Search results for: illicit trading

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

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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

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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: 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: 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: 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: Halilu Babaji, Adamu Buba

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Illicit arms and the emergence of armed groups have witnessed unprecedented situations of political uncertainties in Nigeria, and the twenty-first century globalisation has established the process that has benefited a good number of militia groups and thereby boosting both illicit arms movement and the thriving of terrorist groups, which are largely responsible for the longstanding threat to the national security and stability of the country. This has unleashed unforeseen consequences on the entire Sub-region, following an inflow of weapons and armed fighter which are motivated by weak governance, insecurity and poverty. The social, economic and political environments make it a fertile breeding ground for the penetration and development of terrorist groups in Sub-Saharan Africa.

Keywords: arms, emergence, insecurity, groups

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Authors: Ekaterina Semenova

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The article explores the issue of website owners’ liability for the illicit content. Whilst various issues of secondary liability of internet access providers for the illicit content have been widely discussed in the law doctrine, the liability of website owners has attracted less attention. Meanwhile, the website blocking injunctions influence website owners’ rights most, since website owners have the interest to keep their website online, rather than internet access providers. The discussion of internet access providers’ liability overshadows the necessity to protect the website owners’ rights to due process and proportionality of blocking injunctions. The analysis of Russian website blocking regulation and case law showed that the protection of website owners’ rights depends on the kind of illicit content: some content induces automatic blocking injunctions without prior notice of website owners and any opportunity to appeal, while other content does not invoke automatic blocking and provides an opportunity for the website owner to avoid or appeal an injunction. Comparative analysis of website blocking regulations in European countries reveals different approaches to the proportionality of website blocking and website owner’s rights protection. Based on the findings of the study, we conclude that the global trend to impose website blocking injunctions on wide range of illicit content without due process of law interferes with the rights of website owners.

Keywords: illicit content, liability, Russia, website blocking

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Authors: Meena K. Yadav, Rupak Aryal, Michael D. Short, Ben Van Den Akker, Christopher P. Saint, Cobus Gerber

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Illicit drugs are considered as emerging contaminants of concern that have become an interesting issue for the scientific community from last few years due to their existence in the water environment. A number of the literature has revealed their occurrence in the environment. This is mainly due to the fact that some drugs are partially removed during wastewater treatment processes, and remaining being able to enter the environment and contaminate surface and groundwater and subsequently, drinking water. Therefore, this paper evaluates the occurrence of key illicit drugs in wastewater (influent and effluent) samples in 4 wastewater treatment plants across Adelaide, South Australia over a 1 year period. This paper also compares the efficiency of wastewater treatment plants adopting different technologies in the removal of selected illicit drugs, especially in the context of which technology has higher removal rates. The influent and effluent samples were analysed using Liquid Chromatography tandem Mass Spectrometry (LC-MS/MS). The levels of drugs detected were in the range of mg/L – ng/L in effluent samples; thus emphasising the influence on water quality of receiving water bodies and the significance of removal efficiency of WWTPs(Wastewater Treatment Plants). The results show that the drugs responded differently in the removal depending on the treatment processes used by the WWTPs.

Keywords: illicit drugs, removal efficiency, treatment technology, wastewater

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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: 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: 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: Mahadehe Hassan

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In most countries manufacturing, trading and distribution of gasoline and diesel fuels belongs to the most important sectors of national economy. For Bangladesh, a robust, well-functioning, secure and smartly managed national fuel distribution chain is an essential precondition for achieving Government top priorities in development and modernization of transportation infrastructure, protection of national environment and population health as well as, very importantly, securing due tax revenue for the State Budget. Bangladesh is a developing country with complex fuel supply network, high fuel taxes incidence and – till now - limited possibilities in application of modern, automated technologies for Government national fuel market control. Such environment allows dishonest physical and legal persons and organized criminals to build and profit from illegal fuel distribution schemes and fuel illicit trade. As a result, the market transparency and the country attractiveness for foreign investments, law-abiding economic operators, national consumers, State Budget and the Government ability to finance development projects, and the country at large suffer significantly. Research shows that over 50% of retail petrol stations in major agglomerations of Bangladesh sell adulterated fuels and/or cheat customers on the real volume of the fuel pumped into their vehicles. Other forms of detected fuel illicit trade practices include misdeclaration of fuel quantitative and qualitative parameters during internal transit and selling of non-declared and smuggled fuels. The aim of the study is to recommend the implementation of a National Fuel Distribution Integrity Program (FDIP) in Bangladesh to address and resolve fuel adulteration and illicit trade problems. The program should be customized according to the specific needs of the country and implemented in partnership with providers of advanced technologies. FDIP should enable and further enhance capacity of respective Bangladesh Government authorities in identification and elimination of all forms of fuel illicit trade swiftly and resolutely. FDIP high-technology, IT and automation systems and secure infrastructures should be aimed at the following areas (1) fuel adulteration, misdeclaration and non-declaration; (2) fuel quality and; (3) fuel volume manipulation at retail level. Furthermore, overall concept of FDIP delivery and its interaction with the reporting and management systems used by the Government shall be aligned with and support objectives of the Vision 2041 and Smart Bangladesh Government programs.

