Journal articles: 'One-way trading' – Grafiati (2024)

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Algorithmic trading, often referred to as algo-trading, is a game-changer in the financial industry, revolutionizing the way trading is con- ducted. By harnessing the power of advanced math- ematical algorithms and computational capabilities, algo-trading enables traders to make split-second decisions, execute trades, and manage portfolios with unparalleled speed and precision. In this land- scape of digital finance, Angle One API emerges as a pivotal tool, empowering developers and traders to engage in algorithmic trading with utmost efficiency and sophistication. Keywords-Python,AngleOne Smart API,AWS

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This paper provides a systematic overview of water quality trading programs and one-time offset agreements in the USA. The primary source of information for this overview is a detailed database, collected and compiled by a team of researchers at Dartmouth College. Details discussed include: sources of the pollutant, types of pollutants traded, legal liability, main regulatory drivers, market structure, trading ratios, transaction and administrative costs and difficulties encountered in trading. We find that trading has often been explored as a way to meet more stringent discharge limits or watershed-wide caps. The most common type of trading program in the United States is between point sources and non-point sources. Point sources are usually held liable for non-point source reductions. The pollutants most commonly traded in the USA are nutrients such as phosphorus and nitrogen and almost all offset and trading programs focus on one pollutant only. However, market structures, trading ratios and other details of the trading framework vary widely among programs. No single characteristic appears to be a good predictor of a successful trading program.

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Policies to promote renewable electricity are increasingly seen as a way to reduce the negative environmental impacts associated with electricity consumption and meet growing electricity demand. This paper reviews the international experience with one such policy, namely, renewable energy certificates, and considers important design aspects of a national green certificate system. Within a South African context, a green certificate system would provide a mechanism with which to verify compliance with any future renewable energy obligations, and would encourage renewable electricity generation in the current monopoly environment. In terms of a national green certificate framework, international experience has shown that renewable energy certificates must be both accredited and standardized, with enforcement of penalties for non-compliance with renewable energy quotas. Above all, a long-term and stable policy environment is crucial for developing renewable energy markets.

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One commodity whose consumption level is currently very high and continues to increase is coffee. The government's effort to offset domestic demand for coffee which continues to increase every year is to import coffee from neighboring coffee-producing countries. These efforts resulted in fluctuating domestic coffee prices. One way that can be done to anticipate losses due to fluctuations in the price of coffee commodities is to enter into futures trading contracts, but in reality the price fluctuations that occur cause exchange players, both buyers and sellers, to experience difficulties in determining the price of coffee commodities. futures trading contracts. The average return model is an effective way to price futures trading contracts. This model states that the price of a commodity will tend to return to its average value. If the price fluctuates and moves away from the average value, then at a certain time it will return to the average value

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The present paper discusses the past behaviour of the selected variables in the study and discusses the descriptive analysis of the collected data in the study from 1st April 2009 to 31st March 2023. This section discusses the relationship between trading volume and stock returns of the stocks (50 companies in the Nifty Index) in Indian stock market by applying Mean, S.D, Kurtosis, Skewness, Jarque Bera and One way Anova.The results of one-way ANOVA concluded that“There exists no significant difference between the average response of the stock returns at different quantiles as a result of trading volume at lag one”. It is concluded that the average response of the stock returns at different quantiles is same at different quantiles. However, the means plot of the stock returns at different quantiles is found to increases with the increase in the quantiles. Thus, it can be concluded in the study that the response of the stock returns is higher at higher quantiles and lower at lower quantiles, however no statistical difference is figured out. The stock returns on the next day is concluded to be higher if the trading volume of the stock is high at a particular day.

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Purpose The purpose of this paper is to explore trading strategies that exploit investors’ anchoring bias. Design/methodology/approach This paper forms portfolios based on nearness ratio and other anomaly variables under one- and two-way sorts. The portfolio return series are then regressed on Fama and French three factors to extract abnormal returns. Findings First is to use anchoring as a technical signal. A strategy that trades against anchoring buys stocks with prices near their 52-week high and sells stocks with prices far below their 52-week high. Based on deciles, the strategy generates a significant value-weighted monthly α of 1.13 percent, after accounting for the market, size, and value factors. Further, the strategy is profitable among both large and small stocks; the trading profit is higher among younger firms and more volatile stocks, but is similar between subsamples formed on number of analysts, level of institutional ownership, and number of institutional owners. The strategy is more profitable following periods of high investor sentiment. Second is to combine anchoring with known anomalies. For a broad set of 26 anomalies, a trading strategy that combines anchoring with the anomalies increases the value-weighted monthly α from an average of 0.61 percent to an average of 1.38 percent. While part of the profits can be attributed to momentum, momentum itself does not explain all the profits. Originality/value This paper presents empirical evidence that anchoring bias explains the profitability of a broad set of anomalies and describes practical trading strategies that exploit the anchoring bias.

