Future Asset Research Articles

Source of US market predictability in international equities: the investor attention perspective

US stock returns lead other countries due to delays in information transmission caused by limited investor attention. But two conditions must be held for this argument to be tenable. One is attention’s domestic return predictability, and the other is the US’s internationally central position among attention lead-lag relationships. While many recent studies suggest investors’ limited attention materially influences future asset returns, there lacks direct evidence of investors focusing on the US first and then gradually turning their attention to the rest of the world. This paper fills this gap by demonstrating the international leadership of investor attention on the US equity market.

Algorithmic futuring: predictive infrastructures of valuation and investment in the assetization of edtech

ABSTRACT Futures of education are increasingly defined through predictive technologies and methods. We conceptualize ‘algorithmic futuring’ as the use of data-driven digital methods and predictive infrastructures to anticipate educational futures and animate actions in the present towards their materialization. Specifically, we focus on algorithmic futuring in the education technology investment industry, and the role of predictive infrastructures of valuation and investment in the assetization of edtech. Edtech investment actors make predictions based on the calculated future asset value of digital technologies. Methods and narratives of algorithmic futuring produced through predictive infrastructures are intended to render an assetized future of education seemingly attractive, attainable, and actionable. Our analysis foregrounds three forms of algorithmic futuring practised by an edtech investment organization: forecasting targets; managing uncertainty; and provoking assetization. Each has significant impacts on what futures are put into motion in the present, and exploit a gap in public investment in edtech futures.

Estimating Expected Asset Returns with the Present Value Model of Consumption and Fed Forecasts

Abstract This paper utilizes Greenbook forecasts of consumption and income to estimate expected asset returns through a present value model of consumption. The study finds that, despite the valuable information contained in Greenbook forecasts, the expected asset returns obtained from this approach do not provide meaningful insights into future asset returns. This contrasts with previous literature suggesting predictability using the present-value model.

Disaster Safe Education Unit Program As An Effort To Reduce Disaster Risk In School

Various sectors experienced losses as a result of the disaster. Education is one sector that is badly affected by disasters, especially when disasters occur during school hours. Students, who are the country's future assets, are the biggest victims. In addition, accidents can cause damage to the learning process, school documents, and school facilities. For this reason, disaster risk reduction management must be in place. One way to do this is by providing education about disaster risk reduction. This research uses a qualitative approach, which means describing and explaining what schools are doing to reduce disaster risk by implementing a disaster-safe education unit program. Steps to describe and explain data by referring to expert opinions. This study shows that the Disaster Safe Education Unit (SPAB) program is very important to implement in schools because it can increase the school community's understanding of matters related to disasters. It is hoped that the implementation of the SPAB program will enable schools to remain safe and comfortable and increase students' understanding of how to deal with disasters

Quantitative Portfolio Management: Review and Outlook

This survey aims to provide insightful and objective perspectives on the research history of quantitative portfolio management strategies with suggestions for the future of research. The relevant literature can be clustered into four broad themes: portfolio optimization, risk-parity, style integration, and machine learning. Portfolio optimization attempts to find the optimal trade-off of future returns per unit of risk. Risk-parity attempts to match the exposure of various asset classes such that no single asset class dominates portfolio risk. Style integration combines risk factors on a security level such that rebalancing differences cancel out. Finally, machine learning utilizes large arrays of tunable parameters to predict future asset behavior and solve non-convex optimization problems. We conclude that machine learning will likely be the focus of future research.

Russia-associated sanctions and asset’s value: determination of yield on investment from the liquidity perspective

<p><span lang="EN-US" style="font-size: 14pt; font-family: 'times new roman', times, serif;">In this study, the time-varying pattern of returns with the trading cost is assessed in light of sanctions on the Russia’s economy. The analysis is performed on the Moscow Exchange Index (MOEX) using the ordinary least squares (OLS) method, Bayes’ Theorem technique, and vector error correction model (VECM). Firstly, the transparency of asset’s value in the same trading period was noted to execute the trade with a limited price impact. Thus, the liquidity seems not priced in returns. In addition, the posterior probability of the transparent asset’s value stabilized the market at a limited transaction cost. In this case, there is less likelihood of liquidity acting as a risk element in returns during the same trading session. These results correspond to the era without sanctions, as well as economic restrictions period. Before the economic restrictions, the execution of investment occurred on its real or close value due to the market transparency in the following trading period. This implies, that the past trading cost series is not priced in yields on investment. During the Russia-related sanctions, a higher price impact of transaction was noted due to the ambiguity in the future asset’s value. Therefore, the past liquidity series is a pricing element in the short run and long run. The analysis suggests a potential inference of trading cost to determine the yield on investment. </span></p>