Keywords: fuel adulteration, octane, kerosene, diesel, petrol, pollution, carbon emissions

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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: Abonyi, Emmanuel Ebuka, Amina Jafaru O.

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Background: Illicit substance use among students is a phenomenon that has been widely studied, but it remains of interest due to its high prevalence and potential consequences. It is a major mental health concern among university students which may result in behavioral and academic problems, psychiatric disorders, and infectious diseases. Thus, this study was done to ascertain the prevalence and factors associated with the use of illicit drugs among these groups of people. Methods: A cross-sectional and descriptive survey was conducted among undergraduate students of the University of Lagos for the duration of three(3) months (August to October 2021). A total number of 938 undergraduate students were selected from seventeen faculties in the university. Pretested questionnaires were administered, completed, and returned. The data were analyzed using descriptive statistics and multivariate regression analysis. Results: From the data collected, it was observed that out of 938 undergraduate students of the University of Lagos that completed and returned the questionnaires, 56.3% were female and 43.7% were male. No gender differences were observed in the prevalence of use of any of the illicit substances. The result showed that the majority of the students that participated in the research were females(56.6%); it was observed that there were a total of 541 2nd-year students(57.7%) and 397 final-year students(42.3). Students between the age brackets of 20- 24 years had the highest frequency of 648(69.1%) of illicit drug use and students in none health-related disciplines. The result also showed that the majority of the students reported that they use Marijuana (31.7%), while lifetime use of LSD (6.3%), Heroin(4.8%), Cocaine (4.7%), and Ecstasy(4.5), Ketamine (3.4%). Besides, the use of alcohol was below average(44.1%). Additionally, Marijuana was among the ones that were mostly taken by students having a higher percentage and most of these respondents had experienced relationship problems with their family and intentions (50.9%). From the responses obtained, major reasons students indulge in illicit drug use were; curiosity to experiment, relief of stress after rigorous academic activities, social media influence, and peer pressure. Most Undergraduate students are in their most hyperactive stage in life, which makes them vulnerable to always want to explore practically every adventure. Hence, individual factors and social media influence are identified as major contributors to the prevalence of illicit drug use among undergraduate students at the University of Lagos, Nigeria. Conclusion: Control programs are most needed among the students. They should be comprehensive and focused on students' psycho-education about substances and their related negative consequences, plus the promotion of students' life skills, and integration into the family – and peer-based preventive interventions.

Keywords: illicit drugs, addiction, undergraduate students, prevalence, substances

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Authors: Ana Caroline Ibrahim Lino, Denise Bomtempo Birche de Carvalho

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The background of this research is the international process of control and monitoring of illicit psychoactive substances that has commenced in the early 20th century. This process was intensified with the UN Single Convention on Narcotic Drugs of 1961 and had its culmination in the 1970s with the "War on drugs", a doctrine undertaken by the United States of America. Since then, the phenomenon of drug prohibition has been pushing debates around alternatives of public policies to confront their consequences at a global level and in the specific context of Latin America. Previous research has answered the following key questions: a) With what characteristics and models has the international illicit drug control system consolidated in Latin America with the creation of the Organization of American States (OAS) and the Inter-American Drug Abuse Control Commission (CICAD)? b) What drug policies and programs were determined as guidelines for the member states by the OAS and CICAD? The present paper mainly addresses the analysis of the drug strategies developed by the OAS/CICAD for the Americas from 2004 to 2016. The primary sources have been extracted from the OAS/CICAD documents and reports, listed on the Internet sites of these organizations. Secondary sources refer to bibliographic research on the subject with the following descriptors: illicit drugs, public policies, international organizations, OAS, CICAD, and reducing the demand and supply of illicit drugs. The "content analysis" technique was used to organize the collected material and to choose the axes of analysis. The results show that the policies, strategies, and action plans for Latin America had been focused on anti-drug actions since the creation of the Commission until 2010. The discourses and policies to reduce drug demand and supply were of great importance for solving the problem. However, the real focus was on eliminating the substances by controlling the production, marketing, and distribution of illicit drugs. Little attention was given to the users and their families. The research is of great relevance to the Social Work. The guidelines and parameters of the Social Worker's profession are in line with the need for social, ethical, and political strengthening of any dimension that guarantees the rights of users of psychoactive substances. In addition, it contributed to the understanding of the political, economic, social, and cultural factors that structure the prohibitionism, whose matrix anchors the deprivation of rights and violence.

