Investment Risk Management Research Articles

FINANCIAL INCLUSION (FI): KINERJA UMKM DI INDONESIA DARI PUBLISH OR PERISH

Purpose: This study aims to analyze the financial inclusion of Micro, Small, and Medium Enterprises (MSMEs) that are currently supporting economic growth in Indonesia. Design/Methodology/Approach: Systematic Literature Review (SLR) is a method in this observation by reviewing certain topics of discussion that focus on specific problems that have been identified and classified in a structured manner, assessed, concluded, and selected based on previously determined benchmarks based on evidence and facts from quality research that is relevant to the research question. Research data was obtained from Google Scholar for the period 2011-2022 based on the initial search, there were 198 articles, and only 25 articles were used as data sources and will be studied in more depth. Findings: The results of the research show that through investment diversification, risk management, and wise financial planning, individuals and companies can reduce the negative impact of economic impacts and maintain their financial stability. Research Implications: Insulation can help MSMEs protect their businesses from market shocks, interest rate changes, or other external impacts that can be detrimental. Financial inclusion after the economic crisis that occurred in 2008 hurt financial stability in Indonesia.

Research on gold price prediction based on ARIMA model

Gold is one of the most prevalent currencies in the world and its price has a very strong influence in the global financial markets. Gold has safe-haven properties, which can have a significant impact on its demand and price, especially in times of social unrest or financial crisis. Now, the demand for gold by investors has increased dramatically. Therefore, being able to accurately predict the direction of the gold price can help investors to effectively develop investment strategies and risk management measures. The overall objective of this study is to forecast the price of gold futures for the next six months. In this study, the Kaggle website was searched to find the price of gold from 2020 to 2024 and finally the CLOSE price was chosen as the final predicted price. This paper uses the ARIMA model for gold price forecasting. By comparing the RSME size of each model, ARIMA (1, 1, 2) is finally chosen. From the prediction results the price of gold remains stable in the first half of the year and then increases significantly. From the results of the residual test, there is no autocorrelation, and then it is white noise.

Analyzing the Selective Stock Price Index Using Fractionally Integrated and Heteroskedastic Models

Stock market indices are important tools to measure and compare stock market performance. The Selective Stock Price (SSP) index reflects fluctuations in a set value of financial instruments of Santiago de Chile’s stock exchange. Stock indices also reflect volatility linked to high uncertainty or potential investment risk. However, economic shocks are altering volatility. Evidence of long memory in SSP time series also exists, which implies long-term persistence. In this paper, we studied the volatility of SSP time series from January 2010 to September 2023 using fractionally heteroskedastic models. We considered the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) innovations—the ARFIMA-GARCH model—for SSP log returns, and the fractionally integrated GARCH, or FIGARCH model, was compared with a classical GARCH one. The results show that the ARFIMA-GARCH model performs best in terms of volatility fit and predictive quality. This model allows us to obtain a better understanding of the observed volatility and its behavior, which contributes to more effective investment risk management in the stock market. Moreover, the proposed model detects the influence volatility increments of the SSP index linked to external factors that impact the economic outlook, such as China’s economic slowdown in 2012 and the subprime crisis in 2008.

PREDICTIVE ANALYSIS OF CRYPTOCURRENCY INCOME USING MACHINE LEARNING MODELS

This research explores the potential of various machine learning models to predict cryptocurrency income. With the increasing volatility and widespread use of cryptocurrencies, accurately forecasting income from these digital assets is crucial for investors and stakeholders. By examining a dataset of cryptocurrency transactions, this study addresses the challenge of outliers and assesses the correlations between different variables to ensure robust data analysis. We employ three distinct machine learning models—Linear Regression, Decision Tree Regressor, and Random Forest Regressor—to train and evaluate the dataset. Each model is analysed to calculate performance metrics, such as Mean Squared Error (MSE) and R2 Score, which provide insights into their predictive accuracy and reliability. The detailed analysis and discussion highlight the strengths and weaknesses of each approach, emphasizing the efficiency and accuracy of the models in forecasting cryptocurrency income. The Linear Regression model, known for its simplicity, is compared against more complex models like Decision Tree and Random Forest Regressors, which can capture nonlinear relationships and interactions between variables. The Random Forest Regressor, an ensemble learning method, is particularly noted for its superior performance in handling complex datasets and providing more accurate predictions. This study's findings offer valuable insights for developing more effective investment strategies and risk management practices in the rapidly evolving cryptocurrency market. Through this comprehensive evaluation, the research aims to contribute to the growing body of knowledge on financial forecasting using machine learning techniques, paving the way for future advancements in this field.

