Search results for: Shanghai carbon trading

Authors: Liu Zhiyuan, Sun Zongdi

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In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.

Keywords: Carbon trading price, carbon trading volume, BP neural network model, Shanghai City

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Authors: Zhaojun Wang, Zongdi Sun, Zhiyuan Liu

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First of all, the carbon trading price and trading volume in Shanghai are transformed by Fourier transform, and the frequency response diagram is obtained. Then, the frequency response diagram is analyzed and the Blackman filter is designed. The Blackman filter is used to filter, and the carbon trading time domain and frequency response diagram are obtained. After wavelet analysis, the carbon trading data were processed; respectively, we got the average value for each 5 days, 10 days, 20 days, 30 days, and 60 days. Finally, the data are used as input of the Back Propagation Neural Network model for prediction.

Keywords: Shanghai carbon trading, carbon trading price, carbon trading volume, wavelet analysis, BP neural network model

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Authors: Neha Asthana, Vishal Krishna Prasad

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Reverse logistics and green supply chain form an interconnected and interwoven network of parameters that contribute to enhancement and incremental exchange in the triple bottom line in the consistently changing and fragmenting markets of the globalizing markets of today. Reverse logistics not only contributes to completing the supply chain in a comprehensive and synchronized manner but also contributes to a significant degree in optimizing green supply chains through procedures such as recycling, refurbishing etc. contributing to waste reduction. Carbon trading, owing to its limitations in the global context and being in a nascent stage seeks plethora of research to determine its full application in synergy with reverse logistics and green supply chain.

Keywords: reverse logistics, carbon trading, carbon emissions, green supply chain

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Authors: Bengan Simbarashe Manwere

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The entire African continent, comprising 55 countries, holds a 2% share of the global carbon market. The World Bank attributes the continent’s insignificant share and participation in the carbon market to the limited access to electricity. Approximately 800 million people spread across 47 African countries generate as much power as Spain, with a population of 45million. Only South Africa and North Africa have carbon-reduction investment opportunities on the continent and dominate the 2% market share of the global carbon market. On the back of the 2015 Paris Agreement, South Africa signed into law the Carbon Tax Act 15 of 2019 and the Customs and Excise Amendment Act 13 of 2019 (Gazette No. 4280) on 1 June 2019. By these laws, South Africa was ushered into the league of active global carbon market players. By increasing the cost of production by the rate of R120/tCO2e, the tax intentionally compels the internalization of pollution as a cost of production and, relatedly, stimulate investment in clean technologies. The first phase covered the 1 June 2019 – 31 December 2022 period during which the tax was meant to escalate at CPI + 2% for Scope 1 emitters. However, in the second phase, which stretches from 2023 to 2030, the tax will escalate at the inflation rate only as measured by the consumer price index (CPI). The Carbon Tax Act provides for carbon allowances as mitigation strategies to limit agents’ carbon tax liability by up to 95% for fugitive and process emissions. Although the June 2019 Carbon Tax Act explicitly makes provision for a carbon trading scheme (CTS), the carbon trading regulations thereof were only finalised in December 2020. This points to a delay in the establishment of a carbon trading scheme (CTS). Relatedly, emitters in South Africa are not able to benefit from the 95% reduction in effective carbon tax rate from R120/tCO2e to R6/tCO2e as the Johannesburg Stock Exchange (JSE) has not yet finalized the establishment of the market for trading carbon credits. Whereas most carbon trading schemes have been designed and constructed from the beginning as new tailor-made systems in countries the likes of France, Australia, Romania which treat carbon as a financial product, South Africa intends, on the contrary, to leverage existing trading infrastructure of the Johannesburg Stock Exchange (JSE) and the Clearing and Settlement platforms of Strate, among others, in the interest of the Paris Agreement timelines. Therefore the carbon trading scheme will not be constructed from scratch. At the same time, carbon will be treated as a commodity in order to align with the existing institutional and infrastructural capacity. This explains why the Carbon Tax Act is silent about the involvement of the Financial Sector Conduct Authority (FSCA).For South Africa, there is need to establish they equilibrium stability of the CTS. This is important as South Africa is an innovator in carbon trading and the successful trading of carbon credits on the JSE will lead to imitation by early adopters first, followed by the middle majority thereafter.

