Reduce Investment Risk Research Articles

Empirical Study on BYD Stock Price Prediction Based on LSTM Model

This study employs Long Short-Term Memory (LSTM) neural network models for stock price prediction. LSTM models excel in managing nonlinearities and time-series dependencies in data, thereby enhancing the accuracy of stock price forecasts, reducing investment risks, and increasing returns. Investors can reasonably use the LSTM model to predict stock prices and obtain greater returns. The research utilizes eight years of historical data from BYD, including opening prices, closing prices, highest prices, lowest prices, and trading volumes, as training data. By adjusting various hyperparameters such as epochs, learning rates, and training-test ratios, the study analyzes their impact on the predictive accuracy of the model and identifies optimal configurations through comparative experimental methods. We found that the best parameters were when epoch number was 25, training ratio was 90:10 (minimum mean square error was 0.00175572) and 4 years of training data was selected as the training set (minimum mean square error was 0.00146477).

Research on Quantitative Investment Strategies of Securities Investment Funds

This paper investigates the quantitative investment strategies of securities investment funds, aiming to explore their applications in financial markets and their impact on investment decision-making. Through a review of relevant literature and empirical data, the paper first outlines the basic concepts of quantitative investing and its importance in securities investment funds. Then, it discusses various quantitative strategies, including multi-factor models and machine learning algorithms, and validates their effectiveness through case studies. The research finds that quantitative investing can enhance the scientific and accurate nature of investment decisions while effectively reducing investment risks. Finally, the paper provides suggestions for optimizing quantitative strategies and discusses future trends, offering practical references for the investment industry.

The financialisation of contemporary urban planning policy discourse: insights into Serbian and Irish planning legislation

ABSTRACT While existing literature highlights the role of private real estate interests in shaping urban policy agendas, few studies have examined formal changes in planning legislation as a key site of influence. This paper investigates planning legislation reforms in Serbia and Ireland as illustrative cases of a broader trend of reducing investment risks and enhancing the financial viability of real estate development. Employing critical and context-sensitive narrative analysis of policy change, we present three key insights. First, we reveal how planning has been transformed into a tool for creating real estate assets driven primarily by financial considerations. Second, we demonstrate that public interest is often subordinated to private interests under a policy narrative dominated by financial sector logic and document how legislative and policy changes frequently introduce new barriers that restrict democratic engagement in planning policy formation. Third, we identify an increasing centralization in planning administrative structures, which undermines the principle of subsidiarity and provides opportunities for certain lobby groups to exert undue influence over policymakers. As the financial sector's influence on built environments grows globally, this study calls for more research into how real estate finance and planning systems affect each other, and how this plays out in practice.

Bitcoin's Effect on Selected Crypto Assets: Quantitative Evidence

The main objective of the study is to systematically investigate the effects of Bitcoin on specific crypto assets. In this context, a sample of 135 observations between 2020W01 and 2022W33 for seven crypto assets with the highest value in the financial markets, including Ethereum, Tether, USD Coin, BNB, XRP, and Cardano, has been included in the study sample. These crypto assets, along with Bitcoin, constitute almost 95% of the total crypto asset portfolio. This situation increases the importance of representing a broad universe in the study and contributes to the academic literature. To examine the effects of Bitcoin on the selected crypto assets, a Multivariate Dynamic VAR Model has been used. The model assumes causality among variables. Variables that do not meet this assumption have been excluded from the study sample. The results of the study demonstrate that Bitcoin exhibits significant and one-way effects on all cryptocurrencies. However, the impact of other crypto assets on Bitcoin has been negligible. In this context, it is emphasized that investment decisions obtained from two-way evaluations may be risky and misleading. In order to reduce investment risks for crypto assets to more acceptable levels, the use of comprehensive analysis methods is recommended.

