Multi-factor Model Research Articles

Analysis on the current situation and influencing factors of residents' satisfaction with the built environment of China's Hygienic City Initiative

Objective: To understand the current situation and the main influencing factors of residents' satisfaction with the built environment of China's Hygienic City Initiative. Methods: From the list of China's hygienic cities (excluding county-level cities), 61 cities were randomly selected in equal proportion and the eligible respondents were randomly selected by using the "Questionnaire Star" network platform to carry out the online questionnaire survey. A self-made satisfaction evaluation scale was used to investigate the satisfaction of the included respondents with the urban built environment and search for relevant data on the city level. The two-level multi-factor mixed effect model was constructed to analyze the influencing factors of residents' satisfaction with the built environment of China's Hygienic City Initiative. Results: The age range of 2 465 respondents was mainly between 18 and 40 years old (79.9%), with males being the main group (45.8%). The total score of residents' satisfaction with the built environment of China's hygienic cities was (69.14±13.24) points. Based on four standardized dimensions of sense of gain, the result showed that the satisfaction of urban governance had the highest score (65.08 points), followed by urban environmental sanitation (63.68 points), urban lifestyle (59.97 points) and urban basic function (59.02 points). The analysis results of the two-level multi-factor mixed effect model showed that compared with residents with an annual average concentration of inhalable fine particles in the environment>48 micrograms/cubic meter, residents with an average concentration between 38 and 48 micrograms/cubic meter [β (95%CI): 1.65 (0.08, 3.21)] and≤37 micrograms/cubic meter or less [β (95%CI): 1.98 (0.53, 3.43)] had higher satisfaction. Compared with residents whose proportion of the secondary industry to GDP was≤40.9%, residents in cities with a larger proportion had a lower satisfaction level [residents with a proportion of 40.9%-48.03%, β (95%CI):-2.21 (-3.93, -0.49); residents with a proportion greater than 48.03%, β (95%CI):-2.58 (-4.58, -0.59)]. Compared with residents with a junior high school or lower education level, residents with a higher education level had a lower satisfaction level [β (95%CI):-2.37 (-4.57, -0.17)]. Residents of universities and above [β (95%CI):-3.82 (-6.05, -1.60)], regularly participate in physical exercise [β (95%CI): 5.78 (4.71, 6.84)] and self-rated good health status [β (95%CI): 6.39 (5.33, 7.45)] had a higher satisfaction level. Conclusion: The satisfaction of residents with the built environment of China's hygienic cities is still acceptable. Satisfaction is related to individual characteristics such as residents' cultural level, type of residence, frequent participation in physical exercise, and self-rated good health status, as well as urban-level factors such as green coverage rate in built-up areas, annual average concentration of inhalable fine particles, and the proportion of GDP in the secondary industry.

Quantitative Multi-factor Stock Selection Strategy Based on Internet Service Industry

As a matter of fact, with regard to stock investment practice, quantitative strategies have become popular with the rapid development of China's capital market. Plenty of quantitative strategies have been proposed and implemented into the Chinese markets, where multi-factor stock selection is one of them. Based on the multi-factor model commonly used in quantitative investment, this article selects relevant data of the Internet service industry in the A-share market from 2018 to 2022, conducts an industry-level empirical test on 15 factors in 5 categories that are widely used, and constructs investments accordingly combination. According to the analysis, the research found that the stock portfolio constructed based on the above method can outperform the market, far exceeding the performance of the CSI 300 during the same period, indicating that the multi-factor model is still effective in the Internet service industry, and this industry has high investment value. Overall, these results shed light on guiding further exploration of quantitative analysis of stock market.

Speckle Tracking Echocardiography for the Diagnosis and Prognosis of Light Chain Cardiac Amyloidosis

