Future Transactions Research Articles

Analysis in Decision Making for XAUUSD (Gold/USD) Pair Transactions in Futures Trading

Nowadays, many trading instruments, such as XAU/USD commodity futures trading, a label for spot gold traded in the foreign exchange market, are offered. Gold (XAU) is traded against the US dollar (USD), and its price represents the cost of one ounce of gold in USD. XAU/USD is traded like any other traditional currency in the forex market. This study explores how fundamental and technical analysis can be used as a basis for better decision-making in gold futures trading. The research method used is qualitative, with a descriptive analysis approach based on secondary data in the market. So, in the summary of the analysis results, it is found that fundamental and technical analysis is a tool used for decision-making in gold transactions and futures trading. Decision-making in gold transactions in futures trading should be based on fundamental and technical analysis at a minimum. Decision-making based solely on the results of one analysis alone can be fatal. Various sources of fundamental and technical analysis tools can be used as a decision-making tool in gold and futures trading.

The influence of CSR on cross-border mergers and acquisitions

This paper conducts a comprehensive analysis of Chinese enterprises' cross-border mergers and acquisitions (M&A) events from 2012 to 2022, investigating the influence of corporate social responsibility (CSR) levels on cross-border M&A within the Chinese context. The research reveals that enterprises with higher CSR scores tend to experience lower success rates in cross-border M&A. Both internal and external CSR dimensions exhibit negative impacts on cross-border M&A, with external CSR demonstrating a more pronounced adverse effect. Notably, a history of successful cross-border M&A outcomes significantly enhances the likelihood of smoothly completed future transactions..

Current state of tourism services for tourists: a study in Hanoi City, Vietnam

Assessing tourist satisfaction has become a hot topic for tourism research in recent years. Many studies have shown that improving tourist satisfaction not only has a positive impact on service providers and destination reputation, but also increases tourist loyalty, lowers price elasticity, lowers future transaction costs, and increases production efficiency (Chen et al., 2012; Nguyen Trong Nhan et al., 2014). Some other studies have also shown that improving tourist satisfaction will stimulate word-of-mouth advertising (Ekinci, 2004; Gonzalez et al., 2007; cited by Chen et al., 2012). Therefore, assessing tourist satisfaction is very important, it helps to find out the existing problems, limitations or strengths of service quality from the perspective of tourists, thereby finding solutions to contribute to improving the ability to provide better and better services. Hanoi as the capital, the economic - political - social center of Vietnam. Therefore, the tourism - hotel industry not only attracts tourists but also diplomatic, business and economic - political - social event participants. Hanoi has great potential for tourism - hotel with hundreds of famous landscapes, historical and cultural relics, traditional craft villages, festivals. In terms of natural tourism resources, Hanoi is famous for Huong Tich as "Nam Thien De Nhat Dong". A series of lakes such as West Lake, Hoan Kiem Lake, Dong Mo, Suoi Hai, Quan Son, not to mention the potential of mineral water resources. Hanoi also has many scenic spots around the foot of Ba Vi mountain, creating branded eco-tourism resorts such as Ao Vua, Vua Ba, Khoang Xanh, Suoi Tien, Thac Da, Thac Nga, Ba Vi National Park, etc. Keywords: Satisfaction, Tourists, Service Quality.

Implications of Blockchain-Powered Marketplace of Preowned Virtual Goods

The first‐sale doctrine, which protects consumers’ rights to resell purchased products, has been recognized by the US Supreme Court since 1908. In recent years, consumers have begun to purchase an increasing amount of virtual goods, which renders the first‐sale doctrine unclear. There are two main challenges leading to the uncertainty of the first‐sale doctrine in the digital age: lack of proper technology, and economic implications for developers and consumers. The advent of the blockchain solves the technology challenge, as it can track provenance and establish the chain of custody. In this study, we construct an analytical model to investigate the economic impact of trading preowned virtual items. Specifically, our model captures the decentralized nature of blockchain technology by allowing consumer‐to‐consumer trading, and considers the possibility that consumers prefer preowned virtual items over new ones because preowned items may be upgraded between purchase and resale. Lawmakers seek to strike a balance between the interests of virtual item developers and individual consumers. We show that, surprisingly, the introduction of a blockchain‐based preowned virtual item transaction can actually benefit both developer and consumer. The main intuition is that the developer can adjust the price when forward‐looking consumers incorporate the expected future transaction into their purchase decision. Our analysis also reveals that developers are more willing to embrace the secondary market when they can take a cut during the transaction. Our results provide important policy implications to the burgeoning debate of the first‐sale doctrine in the new digital world.