The asymmetric correlation and causality between the European Union carbon market and fossil energy markets caused by COVID-19

The carbon and fossil energy markets have been significantly affected by the COVID-19 pandemic. This article examines the nonlinear correlation and the spillover effect between the global fossil energy markets and the European Union carbon market prior to and following COVID-19. The nonlinear correlation is captured by the localized Gaussian correlation approach, and the spillover effect is evaluated using a quantile-based Granger causality analysis method. Empirical evidence demonstrates that COVID-19 strengthens the correlation between the carbon and oil markets. Conversely, the correlation between the carbon and coal markets is weakened by COVID-19. Furthermore, COVID-19 only resulted in the contagion between the carbon and Texas gas markets. Finally, the spillover effect between the European Union carbon market and fossil energy markets changes from unidirectional to bidirectional. This article provides inspiration for investors to adjust their portfolios to reduce investment risks and meaningful information for policymakers to maintain market stability.

Implementasi CNN-BiLSTM untuk Prediksi Harga Saham Bank Syariah di Indonesia

Stock price forecasting plays a crucial role in stock investment. Accuracy in predicting stock prices can provide significant financial benefits and help reduce investment risks. Stock price data are time series with high-frequency characteristics, non-linearity, and long memory, which makes stock price prediction a complex challenge. This research proposes a method for predicting the stock prices of Islamic banks in Indonesia using CNN-BiLSTM. This method aims to improve prediction accuracy by utilizing the feature extraction capabilities of CNN and the ability of BiLSTM to understand the temporal sequences of stock data. The data used in this research are the closing stock prices of Bank Syariah Indonesia (BSI), Bank Tabungan Pensiunan Negara Syariah (BTPN Syariah), and Bank Panin Dubai Syariah (PDSB) from January 2, 2020, to July 4, 2024. Testing these three stocks yielded MAPE values of 2.376%, 2.092%, and 0.629%, respectively. The study results show that the CNN-BiLSTM prediction model produced has very good accuracy in predicting stock prices.

The utilization of renewable energy and the economic potential of offshore wind power supported by digital finance

Colombia is well-positioned for the development of sustainable energy due to its abundance of natural resources, which include water, wind, and sun. Regulating the safe and sustainable use of offshore wind energy, which is considered non-conventional, is lacking in the nation, nonetheless. The development of offshore wind technology in Colombia shows potential to meet energy needs during dry hydrological conditions and El Niño/Southern Oscillation events when the hydroelectric system power supply is low. This study examines global initiatives that have encouraged nations to develop plans for cutting their CO2 emissions, stressing both their successes and shortcomings in putting offshore wind technology into practice. An examination of Colombia's renewable energy administrative framework finds a lack of data required to carry out offshore wind projects. Furthermore, a review of previous research on marine energy emphasizes how important it is to expand our knowledge of offshore wind generation. Although the majority of local renewable energy projects concentrate on terrestrial sources, an analysis of wind speed and wind power density in Colombia at different altitudes shows promising magnitudes and good trends.Digital finance plays a crucial role in this context by providing innovative funding mechanisms, enhancing financial accessibility, and reducing investment risks through improved financial technologies. These advancements support the mobilization of capital necessary for the development and expansion of offshore wind energy projects.As a result, the technical, economic, administrative, and legal data pertinent to renewable energy in Colombia is compiled in this study. It proposes to provide information to stakeholders involved in decision-making processes and promotes the possible installation of offshore wind farms in regions close to Colombia's Caribbean coast. Because of its plentiful resources, Colombia offers a great chance to implement offshore wind energy technology, which will lessen dependency on fossil fuels and provide a backup energy source in case of supply shortages. The integration of digital finance is key to unlocking the economic potential of these projects, ensuring sustainable and scalable energy solutions for the future.