Background Cardiac amyloidosis was thought to be a rare disease, but the data shows that its prevalence rate is increasing, and the light chain (AL) cardiac amyloidosis are the most common and worst [1]. Because of the lack of symptoms specificity in the preliminary stages of AL cardiac amyloidosis, resulting in underdiagnosis or severe delays in diagnosis, patients are often delayed in the optimal timing of treatment. A considerable proportion of patients are diagnosed after the onset of symptoms of heart failure which progresses rapidly and results in poor prognosis and high mortality. Patients have a mortality rate of up to 50% within 4-6 months after diagnosis of congestive heart failure and the median survival of these patients were less than 12 months [2]. Therefore, early diagnosis and treatment are particularly important for them. Speckle tracking echocardiography has a good imaging analysis of cardiac amyloidosis, which remains the most often used imaging tool. This study aimed to explore the use of speckle tracking echocardiographic parameters to build models which improve the diagnostic rate of patients with suspected AL cardiac amyloidosis and assess the prognosis of patients with AL cardiac amyloidosis. Methods We studied twenty-four patients of pathologically confirmed amyloidosis from March 2017 to February 2023 at the First Affiliated Hospital of Soochow University. According to the criteria for organ involvement, patients were divided into two groups based on whether the heart was involved or not and SPSS 26.0 was used to analyze data. The differences of characteristics and various parameters of Speckle tracking echocardiography between two groups were compared and the indicators with statistical difference were selected for single factor analysis and multivariate analysis. Statistically significant factors were used to establish diagnosis and prognosis assessment models as below. Result In this study, the rate of cardiac involvement was 62.5%. Until June 30 th, 2023, median follow-up time of twenty-four patients was 31 months, and the 5-year survival rate was 74% in CA group and 85.7% in non-CA group. Characteristics The results showed that CA group has lower baseline blood pressure, and the levels of NT-pro BNP and dFLC were significantly higher in the CA group compared to the non-CA group. In speckle tracking echocardiography parameters, CA group has significantly increased IVSd, LVPWd, RWT, E/e and decreased EF, interventricular septum e', lateral wall e' and left ventricular global strain. Diagnosis The results showed that CA group is more likely to be involved in the heart when monoclonal proteins are formed in the peripheral blood （OR=0.031, 95%CI 0.003-0.365, P=0.002）. In univariate analysis, E/e>10.85, interventricular septum e‘<5.25 cm/s, lateral wall e‘<6.05 cm/s, |left ventricular global strain|<16.3% and left ventricular diastolic function ≥Class II have the effect on CA group with statistically significant. Incorporating them into variables to construct a multivariate logistic regression model showed that: the model was built successfully (Omnibus test: P<0.001) and the goodness of fit model is well (Hosmer-Leme show test: P=1). 88.9% of the study subjects who were CA patients were predicted by the model to be CA patients, while 92.9% of the study subjects who were not CA patients were predicted by the model to be not CA patients, but the 5 factors had no significant effect on cardiac amyloidosis (P > 0.05). Prognosis assessment The result of the study shows that IVPWd>13.5mm, RWT> 0.51, NT pro-BNP>6614pg/mL and dFLC>142.35mg/L are elevated risk factors for prognosis with statistically significant. (Figure1). They were included in the variables to construct a multi-factor Cox proportional hazards model and it turns out: the model was built successfully (Omnibus test: P<0.05), but these 4 factors had no significant effect on cardiac amyloidosis (P > 0.05). Conclusion Speckle tracking echocardiography is of great value in the diagnosis and prognosis of patients with cardiac amyloidosis. The combined analysis of echocardiography parameters and serological abnormalities can help in the diagnosis and prognostic assessment of the disease, it also helps to identify patients with poor prognosis early and increasing the possibility of future treatment strategies based on risk stratification.

The spatial and temporal disaggregation models of high-accuracy vehicle emission inventory

A high-accuracy gridding vehicle emission inventory is not only the foundation for developing refined emission control strategies but a necessary input to air quality model as well. An accurate approach to the spatiotemporal disaggregation is the key step to improving the accuracy of gridding emission inventories. The existing spatial disaggregation method considers relatively fewer impact factors, lacking adequate correlation analysis among impact factors. Additionally, the existing temporal disaggregation method does not correspond with the actual travel behavior of residents. This paper proposes a multi-factor spatial disaggregation model by principal component analysis (PCAM), based on a correlation analysis of the main impact factors. Further, a new temporal disaggregation model is proposed based on the congestion delay index combined with the traffic flow fundamental model (CDITF). The results from a case study in Jinan show that the square of correlation coefficients (RSQ) between the model- disaggregated NO2 emissions based on PCAM and the monitored NO2 concentration increased by 34.4% compared to the traditional disaggregation model based on the standard road length, and the RSQ for CO increased by 13%; the NMD and NME of the simulation results based on CMAQ model compared to standard road length model decrease by approximately 33.7% and 35.5%, respectively. The trend of the monthly, daily, and hourly variations of NO2 and CO emissions disaggregated by the proposed temporal disaggregation model is quite consistent with that of the monitored concentration data. The PCAM method and the CDITF proposed in this paper are more in line with the actual situation using the cumulative emissions on road sections. The vehicle emissions in Jinan are found to be concentrated in the center of each district and county and near high-grade roads. The disaggregation results in areas with large road slopes are more realistic for considering road slope factors. The trend of the monthly, daily, and hourly variations of NO2 and CO emissions disaggregated by the proposed temporal disaggregation model is quite consistent with that of the monitored concentration data, however, the monitored concentration data presents a certain degree of time lag. The proposed spatiotemporal disaggregation model in this paper improves the accuracy of gridding vehicle emission inventory, which is of a great significance for developing precise control strategies of vehicle emissions and improving the urban air quality in general.