Next step for bitcoin: Confluence of technical indicators and machine learning

Cryptocurrencies are quickly becoming a key tool in investment decisions. The volatile nature of bitcoin prices has spurred the demand for robust predictive models. The primary objective of this study is to evaluate and compare the effectiveness of different machine learning models with the combination of technical indicators in predicting bitcoin prices. The study used 27 critical technical indicators to evaluate four machine learning techniques, namely Artificial Neural Network (ANN), a Hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM), Support Vector Machine (SVM), and Random Forest. The results showed that ANN and SVM achieve a significant prediction accuracy of 81% and 82%, respectively, which is higher than the results of traditional models such as standard ARIMA. In practical applications, these methods often improve prediction accuracy by 20-30% over traditional models. The novelty of the analysis lies in the use of temporal and spatial trends via momentum, ROC, and %K features, making for a holistic approach to cryptocurrency market forecasting. This study underscores the critical importance of specific technical indicators and the imperative role of data mining in revolutionizing cryptocurrency market navigation. The research results highlight opportunities to improve investment strategies and risk management policies in the bitcoin market using machine learning models, making the latter valuable to investors and financial experts.

Energy-Related Uncertainty and Idiosyncratic Return Volatility: Implications for Sustainable Investment Strategies in Chinese Firms

This study examines the impact of energy-related uncertainty on idiosyncratic volatility (IVOL) in Chinese firms, leveraging data from the Shanghai and Shenzhen stock exchanges between 2007 and 2022. Utilizing the Energy-Related Uncertainty Index (EUI) and the Fama–French five-factor model, we analyze a comprehensive dataset of 20,998 firm-year observations to understand how macroeconomic uncertainties specific to the energy sector influence firm-specific risk. Our findings reveal that a one-unit increase in the EUI is associated with a 5.1% rise in idiosyncratic volatility across all firms, underscoring the significant impact of energy-related uncertainty on firm-specific risks. The effect is more pronounced in energy-related firms, where a one-unit increase in the EUI leads to a 6.4% increase in IVOL, compared to a 3.7% increase in non-energy-related firms. By incorporating industry-wise, heterogeneity, and phase-based analyses, our findings reveal significant variations in the EUI’s impact across energy and non-energy sectors. State-owned enterprises, firms with high ownership concentration, and smaller firms are more vulnerable to energy uncertainties. Additionally, the effect of the EUI on IVOL is more pronounced during periods of high uncertainty. These insights have important implications for sustainable investment strategies, risk management, and policymaking, providing a deeper understanding of the intricate dynamics of energy markets in fostering sustainable economic growth and development.

Influence of Big Data Analytics on Decision-Making Processes in Financial Firms in South Africa

Purpose: The aim of the study was to assess the influence of big data analytics on decision-making processes in financial firms in South Africa. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: By harnessing vast volumes of structured and unstructured data, these firms can derive valuable insights into market trends, customer behavior, and risk assessment with unprecedented accuracy and speed. This capability enables more informed and data-driven decision-making across various facets of financial operations, including investment strategies, risk management, and customer relationship management. Moreover, big data analytics facilitates the identification of previously hidden patterns and correlations within financial data, enhancing predictive modeling and forecasting capabilities. This proactive approach allows firms to anticipate market shifts, optimize portfolio performance, and mitigate potential risks effectively. Additionally, the adoption of advanced analytics techniques, such as machine learning and artificial intelligence, further enhances decision-making by automating processes and refining predictive accuracy. Implications to Theory, Practice and Policy: Diffusion of innovation theory, technology acceptance model and resource-based view may be used to anchor future studies on assessing influence of big data analytics on decision-making processes in financial firms in South Africa. In practice, financial firms should establish robust data governance frameworks to ensure data accuracy, privacy, and security. Policymakers should develop and enforce regulatory frameworks that support the ethical and effective use of BDA in financial firms.