Optimal planning of hybrid power systems under economic variables and different climatic regions: A case study of Türkiye

Renewable energy-based hybrid power systems (HPS), proposed based on demand in the transition to clean energy-indexed societies, are high-potential investments. However, cost-based optimal sizing and feasibility analyses are complicated due to unforeseen variables and do not guarantee a reliable and robust optimization framework. This study optimizes minimum-cost HPS planning for diverse loads across varied climates and economic conditions, providing holistic comparisons of technical, financial, and environmental viability. According to the analysis, fluctuating economic structure emphasizes that unsubsidized hybrid power system installations are inefficient, the severe imbalance between inflation and interest rates limits the benefits of hybrid power systems and creates an uncertain investment environment for stakeholders. The gradual increase in interest rates to limit inflation has created a more viable renewable energy investment environment, while payback periods are reduced to 7.21 years. Even under identical economic conditions, the renewable fraction of energy in regions with high solar potential can be up to 25 % higher than in regions with less potential. Moreover, payback periods can vary up to 6 years, depending on the variability in solar generation and load profiles. Considering the significant impact of economic uncertainty on HPS investments, optimization plans that reduce investment risks will be helpful to stakeholders.

Role of power-to-heat and thermal energy storage in decarbonization of district heating

The sector integration of the heat and electricity sectors promotes the flexibility of the energy system. This, together with the already almost carbon dioxide-free electricity supply in Finland, facilitates the environmental transition to a carbon-neutral heating system. In this study, pathways to decarbonize the district heating system in Åland (an autonomous region of Finland) are explored. Especially, the roles of different power-to-heat technologies, e.g., heat pumps and direct electric heating, and thermal energy storage are investigated in the decarbonization of the district heating system. The focus is on studying the impact of varying prices in electricity markets on the operation of climate-neutral district heating systems. For this, three scenarios were modeled over the five years of 2018–2022 with varying demand and electricity market prices by using the modeling software PLEXOS. The study showed that electricity prices, but also reserve market prices, have a very significant impact on the operation cost and profit of a district heating system. Overall, electrification has the strong potential to reduce emissions cost-effectively in a district heating system, but it also makes the system vulnerable to electricity price volatility. It can be concluded that thermal energy storage and reserve market participation are crucial for protecting against cost increases and reducing investment risk.

Fiscal Policy to Encourage Investment in the Marine Logistics Sector

The marine logistics sector plays a crucial role in a country's economic growth, particularly for archipelagic nations like Indonesia. However, investment levels in this sector remain low, which can hinder potential economic growth. This research aims to analyze various fiscal policies that can encourage investment in the marine logistics sector. The methods used in this research are qualitative and quantitative descriptive analysis. The results indicate that fiscal policies such as tax incentives, subsidies, and government funds allocated for maritime infrastructure development can increase the attractiveness of investment in this sector. Tax incentives, such as reductions in corporate income tax rates, exemptions or reductions in import taxes for equipment, and tax allowance facilities, can be effective instruments to attract investment. Subsidies, such as capital assistance, low-interest loans, and operational cost compensation, can also help reduce investment risks and encourage investor participation. Additionally, adequate allocation of government funds for the development of maritime infrastructure, such as ports, docks, and other logistics facilities, is crucial for enhancing the sector's efficiency and competitiveness. However, fiscal policies alone are not enough. Improved regulations and better law enforcement are also necessary to create a conducive investment climate in the marine logistics sector.

Multi-stage layout strategies of intercity charging and swapping infrastructure for electric logistics vehicles: random, radical, or conservative?

The arrangement of charging infrastructure has become an urgent issue as electric logistics vehicles (ELVs) transition from urban areas to intercity operations. In this study, we established a multi-stage layout model for intercity charging and swapping infrastructure (CSI) based on the travel characteristics of electric logistics vehicles, to minimize the investment cost of operators and the time cost of vehicle users in Hubei Province, China. Furthermore, the effect of “random”, “conservative”, and “radical” layout strategies for the infrastructure deployment was analyzed based on an improved genetic algorithm. It is found that the random layout strategy leads to a balance between infrastructure operators and users with the lowest total cost, which is 81.2% and 88.8% of the costs based on the radical strategy and the conservative strategy, respectively. The conservative strategy could reduce investment risks for operators with the lowest investment cost, while the radical strategy prioritizes the convenience of electric logistics vehicles, with the lowest user time cost. The case study suggests that choosing conservative strategies can bring benefits when the budget is limited, while radical strategies have more advantages when the budget is abundant. Random strategies bring the best overall social benefits. The configuration of CSI and travel characteristics of ELVs play important roles in determining layout strategies.

