Search Results (30)

Short time series are fundamental in the foreign exchange market due to their ability to provide real-time information, allowing traders to react quickly to market movements, thus optimizing profits and mitigating risks. Economic transactions show a strong connection to foreign currencies, making exchange rate prediction challenging. In this study, the exchange rate estimation between the US dollar (USD) and the Chilean peso (CLP) for a short period, from 2 August 2021 to 31 August 2022, is modeled using the nonlinear Schrödinger equation (NLSE) and calculated with the fourth-order Runge–Kutta method, respectively. Additionally, the daily fluctuations of the current exchange rate are characterized using the Hurst exponent, H, and later used to generate short synthetic fluctuations to predict the USD–CLP exchange rate. The results show that the USD–CLP exchange rate can be estimated with an error of less than $5\%$, while when using short synthetic fluctuations, the exchange rate shows an error of less than $10\%$. Full article

Predicting the directions of financial markets has been performed using a variety of approaches, and the large volume of unstructured data generated by traders and other stakeholders on social media microblog platforms provides unique opportunities for analyzing financial markets using additional perspectives. Pretrained large language models (LLMs) have demonstrated very good performance on a variety of sentiment analysis tasks in different domains. However, it is known that sentiment analysis is a very domain-dependent NLP task that requires knowledge of the domain ontology, and this is particularly the case with the financial domain, which uses its own unique vocabulary. Recent developments in NLP and deep learning including LLMs have made it possible to generate actionable financial sentiments using multiple sources including financial news, company fundamentals, technical indicators, as well social media microblogs posted on platforms such as StockTwits and X (formerly Twitter). We developed a financial social media sentiment analyzer (FinSoSent), which is a domain-specific large language model for the financial domain that was pretrained on financial news articles and fine-tuned and tested using several financial social media corpora. We conducted a large number of experiments using different learning rates, epochs, and batch sizes to yield the best performing model. Our model outperforms current state-of-the-art FSA models based on over 860 experiments, demonstrating the efficacy and effectiveness of FinSoSent. We also conducted experiments using ensemble models comprising FinSoSent and the other current state-of-the-art FSA models used in this research, and a slight performance improvement was obtained based on majority voting. Based on the results obtained across all models in these experiments, the significance of this study is that it highlights the fact that, despite the recent advances of LLMs, sentiment analysis even in domain-specific contexts remains a difficult research problem. Full article

The coffee industry is at a critical juncture where traditional business models and operational practices are no longer sufficient to ensure long-term sustainability and competitiveness. This study explores the imperative need for disruptive innovation across the entire coffee value chain, from coffee farmers and green coffee traders to coffee roasters. Climate change poses a significant threat to coffee cultivation, with rising temperatures, altered rainfall patterns, and increased pest infestations impacting crop yields and quality. Coffee farmers must adopt sustainable agricultural practices, such as agroforestry and precision farming, and leverage technology to mitigate these risks and improve productivity. Additionally, the entire coffee value chain must strive to become climate-neutral, incorporating eco-friendly practices from cultivation to consumption. Green coffee traders face the challenge of enhancing supply chain transparency and embracing digital platforms to streamline operations to meet the increasing demand for traceability. Future coffee trade agreements will not only be based on bean quality but also on comprehensive data accompanying the beans. This includes their precise origin details with geocoordinates, complex risk analyses, and adherence to the rights of indigenous peoples. Consequently, both bean quality and data quality will become fundamental aspects of commercial transactions. Coffee roasters need to innovate in roasting techniques, diversify product offerings, and adopt more sustainable packaging solutions. Furthermore, achieving climate neutrality requires integrating renewable energy sources, reducing waste, and improving energy efficiency throughout the roasting process. This presentation delineates the urgent need for adaptation and innovation at each stage of the coffee value chain. By providing a comprehensive overview of the required adjustments, a roadmap for coffee companies to navigate the complexities of the future market landscape is offered. The presented thesis underscores that only through significant disruption and continuous evolution, combined with a strong commitment to sustainability, can coffee businesses ensure their continued presence and success in the industry. Full article

This review paper thoroughly examines the role of technical analysis in energy and financial markets with a primary focus on its application, effectiveness, and comparative analysis with fundamental analysis. The discussion encompasses fundamental principles, investment strategies, and emerging trends in technical analysis, underscoring their critical relevance for traders, investors, and analysts operating within these markets. Through the analysis of historical price data, technical analysis serves as a crucial tool for recognizing market trends, determining trade timing, and managing risk effectively. Given the complex nature of energy and financial markets, where many factors influence prices, the significance of technical analysis is particularly pronounced. This review aims to provide practical insights and serve as a roadmap for future research in the realm of technical analysis within energy and financial markets. This review contributes to the ongoing discourse and advancement of knowledge in this crucial field by synthesizing existing perspectives and proposing avenues for further exploration. Full article