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Due to the need to present information in a fast and attractive way, organizations are eager to use information visualisations. This study explores the collision between the different experts involved in the production of these visualisations using the model of trading zones supplemented with the learning mechanisms found in the boundary crossing literature. Results show that that there is not one good solution to effective interdisciplinary cooperation in the field of information visualisation. All four types of cooperation that we distinguish – enforced, dominated, fractionated, and attuned – might work well, as long as they are adapted to the situation. In any case the involved experts and initiators have to understand and incorporate approaches that enhance the co-creative, iterative nature of the production process. Overlooking the different forms of collaboration we detect two major forms of trading zones: the one that encompasses the collaboration between an external client and a designer (external trading zone) and the trading zones within an organization between content producer and designer (internal trading zone). Both mechanisms of identifying each other’s expertise and coordinating the different tasks in the production process seem beneficial for the production process.

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A growing number of households benefit from government subsidies to install renewable generation facilities such as PV panels, used to gain independence from the grid and provide cheap energy. In the Romanian electricity market, these prosumers can sell their generation surplus only at regulated prices, back to the grid. A way to increase the number of prosumers is to allow them to make higher profit by selling this surplus back into the local network. This would also be an advantage for the consumers, who could pay less for electricity exempt from network tariffs and benefit from lower prices resulting from the competition between prosumers. One way of enabling this type of trade is to use peer-to-peer contracts traded in local markets, run at microgrid (μG) level. This paper presents a new trading platform based on smart peer-to-peer (P2P) contracts for prosumers energy surplus trading in a real local microgrid. Several trading scenarios are proposed, which give the possibility to perform trading based on participants’ locations, instantaneous active power demand, maximum daily energy demand, and the principle of first come first served implemented in an anonymous blockchain trading ledger. The developed scheme is tested on a low-voltage (LV) microgrid model to check its feasibility of deployment in a real network. A comparative analysis between the proposed scenarios, regarding traded quatities and financial benefits is performed.

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This study aims to analyze the causal relationship between trading volume and return volatility along with macroeconomic variables such as interest rates and exchange rate. The endogenous variables in this study are trading volume and return volatility, while the exogenous variables are interest rates and exchange rates. The sample used in this research is property indexes in Indonesia, Malaysia, Philippines, and Thailand who provide monthly data for the four variables during the observation period in January 2012-December 2019. The analysis technique used to test the hypothesis in this study is Vector Autoregression (VAR) technique. The result of this study indicate that trading volume has positive effect on return volatility in property indexes of Indonesia, Philippines, and Thailand, meanwhile trading volume has no effect on return volatility in Malaysia’s property index. Return volatility has no effect on trading volume in all the countries whether in Indonesia, Malaysia, the Philippines, or in Thailand. There is a one-way causality relationship between trading volume and return volatility in property indexes of Indonesia, Philippines, and Thailand. There is no causality relationship between trading volume and return volatility in Malaysia’s property index.

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The Clean Development Mechanism (CDM) is one of the three “flexibility” mechanisms defined in the Kyoto Protocol. Chinese government implements the CDM projects as the only way and most important means to participate in the global carbon trading market, and provides a lot of policy support; however, there are still many problems and risks during the operation of CDM projects in China: the lack of legal system, Carbon leakage, the insufficient transfer of technology and the passive market position of Chinese enterprises. Starting from the strict GHG reduction target and pilot projects of carbon trading in China, this paper constructs domestic carbon trading market on the basis of experiences and lessons of CDM projects in China; then it puts forward the basic thinking of legal system, market management and supervision system in the process of China’s carbon trading market establishment.