Development of the best investment strategy for insurance companies based on ARIMA-SVM

The purpose of this article is to develop the best investment strategy for insurance companies in the context of extreme weather, aiming to improve the profitability of insurance companies and reduce investment risks. This research focuses on Asia and North America, and constructs an ARIMA model to accurately predict the frequency of extreme weather in the next decade. At the same time, a comprehensive evaluation model was constructed by combining the entropy weight method to quantitatively evaluate the payment ability of local residents. On this basis, the SVM classification model was established to predict whether the insurance company should be insured in a certain place, and the performance of the model was deeply analyzed, and the recall rate and accuracy of the classification model were 0.93 and 0.94, respectively, indicating that the model had good reliability. After a series of rigorous model analysis and data validation, it was finally concluded that China in Asia is the best choice for insurance companies. This strategy not only helps insurers optimize resource allocation, but also achieves stable and sustainable development in the context of complex and volatile extreme weather.

The Path of Empowering Digital Industry with Green Finance for Green and Low Carbon Development

Green and low-carbon development is a consensus among countries around the world on the development of the digital industry. The digital industry, as a pillar industry of China's economy, is a high energy consuming and carbon emitting industry. Therefore, the green and low-carbon transformation of the digital industry is of great significance for China to achieve its "dual carbon" goals. The achievement of the "dual carbon" goal relies on the support of financial services, and green finance plays three roles in guiding resource allocation, market price discovery, and risk management in its development process, mainly including financial products such as green credit, green bonds, green funds, and carbon markets. By building a green financial system that meets the requirements of low-carbon transformation, exploring new growth opportunities in the process of low-carbon transformation, taking precautions, using green finance, enterprise information environment disclosure and other means to guide the rational allocation of funds, using market means for low-carbon investment, reducing investment risks in the process of green transformation, and promoting low-carbon transformation of the digital industry.

Analyzing the connectedness among geopolitical risk, traditional energy and carbon markets

This paper studies time-frequency connectedness using the Diebold Yilmaz method and the Baruník and Křehlík method to examine the relationship among geopolitical risk, traditional energy (coal, oil and gas) and carbon markets. First, geopolitical risk shows stable positive net spillovers in the medium and long term, but fluctuates in the short term. Second, the total connectedness is greater in the short term, and strengthened in the period of rising geopolitical risk with the changes of connectedness paths. Third, in general, the carbon market has greater impact on the geopolitical risk than it has been affected. Finally, as a sub-indicator of geopolitical risk index, geopolitical acts index generally contributes more, but natural gas is more affected by geopolitical threats index. Our results provide useful knowledge for governments to realize their goal of policymaking, and help industrial enterprises make timely adjustments to their energy strategies. In addition, we hope that investors can pay attention to the spillover effect and reduce investment risk.

Data Visualization and Machine Learning Trend Analysis Forecasting Based on Roblox Stock Data

