An Improved BPNN Algorithm Based on Deep Learning Technology to analyze the Market Risks of A+H Shares

The backpropagation neural network (BPNN) algorithm of artificial intelligence (AI) is utilized to predict A+H shares price for helping investors reduce the risk of stock investment. First, the genetic algorithm (GA) is used to optimize BPNN, and a model that can predict multi-day stock prices is established. Then, the Principal Component Analysis (PCA) algorithm is introduced to improve the GA-BP model, aiming to provide a practical approach for analyzing the market risks of the A+H shares. The experimental results show that for A shares, the model has the best prediction effect on the price of Bank of China (BC), and the average prediction errors of opening price, maximum price, minimum price, as well as closing price are 0.0236, 0.0262, 0.0294 and 0.0339, respectively. For H shares, the model constructed has the best effect on the price prediction of China Merchants Bank (CMB). The average prediction errors of opening price, maximum price, minimum price and closing price are 0.0276, 0.0422, 0.0194 and 0.0619, respectively.

An approach for forecasting the Onion Price volatility in Indian Wholesale Markets using hybrid GARCH-LSTM Deep learning models

The fluctuations in the Onion prices have led to political and economic ramifications in countries such as India. In this study, we intend to estimate and then forecast the price volatility of Onion sales prices in major Indian wholesale markets. Initially, we take daily price data from major vegetable wholesale markets across India and simulate them to compute corresponding daily conditional volatilities using the traditional GARCH method. We then forecast the volatilities for the upcoming 10,15 and 21 days using the same traditional GARCH method and compare its forecasting accuracy with recent AI-led models. According to our comparisons, the deep learning-based LSTM model with various configurations provides superior results when compared to other traditional models with the highest accuracy in more than 70% of the cases. We expect that the given study could help the policymakers in managing sufficient buffer stock levels and the food supply chain stakeholders in hedging against the overall market risks due to the fluctuations in prices.

Barriers to Institutional Investment in Rental Housing: A Systematic Review of Market Risks

Housing practitioners and policy experts are advocating for an expansion in rental housing supply in contemporary cities around the world. The objective is to convince institutional investors to include rental housing investment in their investment portfolio to contribute to boosting housing supply. Unfortunately, the rental sector is characterized by numerous uncertainties and challenges, making it unattractive to institutional investors. With the growing attention to institutional investors in various housing market contexts, an understanding of the market risks (also known as barriers), is useful to inform future research and policymaking. Using a systematic literature review methodology, this paper synthesizes the extant literature on the market risks inhibiting institutional investment in rental housing. Findings reveal the following barriers: low profitability, non-progressive rent control policies, unclear target group for rented projects, poor landlord-tenant relations, inadequate property management and unreliable property market information. Among all the barriers identified, low profitability and inadequate property management had great influence on their investment decision. Firstly, institutional investors perceive rental housing investment as less profitable and unattractive in terms of project performance. Secondly, the lack of supporting structures for the property management sector contributes to derailing rental yields. The review also finds that the target group for rental projects are often vague especially for projects under government assistance. The rental sectors in many countries are confronted with numerous problems, some of which greatly inhibit institutional investors from investing in the rental asset. This paper concludes that, although the idea of expanding rental housing supply seems laudable, ignoring these problems may be detrimental to housing markets in the long run. Rental markets in many countries are volatile, and thus not ready to receive institutional investors fully into the sector. An expanded rental sector could be advanced if policy makers take the appropriate steps to resolve the identified challenges. Adequate structural preparations must also be made for large scale rental housing supply.

Long-run Effects of Market Risk Factors on Bank Performance in the SSA Banking System

The study assesses the long-term effects of market risk factors on bank performance in the Sub-Saharan Africa banking system. The article identifies the most influential market risk factor and the most affected bank performance factors in the long term. It covers 40 countries with 350 commercial banks for ten years. The analysis uses dynamic fixed-effects models (ARDL-DFE). The results demonstrated that non-performing loans are the most influencers affecting bank performance factors in the long run. Furthermore, the results show that return on average assets is the most bank performance factor affected mainly by market risks, especially the NPLs in the long run. Finally, the findings surprisingly proved mutual interactions and cointegration movements among bank market risk factors and bank performance measures in the long run. These findings can assist central banks in supervising and regulating SSA commercial banks and inspire regional bank managers in reducing market risks and sharpening long-run performance strategies through resource reallocating.

Characterization and Design for Last Mile Logistics: A Review of the State of the Art and Future Directions

One of the most challenging problems in last mile logistics (LML) has been the strategic delivery due to various market risks and opportunities. This paper provides a systematic review of LML-related studies to find current issues and future opportunities for the LML service industry. To that end, 169 works were selected as target studies for in-depth analysis of recent LML advances. First, text mining analysis was performed to effectively understand the underlying LML themes in the target studies. Then, the novel definition and typology of LML delivery services were suggested. Finally, this paper proposed the next generation of LML research through advanced delivery technique-based LML services, environmentally sustainable LML systems, improvement of LML operations in real industries, effective management of uncertainties in LML, and LML delivery services for decentralized manufacturing services. We believe that this systematic literature review can serve as a useful tool for LML decision makers and stakeholders.