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Efficient market hyphotesis is still an interesting debate, because there are still pros and cons among practitioners and academics in finance. One market anomaly that is contrary to the theory of efficient capital markets is the January effect or year-end effect is one of the effects on a calendar that is small capitalized stocks tend to rise in price in January. This study aims to determine the effect of the January effect on abnormal return and trading volume activity on LQ45 companies listed on the Indonesia Stock Exchange. Analysis using one way anova shows that abnormal returns occur in the January effect phenomenon while the January effect does not occur in trading volume.

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The aim of this paper is to explain how parameters adjustments can be integrated in the design or the control of automates of trading. Typically, we are interested in the online estimation of the market impacts generated by robots or single orders, and how they/the controller should react in an optimal way to the information generated by the observation of the realized impacts. This can be formulated as an optimal impulse control problem with unknown parameters, on which a prior is given. We explain how a mix of the classical Bayesian updating rule and of optimal control techniques allows one to derive the dynamic programming equation satisfied by the corresponding value function, from which the optimal policy can be inferred. We provide an example of convergent finite difference scheme and consider typical examples of applications.

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The world has put various efforts to reduce greenhouse gas emissions to respond to the threat of climate change. Recently, the EU announced to introduce the 'Carbon Border Adjustment Mechanism (CBAM)' which imposes a sort of tariff to the import products from the countries which do not impose the proper level of carbon reduction cost. Korea, as a major exporter to the EC, should respond to the CBAM to reduce the damage. Linking the Korean Emission Trading Scheme with the EU counterpart would be one way. For such a linkage, it is necessary to consider not only technical, administrative, and economic conditions, but also political and popular factors. This study seek several considerations and a proper method for the linkage.

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Grid algorithmic trading has become quite popular among traders because it shows several advantages with respect to similar approaches. Basically, a grid trading strategy is a method that seeks to make profit on the market movements of the underlying financial instrument by positioning buy and sell orders properly time-spaced (grid distance). The main advantage of the grid trading strategy is the financial sustainability of the algorithm because it provides a robust way to mediate losses in financial transactions even though this also means very complicated trades management algorithm. For these reasons, grid trading is certainly one of the best approaches to be used in high frequency trading (HFT) strategies. Due to the high level of unpredictability of the financial markets, many investment funds and institutional traders are opting for the HFT (high frequency trading) systems, which allow them to obtain high performance due to the large number of financial transactions executed in the short-term timeframe. The combination of HFT strategies with the use of machine learning methods for the financial time series forecast, has significantly improved the capability and overall performance of the modern automated trading systems. Taking this into account, the authors propose an automatic HFT grid trading system that operates in the FOREX (foreign exchange) market. The performance of the proposed algorithm together with the reduced drawdown confirmed the effectiveness and robustness of the proposed approach.

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The air transport product permeates national boundaries and therefore makes it impossible for one to localize aircraft engine emissions in terms of calculating the quantum of air pollution by an international flight over the territorial boundary of a nation State. To make matters more complicated, there has been no attempt so far by the international aviation community at reaching a global regulatory system by way of an international treaty that could bind nations of the world to a uniform set of rules applicable to financial instruments that would ensure an equitable and just ‘polluter pays’ system. The furthest we have come is the work carried out so far on emissions trading as a market-based option by the International Civil Aviation Organization (ICAO), which has been working, with a view to evaluating possible ways and means to achieve a global consensus. This article contains a discussion of carbon trading, its consequences, and the legal issues that have emerged as a result of unilateral action in the introduction of an emissions trading scheme. The discussions are presented against the backdrop of the work of ICAO.

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This paper engages with the topic of hospitality in its reading of Kant as a thinker of ‘globality’; that is, as one who is keenly attuned to the various and complex ways humans strive to ‘hospitalize’ this planet in their attempts to transform it into a working and living environment. Despite having no illusions about actual international traders’ practices, who all too often perpetuate injustices and commit crimes, he sets out a project for a different conception of commercium as a cultural practice which should remind us of the finite nature of our planet and its resources, and of our own vulnerable dependence on it. This account of a more enlightened ‘spirit of trade’, which is sensitive to cultural difference and environmental issues, is the product of a mobile way of thinking which would favour a more fluid communication with people on the move.