Keywords: carbon trading scheme (CTS), Johannesburg stock exchange (JSE), carbon tax act 15 of 2019, South Africa

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Authors: Boulis M. Ibrahim, Iordanis A. Kalaitzoglou

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A micro structural pricing model is proposed in which price components account for learning by incorporating changing expectations of the trading intensity and the risk level of incoming trades. An analysis of European carbon futures transactions finds expected trading intensity to increase the information component and decrease the liquidity component of price changes, but at different rates. Among the results, the expected persistence in trading intensity explains the majority of the auto correlations in the level and the conditional volatility of price changes, helps predict hourly patterns in the bid–ask spread and differentiates between the impact of buy versus sell and continuing versus reversing trades.

Keywords: CO2 emission allowances, market microstructure, duration, price discovery

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Authors: Wei Fang, Jian Jun Gu, Di Xue

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Objective: To analyze the employee compensation status of secondary public hospitals of Pudong New Area in Shanghai in order to provide information for compensation reform of public hospitals in Shanghai and as well as in China. Methods: We surveyed all 15 secondary public hospitals of Pudong New Area in Shanghai to collect hospital annual compensation data for their employees and to investigate their suggestions for compensation reform in public hospitals in China. We also collected related annual compensation data of employees in Shanghai and of physicians in the USA from Shanghai statistical Yearbook 2013 and from Bureau of Labor Statistics, U.S. Department of Labor. Results: The average annual compensation for the employees in secondary public hospitals of Pudong New Area in Shanghai in 2012 was 2.65 times of that for overall employees in Shanghai. The physician’s compensation in these public hospitals was relatively lower than that in the USA. Conclusion: The physicians’ compensation in the secondary public hospitals of Pudong New Area in Shanghai should be increased rationally and new compensation reform in public hospitals in Shanghai should be carefully designed.

Keywords: human resource, compensation, public hospital, Shanghai

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Authors: Cristian Păuna

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After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, price prediction

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Authors: Fuqiang Wang, Jun Liu, Liyan Cai

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This paper investigates the coordination of the conflicting two-stage low carbon supply chain consisting of upstream and downstream manufacturers. The conflict means that the upstream manufacturer takes action for carbon emissions reduction under carbon trading mechanism while the downstream manufacturer’s production cost rises. It assumes for the Stackelberg game that the upstream manufacturer plays as a leader and the downstream manufacturer does as a follower. Four kinds of the situation of decentralized decision making, centralized decision-making, the production cost sharing contract and the carbon emissions reduction revenue sharing contract under decentralized decision making are considered. The backward induction approach is adopted to solve the game. The results show that the more intense the conflict is, the lower the efficiency of carbon emissions reduction and the higher the retail price is. The optimal investment of the decentralized supply chain under the two contracts is unchanged and still lower than that of the centralized supply chain. Both the production cost sharing contract and the carbon emissions reduction revenue sharing contract cannot coordinate the supply chain, because that the sharing cost or carbon emissions reduction sharing revenue will transfer through the wholesale price mechanism. As a result, it requires more complicated contract forms to coordinate such a supply chain.

