Future Market Research Articles

Optimizing Genetic Algorithm With Momentum Strategy for Technical Trading Rules: Evidence From Futures Markets

ABSTRACTThis paper introduces an innovative genetically optimized dynamic composite strategy for achieving profitability in futures markets. Utilizing daily data from 35 actively traded futures contracts (1984–2022), we highlight the potential advantages of integrating the momentum effect into dynamic moving average strategies. This enhancement can boost the strategy's capability to capture and capitalize on market trends, ensuring consistent and stable returns. The developed dynamically composite technical trading strategy aspires to be a valuable reference for investors and the finance academic community, contributing to advancements in the field.

The Impact of the CEO's Angry Facial Expression on the Future Market Value of the Enterprise

The Impact of the CEO's Angry Facial Expression on the Future Market Value of the Enterprise

Adoption of Mobile Health Applications for Diabetes Management From a Push–Pull–Mooring Perspective

Diabetes management applications enable diabetes self-management in a more convenient and cost-effective manner. This study develops a push–pull–mooring model in order to understand patient-switching intentions to the conventional offline and the novel mobile diabetes management. Data collected from 412 adult patients with diabetes in China are analyzed to test the proposed hypotheses. The results show that push effects and pull effects have significantly positive effects on switching intention. Mooring effects negatively affect switching behavior. Meanwhile, the moderating effects of all three mooring factors (switching cost, offline habit, and private risk) on the relationship between both push-switching intentions and pull-switching intentions are also detected. These findings contribute to a deeper understanding of patient switching intentions towards diabetes management applications and, accordingly, can help marketers, healthcare providers, and health policymakers develop and appropriate their future marketing and administrative strategies.

Location and Brand Image Outshine Price in Homebuyer Decisions in Indonesia

General Background: The increasing population in Medan has led to a rising demand for housing, prompting competition among companies to provide secure and comfortable upper-middle-class residences. Specific Background: Citraland Helvetia serves as a case study to understand the dominant factors influencing consumer decisions in purchasing homes. This study specifically investigates whether price, location, promotion, brand image, and facilities significantly impact the purchase decisions at Citraland Helvetia. Knowledge Gap: Previous research has not conclusively identified which factors are most influential during the product launch phase for residential properties in this region. Aims: This study aims to identify the dominant factors affecting consumer purchasing decisions during the product launch of Citraland Helvetia, focusing on location, brand image, and price. Results: The study found that location and brand image are the two most influential factors in the decision-making process, while price has a significant impact but is not as dominant. Promotion and facilities also positively influence purchasing decisions. Novelty: The research provides empirical evidence highlighting the critical role of location and brand image over price during the initial launch phase, which contrasts with the common perception that price is the primary determinant. Implications: These findings suggest that developers should prioritize strategic location selection and strong brand image development to attract buyers. Enhancing these elements can lead to better consumer satisfaction and potentially higher investment returns. The study's insights can inform future urban development and marketing strategies in the housing sector, particularly in emerging urban areas like Medan. Highlights: Dominant Factors: Location and brand image drive home purchase decisions. Price Impact: Less significant during initial product launch phase. Strategic Focus: Prioritize location and brand image for buyer attraction. Keywords: Housing demand, Medan, Consumer decision, Location, Brand image

The Post-Internationalization Evolution of the Price Discovery Pattern in China’s Iron Ore Markets

Previous literature provided evidence of a negative impact of internationalization on the quality of China’s iron ore futures market within a 6-month window (i.e. June 1, 2018 to November 31, 2018), but little is known on the long-term impact. Using a longer, 4-year-7-month window from June 1, 2018 to December 30, 2022, we examine the market’s price discovery function, an important measure of market quality. We find significant structural break in China’s iron ore data series in August 2021, which largely coincide with a period of rising commodity prices to all-time high. Before the breaks, the price discovery function of China’s iron ore futures appears quite weak, with the spot market leading the futures market, which is consistent with the findings of Fan et al. During the post-break period, however, the futures market demonstrates a stronger price discovery function and takes over the leading role. The day-of-the-week effects in iron ore futures also seem to weaken post-break as well. Our GARCH model results exhibit significant bi-directional volatility spillover between the futures and spot markets.