The stock market, as a highly complex, unstable and dynamically changing system, is affected by a wide range of factors, such as political, economic, social and technological, in terms of price changes. Therefore, stock price prediction is a challenging problem, but also a problem of great importance. In order to provide a more accurate and reliable method for stock closing price prediction, this paper aims to reduce investment risk and improve investment returns by comparing the prediction effects of different regression models for investors to make investment strategies and decisions. In this paper, Open, High, Low, and Adj Close are used as input parameters, Close is used as the target parameter, the final closing price of the stock is determined from the various stock data, the training set, validation set, and test set are divided in accordance with 6:2:2, and the predictive effect of the model is evaluated using the parameters of MSE, RMSE, MAE, MAPE, and R2. After the comparison of multiple machine learning algorithms, XGBoost regression model and Random Forest Regression model perform the best in the prediction of stock closing price, and their MSEs are 0.061 and 0.072 respectively; followed by LightGBM regression model and decision tree regression model, the MSEs of the two models are 0.113 and 0.299 respectively, but the prediction results of BP neural network regression are poor. By comparing the prediction results of different regression models, it can provide investors with a more accurate and reliable method to predict the closing price of stocks, help investors better formulate investment strategies and decisions, reduce investment risks and improve investment returns. This is of great significance to both individual and institutional investors.

Research on Apple’s Stock Price Trend Forecasting

As the world's largest company by market capitalization, Apple has attracted the attention of many investors. Many investors have developed a strong interest in Apple stocks. However, it is not easy to study the trend of Apple stocks. Because there are many factors affecting Apple's stock price changes, it is very complicated and difficult to review the details of all these factors. Predicting and analyzing stock prices can provide investors with practical tools to raise funds and reduce investment risks. This paper starts with the time series research method of stock prices, which has proved to be a relatively effective method. Through this method, this paper can have an in-depth understanding of the fluctuation of stock prices, so as to better grasp market trends and make more informed investment decisions. By using CNN and LSTM models for prediction, the author finds that it rose by an average of 4% per trading day, and its MSE value was basically below 10, The MSE of other stocks has been optimized from more than 1900 shares predicted in less than 10 a share. This paper splits and analyzes the adjusted data to obtain more reasonable and accurate prediction results. Finally, this article aims to provide valuable reference for those who are interested in stock forecasting or studying the trend of Apple's stock price.

Can NFTs hedge the risk of traditional assets after the COVID-19 pandemic?

Our study analyzes the dynamic spillover effects between the NFT market, Bitcoin market, oil market, gold futures market, S&P 500 stock index, bond market, and US dollar index over three time periods: the full sample period (2017/6/23–2022/9/2), before the COVID-19 outbreak (2017/6/23–2020/1/12), and after the COVID-19 outbreak (2020/1/13–2022/9/2) by employing the DCC-GARCH-based connectedness model. Furthermore, we apply the DCC-GARCH-t-Copula model, Risk Parity Portfolio (RPP) model, and Minimum Connectedness Portfolio (MCoP) model to evaluate the bilateral dynamic hedge ratios, portfolio weights, and the multivariate portfolio performance of these financial assets, respectively. Our findings suggest that NFTs are a source of volatility spillovers during both periods and that the COVID-19 outbreak accelerates the speed and intensity of volatility spillovers from NFTs to traditional financial markets. Moreover, most of the volatility spillovers from NFTs are caused by own shocks, which implies that NFTs are not susceptible to financial risk contagion. The results of bilateral portfolio analysis show that NFTs can effectively hedge other traditional financial assets when used as long positions. The results of multivariate portfolio analysis demonstrate that NFTs can also reduce investment risk despite their smaller weighting. In the two months after the COVID-19 outbreak, MCoP slightly outperforms RPP, although RPP outperforms MCoP in general.

Can Perturbations Help Reduce Investment Risks? Risk-aware Stock Recommendation via Split Variational Adversarial Training