Keywords: cap-and-trade mechanism, carbon emissions reduction, conflict, supply chain coordination

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Authors: Na Li, Yunwei Zhang, Yuhong Niu

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The study discussed the patent protections of health system and enterprises in Shanghai. The comparisons of technical distribution and scopes of patent protections between Shanghai health system and enterprises were used by the methods of IPC classification, co-words analysis and visual social network. Results reflected a decreasing order within IPC A61 area, namely A61B, A61K, A61M, and A61F. A61B required to be further investigated. The highest authorized patents A61B17 of A61B of IPC A61 area was found. Within A61B17, fracture fixation, ligament reconstruction, cardiac surgery, and biopsy detection were regarded as common concerned fields by Shanghai health system and enterprises. However, compared with cardiac closure which Shanghai enterprises paid attention to, Shanghai health system was more inclined to blockages and hemostatic tools. The results also revealed that the scopes of patent protections of Shanghai enterprises were relatively centralized. Shanghai enterprises had a series of comprehensive strategies for protecting core patents. In contrast, Shanghai health system was considered to be lack of strategic patent protections for core patents.

Keywords: co-words analysis, IPC classification, patent protection, technical distribution

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Authors: L. Maiza, A. Cantagrel, M. Forestier, G. Laucoin, T. Regali

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Evaluation of High Frequency Trading (HFT) impact on financial markets is very important for traders who use market analysis to detect winning transaction opportunity. Analysis of HFT data on tobacco commodity market is discussed here and interesting linear relationship has been shown between trading frequency and difference between averaged trading prices above and below considered trading frequency. This may open new perspectives on markets data understanding and could provide possible interpretation of Adam Smith invisible hand.

Keywords: financial market, high frequency trading, analysis, impacts, Adam Smith invisible hand

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Authors: Maxat Bokambayev, Bella Tussupova, Aisha Mamyrova, Erlan Izbasarov

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Optimization of business processes in trading companies is reviewed in the report. There is the presentation of the “Wholesale Customer Order Handling Process” business process model applicable for small and medium businesses. It is proposed to apply the algorithm for automation of the customer order processing which will significantly reduce labor costs and time expenditures and increase the profitability of companies. An optimized business process is an element of the information system of accounting of spare parts trading network activity. The considered algorithm may find application in the trading industry as well.

Keywords: business processes, discrete-event simulation, management, trading industry

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Authors: Lin Ji

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The 1923 HSBC Building is one of the landmarks of Shanghai's Bund complex and is described as "one of the finest buildings from the Suez Canal to the Bering Strait". Mr George Leopold Wilson of Palmer&Turner and his design team combine the latest British design taste with Chinese elements and bring the high standard of London manufacturing to Shanghai. This paper reviews the establishment background and construction process of the Hongkong and Shanghai Bank Corporation in Shanghai, and analyzes the characteristics of various styles and ornament of HSBC. At the same time, using the research method of iconography, compared with Britain's exploration of modern design mode in the early 20th century, we can deeply understand how this "monument of world commerce and prosperity" realizes the identity construction of its financial empire in the Far East in the perfect combination of practicality and artistry.

Keywords: early 20-century Shanghai, the bund, the HSBC building, classical styles, ornament, identity construction

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Authors: Cristian Pauna

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Trading the financial markets is a widespread activity today. A large number of investors, companies, public of private funds are buying and selling every day in order to make profit. Algorithmic trading is the prevalent method to make the trade decisions after the electronic trading release. The orders are sent almost instantly by computers using mathematical models. This paper will present a price prediction methodology based on a multi-dimensional neural network. Using the Fisher transform, the neural network will be instructed for a low-latency auto-adaptive process in order to predict the price evolution for the next period of time. The model is designed especially for algorithmic trading and uses the real-time price series. It was found that the characteristics of the Fisher function applied at the nodes scale level can generate reliable trading signals using the neural network methodology. After real time tests it was found that this method can be applied in any timeframe to trade the financial markets. The paper will also include the steps to implement the presented methodology into an automated trading system. Real trading results will be displayed and analyzed in order to qualify the model. As conclusion, the compared results will reveal that the neural network methodology applied together with the Fisher transform at the nodes level can generate a good price prediction and can build reliable trading signals for algorithmic trading.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, neural network

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Authors: Cameron Truong, Mikhail Bhatia, Yangyang Chen, Viet Nga Cao

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Using a sample of U.S. firms between 1996 and 2011, this paper documents a positive association between options trading volume and future stock price crash risk. This relation is evidently more pronounced among firms with higher information asymmetry, business uncertainty, and short-sale constraints. In a dichotomous cross-sectional setting, we also document that firms with options trading have higher future crash risk than firms without options trading. We further show in a difference-in-difference analysis that firms experience an increase in crash risk immediately after the listing of options. The results suggest that options traders are able of identifying bad news hoarding by management and choose to trade in a liquid options market in anticipation of future crashes.