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The disposition effect is related to the way investors tend to treat unrealized gains and losses on financial assets. In particular, the research found that investors have the tendency to realize gains more quickly than losses. Shefrin and Statman (1985) found that people dislike losing significantly more than they enjoy winning. The disposition effect has been described as “one of the most robust facts about the trading of individual investors" because investors will hold stocks that have lost value yet sell stocks that have gained value. In 1979, Daniel Kahneman and Amos Tversky traced the cause of the disposition effect to the so-called "prospect theory". Given the significance of disposition effect and its impact on investment decisions, the present study investigates factors affecting the disposition effect in the Indian stock market. The results of the study indicate that loss aversion, regret aversion, trading volumes, automatic selling and incremental value of holding positively contribute to the disposition effect.

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Climate change has become one of the most concerned problems throughout the world. Since carbon trading is an essential way to reduce the emission, China has conducted seven ETS pilots and the size of market as well as the level of activity is in the world-leading status. Now Chinese carbon market is in the period of rapid development, and the development of national market has been put on the agenda. All pilots grow fast with highlights in the scheme design, but still, problems are exposed in the first commitment period caused by immature market. Based on the analysis of current development of carbon market and situation of the pilot scheme, the paper explores the existing problems and put forward relevant recommendations on carbon trading market construction.

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Bidding is a competitive way of choosing a counterparty and one of the factors in the formation of a competitive environment. Organized auctions presuppose the presence of a specialized intermediary, and their conduct is conditioned by a number of organizational conditions. The conditions for the admission of goods to trading and the special procedure for concluding contracts at organized auctions act as an institutional restriction of exchange trading and predetermine the fact that not every product is able to be sold on the exchange (recognized as an exchange commodity). Exchange trading pursues the goal of organizing trading activities and, as a secondary effect, forms stock prices (stock quotes, stock indices). The obligation of the seller (manufacturer) to sell part of the goods on the stock exchange (the mechanism of administrative compulsion to sell goods on the stock exchange) is aimed at forming national market price indicators for key groups of goods for regulatory purposes, and not the development of competition as such.

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The purpose of this study is to examine whether president and vice president events of the Constitutional Court's verdict on the winner of the presidential election in 2014 influence the Indonesian capital market activities.The sample of this study is The Indonesian stock exchange listed companies that classified as LQ45 companies. The analysis used one simple t-test, and one way anova.These results indicate that (1) there is not significant abnormal return and trading volume activity are significantly around the events for all companies listed on the LQ-45 (2) there is no difference abnormal return and trading volume activity significantly around the occurrence of events verdict announcement by the president and vice president of the Constitutional Court on the supporting company Joko Widodo, Prabowo and neutral. The average abnormal return as a whole, before and after the event is negative. While positive only when the incident occurred. While on the average trading volume as a whole, before and after the event is negative.

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Pairs Trading is a widely known and used market-neutral trading strategy that utilizes the concept of statistical arbitrage. It is based on the idea of mean-reverting time series and relies on the spread between two assets to demonstrate that property to buy an asset at a relatively undervalued price and an asset at a relatively overvalued price. This allows investors to manage risk if the market moves strongly in only one direction by making money on one side of the bet. The main challenge of pairs trading is selecting pairs that have an actual underlying relationship and their spread has real statistical significance. In this paper, we present the use of machine learning, specifically unsupervised clustering to construct our search space for pair selection and compare it against a traditional way of selecting pairs. We see that not only are we able to pick out more profitable pairs, these pairs are also less volatile and have less exposure to the market.

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Using firm-level data in Canada from 2002 to 2008, I compare the economic performance of three types of firms: those that both export and import (called globally trading firms—GTFs), exporters-only, and importers-only. The results show that GTFs are more productive, larger, more capital intensive, pay higher wages, trade more goods, and trade with more countries than both types of one-way traders. These premia for GTFs were found even before they turned into GTFs (self-selection). Moreover, even after turning into GTFs, the productivity growth of a subset of them was faster than that of one-way traders. The higher the involvement in global value chains (GVCs), the higher was the performance of the “learning-by-turning GTFs”. The GTFs with higher productivity growth were the ones that imported from multiple countries, not those that imported only from China. By another measure, they were both-in-both GTFs—those that traded both final and intermediate goods, and in both directions (exports and imports). Even though they employed only 10% of Canada’s business sector workforce, they contributed 60% of its labour productivity growth.