Smart Contracts as a Tool to Support the Challenges of Buying and Selling Coffee Futures Contracts in Colombia

In Colombia, coffee futures contracts represent essential financial agreements that allow producers and buyers to establish prices, quality, and conditions for future transactions in the coffee market. Despite the evident benefits of stability and predictability, this practice faces significant sustainability challenges that threaten its long-term viability. One of the reasons is the significant lack of transparency in the supply chain. Farmers, affected by abrupt price fluctuations and adverse weather conditions such as the El Niño phenomenon, experience an increase in market prices, leading to the non-delivery of the final product, and contract breaches as they find better prices in the local market. In this context, smart contracts emerge as a promising technological solution to address these problems. These contracts enable the verification of each step in the process, from harvest to final sale, within a blockchain. Therefore, this research designs a smart contract managed through a platform called SmartBeanFutures, which records the clauses of futures contracts using the IERC721 framework, allowing the generation of a unique and non-repeatable asset. It aims to sell, promote, and manage coffee sale prices during the agreement’s signing, creating a transparent environment for chain actors. This proposal undergoes evaluation in a test environment, providing farmers access to the designed platform. Following the validation of the proposal, it was identified that over 74% would use this type of contract in their agricultural processes, highlighting that implementing this technology contributes to eliminating intermediaries in the chain and gives farmers more control over their participation in the market.

An Overview of Customer Relationship Management

Customer satisfaction reflects how effectively needs are met and expectations are exceeded through supplier responses. Achieving it hinges on fostering a strong overall relationship between the customer and the supplier. In today's competitive business landscape, customer satisfaction stands as a vital performance metric and a key factor setting apart business strategies. Therefore, higher levels of customer satisfaction correlate with increased business success and stronger customer connections. Customer loyalty refers to a customer's inclination to continue doing business with a specific supplier and regularly purchase their products or services. This loyalty typically emerges when a customer is highly satisfied with the supplier's offerings, leading them to return for future transactions or consistently choose a particular product or brand from that supplier

Using Content-Based Filtering and Apriori for Recommendation Systems in a Smart Shopping System

This research is motivated by the increasing significance of online shopping platforms and the challenges faced by users in locating products that align with their preferences and requirements, which can significantly influence the sales performance of online retailers. Consequently, the primary objective of this study is to design and implement a recommendation system capable of identifying suitable products and forecasting the purchase frequency for various product combinations, while also integrating this recommendation system with a smart shopping platform. To achieve this objective, the research employs machine learning techniques, specifically content-based filtering and the Apriori algorithm. Content-based filtering is utilized to analyze user preferences and behavioral patterns related to visited products, while the Apriori algorithm is employed to evaluate support and confidence values for item set combinations, thereby generating frequency values for future transactions involving product combinations. Additionally, a smart shopping system is developed and integrated, enhancing the shopping experience through smartphone applications and streamlining the payment process to facilitate seamless product purchases. The research methodology involves data collection pertaining to products and user preferences, followed by several testing involving a sample group of user respondents. The results demonstrate that the developed recommendation system effectively delivers relevant product recommendations based on user preferences, achieving a confidence value up to 98%. Furthermore, the smart shopping system proves capable of independently assisting users throughout the transaction process, thereby enhancing overall user experience and convenience.

Futures trading costs and market microstructure invariance: Identifying bet activity

AbstractMarket microstructure invariance (MMI) stipulates that trading costs of financial assets are driven by the volume and volatility of bets, but these variables are inherently difficult to identify. With futures transactions data, we estimate bet volume as the trading volume of brokerage firms that trade on behalf of their clients and bet volatility as the trade‐related component of futures volatility. We find that the futures bid–ask spread lines up with bet volume and bet volatility as predicted by MMI, and that intermediation by high‐frequency traders does not interfere with the MMI relation.