Keywords: bad news hoarding, cross-sectional setting, options trading, stock price crash

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Authors: Yan-Shen Yang, Bai-Chen Xie

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Background and significance of the study: Both green power trading schemes and interregional power transmission are effective ways to increase green power absorption and achieve renewable power development goals. China accelerates the construction of interregional power transmission lines and the green power market. A critical issue focusing on the close interaction between these two approaches arises, which can heavily affect the green power quota allocation and renewable power development. Existing studies have not discussed this issue adequately, so it is urgent to figure out their relationship to achieve a suitable power market design and a more reasonable power grid construction.Basic methodologies: We develop an equilibrium model of the power market in China to analyze the coupling effect of these two approaches as well as their influence on power generation and interregional transmission in China. Our model considers both the Tradable green certificate (TGC) and green power market, which consists of producers, consumers in the market, and an independent system operator (ISO) minimizing the total system cost. Our equilibrium model includes the decision optimization process of each participant. To reformulate the models presented as a single-level one, we replace the producer, consumer, ISO, and market equilibrium problems with their Karush-Kuhn-Tucker (KKT) conditions, which is further reformulated as a mixed-integer linear programming (MILP) and solved in Gurobi solver. Major findings: The result shows that: (1) the green power market can significantly promote renewable power absorption while the TGC market provides a more flexible way for green power trading. (2) The phenomena of inefficient occupation and no available transmission lines appear simultaneously. The existing interregional transmission lines cannot fully meet the demand for wind and solar PV power trading in some areas while the situation is vice versa in other areas. (3) Synchronous implementation of green power and TGC trading mechanism can benefit the development of green power as well as interregional power transmission. (4) The green power transaction exacerbates the unfair distribution of carbon emissions. The Carbon Gini Coefficient is up to 0.323 under the green power market which shows a high Carbon inequality. The eastern coastal region will benefit the most due to its huge demand for external power.

Keywords: green power market, tradable green certificate, interregional power transmission, power market equilibrium model

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Authors: Luyao Zhang, Tianyu Wu, Jiayi Li, Carlos-Gustavo Salas-Flores, Saad Lahrichi

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Recent advances in AI have made algorithmic trading a central role in finance. However, current research and applications are disconnected information islands. We propose a generally applicable pipeline for designing, programming, and evaluating algorithmic trading of stock and crypto tokens. Moreover, we provide comparative case studies for four conventional algorithms, including moving average crossover, volume-weighted average price, sentiment analysis, and statistical arbitrage. Our study offers a systematic way to program and compare different trading strategies. Moreover, we implement our algorithms by object-oriented programming in Python3, which serves as open-source software for future academic research and applications.

Keywords: algorithmic trading, AI for finance, fintech, machine learning, moving average crossover, volume weighted average price, sentiment analysis, statistical arbitrage, pair trading, object-oriented programming, python3

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Authors: Mohammad Hosein Panahi, Naser Yazdani

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we present a technical trading strategy that leverages the FinBERT language model and financial news analysis with a focus on news related to a subset of Nasdaq 100 stocks. Our approach surpasses the baseline Range Break-out strategy in the Bitcoin market, yielding a remarkable 24.8% increase in the win ratio for all Friday trades and an impressive 48.9% surge in short trades specifically on Fridays. Moreover, we conduct rigorous hypothesis testing to establish the statistical significance of these improvements. Our findings underscore considerable potential of our NLP-driven approach in enhancing trading strategies and achieving greater profitability within financial markets.