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Abstract Trading and investment on financial markets are common activities today. A very high number of investors, companies, public or private funds are buying and selling every day with a single purpose: the profit. The common questions for any market participant are: when to buy, when to sell and when is better to stay away from the market risk. In order to answer all these questions, many trading strategies are used to establish the best moments to entry or to exit the trades. Due to the large price volatility, a significant part of the trades is set up automatically today by computers using algorithmic trading procedures. For this particular field, special aspects must be met in order to automate the trading process. This paper presents one of these mathematical models used in automated trading systems, a method based on the Fisher transform. A general form of this method will be presented, the functional parameters and the way to optimize them in order to reduce the risk. It will be also suggested a method to build reliable trading signals with the Fisher function in order to be automated. Three different trading signal types will be explained together with the significance of the functional parameters in the price field. A code sample will be included in this paper to prove the simplicity of this method. Real results obtained with the Fisher trading signals will be also presented, compared and analyzed in order to show how this method can be implemented in algorithmic trading.

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This research is related to study the new wave is coming into a Stock Market to earn easy money though online trading with the respect of industries and brokerages. The Indian Stock Market is one of the important elements in Indian economy which determines the growth in our Indian economy. In today’s competitive world there are millions of people having an Internet facility in their home and offices. From last 32 years since 1991, Globalization, privatization and Liberalization) the internet has impacted much on people perception and the present study is a way to find out the customer awareness about online trading. The main objective of the study is to understand how the investor or trader takes a decision in market and how this process can be made in better way with the help of traders based in Mumbai.

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We introduce and study a general version of the fractional online knapsack problem with multiple knapsacks, heterogeneous constraints on which items can be assigned to which knapsack, and rate-limiting constraints on the assignment of items to knapsacks. This problem generalizes variations of the knapsack problem and of the one-way trading problem that have previously been treated separately, and additionally finds application to the real-time control of electric vehicle (EV) charging. We introduce a new algorithm that achieves a competitive ratio within an additive factor of the best achievable competitive ratios for the general problem and matches or improves upon the best-known competitive ratio for special cases in the knapsack and one-way trading literatures. Moreover, our analysis provides a novel approach to online algorithm design based on an instance-dependent primal-dual analysis that connects the identification of worst-case instances to the design of algorithms. Finally, in the full version of this paper, we illustrate the proposed algorithm via trace-based experiments of EV charging.

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This paper considers the impact of the economic rise of China on both firms and competition in middle income countries (locally) and on the world trading system (globally). It examines the size and nature of the shock that China has administered to the world economy, the way in which firms and export sectors in one middle income country have accommodated that rise, some of the frictions and adjustment strains that China’s rise pose for the world trading system, and two cases which I believe to pose threats to the world trading system if the parties involved do not behave with great care. I will argue that integrating China into the global economy in a way that benefits nearly all presents perhaps the most important international trade and trade policy issue of the present era. The shock that the emergence of China is administering to the world economy is larger than any seen previously—and by a large margin. While the huge increase in global production that China has generated brings widespread benefits, there are inevitably stresses and indeed possibly some losers. I start to identify these in two exercises that are reported here, both, for reasons of data availability, carried out on Mexico. One looks at firm adjustment and the other at export margins. I then discuss China’s role in the wider trading system—the WTO and in global imbalances—and finally identify two areas in which the poor handling of the integration of China into the world economy could derail the world trading system. I mention these latter issues not as inevitable disasters but as issues that are sensitive enough to explode if not handled delicately. An important role of economists in policy-making is to discourage inappropriate policies and descent into trade war as a result of the competition that China brings would certainly count as ‘inappropriate’. It is as a warning, no more, that I address them in this paper.