A Novel Similarity-Based Link Prediction Approach for Transaction Networks

A network consists of nodes and links, which represent components of a system and interactions between them, respectively. An example of networks is transaction networks, in which nodes and links represent firms and transactions, respectively. Link prediction in transaction networks is an important problem, which aims to estimate the likelihood of a transaction between two firms. It can be used to predict missing link information between firms to gain fuller knowledge as transaction information is not easily accessible between firms. In addition, firms can use it to predict future transactions as transactions evolve over time for various reasons such as a shift in customer demand, resource availability, etc. Many link prediction methods have been proposed for networks. However, to the best of our knowledge, there is no existing link prediction method for transaction networks. Existing methods are not suitable for transaction networks as they assume homophily, the tendency of individuals to associate with similar others, which may not be true in transaction networks. In addition, they do not consider the hierarchy structure exhibited in transaction networks. In this article, we propose a new similarity score for transaction networks that account for multiple, temporal, and directed transactions. We then propose a link prediction procedure based on the proposed similarity score to predict new transactions in transaction networks, which avoids the homophily assumption and exploits the hierarchical structure of transaction networks. The proposed method is tested on real-world transaction networks and yields better area under the receiver operating characteristic curve compared to existing methods.

Modelling Risk for Commodities in Brazil: An Application for Live Cattle Spot and Futures Prices

This study analyses a series of live cattle spot and futures prices from the Boi Gordo Index (BGI) in Brazil. The objective is to develop a model that best portrays this commodity’s behaviour to estimate futures prices more accurately. The database created contains 2010 daily entries in which trade in futures contracts occurs, as well as BGI spot sales in the market, from 1 December 2006 to 30 April 2015. One of the most important reasons why this type of risk needs to be measured is to set loss limits. To identify patterns in price behaviour in order to improve future transaction results, investors must analyse fluctuations in asset values for longer periods. Bibliographic research reveals that no other study has conducted a comprehensive analysis of this commodity using this approach. Cattle ranching is big business in Brazil given that in 2021, this sector moved BRL 913.14 billion (USD 169.29 billion). In that year, agribusiness contributed 26.6% of Brazil’s total gross domestic product. Using the proposed risk modelling technique, economic agents can make the best decision about which options within these investors’ reach produce more effective risk management. The methodology is based on Holt–Winters exponential smoothing algorithm, autoregressive integrated moving-average (ARIMA), ARIMA with exogenous inputs, generalised autoregressive conditionally heteroskedastic and generalised autoregressive moving-average (GARMA) models. More specifically, five different methods are applied that allow a comparison of 12 different models as ways to portray and predict the BGI commodity behaviours. The results show that GARMA with order c(2,1) and without intercept is the best model. Investors equipped with such precise modelling insights stand at an advantageous position in the market, promoting informed investment decisions and optimising returns.

An enhanced hybrid model based on multiple influencing factors and divide-conquer strategy for carbon price prediction

Accurate prediction of carbon price is of great significance for promoting the steady development of carbon trading market. However, as the unstable and nonlinear characteristics of carbon prices, it is challenging to obtain accurate future transaction prices. Therefore, this paper proposes a hybrid system for carbon price prediction. Specifically, the two-stage data preprocessing can decompose and reconstruct the carbon price sequence, which can reduce noise and chaotic disturbance and improve the quality of input data. Moreover, extreme gradient boosting (XGBoost) and partial autocorrelation function (PACF) are proposed to screen the influencing factors and carbon price subsequences to obtain the optimal features. The prediction and combination module uses the strategy of subsequence prediction and nonlinear integration, leveraging the strengths of each model to achieve the ideal results. In this system, an effective slime mold algorithm (SMA) is used to ensure the accuracy and stability of the prediction system. The research results show that: (a) The developed system has the best predictive performance compared to the benchmark models. (b) Energy, economy and commodity markets have a more significant long-term impact on carbon prices, and news sentiment and Baidu index contribute to their short-term fluctuations.