Keywords: quantitative finance, technical analysis, bitcoin market, NLP, language models, FinBERT, technical trading

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Authors: Shabnam Dadparvar, Laijin Shen

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After a period of conflict between Russia and the West, new signs of confrontation between the United States and China are observed. China, as the most populous country in the world with a high rate of economic growth, neither stands the hegemonic power of the United States nor has the intention of direct confrontation with it. By raising the costs of the United States’ leadership at the international level, China seeks to find a better status without direct confrontation with the US. Meanwhile, the Shanghai Cooperation Organization (SCO), as a soft balancing strategy against the hegemony of the United States is used as a tool to reach this goal. The authors by using a descriptive-analytical method try to explain the policies of China and the United States on Shanghai Cooperation Organization as well as confrontation between these two countries within the framework of 'balance of soft power theory'.

Keywords: balance of soft power, Central Asia, Shanghai cooperation organization, terrorism

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Authors: Sarthak Kumar Jena, Chandra Sekhar Mishra, Prabina Rajib

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Purpose: This paper aims to derive undervaluation signal from the insiders trading of Indian companies where the ownership is complex and concentrated, investors protection is weak, and the insider rules and regulations are not stringent like developed country. This study examines the relationship between insider trading with short term and long term abnormal return. The study also examines the relationship between insider trading and the actual share repurchase by the firm. Methodology: A sample of 78 companies over the period 2008-2013 are analyzed in the study due to not availability of insider data in Indian context. For preliminary analysis T-test and Wilcoxon rank sum test is used to find the difference between the insider trading before and after the share repurchase announcement. Tobit model is used to find out whether insider trading influence shares repurchase decisions or not. Return on the basis of market model and buy hold are calculated in the previous year and the following year of share repurchase announcement. Findings: The paper finds that insider trading around share repurchase is more than control firms and there is positive and significant difference in insider buying between the previous year of share buyback announcement and the following year of buyback announcement. Insider buying before share repurchase announcement has a positive influence on share repurchase decisions. We find insider buying has a positive and significant relationship with announcement return, whereas insider selling has a negative significant relationship with announcement return. Actual share repurchase and program completion also depend on insider trading before share repurchase. Research limitation: The study is constrained by the small sample size, so the results should be viewed by keeping this limitation in mind. Originality: The paper is to our best knowledge the first study based on Indian context to extend the insider trading literature to share repurchase event and examine insider trading to find out undervaluation signal associated with insider buying.

Keywords: insider trading, buyback, open market share repurchase, signalling

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Authors: Hosein Hamisheh Bahar, Mohammad Hossein Fazel Zarandi, Akbar Esfahanipour

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In this paper, a hybrid expert system is developed by using fuzzy genetic network programming with reinforcement learning (GNP-RL). In this system, the frame-based structure of the system uses the trading rules extracted by GNP. These rules are extracted by using technical indices of the stock prices in the training time period. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In the other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. Finally, the obtained rules are used in a frame-based system implemented in Kappa-PC software. This developed trading system has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in the testing time period shows that the developed system has more favorable performance in comparison with the Buy and Hold strategy.

Keywords: fuzzy genetic network programming, hybrid expert system, technical trading signal, Tehran stock exchange

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Authors: Musa Njabulo Shongwe

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The multilateral trading system is facing a great danger of fragmentation. Its modus operandi, multilateralism, is increasingly becoming clogged by trade barriers created by the proliferation of preferential regional trading blocs. The paper explores the fragmentation of the multilateral trade regulation system (WTO law) by analysing whether and to what extent Regional Trade Agreements (RTAs) have conflicted with the Multilateral Trading System. The paper examines the effects of RTA dominance in view of the WTO's quest for trade liberalization. This is an important inquiry because the proliferation of RTAs implies the erosion of the WTO law’s core principle of non-discrimination. The paper further explores how the proliferation of RTAs has endangered the coherence of the multilateral trading system. The study is carried out with the initial assumption that RTAs could be complementary and coherent with WTO law, and thus facilitate international trade and enhance development prospects. There is evidence that is tested by this study which suggests that RTAs can be divergent and hence undermine the WTO multilateral rules of regulating international trade. The paper finally recommends legal tools of regulating and managing the WTO-RTA interface, as well as other legal means of ensuring a harmonious existence between the WTO and regional trade arrangements.