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The aim of the article. Domestic stocks suffer a problem of ensuring information transparency and objectivity of trading results, manipulations caused by the distortion of trading results in favor of certain participants. One way to solve this problem is to introduce e-bidding, which reduces the degree of subjectivity in determining best offers during the trading process. However, the domestic practice of digital stock trading proves that existing procedures do not prevent the manipulation of dishonest participants. This situation undermines confidence in e-trading and stock trading in general, causing significant economic losses. This is a burning problem in the stock trading of unprocessed timber, which is due to deficient legislation and governmental regulations of this process. These facts, as well as the significant public response associated with large-scale theft of timber, arise the task of changing principles and improving the system of stock electronic trading of raw timber. The aim of the article is to improve principles and approaches to e-trading of raw timber to increase its efficiency and transparency. The results of the analyses. Main factors contributing to the introduction of digital technologies in various types of business are identified and systematized. The generalized scheme of carrying out effective and transparent electronic trading with application of modern digital technologies is developed, the system of principles of its functioning is formed. The scheme of interaction of stock trading participants at stages of decision-making is developed. Basic characteristics of an improved system of electronic trading of unprocessed timber are defined, the basic approaches to their maintenance are offered. The advantages of practical implementation of the proposed approach to the introduction of a new system of electronic bidding are outlined: wider functionality and increased bidding efficiency; improved informational support of substantiated decisions on purchase and sale; simpler, efficient and non-bureaucratized documentary; ensured bidding objectivity and transparency; advanced control and analysis of bidding, prevention of manipulation. Conclusions and perspectives for further research. The obtained results deepen the theoretical and methodological principles of the organizational and economic mechanism of electronic trading of raw timber in terms of increasing its efficiency and transparency. Further research should be aimed at assessing the feasibility and adaptation of developments for application in stock trading in other fields of the domestic economy. Key words: stock trading, digital trading technologies, trading transparency, trading objectivity, trading efficiency, raw timber.

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Abstrak The Qur'an, as a direction and guide for humans, requires Muslims to understand the verses of the Qur'an. The Qur'an has set rules for humans in seeking sustenance, one of which is by way of muamalah or trading (buying and selling). Trading should not be done with the will of vanity. Allah forbids humans to eat other people's property and harming others in a vanity way. Trading or buying and selling are required to be based on an-taradin' or the willingness between the two parties. This study aims to analyse the concept of 'an-taradin in online buying and selling, which refers to the interpretation of Al-Munir by Wahbah az-Zuhaili. This is a qualitative study. The data were analysed using a literature study. According to Shari'a rules, the results showed that 'An-taradin or mutual consent in Surah An-Nisa' verse 29 is the willingness between the two parties. Mutual willingness must be following the limits of sharia. Online buying and selling transactions are required to have open ethics and trust in the transaction to avoid the detrimental of both parties. Honesty and benefit can be the basic principles in conducting online buying and selling transactions.

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AbstractThe impact of statutory Sunday retail restrictions on the transport behaviours of people living in the Polish post-socialist city of Lodz is investigated in this article. One carrier of information on journeys undertaken in the city is data from induction loops – a part of the city’s Intelligent Transportation System (ITS). The second source of data is a two-stage questionnaire survey (concerning trading and non-trading Sundays) of the city’s inhabitants, aimed at defining any changes in their transport behaviours with reference to the introduction of retail restrictions. The research was conducted to assess the way in which the new statutory restrictions affect transport behaviour discharged after the political transformation. The results of the research conducted on the transport behaviours of Lodz residents indicate that the majority of their transport behaviours clearly depend on whether a given Sunday is a trading or non-trading day. The traffic load of the urban road network (perceived as the manifestation of residents’ spatial mobility) is characterised by a distinct changeability due to the legislative restrictions related to Sunday trading. There is both a time (daily and hourly) differentiation of traffic flows and a spatial changeability of the load in the urban space, when a comparative analysis is conducted of the results of observations made in the weeks preceding trading and non-trading Sundays. The study also demonstrates that the time previously devoted to Sunday shopping is currently spent not only at home, but also allocated to new (and until now unperformed) activities that often require travelling.

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Penelitian ini bertujuan untuk menganalisis apakah terjadi fenomena (January Effect) pada Saham Perusahaan Perbankan yang termasuk dalam LQ-45 dan terdaftar di Bursa Efek Indonesia Periode 2016 - 2019, dengan melihat dari perbedaan return saham dan trading volume activity bulan Januari dan bulan selain Januari. Populasi dari penelitian ini adalah Saham Perusahaan Perbankan yang termasuk dalam LQ-45 yang terdaftar di Bursa Efek Indonesia, dimana 3 perusahaan dipilih sebagai sampel menggunakan teknik purposive sampling. Teknik analisis data dilakukan dengan Uji One Way ANOVA dan Non Parametrik Kruskall Wallis. Hasil analisis penelitian menunjukkan bahwa terdapat perbedaan yang signifikan return saham bulan Januari dengan bulan lainnya, dan terdapat perbedaan yang signifikan pada trading volume activity pada bulan Januari dengan bulan lainnya, namun return saham dan trading volume activity bulan Januari bukan yang tertinggi diantara bulan yang lainnya, sehingga January Effect tidak terbukti terjadi pada Perusahaan Perbankan yang termasuk dalam LQ-45 dan terdaftar di Bursa Efek Indonesia Periode 2016 -2019.