Climate protection as an opportunity for banks to increase earnings and consumer trust

Banks are currently facing numerous challenges. In addition to the ongoing cheap money policy of the European Central Bank, a regulated market environment and a rapidly progressive digitization, financial institutions are increasingly confronted with topics such as sustainability and climate protection. From the latter derive not only risks but also chances for banks. Sustainability risks can impact different risk categories such as market risks, credit risks, operational risks, and liquidity risks. Moreover, reputational risks can occur in this context. This is especially important as bank customers constantly develop a greater awareness of ecological issues, and thus, develop increasing expectations on how companies – like banks – deal with issues like climate protection and sustainability. For this reason, we will start with a theoretical explanation of the key words and then present the results of our customer survey to highlight the current expectations of bank customers in the context of climate protection. Based on this, we formulate recommendations for banks on how to generate a competitive advantage by engaging in climate protection and by taking sustainable actions.

Risk analysis model and agricultural derivative market use

This research aims to build conceptual guidelines regarding price risk management through the agricultural derivatives market. Specifically, to identify the common price risk management methods and strategies employed, the risk analysis models of derivative markets, and the barriers to agricultural risk management. This is an integrative review, the search for literature on the models of risk management analysis of agricultural derivatives started by listing the largest possible number of keywords on the topic, in the Scopus and Web of Science. Forty-five publications were found meeting the pre-established criteria that served as the basis for this research. Based on the literature review, we list the main information on the subject and we also propose a theoretical model for analyzing the market risks of agricultural derivatives. Still, it was possible to notice that among the methodologies for measuring market risk, Value at Risk (VaR) stands out. We exemplify and demonstrate the existence of several statistical analyzes and mathematical models, as well as software available for the management of price risks. It is concluded that strategies with the futures and options market, even though they are the most efficient for risk management, lack incentives to become practical.

Using Fuzzy AHP-PROMETHEE for Market Risk Assessment of New-Build River Cruises on the Yangtze River

Cruise tourism on the Yangtze River Basin has developed rapidly in recent years. However, it is still facing such challenges as homogenization of itinerary and shore attraction arrangement, as well as aging cruise ships and simplification of service facilities, while it is also difficult to satisfy hierarchical and personalized tourist needs. To change such circumstances, new-build river cruise ships are inevitable. Complexity of market supply and demand environment, together with variability of tourist preferences, have increased market uncertainties of new-build cruise products. This study aims to assess market risks of new-build river cruise ships first by identifying risk factors, from the perspective of supply and demand under the actual conditions of the Yangtze River cruise market, then by evaluating potential impacts, caused by risk factors based on multi-criteria decision-making considerations. Fuzzy AHP-PROMETHEE was employed to prioritize the risk factors. The results reveal that among the most significant market risk factors are the following, in sequence: backwardness of support facilities; sudden security crisis; homogenization of cruise products; simplification of tourism route design; and inadequate management of the tourism market. Such findings will provide beneficial insights for strategic and sustainable development of river cruises on the Yangtze River.

Effective Mainstreaming of Agricultural Emissions into Climate Action Agenda: The Case of Institutions and Smallholder Dairy Production Systems, Western Kenya

Small-scale farming production systems are integral drivers of global sustainability challenges and the climate crisis as well as a solution space for the transition to climate compatible development. However, mainstreaming agricultural emissions into a climate action agenda through integrative approaches, such as Climate Smart Agriculture (CSA), largely reinforces adaptation–mitigation dualism and pays inadequate attention to institutions’ linkage on the generation of externalities, such as Greenhouse Gas (GHG) emissions. This may undermine the effectiveness of local–global climate risk management initiatives. Literature data and a survey of small-scale farmers’ dairy feeding strategies were used in the simulation of GHG emissions. The effect of price risks on ecoefficiencies or the amount of GHG emissions per unit of produced milk is framed as a proxy for institutional feedbacks on GHG emissions and effect at scale. This case study on small-scale dairy farmers in western Kenya illustrates the effect of local-level and sectoral-level institutional constraints, such as market risks on decision making, on GHG emissions and the effectiveness of climate action. The findings suggest that price risks are significant in incentivising the adoption of CSA technologies. Since institutional interactions influence the choice of individual farmer management actions in adaptation planning, they significantly contribute to GHG spillover at scale. This can be visualised in terms of the nexus between low or non-existent dairy feeding strategies, low herd productivity, and net higher methane emissions per unit of produced milk in a dairy value chain. The use of the Sustainable Food Value Chain (SFVC) analytical lens could mediate the identification of binding constraints, foster organisational and policy coherence, as well as broker the effective mainstreaming of agricultural emissions into local–global climate change risk management initiatives. Market risks thus provide a systematic and holistic lens for assessing alternative carbon transitions, climate financing, adaptation–mitigation dualism, and the related risk of maladaptation, all of which are integral in the planning and implementation of effective climate action initiatives.