Keywords: fragmentation of international trade law, regionalism, regional trade agreements, WTO law

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Authors: Sriram Kashyap Prasad, Ionut Florescu

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This study models the intraday asset prices as driven by Markov process. This work identifies the latent states of the Hidden Markov model, using limit order book data (trades and quotes) to continuously estimate the states throughout the day. This work builds a trading strategy using estimated states to generate signals. The strategy utilizes current state to recalibrate buy/ sell levels and the transition between states to trigger stop-loss when adverse price movements occur. The proposed trading strategy is tested on the Stevens High Frequency Trading (SHIFT) platform. SHIFT is a highly realistic market simulator with functionalities for creating an artificial market simulation by deploying agents, trading strategies, distributing initial wealth, etc. In the implementation several assets on the NASDAQ exchange are used for testing. In comparison to a strategy with static buy/ sell levels, this study shows that the number of limit orders that get matched and executed can be increased. Executing limit orders earns rebates on NASDAQ. The system can capture jumps in the limit order book prices, provide dynamic buy/sell levels and trigger stop loss signals to improve the PnL (Profit and Loss) performance of the strategy.

Keywords: algorithmic trading, Hidden Markov model, high frequency trading, limit order book learning

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Authors: Jie Ji, Jing Xu, Yuehong Zhuang, Xiangqing Kang, Ying Qian, Ping Zhou, Di Xue

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Background: In 2014, there were 28,341 health professionals in Pudong new area of Shanghai and the number per 1000 population was 5.199, 55.55% higher than that in 2006. But it was always less than the average number of health professionals per 1000 population in Shanghai from 2006 to 2014. Therefore, allocation planning for the health professionals in Pudong new area has become a high priority task in order to meet the future demands of health care. In this study, we constructed an objective-based system dynamics model to forecast the number of health professionals in Pudong new area of Shanghai in 2020. Methods: We collected the data from health statistics reports and previous survey of human resources in Pudong new area of Shanghai. Nine experts, who were from health administrative departments, public hospitals and community health service centers, were consulted to estimate the current and future status of nine variables used in the system dynamics model. Based on the objective of the number of health professionals per 1000 population (8.0) in Shanghai for 2020, the system dynamics model for health professionals in Pudong new area of Shanghai was constructed to forecast the number of health professionals needed in Pudong new area in 2020. Results: The system dynamics model for health professionals in Pudong new area of Shanghai was constructed. The model forecasted that there will be 37,330 health professionals (6.433 per 1000 population) in 2020. If the success rate of health professional recruitment changed from 20% to 70%, the number of health professionals per 1000 population would be changed from 5.269 to 6.919. If this rate changed from 20% to 70% and the success rate of building new beds changed from 5% to 30% at the same time, the number of health professionals per 1000 population would be changed from 5.269 to 6.923. Conclusions: The system dynamics model could be used to simulate and forecast the health professionals. But, if there were no significant changes in health policies and management system, the number of health professionals per 1000 population would not reach the objectives in Pudong new area in 2020.

Keywords: allocation planning, forecast, health professional, system dynamics

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Authors: Diqin Qi, Jiaming Li, Siman Li

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Based on the use of the three-point smoothing method for selectively processing original data columns, this paper establishes a group of grey GM(1,1) models to predict the concentration ranges of four major air pollutants in Shanghai from 2023 to 2024. The results indicate that PM₁₀, SO₂, and NO₂ maintain the national Grade I standards, while the concentration of PM₂.₅ has decreased but still remains within the national Grade II standards. Combining the forecast results, recommendations are provided for the Shanghai municipal government's efforts in air pollution prevention and control.