In the stock market, a successful investment requires a good balance between profits and risks. Based on the learning to rank paradigm, stock recommendation has been widely studied in quantitative finance to recommend stocks with higher return ratios for investors. Despite the efforts to make profits, many existing recommendation approaches still have some limitations in risk control, which may lead to intolerable paper losses in practical stock investing. To effectively reduce risks, we draw inspiration from adversarial learning and propose a novel Split Variational Adversarial Training (SVAT) method for risk-aware stock recommendation. Essentially, SVAT encourages the stock model to be sensitive to adversarial perturbations of risky stock examples and enhances the model’s risk awareness by learning from perturbations. To generate representative adversarial examples as risk indicators, we devise a variational perturbation generator to model diverse risk factors. Particularly, the variational architecture enables our method to provide a rough risk quantification for investors, showing an additional advantage of interpretability. Experiments on several real-world stock market datasets demonstrate the superiority of our SVAT method. By lowering the volatility of the stock-recommendation model, SVAT effectively reduces investment risks and outperforms state-of-the-art baselines by more than 30% in terms of risk-adjusted profits. All the experimental data and source code are available at https://drive.google.com/drive/folders/14AdM7WENEvIp5x5bV3zV_i4Aev21C9g6?usp=sharing .

Do natural resources rents reduce income inequality? A finite mixture of regressions approach

The World Bank report (2023) highlights that almost all types of natural capital, the global stock of resources and services provided by nature, are in decline while inequalities persist. Based on this context, the objective of this article is to analyze the effect of natural resources on income inequality in 73 developing countries over the period 2005–2020. Unlike previous works, this article examines the possibility that countries follow different inequality regimes and test the hypothesis that whether natural resources, reduce or increase income inequality depending on the inequality regime to which countries belong. We used a finite mixture regression model, and we found that our sample is best described by a model with five regimes of countries. The results show that the effect of natural resource rents on income inequality varies across five distinct regimes of countries. In regimes 1 and 2, natural resources have a positive effect on inequality, while in regimes 3 and 4, they have a negative effect on inequality. In regime 5, the effect is not significant. Furthermore, our analysis shows that countries that have low levels of political risk are likely to be in the regime where the dependence of natural resources decreases income inequality. These results clearly emphasize that for countries with abundant natural resources to fully benefit from reduced inequality, they must undertake strong reforms to reduce political risk. These reforms include improving the quality of the judicial system, intensifying the fight against corruption, and reducing investment risks.

Leveraging FinTech Compliance to Mitigate Cryptocurrency Volatility for Secure US Employee Retirement Benefits: Bitcoin ETF Case Study

The integration of cryptocurrencies, particularly Bitcoin, into retirement savings plans has recently garnered significant attention. This interest has been amplified by the U.S. Securities and Exchange Commission's approval of Bitcoin Exchange-Traded Funds (ETFs) in January 2024 and Fidelity Investments' decision to include Bitcoin in their 401(k) plans. These landmark developments represent a paradigm shift in retirement investment strategies, merging traditional financial planning with the dynamic and volatile world of cryptocurrencies. The entry of Bitcoin introduces novel challenges, including increased volatility and regulatory uncertainty, necessitating a comprehensive examination of its impacts on retirement savings. The study sought to explore the role of Financial Technology (FinTech) in managing these risks and assess the adequacy of current regulatory frameworks. Employing a quantitative research approach, the study collected data from 386 participants, including FinTech practitioners and investment portfolio managers through a survey that combining closed-ended and open-ended questions. Multiple regression was used to analyze the relationships between variables such as FinTech integration, regulatory compliance, and the risk associated with cryptocurrency-inclusive retirement portfolios. The study revealed that the inclusion of Bitcoin significantly increases portfolio volatility. It also found that advanced FinTech data management techniques significantly enhance risk mitigation, while current regulatory frameworks are inadequate for governing the inclusion of cryptocurrencies in retirement plans. A comprehensive framework combining FinTech and regulatory compliance was shown to effectively reduce investment risks. The study recommends cautious consideration of cryptocurrencies in retirement portfolios, with an emphasis on assessing the risk appetite of participants. It advocates for dynamic regulatory frameworks and enhanced use of FinTech for real-time risk management. The study suggests that retirement plan providers should adopt an integrated approach, combining technological innovations with regulatory oversight, to navigate the complexities of cryptocurrency investments effectively.