Predicting transaction outcomes under customized pricing with discretion: A structural estimation approach

We consider a firm selling heterogeneous products with prices customized for each customer and the final price is set by negotiations between the seller and the customer. This type of pricing modality is referred to as customized pricing with discretion and is commonly used in insurance, consumer loans, mortgages, and many business‐to‐business markets. We assume that each sales agent has a reserve price and each customer has a willingness‐to‐pay, which are jointly drawn from a distribution and, if the transaction is successful, they agree on a price between these two values based on their relative bargaining power. Given the outcomes of a series of negotiations, our goal is to estimate the underlying joint distribution of reserve price and willingness‐to‐pay, and predict the outcomes of future transactions. We assume that the price that prevails as the outcome of the negotiation can be represented as a generalized Nash bargaining equilibrium. We develop a structural estimation method based on the expectation‐maximization algorithm to estimate the parameters of reserve price and willingness‐to‐pay distribution. Using a real‐world data set from indirect auto‐lending industry, we show that our proposed method, which accounts for heterogeneity in sales agent's reserve price and customer's willingness‐to‐pay, improves the predictive accuracy of final price (APR) and take‐up probability (i.e., probability of a customer accepting the loan) on real‐world test data by about 8.70% and 3.68%, respectively, compared to a (Tobit‐based) model, which accommodates unobserved heterogeneity in customer's willingness‐to‐pay only. Using the structural EM estimates, we conduct counterfactual analyses to understand the impact of different pricing policies, which vary in the amount of discretion provided to the sales agent during the negotiation process. For example, we find that the lender may increase profits by 13.63% compared to the status quo by optimally imposing a customized minimum price (annual percentage rate) below which a loan should not be offered to the customer.

The Green Food Supply Chain: Factors Affecting Food Distributor's Sustainability Through Positive WOM

The research intended to uncover factors affecting customers using and repeating orders and transactions with logistics companies in five major logistics hubs in Indonesia. The quality of the food delivered, the service provided, and perceived value significantly impact logistics user satisfaction and bring them back in future transactions. Word of Mouth construct took place as a trigger and supported the fact that it pushed the customers to come back again and take the same logistics service. The result showed that logistics customer satisfaction has a positive influence on positive word of mouth, where the P value was < 0.05 so that when consumers are satisfied with their experience in using the services of a logistics company, it will increase consumers in recommending friends or relatives to take the same services. Positive word of mouth positively influences repurchase intention, where the P value was <0.05, so when consumers recommend friends or relatives to use logistics services, it will increase repurchase intention.

IUS Constituendum of Expert Advisor in Commodity Futures Trading: A Legal Certainty

This study aims to construct forward arrangements regarding Expert Advisors in commodity futures trading. The legal issue being studied is the legal vacuum of regulating trading robot software or commonly known as an Expert Advisor in commodity futures trading. The novelty of this research is legal discovery through legal construction of the development of Expert Advisors in commodity futures trading. This research is juridical-normative legal research by prioritizing statutory and conceptual approaches. The results of the study confirm that the urgency of setting up an Expert Advisor as a futures adviser in commodity futures trading is needed in order to provide legal certainty for commodity futures transaction actors. Legal certainty related to the arrangement of Expert Advisors as futures advisers in commodity futures trading is also needed for CoFTRA as a supporting element of the ministry of trade as well as being a supervisor and enforcer of various legal provisions in the practice of commodity futures trading so that the supervision and enforcement process can be more optimal and can guarantee legal certainty, benefits, and fairness for commodity futures trading actors. Ius constituendum Expert Advisor as a futures adviser in commodity futures trading to ensure legal certainty can be carried out by revising the Law on commodity futures trading, including conducting a judicial review at the Constitutional Court regarding the provisions in the Law on commodity futures trading.

Predictive-Analysis-based Machine Learning Model for Fraud Detection with Boosting Classifiers

Fraud detection for credit/debit card, loan defaulters and similar types is achievable with the assistance of Machine Learning (ML) algorithms as they are well capable of learning from previous fraud trends or historical data and spot them in current or future transactions. Fraudulent cases are scant in the comparison of non-fraudulent observations, almost in all the datasets. In such cases detecting fraudulent transaction are quite difficult. The most effective way to prevent loan default is to identify non-performing loans as soon as possible. Machine learning algorithms are coming into sight as adept at handling such data with enough computing influence. In this paper, the rendering of different machine learning algorithms such as Decision Tree, Random Forest, linear regression, and Gradient Boosting method are compared for detection and prediction of fraud cases using loan fraudulent manifestations. Further model accuracy metric have been performed with confusion matrix and calculation of accuracy, precision, recall and F-1 score along with Receiver Operating Characteristic (ROC )curves.