Keywords: atmospheric pollutant prediction, Grey GM(1, 1), model group, three-point smoothing method

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Authors: Khemnath Kharel

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Forest itself is a factory as well as product. It supplies tangible and intangible goods and services. It supplies timber, fuel wood, fodder, grass leaf litter as well as non timber edible goods and medicinal and aromatic products additionally provides environmental services. These environmental services are of local, national, or even global importance. In Nepal more than 19 thousands community forests are providing environmental service in less economic benefit than actual efficiency. There is a risk of cost of management of those forest exceeds benefits and forests get converted to open access resources in future. Most of the environmental goods and services don’t have markets which mean no prices at which they are available to the consumers therefore the valuation of these services goods and services establishment of paying mechanism for such services and insure the benefit to community is more relevant in local as well as global scale. There are few examples of carbon trading in domestic level to meet the country wide emission goal. In this contest the study aims to explore the public attitude towards carbon offsetting and their responsibility over service providers. This study helps in promotion of environment service awareness among general people and service provider; community forest. The research helps to unveil the carbon pool scenario in community forest and willingness to pay for carbon offsetting of people who are consuming more energy than general people and emitting relatively more carbon in atmosphere. The study has assessed the carbon pool status in two community forest. In the study in two community forests carbon pools were assessed following the guideline “Forest Carbon Inventory Guideline 2010” prescribed by Ministry of Forest and soil Conservation, Nepal. Final out comes of analysis in intensively managed area of Hokse CF recorded as 103.58 tons C /ha with 6173.30 tons carbon stock. Similarly in Hariyali CF carbon density was recorded 251.72 mg C /ha. The total carbon stock of intensively managed blocks in Hariyali CF is 35839.62 tons carbon.

Keywords: carbon, offsetting, sequestration, valuation

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Authors: Ching-Feng Chen

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The floating photovoltaic (FPV) system highlights economic benefits and energy performance to carbon dioxide (CO₂) discharges. Due to land resource scarcity and many negligent water territories, such as reservoirs, dams, and lakes in Japan and Taiwan, both countries are actively developing FPV and responding to the pricing of the emissions trading systems (ETS). This paper performs a case study through a synthesis approach to compare the economic indicators between the FPVs of Taiwan’s Agongdian Reservoir and Japan’s Yamakura Dam. The research results show that the metrics of the system capacity, installation costs, bank interest rates, and ETS and Electricity Bills affect FPV operating gains. In the post-Feed-In-Tariff (FIT) phase, investing in FPV in Japan is more profitable than in Taiwan. The former’s positive net present value (NPV), eminent internal rate of return (IRR) (11.6%), and benefit-cost ratio (BCR) above 1 (2.0) at the discount rate of 10% indicate that investing the FPV in Japan is more favorable than in Taiwan. In addition, the breakeven point is modest (about 61.3%.). The presented methodology in the study helps investors evaluate schemes’ pros and cons and determine whether a decision is beneficial while funding PV or FPV projects.

Keywords: carbon border adjustment mechanism, floating photovoltaic, emissions trading systems, net present value, internal rate of return, benefit-cost ratio

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

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This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Peter Shi

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Algorithmic trading is a rapidly expanding domain within quantitative finance, constituting a substantial portion of trading volumes in the US financial market. The demand for rigorous and robust mathematical theories underpinning these trading algorithms is ever-growing. In this study, the author establishes a new stock market model that integrates the Efficient Market Hypothesis and the statistical arbitrage. The model, for the first time, finds probabilistic relations between the rational price and the market price in terms of the conditional expectation. The theory consequently leads to a mathematical justification of the old market adage: buy-low and sell-high. The thresholds for “low” and “high” are precisely derived using a max-min operation on Bayes’s error. This explicit connection harmonizes the Efficient Market Hypothesis and Statistical Arbitrage, demonstrating their compatibility in explaining market dynamics. The amalgamation represents a pioneering contribution to quantitative finance. The study culminates in comprehensive numerical tests using historical market data, affirming that the “buy-low” and “sell-high” algorithm derived from this theory significantly outperforms the general market over the long term in four out of six distinct market environments.