CHILDRENS PRIVACY RIGHTS BASED ON THE THEORY OF DIGNIFIED JUSTICE IN INDONESIA

Child data protection is a mandate of the constitution of the Republic of Indonesia. The purpose of this study is first, to find out the provisions of childrens personal data protection in Indonesia, Second, critical notes on the regulation towards protection of childrens personal data. Third, the perspective of the theory of dignified justice related to the protection of childrens personal data. The research method used is normative legal research. The results of the study are first, the protection of childrens personal data in Indonesia has been regulated in the constitution, namely Law Number 27 of 2022 concerning the Protection of Personal Data (UU PDP), which is the embodiment of Article 28 G paragraph (1) of the 1945 Constitution of the Republic of Indonesia. Second, the PDP Law regarding the protection of childrens personal data still seems simple as assessed from the legal substance of the parental consent mechanism, verification, audit monitoring and the lack of a risk-based approach. Third, dignified justice aims to humanize humans as creatures of God Almighty, by placing children in their true position in technology and personal data. Dignified justice is carried out through a multi-stakeholder cooperation through the synergy of parents, companies, government, educational institutions, and the press. In the end, the goal is the creation of sustainable protection for childrens privacy data because children are the future assets of a nation.

Model predictive control for multi-period portfolio optimization: a trading-oriented learning approach

Portfolio optimization aims at finding the optimal allocation of a given wealth among different assets to maximize an investor's utility function. A major critical issue concerns the prediction of future asset returns to forecast market evolution, due to the non-stationarity and volatility of asset prices. In this work, a learning-based Model Predictive Control (MPC) strategy for multi-period portfolio optimization is proposed, where the return prediction model is estimated via a novel trading-oriented learning paradigm. According to such a perspective, the model parameters are not the ones minimizing the prediction error but those that directly maximize the investor's utility. An extensive experimental study carried out on real-market data shows the potential improvements introduced by the proposed methodology compared to benchmark financial strategies.

Crime Rate Prediction using Python

It has never been easy to invest in a set of assets, the abnormality of the financial market does not allow simple models to predict future asset values with higher accuracy. Machine learning, which consists of making computers perform tasks that normally require human intelligence is currently the dominant trend in scientific research. This article aims to build a model using Machine learning Model. The main objective of this paper is to see in which precision a Machine learning algorithm can predict. Predicting crime prediction rate is a complex task that traditionally involves extensive human-computer interaction.. The network is trained and evaluated for accuracy with various sizes of data, and the results are tabulated. This project is to predict crime to make more acquainted and precise crime in the area

Worst-case Conditional Value at Risk for asset liability management: A framework for general loss functions

Asset–liability management (ALM) is a challenging task faced by pension funds due to the uncertain nature of future asset returns, employees’ wages, and interest rates. To address this challenge, this paper presents a new mathematical model that uses a Worst-case Conditional Value-at-Risk (WCVaR) constraint to ensure that, with high probability, the funding ratio remains above a regulator-mandated threshold under the worst-case density function that plausibly explains historical sample data. A tractable reformulation of this WCVaR constraint is developed based on the definition of the Worst-case Lower Partial Moment (WLPM) for a general loss function. Additionally, a data-driven moment-based ambiguity set is constructed to capture uncertainty in the moments of the density functions of random variables in the ALM problem. The proposed approach is evaluated using real-world data from the Canada Pension Plan (CPP) and is shown to outperform classical ALM models, based on either CVaR or WCVaR with fixed moments, on out-of-sample data. The proposed framework for handling correlated uncertainty using WCVaR with nonlinear loss functions can be used in other application areas.

Information Asymmetry and Agency Problems in the Financial Market

This paper discusses that the agency problem stems from the information asymmetry between shareholders and professional managers, this comes from the fact that shareholders cannot have expertise in every aspect. Therefore, in the actual operation of the company, it is necessary to hire professional managers to manage the company. However, at the same time, the two parties with asymmetric information have different ways of income. The professional manager whose income mainly comes from a fixed salary is bound to be more interested in short-term development than like the shareholders. The reason. The main income is profiting sharing and future asset appreciation. As a result, managers will not value long-term development as much as shareholders. As well as this paper will discuss whether it can minimize the agency cost by changing the remuneration method of professional managers to equity incentive value. This paper will also discuss the feasibility and possible problems by having directors directly involved in the management of the company, or creating a regulator to oversee the professional managers.