Research on the Mechanism and Strategy of Equity Investment Driving the Transformation of New Material Innovation Achievements

Under the wave of global technological revolution, the new materials industry is facing difficulties in innovation and transformation. Equity investment has become a key driving force due to its flexible mechanism, risk sharing, and deep participation. Based on introducing the current development status of China’s new materials industry, this article reveals the mechanism of equity investment in financial support, risk management, incentive constraints, and resource integration. The article proposes optimization strategies, such as building an information-sharing mechanism and a diversified investment fund system, to promote the high-quality development of the new materials industry driven by innovation and provide strategic guidance for policymakers, investors, and enterprises.

Exploring the nexus between ESG risk variations and investment preferences: Insights from sustainable ETFs during the COVID-19 era

This study analyzes the impact of Environmental, Social, and Governance (ESG) risk metrics on the financial performance of ETFs in the US and Europe from January 2020 to December 2023, particularly during the COVID-19 pandemic. It explores the influence of ESG investment trends on ETF performance and investor preferences between equities and bonds in response to ESG risk profiles. Results show a positive correlation between higher ESG standards and financial performance, measured by the Sharpe ratio, with a marked preference for bonds amidst increased ESG risks. This highlights the critical role of ESG considerations in investment strategies and risk management, providing insights for sustainable finance.

AI-enabled FinTech for innovative sustainability: promoting organizational sustainability practices in digital accounting and finance

Purpose The purpose of this study is to delve into the intricate terrain of assimilating sustainability practices into digital accounting and finance, centring on the transformative dynamics introduced by artificial intelligence (AI)-enabled FinTech. The primary objective is to scrutinize critical lacunae in existing literature, exploring how organizations can meticulously construct comprehensive sustainability frameworks. Simultaneously, the study investigates the protracted repercussions of AI-enabled FinTech on the enduring sustainability paradigms. Design/methodology/approach Executing a systematic literature review, the research engaged in the meticulous identification and assessment of a voluminous pool of 1,158 articles. Using a judicious two-phase strategy, the scrutiny distilled a mere 64 pertinent articles, subjecting them to rigorous evaluation encompassing methodologies, contributions and overall quality. The Fuzzy Delphi method was used to elicit expert opinions and facilitate consensus-building, leveraging fuzzy logic to accommodate uncertainties in the data. Findings The review navigates the convoluted impact of AI across diverse sectors, accentuating its transformative imprint on realms such as health care, finance and transportation. Specifically, in the financial domain, the discerning eye of AI-enabled FinTech optimizes investment portfolios, augments risk assessment, propels financial inclusion and streamlines the intricate landscape of sustainability reporting. The study meticulously pinpoints research gaps encompassing investment optimization, risk management, financial inclusion, sustainability reporting and ethical considerations within the intricate milieu of AI-enabled FinTech. This research contributes to the existing body of knowledge by synthesizing intricate thematic strands, discerning overarching trends and spotlighting critical voids in the synthesis of sustainability practices and AI-enabled FinTech. The findings resonate with far-reaching implications, emphasizing the exigency of comprehensive investigations into the longitudinal sustainability ramifications instigated by AI-enabled FinTech. Originality/value The study underscores the imperative of crafting robust ethical frameworks for the equitable and transparent deployment of AI solutions within the intricate landscape of FinTech. Moreover, this research stands poised to shape organizational strategies, inform regulatory frameworks and guide investment decisions, thereby catalyzing the cultivation of conscientious and sustainable financial practices.

Multiscale quantile dependence between China's green bond and green equity: Fresh evidence from higher-order moment perspective

This study investigates the multiscale quantile dependence between green bond and green equity in China at different moments and investment horizons. A novel Ensemble Empirical Mode Decomposition based Copula Quantile-on-Quantile Regression model (EC-QQR) has been proposed to quantify the multiscale quantile dependence between two green finance markets for different moments. The empirical findings reveal that, first, green bonds have positive correlations with green equities at kurtosis and negative correlations at skewness. Second, the “tail effect” can be found in extreme quantiles for most short- and medium-terms. Third, the rolling window analysis provides evidence of dynamic dependence, especially for the kurtosis moment in the short term, and interdependence is susceptible to major events such as the Sino-US trade war and COVID-19. These findings have implications for investment strategies and risk management in the green finance markets.