Beyond fear and safety: revenge travel and international tourists’ intentions in the post-crisis era

Purpose This study aims to investigate the evolving travel intentions of tourists in the aftermath of the relaxation of international mobility restrictions in 2022. It aims to understand how the concept of “revenge travel” – travelling with the intent to make up for lost time during crisis periods – impacts tourists' travel intentions in the post-crisis era. Design/methodology/approach Employing a quantitative approach, the study uses Structural Equation Modelling (SEM). Data were collected through a survey of 320 respondents in 2022. This methodology enables a comprehensive understanding of travel intentions, including motivations, perceptions of destination safety and the influence of revenge travel. Findings The findings reveal that revenge travel is a significant predictor of travel intentions post-crisis. The results indicate that tourists are less concerned with health and safety and are more driven by the desire to compensate for lost travel opportunities. This shift suggests that travel intentions in the post-crisis era are not predominantly fear-induced. The study also offers insights into how these intentions can inform the recovery and sustainable planning of the tourism industry. Originality/value This paper recognises the impact of revenge travel on travel intentions following the lifting of crisis-related mobility restrictions. It provides novel insights into tourists' post-crisis travel behaviour, extending the understanding of travel motivations in extraordinary circumstances. The findings are valuable for tourism practitioners and researchers, offering guidance for future tourism development and marketing strategies within a post-crisis context.

An Investigation of the Co-Movement between Spot and Futures Prices for Chinese Agricultural Commodities

We employed a non-parametric causality test based on Singular Spectrum Analysis (SSA) and used the Vector Error Correction Model (VECM) and Information Share Model (IS) to measure the relationship between the futures and spot prices for seven major agricultural commodities in China from 2009 to 2017. We found that the agricultural futures market has potential leading information in price discovery. The results of an Impulse Response Function (IRF) analysis also showed that the spot prices react to shocks from the future market and have a lasting impact. This confirms our findings reported for the causality test and information share analysis.

Social life cycle hotspot analysis of future hydrogen use in the EU

Abstract Purpose The widespread use of hydrogen in the EU aimed at reducing greenhouse gas emissions may involve complex value chains (e.g. importation from third countries) with potential effects (positive or negative) on the different sectors of society. Achieving sustainable hydrogen deployment must be motivated not only by environmental and economic aspects but also by social responsibility and the search for human well-being. Given this, and the scarcity of studies currently available on prospective social impacts of hydrogen production, the present purpose of this article is to unveil and assess the main social impacts linked to the future hydrogen value chains. Methods The methodological approach adopted in this article encompasses the following steps: (i) analysis of two potential value chains for hydrogen use in EU: an on-site option, where hydrogen is produced and used in the same European country, and an off-site option, where hydrogen is produced in a European country different from its usage involving more unit processes, in terms of storage and transport activities, and working time to deliver the same quantity of hydrogen. This framework will include (i) scenario analysis and a forward-looking perspective taking into account the critical raw materials employed across the entire value chain, (ii) identification of a list of relevant social impact categories and indicators through a systematic procedure, (iii) social hotspot analysis using Product Social Impact Life Cycle Assessment (PSILCA) to assess the selected representative value chains, and (iv) conducting scenario analysis and subsequently interpreting of results. Results and discussion The off-site value chain shows a relatively worse social performance (6 to 72 times) than the on-site value chain across most selected indicators due to the more complex value chain. Although the identification of social hotspots depends on the specific social indicator under evaluation, the power source components (wind and solar PV) manufacturing processes and the relatively increased complexity of the off-site option highly conditioned the social performance of the hydrogen value chains in most of the indicators considered. A scenario analysis was carried out comparing both value chains with two additional locations for hydrogen production: Northern Africa and Western Asia. The findings indicate that the on-site value chain presents the lowest impact scores. For the off-site option, the production of hydrogen in a European country is the most preferable scenario in terms of the social indicators evaluated. Conclusions According to findings, producing hydrogen in a different location than where it is consumed increases the social impacts of its deployment. Measures at mid and long term should be considered for improving the social impact of hydrogen deployment in Europe. This includes increasing reuse and recycling, responsibly sourcing raw materials, and creating regulatory frameworks ensuring safe working conditions across global value chains. Furthermore, this article highlights the crucial role of the S-LCA methodology in evaluating social aspects as a support for targeted policy interventions, and the need to adapt this to the specific case study. At the same time, it acknowledges that other relevant social aspects that can influence the social sustainability of the hydrogen technology are not captured with this methodology (in particular social acceptance, affordability and energy security). Improvements in selecting indicators and refined geographical and temporal representations of the value chains to better represent hydrogen technologies and future size market are research gaps filled in the present scientific work.