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Purpose – The purpose of this paper is to discuss the ways in which information acts as a commodity in massively multiplayer online role-playing games (MMORPGs), and how players pay for items and services with information practices. Design/methodology/approach – Through meta-theoretical analysis of the game environment as a set of information systems, one of retrieval and one social, the paper shows how players’ information practices influence their access to game content, organizational status and relationship to real-money trade. Findings – By showing how information trading functions in MMORPGs, the paper displays the importance of information access for play, the efficiency of real money trade and the significance of information practice -based services as a relatively regular form of payment in virtual worlds. Players furthermore shown to contribute to the information economy of the game with the way in which they decide not to share some information, so as to prevent others from a loss of game content value due to spoilers. Originality/value – The subject, despite the popularity of online games, has been severely understudied within library and information science. The paper contributes to that line of research, by showing how games function as information systems, and by explaining how they, as environments and contexts, influence and are influenced by information practices.

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AbstractThe costs of implementing investment strategies represent a significant drag on the performance of mutual funds and other institutional investors. It is the responsibility of institutional investors, and in the interests of the individual investors they represent, to seek market mechanisms that mitigate trading costs. We investigate an example of one such liquidity provision mechanism whereby liquidity demanders auction a set of trades as a package directly to potential liquidity providers. A critical feature of the auction is that the identities of the securities in the package are not revealed to the bidder. We demonstrate that this mechanism provides a transactions cost savings relative to more traditional trading mechanisms for the liquidity demander as well as an efficient way for liquidity suppliers to obtain order flow. We argue that the cost savings afforded this new mechanism are due to the potential for low cost crosses with the bidder's existing inventory positions and through the longer trading horizon, and superior trading ability, of the bidders. This research suggests that the ability to innovate via new liquidity provision mechanisms can provide market participants with transaction cost savings that cannot be easily duplicated on more traditional exchanges.

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For carbon emissions under the limit of supply chain cost sharing problem, based on the Shared contract perspective starkerberg game model includes the manufacturers and retailers, analyzes the cost sharing ratio, carbon trading price and unit carbon tax emissions on the supply chain, and the optimal strategy combination and carbon reduction cost allocation ratio is studied. The results show that the one-way cost sharing model is beneficial to the reduction of low carbon supply chain, while the two-way cost sharing model has a positive effect on the reduction of supply chain within the small allocation ratio. In addition, the sharing proportion of manufacturers is within a certain range, and the two-way emission reduction mode is the optimal choice for the supply chain. Meanwhile, the sharing proportion will affect the choice between decentralized and centralized decisions. In addition, the numerical analysis shows that the carbon emission reduction levels increase with the increase of carbon trading prices. The results have good reference significance for the carbon emission reduction decision and carbon reduction cost sharing contract among members of the low-carbon supply chain.

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In recent years, trading applications have revolutionized the way individuals engage with the financial markets, providing unprecedented access to real-time data and advanced trading strategies. This paper presents "Wealth Secret," a cutting-edge trading application designed to cater to the needs of both novice and experienced traders. The application leverages real-time stock and index data sourced from yfinance, focusing on the top 50 stocks of Nifty-50, leading stocks in the banking sector, and a comprehensive range of mutual funds including mid-cap, small-cap, and large-cap categories. "Wealth Secret" incorporates a robust backend developed in Python and deployed on PythonAnywhere, ensuring reliable and scalable performance. The frontend, built using Kotlin, offers an intuitive and user-friendly interface. Firebase Realtime Database and Firebase Auth provide secure and efficient data management and user authentication, complemented by local storage solutions through SQLite. One of the standout features of "Wealth Secret" is its extensive coverage of options trading strategies, tailored to various market conditions—bullish, bearish, and sideways. This strategic depth equips users with the necessary tools to navigate the complexities of the financial markets effectively. Additionally, the application includes a dedicated section for purchasing, enhancing the user experience by facilitating seamless transactions within the platform. This paper delves into the technical architecture, real-time data integration, and strategic functionalities of "Wealth Secret," highlighting its potential to empower traders with actionable insights and sophisticated trading capabilities. Through this research, we aim to demonstrate how innovative technology can transform trading practices, making them more accessible and efficient for a broader audience.