Residential building and sub-building level flood damage analysis using simple and complex models

Flood damage assessment is critical for optimal risk management investments. Damage models evaluate physical damage or monetary loss from direct building exposure to flood hazard processes. Traditional models represent a simple relationship whereby physical damage increases with water depth. More complex models offer an improved understanding of vulnerability, analysing interactions between multiple hazard and exposure variables that drive damage. Our study investigates whether increasing model complexity and explanatory damage variables improves prediction precision and reliability at residential building and sub-building (component) levels. We evaluate simple and complex empirical univariable and multivariable models for flood damage prediction. The Random Forest algorithm learned on multiple hazard and exposure explanatory variables outperformed linear and non-linear univariable regression approaches. Random Forest model predictive precision was highest when learning was limited to water depth and several important explanatory damage variables (flow velocity, area and floor height). Component damage models demonstrated high predictive precision for internal finishes and services. Precision reduced for structure and external finishes as damage samples for model learning were limited. High performing but complex multivariable models require further spatio-temporal transfer investigation to determine opportunities for accurate and reliable object-specific flood damage prediction in data scarce locations.

Comparing ChatGPT And LSTM In Predicting Changes In Quarterly Financial Metrics

In the financial industry, the ability to predict financial metrics accurately and in a timely manner can significantly impact investment decisions, risk management, and strategic planning. In recent years, machine learning has emerged as a powerful tool for such predictions. This study aims to explore the heretofore underexplored predictive potential of ChatGPT by predicting positive or negative changes in quarterly financial metrics, such as revenue and sales numbers, using textual data from social media. The performance of ChatGPT is compared against Long Short-Term Memory (LSTM) neural network models developed as part of this research. The methodology involves preprocessing large datasets from Twitter concerning major companies such as Amazon, Google, and Tesla, training LSTM models, and prompt engineering for ChatGPT-based predictions. Initial findings indicate that LSTM models can predict quarterly financial metric changes with up to 87% accuracy, significantly outperforming ChatGPT, which achieves a maximum accuracy of 67%. Therefore, at the current time, ChatGPT cannot be considered a reliable predictive tool for changes in quarterly financial metrics.

Risk Assessment of Highway Infrastructure REITs Projects Based on the DEMATEL—ISM Approach

Highway infrastructure projects financed by real estate investment trusts (REITs) have garnered significant attention due to their potential for generating stable returns. However, assessing the risks associated with such projects remains a critical challenge. This study addresses this gap by proposing a novel approach to risk assessments utilizing the Decision-Making Trial and Evaluation Laboratory (DEMATEL) integrated with Interpretive Structural Modeling (ISM). Unlike traditional risk assessment methods, which often treat risk factors as isolated entities, the DEMATEL—ISM approach provides a comprehensive framework that reveals the direct and indirect relationships between risk factors, thus offering a systemic understanding of the interactions. The results found that falling market prices, insufficient operating revenues, and operational inefficiencies were direct influences, and high financing costs and force majeure were the fundamental influences, while other factors acted as intermediate influences. In addition, the study identifies direct and indirect relationships between different risk factors, providing a hierarchical topology of risk factors to reflect the relationships between the risk factors. The findings not only contribute to enhancing the understanding of risk dynamics in highway infrastructure REITs projects, but also offer a structured framework for risk assessment and mitigation. This research represents a significant innovation in the field of infrastructure investment risk management, offering practical implications for project planning, decision-making, and risk management strategies.

Application of Financial Mathematical Models Combined with Root Algorithms in Finance

Investors in the financial markets must deal with various hazards, for which they must create prudent investment portfolios and risk management plans. A multi-objective optimisation approach is proposed using the root algorithm to create a multi-objective root system growth model based on many clusters. An investment risk management optimisation model based on root system growth is built into the study using distributed decision-making. To create a multi-objective root algorithm-based portfolio optimisation model, the Markowitz mean-variance model and a multi-objective root algorithm are employed. Accordingto the findings, the multi-group multi-objective root system method has a real Pareto frontier solution that is more accessible, has a faster convergence rate, and has lower fitness values. The root algorithm’s solutions are workable, and the final risk values of 0.0105, 0.0082, and 0.4623 for the 2, 4, and 6 investment objectives are all in the low-risk class range. The optimal set of solutions discovered by the algorithm had better distributivity and convergence. The Hypervolume values for the multi-group multi-objective root algorithm were 5.5298 and 3.9628 for the dual-objective portfolio and the tri-objective portfolio of investment return costs, respectively. The findings of this study can guide the development of portfolio and risk management strategies.