We revisit, and document new facts regarding, the financialization of U.S. energy markets in 2000–2010. We show that, after controlling for macroeconomic factors and physical energy market fundamentals, the strength of energy markets’ co-movements with the U.S. stock market is positively related to the energy paper market activity of hedge funds that trade both asset classes. This relation weakens when credit risk is elevated. We find, in contrast, no link with the aggregate positions of commodity index traders in energy futures markets. Our findings have implications for the ongoing debate regarding the financialization of commodities. Full article

We analyze the role of hedge fund, swap dealer, and arbitrageur activity in the crude oil market. The contribution of our work is to examine the role of institutional traders in switching between high-volatility and low-volatility regimes. Using confidential position data on institutional investors, we first analyze the linkages between trader positions and fundamentals. We find that these institutional position changes reflect fundamental economic factors. Subsequently, we adopt a Markov regime-switching model with time-varying probabilities and find that institutional position changes contribute incrementally to the probability of regime changes. Full article

This article constructs a behavioral financial model that provides feedback on both historical prices and company fundamentals without considering asset liquidation to discuss the long-term impact of investor sentiment and feedback trading on asset price fluctuations. The research conclusion shows that the abnormal volatility of asset prices is captured by the value effect, the cognitive bias effect, the sentiment shock effect, and the trading inductive effect. The value effect is the volatility of asset prices that is completely determined by fundamental factors; the higher the degree of cyclical fluctuations in fundamental factors, the higher the volatility of prices. The bias effect refers to investors’ misreading of basic information and trends in asset prices; the greater the instability of emotional shocks, the greater the abnormal volatility of asset prices. The trading inductive effect is also called the Keynes effect, which reflects the role played by rational traders. Full article

In this study, we explore the dynamics of the stock market using an agent-based simulation platform. Our approach involves creating a multi-strategy market where each agent considers both fundamental and technical factors when determining their strategy. The agents vary in their approach to these factors and the time interval they use for technical analysis. Our findings indicate that investing heavily in reducing the value–price gap was a successful strategy, even in markets where there were no trading forces to reduce this gap. Furthermore, our results remain consistent across various modifications to the simulation’s structure. Full article

Sentiment and extrapolation are ubiquitous in the financial market, and they are not only the embodiment of human nature, but also the primary drivers of asset price bubbles. In this study, we first constructed a theoretical model that included fundamental traders and extrapolated investors, and we assessed the time series characteristics of asset prices under different types of information shocks. According to the research results, good news about the fundamentals can lead to positive asset price bubbles, and correspondingly, bad news can lead to negative asset price bubbles; however, the decrease in asset prices in the case of negative bubbles is not as substantial as the increase in prices in the case of positive bubbles, and the time for prices to reverse is also long, which can be explained by the short-selling constraints. According to the comparative static analysis, the scales of the positive and negative foams depend on the proportion of investors in the market and the extrapolation coefficient. We verified the conclusion of the theoretical model from two aspects: (1) we analyzed the relationship between investor sentiment and the prevalence of informed trading, and according to the results, the increase (decrease) in investor sentiment can reduce the information content of asset prices and increase price volatility; however, the impact of low sentiment is not substantial, which preliminarily tests the conclusion of the theoretical model; (2) we examined the relationship between the cumulative change in investor sentiment and future portfolio returns, and we found that the cumulative increase in investor sentiment can have a positive impact on future portfolio returns at the initial stage, and depress future portfolio returns in the long term, which forms positive asset price bubbles. The cumulative depression of investor sentiment can depress the future portfolio returns at the initial stage, and positively influence the future portfolio returns in the long term, which forms negative asset price bubbles. Moreover, these two nonlinear relationships exhibit cross-sectional differences in different types of asset portfolios, which further validates the key proposition of the theoretical model. Full article

Modern communication habits are largely shaped by the extensive use of social media and other online communication platforms. The enormous amount of available data and speed with which new information arises, however, often suffices to cause misunderstandings, false conclusions, or otherwise disturbed opinion formation processes. To investigate some of these effects we use an agent-based model on gossip and reputation dynamics with 50 agents, including Bayesian knowledge updates under bounded rationality and up to the second-order theory of mind effects. Thereby, we observe the occurrence of reputation boosts from fake images, as well as the advantage of hiding one’s opinion in order to become a strong information trader. In addition, the simulations show fundamentally different mechanisms for reaching high agreement with others and becoming well-informed. Additionally, we investigate the robustness of our results with respect to different knowledge-update mechanisms and argue why it makes sense to especially emphasize the margins of distribution when judging a bounded quantity such as honesty in a reputation game simulation. Full article

