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Embracing finance, economics, operations research, and computers, this book applies modern techniques of analysis and computation to find combinations of securities that best meet the needs of private or institutional investors.
In the existing works, stochastic sets $\mathbb{B}$ of interval type, along with $\mathbb{B}$-stochastic processes, were introduced within the framework of stochastic analysis. In this paper, we undertake the construction of $\mathbb{B}$-stochastic integration by exploring three novel types of $\mathbb{B}$-stochastic integrals: Stieltjes integrals of $\mathbb{B}$-predictable processes with respect to $\mathbb{B}$-adapted processes with finite variation, stochastic integrals of $\mathbb{B}$-predictable processes with respect to $\mathbb{B}$-inner local martingales, and stochastic integrals of $\mathbb{B}$-predictable processes with respect to $\mathbb{B}$-inner semimartingales. These $\mathbb{B}$-stochastic integrals are exclusively defined on subsets $\mathbb{B}$, with values outside the scope of $\mathbb{B}$ being deemed irrelevant. Additionally, we present several notable consequences, including the relationship between $\mathbb{B}$-stochastic integrals and existing stochastic integrals, as well as Itô's formula for $\mathbb{B}$-inner semimartingales. In the context of models pertaining to uncertain time-horizons in mathematical finance, we establish essentials of mathematical finance for general markets characterized by sudden-stop horizons. This is achieved by defining self-financing strategies, admissible strategies, and no-arbitrary conditions. In such financial markets, the exclusivity characteristic inherent in $\mathbb{B}$-stochastic integrals offers investors a viable alternative approach. This approach enables them to effectively filter out unnecessary information pertaining to asset price dynamics and portfolio strategies that extend beyond the predefined time-horizons.
Héctor Jasso-Fuentes, Alejandra Quintos, Xinta Yang
In the context of micro-finance, a group of individuals undertake business projects that may interfere with one another. A contagious default happens if one person's project failure leads to the default of another group member. In this paper, we apply a probabilistic approach to analyze the impact of such contagion among investment group members. Firstly, a general formula is provided to compute the group survival probability with the presence of contagion effect. Then, special cases of this probability model are examined in detail. In particular, we show that if the investment group is homogeneous, defined in the paper, then including more members into the group will eventually lead to default with probability 1. This differs from the non-contagious scenario, where the default probability decreases monotonically with respect to the group size. Afterwards, we provide an upper bound of the optimal group size under the homogeneous setup; so, one can run a linear search within finite time to locate this optimizer.
Luca Pennella, Pietro Saggese, Fabio Pinelli
et al.
Decentralized Finance (DeFi) applications introduce novel financial instruments replicating and extending traditional ones through blockchain-based smart contracts. Among these, derivatives protocols enable the decentralized trading of cryptoassets that are the counterpart of derivative products available in traditional finance. Despite their growing significance, DeFi derivatives protocols remain relatively understudied compared to other DeFi instruments, such as lending protocols and decentralized exchanges with automated market makers. This paper systematically analyzes DeFi derivatives protocols - categorized into perpetual, options, and synthetics - in the field, highlighting similarities, differences, dynamics, and actors. As a result of our study, we provide a formal characterization of decentralized derivative products and introduce a unifying conceptual framework that captures the design principles and core architecture of such protocols. We complement our theoretical analysis with numerical simulations: we evaluate protocol dynamics under various economic conditions, including changes in underlying asset prices, volatility, protocol-specific fees, leverage, and their impact on liquidation and profitability.
Abstract
This study aims to determine whether the application of the Group Investigation model can increase student’s teamwork skills in elements of business economics at SMKN 2 Magelang. This research is motivated by the importance of teamwork skills in vocational high school in preparation for work as the main outcome. The research was Classroom Action Research that was performed within 2 cycles used. The subjects of this study were 35 students of class XI AKL 1 of SMKN 2 Magelang. This research instrument uses an observation sheet that contains 5 indicators of teamwork including positive interdependence, face-to-face interaction, personal responsibility, interpersonal relationships, and group processing. The research used qualitative descriptive analysis techniques. Cycle I showed that the average student’s teamwork skills are 74,14%. Student’s teamwork skills increased by 10,07% in cycle II to 84,21%. The results showed an increase in student’s teamwork skills using Group Investigation model.