Strategi Digital Marketing Tiktok Untuk Meningkatkan Brand Awareness Koding Next Surabaya

In the digital era, social media marketing is crucial for enhancing brand awareness and expanding audience reach. TikTok has emerged as a popular platform among the youth, known for its creative and interactive content. Recognizing this potential, Koding Next Surabaya, a leading coding education institution for children and teenagers in Southeast Asia, aims to leverage TikTok to increase its brand awareness. Established in 2015 by Bartek Wasik and Mateusz Rybinski and present in Surabaya since 2022, Koding Next focuses on delivering top-quality programming education. This internship involves creating relevant content and studying innovative digital marketing strategies, particularly through interactive content and TikTok's promotional features, to reach a broader audience. This study seeks to develop an effective digital marketing strategy on TikTok, identify the most engaging content types, and measure the impact of TikTok's promotional features on brand awareness. The research focuses on the period from December to June, using metrics such as view count, engagement, and follower growth, within Surabaya's geographical context. The findings are expected to provide practical guidance for Koding Next Surabaya, enhance its brand awareness, and serve as a reference for future digital marketing research on TikTok.

Ambiguous investor sentiment

This study examines the interaction between investor sentiment, ambiguity, and asset pricing. While the existing literature highlights the importance of sentiment in times of higher uncertainty, our study further shows the nuanced distinction between ambiguity and standard uncertainty. We examine their effects on the relationship between sentiment and future stock market returns at the daily and monthly frequencies. Our analysis demonstrates that ambiguity weakens the predictive ability of sentiment on stock returns, whereas standard uncertainty increases it. We also present additional evidence suggesting that lower market participation during periods of high ambiguity is the likely driver of this effect.

An EEMD‐LSTM, SVR, and BP decomposition ensemble model for steel future prices forecasting

AbstractThe forecasting of steel futures prices is important for the steel futures market, even for the steel industry. We propose a decomposition ensemble model that incorporates the Ensemble Empirical Mode Decomposition (EEMD), Long Short‐Term Memory (LSTM), Support Vector Regression (SVR), and Back Propagation (BP) neural network to forecast steel futures prices. The forecasting procedures are as follows: (1) The price data are initially decomposed into several relatively independent Intrinsic Mode Functions (IMFs) and a residue using EEMD. (2) The IMFs are then reconstructed as components representing short‐term, medium‐term, and long‐term frequencies via fine‐to‐coarse. (3) LSTM, SVR, and BP neural network are utilized to forecast the short‐term, medium‐term, and long‐term reconstructed components, respectively. (4) The prediction results for each component are simply added to the final prediction results. The accuracy of the proposed model is compared with several benchmark models by experiments and evaluated by some prediction evaluation indexes. The experimental results show that our model outperforms other models in terms of forecast accuracy, confirming its strong predictive capabilities. This study provides some suggestions for investment and decision making by participants in the steel futures market. It may promote the smooth operation of the steel futures market and shed some light on the operation of the steel industry.

Mechanisms and implications of autonomous vehicle market penetration: Insights from a Markov forecasting model

Due to the rapid evolution of autonomous driving technology and the complexity of market penetration mechanisms, establishing a reliable quantitative research approach for measuring autonomous vehicle (AV) penetration and effectively validating forecasted outcomes poses significant challenges. To address this issue, this paper overcomes data limitations by starting from the perspective of Chinese automotive market. It introduces a quantifiable Markov forecasting model that establishes the link between transition probabilities and penetration influencing factors. Through a penetration network, it visually represents the correlation and evolutionary states of AVs. Building upon the model, a framework for data quantification and analysis is formed. By quantifying model indicators with market data such as car performance and historical sales, the network parameters and transition probabilities are continuously updated in real-time. This drives the model to output short-term forecasts for AV penetration in the automotive market. In addition, we devise a two-stage simulation algorithm to accomplish parameter calibration and model validation. Through validation and comparative analysis, it is observed that, compared to direct learning from historical data, our model can more accurately forecast real market penetration trends. Furthermore, sensitivity analysis experiments on market strategies indicate that, compared to technical investment, the market exhibits a higher sensitivity to price adjustments. A strategy combination of increased technical investment in high-level vehicles and judiciously raising prices proves more advantageous for intelligent transformation in the automotive sector than a singular strategy. Additionally, as the AV market evolves, the sensitivity to favorable strategies will gradually increase. Therefore, the developmental stage of the market is a crucial factor for both car companies and investors to consider. The insights gleaned from this paper offer actionable guidance for policymakers and automotive corporations in shaping future market strategies, thereby fostering the continued growth of autonomous driving technologies within the industry.