This paper discusses the impact of noise trader risk on total consumption and investor consumption. The model predicts that: (1) If noise traders show optimistic beliefs, they will have a restraining effect on the total consumption when the noise trading intensity is high enough, they will expand consumption at t = 1 and reduce consumption at t = 2, and rational investors will reduce consumption at t = 1 and expand consumption at t = 2; (2) if the beliefs of noise traders do not show bias, the consumption of rational investors is always higher than that of noise traders and exceeds the market benchmark; (3) the relative consumption of rational investors and noise traders depends on the risk, risk aversion, fundamental risk and market ratio of noise traders; (4) based on the reasonable range of noise traders’ beliefs, the lifetime consumption of noise traders will be higher than that of rational investors and the market, and the excess consumption will change with a series of parameters. Full article

We use a 2018 survey of FX margin traders in Japan to investigate which key factors influence their trading performance: socio-demographic and economic situation, investment strategy and trading behaviour, and/or financial literacy. We study this question using general-to-specific modelling and impulse-indicator saturation. First, the data show that variables from all three groups are significant predictors of traders’ performance. Second, we find that older traders and those without a specific trading strategy demonstrate lower performance. Performance is higher for those who trade greater amounts, rely more on fundamental analysis, and report having profitable FX trade skills. Third, respondents’ subjectively stated claim of having FX trade skills is based on a more advanced understanding of FX trading and a reliance on professional advice. Neither objective financial knowledge nor over/underconfidence play a noteworthy role in the performance of margin traders. Full article

Information is the basis for the sustainable and stable development of financial markets. Advanced internet technology has accelerated the dissemination of information. To investigate the impacts of private information dissemination on the sustainability of the financial market, we construct a rational expectation equilibrium (REE) model. The dissemination of private information favors noise traders who receive private information and weakens the advantage of informed traders who have direct access to private information, thus reducing noise-driven volatility and increasing fundamental-driven volatility, which is not conducive to the sustainability and stability of the financial market. Private information dissemination increases information asymmetry, reduces the number of noise traders in the market, decreases market liquidity, and hurts price efficiency for both exogenous and endogenous information acquisition, which is harmful to the sustainability of the financial market. Additionally, we numerically analyze the effects of private information on noise traders, market liquidity, and price efficiency. The numerical results are consistent with the theoretical analysis. The findings highlight the potential of private information dissemination to noise traders in financial market analysis. This study contributes to the analysis of financial market sustainability. Full article

Background: People nowadays are more concerned about their financial status and how to improve their quality of life; one method is day trading. This study aims to investigate the association between stress or anxiety and day trading among day traders in Saudi Arabia. Methods: We collected the data through DASS-21, a set of three self-report scales designed to measure the emotional states of depression, anxiety, and stress. It tells if the person has mild, moderate, severe, or extremely severe emotional status. Our study will focus on two domains: stress and anxiety. Day traders scoring between 0 and 7 on the anxiety scale were classified as normal anxiety. Scoring between 8 and 9 on the anxiety scale, mild anxiety, and between 10 and 14 on the anxiety scale as moderate anxiety. Those scoring between 15 and 19 were classified as severe, and those scoring >20 as extremely severe. Results: Our results showed that out of 387 valid surveys, day traders scoring < 14 on the stress scale were classified as everyday stress (N = 249, 64.3%), and those scoring between 15 and 18 as mild (N = 49, 12.7%) and those scoring between 19 and 25 as moderate (N = 46, 11.9%), those scoring between 26 and 33 as severe (N = 34, 8.8%), and those scoring > 34 were classified as extremely severe (N = 9, 2.3%). Conclusions: The prevalence of anxiety and stress is considerable among day-traders. Therefore, it is fundamental to develop more effective health promotion strategies for the target population to make them aware of and learn how to control and prevent these harmful emotional feelings. Full article

In the last decade, cryptocurrency trading has attracted the attention of private and professional traders and investors. To forecast the financial markets, algorithmic trading systems based on Artificial Intelligence (AI) models are becoming more and more established. However, they suffer from the lack of transparency, thus hindering domain experts from directly monitoring the fundamentals behind market movements. This is particularly critical for cryptocurrency investors, because the study of the main factors influencing cryptocurrency prices, including the characteristics of the blockchain infrastructure, is crucial for driving experts’ decisions. This paper proposes a new visual analytics tool to support domain experts in the explanation of AI-based cryptocurrency trading systems. To describe the rationale behind AI models, it exploits an established method, namely SHapley Additive exPlanations, which allows experts to identify the most discriminating features and provides them with an interactive and easy-to-use graphical interface. The simulations carried out on 21 cryptocurrencies over a 8-year period demonstrate the usability of the proposed tool. Full article