Bahasa Indonesia Abstrak
Penelitian ini bertujuan untuk mengetahui peningkatan kemampuan kerja sama siswa pada elemen ekonomi bisnis melalui penerapan model pembelajaran Group Investigation di SMKN 2 Magelang. Penelitian ini dilatarbelakangi oleh pentingnya kemampuan kerja sama pada siswa SMK untuk persiapan menuju dunia kerja sebagai luaran utama. Jenis Penelitian ini adalah Penelitian Tindakan Kelas (PTK) dimana dilaksanakan dalam 2 siklus. Subyek penelitian ini adalah siswa kelas XI AKL 1 yang berjumlah 35 siswa. Penelitian ini menggunakan lembar observasi yang memuat 5 indikator kerja sama yakni saling ketergantungan positif, interaksi tatap muka, tanggung jawab individu, hubungan interpersonal, dan proses kelompok. Analisis data dilakukan menggunakan teknik deskriptif kualitatif. Siklus I menunjukkan kemampuan kerja sama siswa sebesar 74,14%. Kemampuan kerja sama siswa meningkat sebesar 10,07% pada siklus II menjadi 84,21%%. Hasil penelitian menunjukkan adanya peningkatan kemampuan kerja sama siswa menggunakan model Group Investigation .
The article is devoted to stimulating the development of housing construction through mortgage lending. The purpose of the article is to determine the role of mortgage lending in the development of the housing sector, in particular through its impact on supply and demand in the real estate market and pricing in this sector. In the course of the research, data analysis, correlation analysis, and methods of forecasting economic trends were used. Graphical methods were also used to provide a clear understanding of how changes in the mortgage market affect the development of the real estate sector. The results of the study show that mortgage lending is a key element in stimulating the development of the housing sector, as it not only directly facilitates access to finance for potential property buyers, but also indirectly affects the pricing and investment attractiveness of the housing sector. The paper shows that fluctuations in mortgage rates have a significant impact on the dynamics of supply and demand in the residential real estate market, as well as on price trends. In particular, rising mortgage rates tend to reduce demand for housing, as households expect better investment opportunities. There is also a strong correlation between mortgage rate increases and slower price growth. The expansion of the mortgage lending market and, consequently, a reduction in mortgage rates boosts supply in the real estate market. Nevertheless, it is found that such changes can have complex and ambiguous consequences, including the risks of market overheating and the formation of price "bubbles" that threaten the stability of the sector in the long run. In addition, the study found that mortgage lending facilitates the implementation of new construction projects and the expansion of the housing stock, which is an important factor in stimulating economic growth. At the same time, the analysis showed that the impact of mortgage rates on the real estate market depends on a wide range of factors, including the economic situation, central bank policy, consumer confidence and other macroeconomic indicators. The practical significance of the publication is to provide recommendations for the development of a balanced policy in the field of mortgage lending aimed at supporting the stable development of the housing sector and preventing potential destabilising factors in the real estate market.
As Islamic banks grow and evolve, pricing methods for their services have become essential to study and implement. This study highlights the significance of understanding the factors influencing Islamic banking service pricing in Algeria. The study aims to analyze how Islamic banks price their services, with a focus on cost, market, and value strategies. Additionally, it seeks to evaluate and recommend ways to enhance the current practices of banks operating in the national market. Algeria is experiencing rapid growth in Islamic banking, making it an ideal location to study this subject. The country is home to two Islamic banks, Al Baraka Bank and Al Salam Bank. Algeria was selected as a new market to allow the findings to be applicable to similar situations elsewhere. The research utilizes secondary data obtained from available information on Islamic bank service fees, comparing them with those of traditional banks. It also conducts financing simulations in both banks and compares them with the traditional theoretical framework. Data was gathered from various sources, including bank websites, annual reports, and previous studies. The research reveals that Algerian Islamic banks do not prioritize scientific methods in pricing their services. The results suggest that these banks operate within a traditional framework under the oversight of the central bank. The central bank's rules depend on the prices of services conventional banks offer. This shapes how customers perceive these banks as representatives of Islamic banking. Islamic banks can utilize the study's results to develop pricing strategies that are more effective and compliant with Islamic law. Regulators can utilize these findings to formulate enhanced policies to bolster the Islamic banking sector. The results also assist researchers in delving deeper into the realm of Islamic banking service pricing. This study refutes the hypothesis that Algerian Islamic banks have enhanced the efficiency of their service pricing by adopting models in line with Islamic finance principles, such as profit-sharing, while considering market conditions and service value. They should embrace more pragmatic and beneficial pricing strategies that align with Islamic law, cater to customer needs, and enhance their competitiveness and value in the national banking market.