Time-Varying Structure of the Optimal Hedge Ratio for Emerging Markets

Emerging markets are more exposed to risk than developed markets. Therefore, they require risk management using futures market instruments. This study aims to determine the hedging effectiveness of the spot index market risks in the stock index futures market in Brazil, Russia, India, South Africa, and Turkey. Measuring the hedging effectiveness level of futures markets is vital for these countries because investors must remain in the stock markets for the sustainability of the financial markets and economies. Weekly closing data for the period from January 2009 to October 2021 were analyzed via a dynamic method referred to as flexible least squares (FLS). Although the FLS results show that futures transactions provide high hedging effectiveness for all countries within the scope of this study, country-specific conditions may reduce the hedging effectiveness.

“Greasy Thumb”—The Man Who Made the Chicago Mob

“Greasy Thumb”—The Man Who Made the Chicago Mob

A Comprehensive Study on Namecoin

Abstract: Financial transaction networks are among the world's biggest networks. The digital (crypto) currency network, such as Bitcoin, is a relatively new sort of financial network. Namecoin is a cryptocurrency that is based on Bitcoin and includes functionalities such as DNS. The Namecoin network has nearly 17 million edges and over 2 million nodes. The analysis of such a crypto currency network can aid in the modelling or prediction of future transaction network growth. We evaluated the Namecoin blockchain data in 7 six-month periods in order to analyse the transaction network graph over time. In comparison to Bitcoin, our data imply that user behaviour and development patterns are different [1]. "An experimental open-source technology that improves the decentralisation, security, censorship resistance, privacy, and speed of certain components of the Internet infrastructure, such as DNS and identities," according to Namecoin's definition.

AMERICAN COTTON FUTURE MARKETS AND THE RELATIONSHIP OF CURRENCY WITH BIST TEXTILE LEATHER INDUSTRY

By the effect of globalization, the importance that countries attach to foreign trade is increasing, and therefore every market that contributes to foreign trade is carefully examined. One of the indispensable market for both national and international economies is the textile sector. The materials used in this sector, which has a large share in foreign trade, are also economically important factors. For example, cotton plant is one of the most important input used in production processes for the textile industry. For this reason, in this work, studies are carried out on important indices that are traded on the Turkish stock market, especially with the theme of "cotton". The aim of this study is to reveal the cointegration and causality relations of BIST Textile Leather (XTEKS) index, American Cotton Futures Markets (CTZ2) and Dollar rate using daily data between 2.1.2019-31.12.2021 with Engle Granger cointegration test and VAR Granger causality test. According to the results of the study, an Engle Granger cointegration relationship has been found between the BIST Textile Leather (XTEKS) index with the American Cotton Futures Transactions (CTZ2) and the Dollar exchange rate and it has been determined that these variables come to equilibrium and act together in the long term. According to the VAR Granger causality test related to the variables, no statistical causality relationship could be detected.

CATCHM: A novel network-based credit card fraud detection method using node representation learning

Advanced fraud detection systems leverage the digital traces from (credit-card) transactions to detect fraudulent activity in future transactions. Recent research in fraud detection has focused primarily on data analytics combined with manual feature engineering, which is tedious, expensive and requires considerable domain expertise. Furthermore, transactions are often examined in isolation, disregarding the interconnection that exists between them.In this paper, we propose CATCHM, a novel network-based credit card fraud detection method based on representation learning (RL). Through innovative network design, an efficient inductive pooling operator, and careful downstream classifier configuration, we show how network RL can benefit fraud detection by avoiding manual feature engineering and explicitly considering the relational structure of transactions. Extensive empirical evaluation on a real-life credit card dataset shows that CATCHM outperforms state-of-the-art methods, thereby illustrating the practical relevance of this approach for industry.