Measures for Investment Risk Management of Enterprise Technological Innovation Projects

Enterprise technological innovation project investment risk management refers to the enterprise taking a series of measures to manage and control possible investment risks when making technological innovation project investment decisions. Investment risks in technological innovation projects refer to various risks that may have a negative impact on investment due to the uncertainty of technological innovation and changes in the market environment. This article, from an industry professional perspective, analyzes and discusses measures for investment risk management of corporate technological innovation projects.

LLMs and NLP Models in Cryptocurrency Sentiment Analysis: A Comparative Classification Study

Cryptocurrencies are becoming increasingly prominent in financial investments, with more investors diversifying their portfolios and individuals drawn to their ease of use and decentralized financial opportunities. However, this accessibility also brings significant risks and rewards, often influenced by news and the sentiments of crypto investors, known as crypto signals. This paper explores the capabilities of large language models (LLMs) and natural language processing (NLP) models in analyzing sentiment from cryptocurrency-related news articles. We fine-tune state-of-the-art models such as GPT-4, BERT, and FinBERT for this specific task, evaluating their performance and comparing their effectiveness in sentiment classification. By leveraging these advanced techniques, we aim to enhance the understanding of sentiment dynamics in the cryptocurrency market, providing insights that can inform investment decisions and risk management strategies. The outcomes of this comparative study contribute to the broader discourse on applying advanced NLP models to cryptocurrency sentiment analysis, with implications for both academic research and practical applications in financial markets.

Investment Risk Management in Sharia Banking : A Literature Study

Investment Risk Management in Sharia Banking : A Literature Study

Extreme time-frequency connectedness across U.S. sector stock and commodity futures markets

This study investigates the extreme time-frequency return connectedness between ten U.S. sectors and commodities from January 2014 to May 2023. Using quantile time-frequency measures, we find the following: Firstly, the total connectedness estimates are more sensitive at shorter frequencies than at longer ones. Secondly, the study reveals varied degrees of contagion during crisis periods. Notably, during COVID-19 (Russia-Ukraine conflict), the contagion is driven by short-term (long-term) shocks, specifically during the bearish (bullish) phase. Thirdly, quantile connectedness measures depict intense correlations around market extremes, underlining dynamic net return-contagion with tailored risk strategies. The shifts in shock transmission roles during bearish and bullish scenarios, along with evolving dynamics across time-frequency horizons, emphasize substantial interconnectedness within the network. Our findings suggest limited diversification scope under extreme market conditions, informing investment decisions, risk management, and portfolio optimization.

Multi-dimensional identification of investment risks in renewable energy projects along the Belt and Road: an integrated framework based on HHM and WRT

ABSTRACT Due to the complexity of various risk factors and their interactive effects in the risk management of renewable energy project investment, this study uses Hierarchical Holographic Modelling (HHM) and WSR–RFRM–TSS (WRT) methods to construct a risk assessment model for energy project investment. Firstly, we comprehensively identify the risk sources by applying HHM method, and then construct the risk assessment index framework based on the Wuli–Shili–Renli (WSR) methodology. To identify the most significant risk indicators, the risk factors are secondarily filtered by using the Risk Filtering, Ranking, and Management Framework (RFRM). Following that, multi-dimensional risk scenarios are created by incorporating the Theory of Scenario Structuring (TSS). Finally, an empirical study is conducted using the Karot Hydroelectric Project project as a case study. The results show that in the two-dimensional risk scenarios, the highest risk value is the coupling of political stability risk and institutional imperfection risk. In the three-dimensional risk scenarios, the highest risk value is the coupling of political stability risk, institutional imperfection risk and communication barrier risk. The risk values of two-dimensional risk scenarios are generally higher than those of one-dimensional risk scenarios and three-dimensional risk scenarios.