With the continuous penetration of Internet applications in our lives, the ever-increasing data on clicking behavior has made online services a critical component of the economic sectors of internet companies over the past decade. This development trend has brought a large amount of information that reflects user needs but is relatively chaotic. Extracting user interests and needs from complex click behaviors is crucial for advancing online business development and precisely targeting product information The interactive attention-based capsules (IACaps) network is proposed in this paper to collate and analyze complex and changing click information for user behavior representation. Specifically, an interactive attention dynamic routing mechanism is proposed to mine the potential association information among different browsing behaviors, which facilitates the extraction and understanding of seemingly irrelevant information hidden in massive click data. To ensure the practicability of the proposed method, three different types of datasets were selected from Amazon Dataset for experiments, and the results of which shows the superior performance of the proposed method when compared with other models. Specifically, the reasonableness and effectiveness of the reported model are further proved by improvements of metrics obtained in the main experiments and ablation studies. Optimization of Hyper-parameters is also analyzed from the number of iterations, the number of capsules, and the dimension of capsules for better understanding of operating principles.
Large Vision-Language Models (LVLMs) evolve rapidly as Large Language Models (LLMs) was equipped with vision modules to create more human-like models. However, we should carefully evaluate their applications in different domains, as they may possess undesired biases. Our work studies the potential behavioral biases of LVLMs from a behavioral finance perspective, an interdisciplinary subject that jointly considers finance and psychology. We propose an end-to-end framework, from data collection to new evaluation metrics, to assess LVLMs' reasoning capabilities and the dynamic behaviors manifested in two established human financial behavioral biases: recency bias and authority bias. Our evaluations find that recent open-source LVLMs such as LLaVA-NeXT, MobileVLM-V2, Mini-Gemini, MiniCPM-Llama3-V 2.5 and Phi-3-vision-128k suffer significantly from these two biases, while the proprietary model GPT-4o is negligibly impacted. Our observations highlight directions in which open-source models can improve. The code is available at https://github.com/mydcxiao/vlm_behavioral_fin.
Cassandra Crowe, Belinda Middleweek, Laura Ryan
et al.
We surveyed Australian finance professionals and tested whether there are statistically significant differences in promotional propensity according to gender identity. The findings indicate men and women are equally likely to ask for promotion, however, 'gifted advancements' account for the higher statistical frequency of promotions among men. These gender-based differences in behaviors have been overlooked in existing research on promotion. We call for a standardized framework for the development of promotion policies to address this industry-wide problem.
The increasing reliance on Large Language Models (LLMs) in sensitive domains like finance necessitates robust methods for privacy preservation and regulatory compliance. This paper presents an iterative meta-prompting methodology designed to optimise hard prompts without exposing proprietary or confidential context to the LLM. Through a novel regeneration process involving feeder and propagation methods, we demonstrate significant improvements in prompt efficacy. Evaluated on public datasets serving as proxies for financial tasks such as SQuAD for extractive financial Q&A, CNN/DailyMail for news summarisation, and SAMSum for client interaction summarisation, our approach, utilising GPT-3.5 Turbo, achieved a 103.87% improvement in ROUGE-L F1 for question answering. This work highlights a practical, low-cost strategy for adapting LLMs to financial applications while upholding critical privacy and auditability standards, offering a compelling case for its relevance in the evolving landscape of generative AI in finance.
In recent years, Large Language Models (LLMs) like ChatGPT have seen considerable advancements and have been applied in diverse fields. Built on the Transformer architecture, these models are trained on extensive datasets, enabling them to understand and generate human language effectively. In the financial domain, the deployment of LLMs is gaining momentum. These models are being utilized for automating financial report generation, forecasting market trends, analyzing investor sentiment, and offering personalized financial advice. Leveraging their natural language processing capabilities, LLMs can distill key insights from vast financial data, aiding institutions in making informed investment choices and enhancing both operational efficiency and customer satisfaction. In this study, we provide a comprehensive overview of the emerging integration of LLMs into various financial tasks. Additionally, we conducted holistic tests on multiple financial tasks through the combination of natural language instructions. Our findings show that GPT-4 effectively follow prompt instructions across various financial tasks. This survey and evaluation of LLMs in the financial domain aim to deepen the understanding of LLMs' current role in finance for both financial practitioners and LLM researchers, identify new research and application prospects, and highlight how these technologies can be leveraged to solve practical challenges in the finance industry.