Journal of Futures Markets: Volume 44, Number 8, August 2024

Journal of Futures Markets: Volume 44, Number 8, August 2024

Volatility spillover across spot and futures markets: Evidence from dual financial system

This paper investigates dynamic returns and volatility spillovers between spot and futures markets in a dual financial system. It further analyses the shock transmission of both volume trading and open interest in the futures market. Empirical results suggest that both spot and futures indices are net transmitters of return spillovers to the volume and open interest of the futures market, whereas the futures volume is the only net transmitter of volatility spillovers to all other variables. This is consistent with the Information Arrival Hypothesis theory. The empirical analysis also evidences the presence of a dynamic interdependence between both Islamic and conventional spot market volatilities and the futures market. In particular, the returns and volatility spillover are bidirectional and ricocheting-off transmission in nature. Specifically, such interdependence is stronger in the case of the Islamic spot index than the conventional spot index and during financial shocks.

Exploring calendar effects: the impact of WASDE releases on grain futures market volatility

ABSTRACT The monthly issuance of the World Agriculture Supply and Demand Estimates (WASDE) by the United States Department of Agriculture (USDA) plays a pivotal role in shaping market dynamics through its comprehensive projections and assessments of agricultural commodities. This paper examines the influence of WASDE reports on the intraday volatility of grain futures. We analyse the calendar effect on volatility, focusing on the diurnal patterns on WASDE release days compared to other trading days. This study leverages high-frequency intraday data to explore the immediate and sustained market reactions to the reports, aiming to identify distinct volatility patterns and their implications for traders and market analysts. Our findings confirm the presence of unique diurnal volatility patterns on WASDE release days, significantly diverging from non-release days. This research contributes to the literature by providing refined insights into how public information releases affect market volatility, offering valuable perspectives for understanding and anticipating market movements in response to agricultural reports.

Will the carbon emissions trading system promote enterprises' willingness to pay for carbon emission reduction?

AbstractThe emissions trading system (ETS) is one of the effective measures for energy conservation and emissions reduction, and the impact of the carbon ETS can be reflected in the willingness of enterprises to pay for carbon emissions reductions. However, there has been little research exploring the relationship between the two. Therefore, this study fills this knowledge gap by comprehensively investigating the influence of China carbon ETS policy (ETSP) on the willingness of enterprises to pay for carbon emissions reductions, and incorporates environmental awareness (EA), green technology innovation (GTI), and climate risk awareness (CRA) into the research model, thus constructing a new theoretical framework. Empirical analysis is conducted using the structural equation modeling method based on survey data from 478 Chinese enterprises. The results indicate that: (1) the China carbon ETSP can promote the willingness of enterprises to pay for carbon emissions reductions. (2) The China carbon ETSP can also promote the EA, GTI, and CRA of enterprises. (3) EA, GTI, and CRA can also promote the willingness of enterprises to pay for carbon emissions reductions. (4) Mediation effect analysis revealed that EA and CRA, respectively, play a mediating role in the influence process of the China carbon ETSP on the willingness of enterprises to pay for carbon emissions reductions. However, GTI does not have a mediating effect in this process. (5) Comparative mediation analysis found no significant differences in the mediating effects of EA and CRA. This study provides constructive practical recommendations for future carbon market mechanism construction and enterprise emission reduction policy formulation. It also offers guidance and reference for the behavioral decision‐making of enterprises in carbon market transactions, and carries practical significance and policy advocacy value.

