rm{CO}}_2$ emissions. However, this study shows that CO 2 ${\rm{CO}}_2$ emissions quickly returned to normal levels after a brief downturn, and we provide information on the order of CO 2 ${\rm{CO}}_2$ emissions recovery for different sectors.

We study the effects of fee structures on fund managers’ strategies for locking in profits. Utilizing the optimal stopping time method, we identify two critical portfolio value thresholds that signal when a manager will choose to lock in profits. Fee components such as management fees, self-investment ratios, and high-water marks significantly influence these decisions. Specifically, higher management fees are associated with increased risk aversion, leading to a narrower continuation region, indicating a preference for lower risk. Conversely, performance fees encourage greater risk-taking. We use the S&P 500 Index and NASDAQ Composite index as representatives of managers’ portfolios and apply our model to illustrate how managers adjust their profit-locking strategies in response to their desired rewards.

We examine the effectiveness of deep learning models in implementing the time-series momentum strategy in the Chinese futures market. Our empirical analysis shows that the long short-term memory (LSTM) model performs better than other machine learning methods in terms of profitability and risk management. Importantly, incorporating the Sharpe ratio into model training significantly increases returns while decreasing risks. Additionally, our findings indicate that considering momentum turning points and combining short- and long-term predictions further enhances the performance of the LSTM model.

Since investors have diverse perspectives and limited information, their expectations can be subjective and prone to inaccuracies. Hence, price fluctuations are influenced by heterogeneous beliefs regarding future expectations, and both surveys and straightforward models can only partially capture the intricate nature of expectations. To address this issue, we employ a noncausal AR-GARCH model with a quasi-maximum likelihood technique to mitigate the impact of heterogeneous beliefs. Our approach allows us to determine the asymptotic distribution of estimated parameters and perform hypothesis tests. These empirical findings indicate that the error term in the US stock market is causal; in contrast, in the Chinese stock market, noncausal errors significantly impact price volatility. Furthermore, our models have the capability to discern nuanced distinctions between Brent and WTI crude oil prices, indicating that the price pattern of WTI may be more influenced by heterogeneous beliefs among market participants.

Many sequentially observed functional data objects are available only at the times of certain events. For example, the trajectory of stock prices of companies after their initial public offering (IPO) can be observed when the offering occurs, and the resulting data may be affected by changing circumstances. It is of interest to investigate whether the mean behavior of such functions is stable over time, and if not, to estimate the times at which apparent changes occur. Since the frequency of events may fluctuates over time, we propose a change point analysis that has two steps. In the first step, we segment the series into segments in which the frequency of events is approximately homogeneous using a new binary segmentation procedure for event frequencies. After adjusting the observed curves in each segment based on the frequency of events, we proceed in the second step by developing a method to test for and estimate change points in the mean of the observed functional data objects. We establish the consistency and asymptotic distribution of the change point detector and estimator in both steps, and study their performance using Monte Carlo simulations. An application to IPO performance data illustrates the proposed methods.

The filter rule is a popular investment strategy, but its effectiveness and rationality are still controversial. We consider the process of the maximum stock price and the short loss aversion in the utility function and establish an optimal stopping time model. Our model describes the decision-making process of investors applying the filter rule strategy. The filter size of our model is dynamic and depends both on the characteristics of the price process and the investor utility function. A series of numerical simulations present the optimal times to sell the stock under different situations. We also conduct empirical analyses on the Shanghai Stock Exchange Index and the S&P 500 Index, and prove that the filter rule is effective.

This paper studies the effect of corporate social responsibility (CSR) and customer relationships on the stock price during the COVID-19 pandemic. The empirical results show that CSR practices improve firms’ resilience to the negative health crisis shocks. The functional principal component analysis helps display the relationship between CSR and cumulative abnormal returns (CAR). It shows that CSR practices improve customers’ cooperation willingness. Customers of high-CSR firms pay invoices faster during the crisis, which results in less increment of accounts receivable. Hence, high-CSR firms gain more cash support from their customers to overcome the COVID-19 pandemic, resulting in higher cumulative abnormal returns.

Over-the-counter (OTC) markets dominate trading in many asset classes. We examine the role of OTC trading institutions on price discovery and bubble formation by constructing an agent-based model with artificial adaptive traders placed in a laboratory market environment. We compare (i) OTC markets without dealers; (ii) OTC markets with dealers; and (iii) standard double auction (DA) markets. Under the same parameters estimated from the DA experimental data, we find that replacing DA with OTC trading institutions does not mitigate price bubbles. After recalibrating the model using the OTC experimental data, we find that anchoring effects are lower and selling pressures are higher in OTC markets than DA markets. The simulated results show smaller price bubbles in OTC markets than in DA markets, which is consistent with the experimental data. This result highlights the important role played by the interaction between trading institutions and the agent’s behavioral biases in bubble formation.

Timing the selling of crude oil futures to control risk is a worth studying question given the swift fall of their prices. This paper proposes an optimal stopping model to find the optimal selling time at the beginning of the downtrend. The model depends on the crude oil futures prices drawdown and the boundary to identify the occurrence of downtrend in real-time. The numerical simulation and empirical analyses help verify the effectiveness of the proposed optimal stopping time model, especially, in 2007, when the model can effectively avoid losses. The conclusions of the paper provide a new perspective for investors to control risk.

With the application of the optimal stopping techniques, this paper proposes a filter rule for investors in emerging stock markets. In a bull market, once the stock price falls down to the optimal filter size, investors should sell the stock to avoid massive losses. We show that the optimal filter size is a function of the historical highest price, the weights of the future returns and the current drawdown in the investor’s utility function, the characteristics of the underlying stochastic price process, and the discount rate. Out-of-sample tests verify that this filter rule is valid, and the selling signals generated by the filter rule are at the beginning of the downtrend in the most emerging stock markets.

We investigate the impacts of fee structure on a typical on the fund manager's optimal selling price. The fee structure includes the self-investment ratio in the fund, management fee, incentive fee, and the high watermark. Based on the explicit solution of the optimal selling price, we find that the optimal selling price is negatively related to the self-investment ratio and management fee, and positively related to the incentive fee and high watermark. This conclusion inspires designing an incentive contract for the manager: the fund manager should be offered more incentive fee and higher watermark, lower management fee, and self-investment ratio, which will possibly improve the fund performance.