Climate change presents challenges to policy and economic stability, necessitating effective trading strategies to reduce environmental risks. This article addresses gaps in existing studies by using a Markov-switching model to consider climate risk. Backward stochastic differential equations are used to optimize utility with three hedging strategies based on the concept of risk aversion. Numerical scenarios confirm the model\'s superiority in incorporating exogenous events, with our risk-averse strategy outperforming classical approaches. Our strategy outperforms classical strategies by taking a flexible risk trading when investors face risk-averse behavior due to climate risk events. The findings presented in this article have important implications for the development of more resilient investment portfolios and can contribute to climate policy.

Efficient stock status analysis and forecasting are important for stock market participants to be able to improve returns and reduce associated risks. However, stock market data are replete with noise and randomness, rendering the task of attaining precise price predictions arduous. Moreover, the lagging phenomenon of price prediction makes it hard for the corresponding trading strategy to capture the turning points, resulting in lower investment returns. To address this issue, we propose a framework for Important Trading Point (ITP) prediction based on Return-Adaptive Piecewise Linear Representation (RA-PLR) and a Batch Attention Multi-Scale Convolution Recurrent Neural Network (Batch-MCRNN) with the starting point of improving stock investment returns. Firstly, a novel RA-PLR method is adopted to detect historical ITPs in the stock market. Then, we apply the Batch-MCRNN model to integrate the information of the data across space, time, and sample dimensions for predicting future ITPs. Finally, we design a trading strategy that combines the Relative Strength Index (RSI) and the Double Check (DC) method to match ITP predictions. We conducted a comprehensive and systematic comparison with several state-of-the-art benchmark models on real-world datasets regarding prediction accuracy, risk, return, and other indicators. Our proposed method significantly outperformed the comparative methods on all indicators and has significant reference value for stock investment.

Pantoea dispersa is a Gram-negative bacterium that exists in a variety of environments and has potential in many commercial and agricultural applications, such as biotechnology, environmental protection, soil bioremediation, and plant growth stimulation. However, P. dispersa is also a harmful pathogen to both humans and plants. This \"double-edged sword\" phenomenon is not uncommon in nature. To ensure survival, microorganisms respond to both environmental and biological stimuli, which could be beneficial or detrimental to other species. Therefore, to harness the full potential of P. dispersa, while minimizing potential harm, it is imperative to unravel its genetic makeup, understand its ecological interactions and underlying mechanisms. This review aims to provide a comprehensive and up-to-date overview of the genetic and biological characteristics of P. dispersa, in addition to potential impacts on plants and humans, as well as to provide insights into potential applications.

The objective of this paper is to examine the overreaction behavior of 20 commodity futures based on intraday data from November 20, 2019 to June 3, 2020 with a focus on the impact of the Covid-19 pandemic. A dynamic and non-parametric approach is applied on intraday data for four different frequencies (from 1 min to 1 h) and two different sub-periods (pre-Covid-19 pandemic and during Covid-19 pandemic) in order to detect overreaction behavior which is defined as a large change of prices followed by proportional price reversals. Our empirical findings show that the overreaction hypothesis is confirmed for the considered commodity futures. Furthermore, both the number and the amplitude of overreactions is higher during the Covid-19 pandemic. Our findings also indicate that soft and metal commodities show much less overreactions than precious metals and especially energy commodities. In particular, crude oil futures exhibit a different overreaction behavior compared to other commodities since it has a higher number of negative than positive overreactions during the Covid-19 pandemic. We also find that the data frequency is independent of the overreacting behavior in both periods as the results continuously improve when having more observations due to higher frequencies. Finally, we find that extreme overreactions during the Covid-19 pandemic provide a great potential for profitable trading returns, which can be exploited by traders.

At present, gold and bitcoin have become mainstream assets in market transactions. Due to the volatility of gold and bitcoin prices, we can buy and sell assets like gold and bitcoin the same way we buy and sell stocks. The research goal of this article is to develop an optimal trading strategy that maximizes our post-trade returns. By studying the relationship between the two, on the one hand, it supplements and enriches the theoretical research on the rate of return of gold and Bitcoin, on the other hand, it provides a certain reference for investors to construct investment strategies. The research on the cointegration relationship between them has important practical significance. At the same time, it has important practical significance for the research on the cointegration relationship between bitcoin and gold.

This study demonstrates whether analysts\' sentiments toward individual stocks are useful for stock investment strategies. This is achieved by using natural language processing to create a polarity index from textual information in analyst reports. In this study, we performed time series forecasting for the created polarity index using deep learning, and clustered the forecasted values by volatility using a regime switching model. In addition, we constructed a portfolio from stock data and rebalanced it at each change point of the regime. Consequently, the investment strategy proposed in this study outperforms the benchmark portfolio in terms of returns. This suggests that the polarity index is useful for constructing stock investment strategies.

In recent years, machine learning for trading has been widely studied. The direction and size of position should be determined in trading decisions based on market conditions. However, there is no research so far that considers variable position sizes in models developed for trading purposes. In this paper, we propose a deep reinforcement learning model named LSTM-DDPG to make trading decisions with variable positions. Specifically, we consider the trading process as a Partially Observable Markov Decision Process, in which the long short-term memory (LSTM) network is used to extract market state features and the deep deterministic policy gradient (DDPG) framework is used to make trading decisions concerning the direction and variable size of position. We test the LSTM-DDPG model on IF300 (index futures of China stock market) data and the results show that LSTM-DDPG with variable positions performs better in terms of return and risk than models with fixed or few-level positions. In addition, the investment potential of the model can be better tapped by the reward function of the differential Sharpe ratio than that of profit reward function.

This paper aims to explore the relationship between information asymmetry and stock momentum. Using winner and loser approach, we find that winners with exaggerated forecast of earnings per share are more likely to have contrarian profits in subsequent holding periods. On the contrary, winners with low or middle-low information asymmetry tend to continue their good returns in future holding periods. In addition, the losers with middle information asymmetry achieve the highest contrarian profits, which may be called \"white lie effects.\"