Assessing network risk with FRM: links with pricing kernel volatility and application to cryptocurrencies

The Financial Risk Meter (FRM) employs Quantile-LASSO regression to identify systemic financial risk and dependencies among tail events across financial assets. This paper establishes, both theoretically and empirically, a meaningful economic relationship between the FRM index, derived from the penalization parameter in quantile LASSO regression, and the volatility of assets' pricing kernels, the attainable maximal Sharpe ratio, and market volatility. Despite the rapid growth of the crypto market and its increasing integration with traditional financial markets, there remains a dearth of risk measures in this space. FRM @ Crypto exhibits robust predictive capabilities in anticipating future market risk, potentially filling a critical void in this market.

Asymmetric TVP-VAR connectedness between highly traded commodities and hedging strategies: Evidence from major contagions

The objective of this study is to examine the return interconnectedness and asymmetric spillover effects in global commodity futures markets, with a focus on the impact of contagion. A competent asymmetric time-varying parameter vector autoregressive (TVP-VAR) model was employed for highly traded commodity futures (cocoa, coffee, corn, cotton, soy, sugar, wheat, and oil) between January 1, 2000, and March 31, 2024. This study investigates the connectedness of commodities in three dimensions: asymmetric spillovers, the influence of oil and oil substitutes on the network, and the impact of major contagions. The average total connectedness index (TCI) indicates that the connectedness is significant throughout the period and increases during the invasion. The findings imply that contagion effects trigger a potential alteration in the structure of the network integration level of the commodities, amplifying system-wide dynamic connectivity due to disurptions caused by oil and oil substitutes. The net plot depicts corn and soy as the net transmitters, with their magnitude increasing during the contagions. The pairwise connectedness index (PCI) revealed that corn-soy, corn‒wheat, and soy-wheat were the primary interactors, while oil became a significant interactor, particularly during the oil crash and the COVID-19 outbreak. Additionally, compared with other contagions, GFC had a potential asymmetric effect on the network. Positive returns dominate the interaction between the primary transmitter and receivers, whereas negative returns do not significantly dominate the total network. These investigations contribute to the literature on the food-fuel nexus in terms of asymmetries and the impact of contagions on the futures market. It also identified the optimal portfolio allocation based on the hedging effectiveness of three portfolio construction strategies.

The Mixed Tangible Catalog: Toward Tangible and Sustainable B2B Metaverse Fashion Showrooms.

The metaverse, driven by mixed reality (MR), is positioned as the future market, revolutionizing product exploration in virtual space. Existing literature on this subject mainly focuses on business-to-consumer perspectives, leaving a gap in understanding business-to-business (B2B) applications, particularly in the fashion industry. This article introduces a "Mixed Tangible Catalog" (MTC) for B2B that combines a physical, foldable cardboard booth with an MR application linked to a head-mounted display. Targeting the fashion sector's need for high standards in material evaluation, the MTC allows retailers and distributors to browse garments, customize material attributes, and receive visual and tangible feedback. Evaluation through a focus group of 10 industry experts revealed positive feedback. The MTC maintains the tangibility of traditional B2B showrooms and reduces the environmental impact by minimizing transportation, samples, and waste. This innovative approach offers an efficient and sustainable alternative to conventional physical showrooms, enhancing both economic and ecological aspects.

Dual-market quantitative trading: The dynamics of liquidity and turnover in financial markets

Financial market liquidity is a popular research topic. Investor-driven research uses the turnover rate to measure liquidity and generally finds that the higher the stock turnover rate, the lower the returns. However, the traditional financial liquidity theory has been impacted by new machine-driven quantitative trading models. To explore high machine-driven liquidity and the impact of high turnover rates on returns, this study establishes a dual-market quantitative trading system, introduces a variational modal decomposition (VMD)-bidirectional gated recurrent unit (BiGRU) model for data prediction, and uses the back-end Hong Kong foreign exchange market to develop a quantitative trading strategy using the same rotating funds in the U.S. and Chinese stock markets. The experimental results show that given a principal amount of 210,000.00 CNY, the final predicted net return is 226,538.30 CNY, a net return of 107.86%, which is 40.6% higher than the net return of a single Chinese market. We conclude that, under machine-driven trading, increasing liquidity and turnover increase returns. This study provides a new perspective on liquidity theory that is useful for future financial market research and quantitative trading practices.