The Influence Between the Decay Speed of Information and Future Asset Price—A New Herd Behavior Model with the Decay Factor

The Influence Between the Decay Speed of Information and Future Asset Price—A New Herd Behavior Model with the Decay Factor

FOSTERING LOYALTY AMONG YOUNG CONSUMERS: STRATEGIC APPROACHES FOR BANK SUSTAINABILITY

Objective: This study examines the influence of service quality, satisfaction, bank image, and trust on bank loyalty among youngsters in Malaysia, represented by university students. Theoretical Framework: This study integrates cognitive and behavioral approaches from the Learning Theory to model customer loyalty. Cognitive processes involve assessing service quality for future decisions, while behavioral aspects focus on observable actions. Both perspectives are essential for understanding and predicting consumer behaviors in the context of loyalty. Method: Using a sample of 290 university students, data was collected through a self-administered survey and analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM). Results and Discussion: The results reveal that only bank image and trust significantly affect loyalty, while satisfaction, bank image, and trust are influenced by service quality but not on students’ loyalty. Interestingly, bank image also influences satisfaction. These findings suggest that banks should prioritize building a positive image and trust among customers to enhance loyalty, rather than solely focusing on service quality and satisfaction. Research Implications: The study offers practical implications for Malaysian banks, highlighting the importance of loyalty for long-term sustainability and targeting undergraduate students as future assets. It contributes to the literature by exploring these relationships specifically among undergraduate students in Malaysia, offering insights for banks to consolidate their market share and tailor products and services for young consumers Originality/Value: This pioneering study examines how service quality, satisfaction, image, trust, and loyalty interrelate among Malaysian undergraduate students, offering insights for banks targeting young consumers

Developing a predictive maintenance model for the rehabilitation of roads’ assets

Maintaining road infrastructure is a significant challenge in minimizing public fund waste from reactive maintenance. Preventive maintenance is considered a new era in this field. To achieve cost savings and prolong the life cycle of road networks, it’s crucial to determine their lifespan at the time of construction. This article proposes an emerging predictive methodology to select road network rehabilitation policy. This methodology facilitates long-term planning based on technical and budgetary perspectives to identify the most cost-effective rehabilitation policy for the road network’s assets over the planning horizon. To accomplish this, the Pavement Rehabilitation Predictive Model (PRPM) is developed for long-term forecasting of future asset conditions as the network deteriorates over time. It takes into account effective planning for different rehabilitation policies predicts asset deterioration, and aids in making informed decisions by identifying the best rehabilitation policy for pavement sections. An empirical example is used to illustrate the proposed approach in detail, based on the evaluation of three scenarios of the rehabilitation policies. The results indicate that the prediction model is highly efficient, resulting in an average budget savings of up to 20% compared to other relevant maintenance methods.

Parents’ Awareness of Healthy Food Intake among Toddlers in Kuantan, Pahang, Malaysia

Parents play a crucial role in the upbringing and education of children, who are the country's future assets. Early education should commence at home, focusing not only on shaping the child's attitude and spirituality but also on providing a balanced diet that promotes overall health. Nutritious food is vital as it provides energy for active movement, supports the proper functioning of cells and organs, and strengthens the child's immune system. In 2019, revealed that one in five children under the age of five in Malaysia suffers from stunting. This alarming situation underscores the need for parents to address this issue seriously to safeguard the well-being of future generations. Furthermore, children are influenced by media and peers, often preferring sugary and high-calorie foods such as cakes, ice cream, snacks, and fast food. Hence, this study aimed to assess the level of parental awareness regarding the selection of daily food for children. Utilizing a quantitative approach and descriptive and regression analysis, the study involved 80 parents from various day care centers in the Kuantan district. The study's results indicated that parents with higher educational backgrounds displayed greater knowledge of healthy food intake. This suggests that education might play a role in increasing awareness and understanding of healthy eating habits among parents. Parents are clearly attentive and dedicated to offering their children nutritious and well-balanced meals.

Stochastic Volatility Models with Skewness Selection.

This paper expands traditional stochastic volatility models by allowing for time-varying skewness without imposing it. While dynamic asymmetry may capture the likely direction of future asset returns, it comes at the risk of leading to overparameterization. Our proposed approach mitigates this concern by leveraging sparsity-inducing priors to automatically select the skewness parameter as dynamic, static or zero in a data-driven framework. We consider two empirical applications. First, in a bond yield application, dynamic skewness captures interest rate cycles of monetary easing and tightening and is partially explained by central banks' mandates. In a currency modeling framework, our model indicates no skewness in the carry factor after accounting for stochastic volatility. This supports the idea of carry crashes resulting from volatility surges instead of dynamic skewness.