Keywords: efficient market hypothesis, behavioral finance, Bayes' decision, algorithmic trading, risk control, stock market

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Authors: Nkechi V. Osuji

Abstract:

The use of blockchain to address the issue of climate change is increasingly a discourse among countries, industries, and stakeholders. For a long time, the European Union (EU) has been combating the issue of climate action in industries through sustainability programs. One of such programs is the EU monitoring reporting and verification (MRV) program of the EU ETS. However, the system has some key challenges and areas for improvement, which makes it inefficient. The main objective of the research is to look at how blockchain can be used to improve the inefficiency of the EU ETS program for the Danish energy industry with a focus on its monitoring and reporting framework. Applying empirical data from 13 semi-structured expert interviews, three case studies, and literature reviews, three outcomes are presented in the study. The first is on the current conditions and challenges of monitoring and reporting CO₂ emission trading. The second is putting into consideration if blockchain is the right fit to solve these challenges and how. The third stage looks at the factors that might affect the implementation of such a system and provides recommendations to mitigate these challenges. The first stage of the findings reveals that the monitoring and reporting of CO₂ emissions is a mandatory requirement by law for all energy operators under the EU ETS program. However, most energy operators are non-compliant with the program in reality, which creates a gap and causes challenges in the monitoring and reporting of CO₂ emission trading. Other challenges the study found out are the lack of transparency, lack of standardization in CO₂ accounting, and the issue of double-counting in the current system. The second stage of the research was guided by three case studies and requirement engineering (RE) to explore these identified challenges and if blockchain is the right fit to address them. This stage of the research addressed the main research question: how can blockchain be used for monitoring and reporting CO₂ emission trading in the energy industry. Through analysis of the study data, the researcher developed a conceptual private permissioned Hyperledger blockchain and elucidated on how it can address the identified challenges. Particularly, the smart contract of blockchain was highlighted as a key feature. This is because of its ability to automate, be immutable, and digitally enforce negotiations without a middleman. These characteristics are unique in solving the issue of compliance, transparency, standardization, and double counting identified. The third stage of the research presents technological constraints and a high level of stakeholder collaboration as major factors that might affect the implementation of the proposed system. The proposed conceptual model requires high-level integration with other technologies such as the Internet of Things (IoT) and machine learning. Therefore, the study encourages future research in these areas. This is because blockchain is continually evolving its technology capabilities. As such, it remains a topic of interest in research and development for addressing climate change. Such a study is a good contribution to creating sustainable practices to solve the global climate issue.

Keywords: blockchain, carbon emission trading, European Union emission trading system, monitoring and reporting

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Authors: Frank C. Amaechi, Adeyinka A. Aderinto, Usman A. Ojedokun, Oludayo Tade

Abstract:

Illicit trading in new naira notes has become a common practice in most communities in Nigeria despite the Central Bank Act’s in 2007 proscription of all forms of naira abuse. This study investigated the socio-economic factors sustaining illicit trading in new naira notes in Ibadan metropolis. The study was exploratory and cross-sectional in design. Neutralization theory was adopted as theoretical framework. Data were generated through the combination of in-depth interview and key informant interview methods. The purposive sampling technique was utilised to select five illicit traders of new naira notes, 32 patrons of the trade and six bank officials. Findings revealed that illicit trading in Nigeria’s national currency is flourishing because of the frequent demand for new naira notes that are not readily available in Nigerian banks. Also, the norm of cash spraying at social events is sustaining the illicit markets for new naira notes in Ibadan metropolis. In addition, a chain of network, comprising three principal actors, is behind the illegal business. A strict enforcement of the law banning cash spraying is advocated as a means of arresting this phenomenon.

Keywords: illicit trading, naira notes, national currency, Nigeria

Procedia PDF Downloads 314