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Abstract Climate change and increasing demand of water aggravate the frequency and intensity of trans-boundary water conflicts, which are evolving into one of the most sensitive economic and social issues in trans-boundary areas. This paper analyzes the inefficiency of traditional regional negotiation models to deal with trans-boundary water conflicts, and argues that Coase's theory of property rights is more suitable for dealing with trans-boundary water conflicts. Based on the Bayesian evolutionary game model with incomplete information of property rights, we put forward the following two ways to promote the smooth progress of water rights trading and, furthermore, resolve water resources conflicts: first, to reduce the transaction costs of the upstream and downstream regions; second, to increase utilization efficiency of water resources in the upper reaches. Finally, taking the water conflict of Dayankeng Hydropower Station as a case simulation, we give answers to the three questions: (1) under what conditions, both sides of the conflicts will choose water rights trading; (2) what is the impact of transaction costs on water rights trading, which provided a new way to solve trans-boundary water conflicts; (3) what is the improvement of welfare effects of water conflict participants because of water rights trading.

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Deep reinforcement learning (DRL) has achieved significant results in many machine learning (ML) benchmarks. In this short survey, we provide an overview of DRL applied to trading on financial markets with the purpose of unravelling common structures used in the trading community using DRL, as well as discovering common issues and limitations of such approaches. We include also a short corpus summarization using Google Scholar. Moreover, we discuss how one can use hierarchy for dividing the problem space, as well as using model-based RL to learn a world model of the trading environment which can be used for prediction. In addition, multiple risk measures are defined and discussed, which not only provide a way of quantifying the performance of various algorithms, but they can also act as (dense) reward-shaping mechanisms for the agent. We discuss in detail the various state representations used for financial markets, which we consider critical for the success and efficiency of such DRL agents. The market in focus for this survey is the cryptocurrency market; the results of this survey are two-fold: firstly, to find the most promising directions for further research and secondly, to show how a lack of consistency in the community can significantly impede research and the development of DRL agents for trading.

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E-commerce is a trading or facilitation of trading in products or services using computer networks, such as Internet. E-Commerce is one of the biggest forms of doing E-business, that has happened to the Indian cashless economy in recent years. This has created a new flavor of doing business, which has a huge potential and is fundamentally changing the way businesses are done. This provides advantage for both buyers as well as sellers at the core of its phenomenal rise. The economic reforms of India that were amended in 1991, has resulted in opening of the economy with a view to integrate itself with the worldwide economy. As a result, in last few years we have witnessed a technological revolution accompanied by the widespread use of the Internet, web technologies and their applications. As a symbol of globalization, E-commerce represents the cutting edge of success in this digital age and it has changed and is still changing the way business is conducted around the world

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This paper identifies hidden patterns between trading volumes and the market value of an asset. Based on open market data, we try to improve the existing corpus of research using new, innovative neural network training methods. Dividing into two independent models, we conducted a comparative analysis between two methods of training Proximal Policy Optimization (PPO) models. The primary difference between the two PPO models is the data. To showcase the drastic differences the PPO model makes in market conditions, one model uses historical data from Binance trading history as a data sample and the trading pair BNB/USDT as a predicted asset. Another model, apart from purely price fluctuations, also draws data on trading volume. That way, we can clearly illustrate what the difference can be if we add additional markers for model training. Using PPO models, the authors conduct a comparative analysis of prediction accuracy, taking the sequence of BNB token values and trading volumes on 15-minute candles as variables. The main research question of this paper is to identify an increase in the accuracy of the PPO model when adding additional variables. The primary research gap that we explore is whether PPO models specifically trained on highly volatile assets can be improved by adding additional markers that are closely linked. In our study, we identified the closest marker, which is a trading volume. The study results show that including additional parameters in the form of trading volume significantly reduces the model's accuracy. The scientific contribution of this research is that it shows in practice that the PPO model does not require additional parameters to form accurately predicting models within the framework of market forecasting. Doi: 10.28991/ESJ-2023-07-04-012 Full Text: PDF