IMPLEMENTING OF VIRTUAL REALITY TECHNOLOGY IN SCIENCE SUBJECTS AS EFFORT TO BUILD A MEANINGFUL LEARNING

The post-COVID-19 pandemic has resulted in learning loss in almost all subjects, including science learning. In addition, there is a decrease in motivation in capturing learning. In contrast, 21st-century education demands literacy skills accompanied by competencies and characters that lead to lifelong learning. Therefore, services in the form of technology that can improve students' skills and interests are needed, one of which is using virtual reality. This technology is believed to be a future asset that can help students construct their cognitive structures. Thus, this study aims to examine the use of virtual reality technology in improving meaningful learning in science learning at school. The method used in this study is a qualitative method with a descriptive approach through a literature review of Google Scholar and Scopus-indexed journals. The data that has been collected is analyzed by correlating the relationship between the use of virtual reality with meaningful learning in science subjects. After that, it ends by elaborating on the ideas in this article. Based on studies that have been conducted, it is found that virtual reality is a 3D technology that helps visualize science concepts that combine the real and virtual worlds (immersive) so that learning is more interactive. Despite its great potential, this technology should be used as a supporting tool, not a substitute for human interaction in learning. Virtual reality can also create meaningful learning by directly involving students in constructing their cognitive structures related to facts, concepts, procedures, and metacognition. In addition, this technology is also able to integrate science concepts that were previously fragmented to be integrated comprehensively through 3D virtual simulations. This will help create meaningful learning because students can understand science concepts comprehensively by connecting their experiences to real learning materials through immersive technology. Keywords: Immerse Technology; Meaningful Learning; Science Subject; Virtual Reality

Extended model to explain customer attitude toward NFT and moderating effect of technology optimism

PurposeThis study empirically examined consumer adoption attitudes and behaviors toward nonfungible tokens (NFTs). Findings indicate that consumer attitudes toward NFTs are influenced by perceived usefulness, reliability and profit expectancy and that strong attitudes are associated with purchase intentions. Additionally, the relationship between attitudes and purchase intentions was moderated by technology optimism.Design/methodology/approachThe authors utilized a partial least squares (PLS) model to examine the hypotheses in this empirical analysis. Obtaining a sample of actual NFT holders or experienced users is challenging. A total of 105 individuals participated in the study as valid responders by answering the screening question in the questionnaire. The authors opted for the PLS model as a research approach due to the limited size of the consumer population in the NFT market.FindingsThis study discovered that the adoption of NFTs was affected by technical aspects such as usefulness and reliability and the potential for future asset growth. Furthermore, the degree of attitude-to-intention conversion varied based on optimism, an inherent characteristic.Research limitations/implicationsThis study offers valuable insights for NFT owners, content providers and trading firms. For the NFT market to expand, it must meet consumers' expectations for the desired content features and asset investment attributes. Additionally, customer targeting strategies should attract and appeal to technology enthusiasts with an optimistic outlook on technology.Originality/valueThe authors conducted an initial empirical analysis of actual NFT consumers, an area of research studied sparingly despite its significance.

A Novel Hybrid Decision Making Framework for Comparing Market Performance of Metaverse Crypto Assets

With proliferation of smartphone usage and rapidly increasing internet access, the financial sectors has been witnessing a transformational change in the last few years, leading to the digital economy. The cryptocurrencies (digital assets) and the metaverse assets that use blockchain technology extensively have emerged as an attractive investment option to the investors. The review of the extant literature shows a scantiness of research in comparing the performance of metaverse crypto assets based on market indicators. In this regard, the current work aims to provide a framework to compare the metaverse crypto assets based on their market performance. To this end, the present paper proposes a novel hybrid framework such as Logarithmic Percentage Change driven Compromise Solution based Appraisal (LOPCCSA). To compare the alternatives the variables like return, momentum of the daily closing price, market capitalization, trading volume and risk (in terms of historical volatility realized over the study period 2022). From the result we observe that the momentum of the closing prices and volatility of the price movements hold the higher importance as derived by calculation of objective weights. Theta Network (A10) comes out as the top performer ahead of ApeCoin (A1) or MANA (A3) or Internet Computer (A6). The comparison of the results obtained by LOPCCSA with other MCDM models show considerable consistency. The sensitivity analysis indicates that LOPCCSA provides a stable solution. The present paper is a distinct attempt that shall attract the decision makers and investors to carry out a deeper and comprehensive analysis of the metaverse crypto assets in near future.

Pembinaan Literasi Baca Pada Anak-Anak dengan Metode CABACA di Desa Kanrung Kec. Sinjai Tengah Kab. Sinjai

Education makes a person get information and also community members, especially elementary school children, are future assets. Therefore, it is necessary to develop reading and writing. One way to carry out reading and writing coaching is to use the CABACA book media. The purpose of using CABACA media is to make it easier for students to recognize letters, spell words, read words, and read sentences. The purpose of this training is to reduce the illiteracy of students in Kanrung Village, especially students at SDN No 213 Sabbang by coaching learning to read and to write in school. The type of CABACA method used is phenomenological research with a qualitative approach located in Kanrung Village, Central Sinjai District, Sinjai Regency, while the data collection techniques used were interviews, observation and documentation. The results of fostering learning to read and write using CABACA media can reduce illiteracy and improve the reading ability of students in Kanrung Village.