Keywords: illicit drug policies, international organizations, latin America, prohibitionism, reduce the demand and supply of illicit drugs

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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: Mohsen Qasemi

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In the international economic order multilateral trading system which established by General Agreement on Tariffs and Trade 1947 (GATT) was dominant until about two decades ago. Regional Trade Agreements (RTAs) have changed this order and become an important phenomenon. One of the main objectives of the World Trade Organization (WTO) as a central institution of multilateral trading system is raising standards of living. There are many scholars who suggest that WTO should take steps to protect human rights in its activities. Although it has always been opposing views who declare that since WTO has no explicit rule for human rights, it has no human rights related obligations. At the time that the WTO was established, member states began to join RTAs and since then, the escalating growth of these agreements and their effects on multilateral trading system has been controversial. There are some aspects of RTAs that have received too little attention from scholars. It is important to take a different view and evaluate the RTAs based on non-commercial aspects. The present paper seeks to answer this question: which system could be more useful in protecting human rights, RTAs or WTO?

Keywords: WTO, RTAs, human rights, multilateral trading system, non discrimination

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Authors: Cristian Pauna

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With few steps away from the 2020, investments in financial markets is a common activity nowadays. In the electronic trading environment, the automated investment software has become a major part in the business intelligence system of any modern financial company. The investment decisions are assisted and/or made automatically by computers using mathematical algorithms today. The complexity of these algorithms requires computer assistance in the investment process. This paper will present several investment strategies that can be automated with algorithmic trading for Deutscher Aktienindex DAX30. It was found that, based on several price action mathematical models used for high-frequency trading some investment strategies can be optimized and improved for automated investments with good results. This paper will present the way to automate these investment decisions. Automated signals will be built using all of these strategies. Three major types of investment strategies were found in this study. The types are separated by the target length and by the exit strategy used. The exit decisions will be also automated and the paper will present the specificity for each investment type. A comparative study will be also included in this paper in order to reveal the differences between strategies. Based on these results, the profit and the capital exposure will be compared and analyzed in order to qualify the investment methodologies presented and to compare them with any other investment system. As conclusion, some major investment strategies will be revealed and compared in order to be considered for inclusion in any automated investment system.

Keywords: Algorithmic trading, automated investment systems, limit conditions, trading principles, trading strategies

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Authors: Liwen Wang, Yuting Chen, Tong Wu, Shaolei Hu

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In order to enhance the security of critical financial infrastructure, this study carries out a transformation of the architecture of a financial trading terminal to a zero trust architecture (ZTA), constructs an active defense system for cybersecurity, improves the security level of trading services in the Internet environment, enhances the ability to prevent network attacks and unknown risks, and reduces the industry and security risks brought about by cybersecurity risks. This study introduces the SDP technology of ZTA, adapts and applies it to a financial trading terminal to achieve security optimization and fine-grained business grading control. The upgraded architecture of the trading terminal moves security protection forward to the user access layer, replaces VPN to optimize remote access, and significantly improves the security protection capability of Internet transactions. The study achieves 1. deep integration with the access control architecture of the transaction system; 2. no impact on the performance of terminals and gateways, and no perception of application system upgrades; 3. customized checklist and policy configuration; 4. introduction of industry-leading security technology such as single-packet authorization (SPA) and secondary authentication. This study carries out a successful application of ZTA in the field of financial trading and provides transformation ideas for other similar systems while improving the security level of financial transaction services in the Internet environment.

Keywords: zero trust, trading terminal, architecture, network security, cybersecurity

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Authors: Yuanxiaoyue Yang

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Emissions trading, or ‘cap-and-trade', has been long promoted by economists as a more cost-effective pollution control approach than traditional performance standard approaches. While there is a large body of empirical evidence for the overall effectiveness of emissions trading, relatively little attention has been paid to other unintended consequences brought by emissions trading. One important consequence is that cap-and-trade could introduce the risk of creating high-level emission concentrations in areas where emitting facilities purchase a large number of emission allowances, which may cause an unequal distribution of environmental benefits. This study will contribute to the current environmental policy literature by linking trading activity with environmental injustice concerns and empirically analyzing the causal relationship between trading activity and emissions reduction under a cap-and-trade program for the first time. To investigate the potential environmental injustice concern in cap-and-trade, this paper uses a differences-in-differences (DID) with instrumental variable method to identify the causal effect of allowance trading behaviors on emission reduction levels under the clean air interstate rule (CAIR), a cap-and-trade program targeting on the power sector in the eastern US. The major data source is the facility-year level emissions and allowance transaction data collected from US EPA air market databases. While polluting facilities from CAIR are the treatment group under our DID identification, we use non-CAIR facilities from the Acid Rain Program - another NOx control program without a trading scheme – as the control group. To isolate the causal effects of trading behaviors on emissions reduction, we also use eligibility for CAIR participation as the instrumental variable. The DID results indicate that the CAIR program was able to reduce NOx emissions from affected facilities by about 10% more than facilities who did not participate in the CAIR program. Therefore, CAIR achieves excellent overall performance in emissions reduction. The IV regression results also indicate that compared with non-CAIR facilities, purchasing emission permits still decreases a CAIR participating facility’s emissions level significantly. This result implies that even buyers under the cap-and-trade program have achieved a great amount of emissions reduction. Therefore, we conclude little evidence of environmental injustice from the CAIR program.

Keywords: air pollution, cap-and-trade, emissions trading, environmental justice

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Authors: Tian Lan

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Arbitrageurs participate in a variety of techniques in response to the existence of fluctuating sentiment, resulting in sparse sentiment exposures. This paper found that Commodity Trading Advisor (CTA) funds in the top decile rated by sentiment beta outperformed those in the bottom decile by 0.33% per month on a risk-adjusted basis, with the difference being larger among skilled managers. This paper also discovered that around ten percent of Commodity Trading Advisor (CTA) funds could accurately predict market sentiment, which has a positive correlation with fund sentiment beta and acts as a determinant in fund performance. Instead of betting against mispricing, this research demonstrates that a competent manager can achieve remarkable returns by forecasting and reacting to shifts in investor sentiment.

Keywords: investment sentiment, CTA fund, market timing, fund performance

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Authors: Helee Dave

Abstract:

Globalization has helped in the growth and change of the Indian economy to a great extent. The purchasing power of Indians has increased. IT Infrastructure has considerably improved in India. There is an increase in the usage of smartphones. The smartphones facilitate all sorts of work now a day, from getting groceries to planning a tour; it is just one click away. Similar is the case with equity trading. The traders in equity market can now deal with their stocks through mobile applications eliminating the middle man. The traders do not have an option but to open a dematerialization account with the banks which are compulsory enough irrespective of their mode of transaction that is online or offline. Considering that India is a young country having more than 50% of its population below the age of 25 and 65% of its population below the age of 35; this youth is comfortable with the usage of smartphones. The banking industry is also providing a virtual platform supporting equity market industry. Yet equity trading through online applications is at an infant stage. This paper primarily attempts to understand challenges and opportunities faced by equity trading through mobile apps in India.

Keywords: BPO, business process outsourcing, de-materialization account, equity, ITES, information technology enabled services

Procedia PDF Downloads 275

Authors: Usama Bin Zubair, Muhammad Azeem

Abstract:

Introduction: Depression has been one of the most commonly diagnosed mental health disorders in Pakistan. A Census conducted by the government of Pakistan in 2017 showed that more than 10000 trans-genders live in Pakistan. HIV, illicit substance use and mental health issues, including depression, have been the main health problems faced by them. Trans-gender population has been suffering from depressive illness more than normal population all over the world. Aim: To assess the prevalence of depression among the transgender population and analyze the relationship of socio-demographic factors with depression. Subjects and Methods: The sample population comprised of one hundred and forty-two transgender people of Rawalpindi and Islamabad. Beck depressive inventory II (BDI-II) was used to record the presence and severity of the depressive symptoms. Depressive symptoms were categorized as mild, moderate and severe. Relationship of the age, smoking, family income, illicit substance use and education were studied with the presence of depressive symptoms among these transgender people of twin cities of Pakistan. Results: A total of 142 transgender people were included in the final analysis. The mean age of the study participants was 39.55 ± 6.18. Out of these, 45.1% had no depressive symptoms while 31.7% had mild, 12.7% had moderate and 10.6% had severe depressive symptomatology. After applying the binary logistic regression, we found that the presence of depressive symptoms had a significant association with illicit substance use among the target population. Conclusion: This study showed a high prevalence of depressive symptoms among the transgender population in the twin cities of Pakistan. Use of illicit substances like tobacco, cannabis, opiates, and alcohol should be discouraged to prevent mental health problems.

Keywords: depression, transgender, prevalence, sociodemographic factors

Procedia PDF Downloads 90

Authors: Neha Asthana, Vishal Krishna Prasad

Abstract:

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

Procedia PDF Downloads 385