The value and growth effect in the Vietnamese stock market: a mispricing explanation

PurposeAlthough the value effect is comprehensively investigated in developed markets, the number of studies examining the Vietnamese stock market is limited. Hence, the first aim of this research is to provide empirical evidence regarding returns on value and growth stocks in Vietnam. The second aim is to explain abnormal returns on Vietnamese growth and value stocks using both risk-based and behavioral points of view.Design/methodology/approachFrom the risk-based explanation, the Capital Asset Pricing Model (CAPM), Fama–French three- and five-factor models are estimated. From the behavioral explanation, to construct the mispricing factor, this paper relies on the method of Rhodes-Kropf et al. (2005), one of the most popular mispricing estimations in the financial literature with numerous citations (Jaffe et al., 2020).FindingsWhile the CAPM and Fama–French multifactor models cannot capture returns on growth and value stocks, a three-factor model with the mispricing factor has done an excellent job in explaining their returns. Three out of four Fama–French mimic factors do not contain additional information on expected returns. Their risk premiums are also statistically insignificant according to the Fama–MacBeth second-stage regression. By contrast, both robustness tests prove the explanatory power of a three-factor model with mispricing. Taken together, mispricing plays an essential role in explaining returns on Vietnamese growth and value stocks, consistent with the behavioral point of view.Originality/valueThere are several value-enhancing aspects in the field of market finance. First, this paper contributes to the literature of value effect in emerging markets. While the evidence of value effect is obvious in numerous developed as well as international markets, both growth and value effects are discovered in Vietnam. Second, the explanatory power of Fama–French multifactor models is evaluated in the Vietnamese context. Finally, to the best of the author's knowledge, this is the first paper that incorporates the mispricing estimation of Rhodes-Kropf et al. (2005) into the asset pricing model in Vietnam.

Startup industry in Ukraine: a catalyst for post-war recovery and strengthening monetary transmission

This article examines the role of the startup industry in Ukraine as a catalyst for post-war economic recovery and the strengthening of monetary transmission. The study highlights the significance of supporting innovation and attracting foreign investment to foster economic growth, drawing upon the analysis of the Ukrainian startup ecosystem across diverse sectors, including defense, healthcare, and technology. To quantitatively assess the relationship between the number of startup projects and the prevailing macroeconomic conditions, the author employs a multifactor regression model incorporating variables such as the short-term bank lending rate with a lag of one, past foreign direct investments, real wage index, differences in the National Bank of Ukraine’s policy rate with a lag of two, and economic agents’ inflation expectations for the next 12 months. To approximate the number of startups, the proxy variable of registered patents and inventions is used. The analysis underscores the comparative advantages of Ukrainian startups, particularly in terms of accessing talented IT professionals, and emphasizes the need for supporting the promotion of young innovative entrepreneurs. Ultimately, the research reveals that the growth of the startup industry can contribute to the strengthening of monetary transmission and reduce dependence on traditional sectors, enhancing the economy’s resilience to external shocks. Furthermore, Ukraine possesses significant potential for developing the startup business sector, driven by the successful resistance to Russian aggression, which has led to new initiatives by war veterans in drone manufacturing, improving the technical specifications of various weapons, and integrating into the global investment landscape. This, coupled with the country’s growing reputation as a new geopolitical investment hub for foreign investors, contributes to the increasing popularity of Ukraine in the global investment space.
 JEL classification: C22, M13, O31, E52, E58, F35

PREDICTING PAKISTANS’ MUTUAL FUND PERFORMANCE: AN EVALUATION OF TRADITIONAL AND MODERN MEASURES

Mutual Funds enable small investors to benefit from the capital market with minimal investments, thanks to the expertise of professional managers. This study focuses on the Pakistani Mutual Fund industry and evaluates the suitability of traditional and multifactor asset pricing models in assessing Mutual Fund performance. Since multifactor models are rarely used in Pakistani research, this study employs the CAPM, Fama French, and Carhart models to assess the performance of Pakistan Mutual Funds. Data from 100 open-end Mutual Funds between 2005 and 2017 was collected from the Mutual Fund Association of Pakistan, while risk-free rates data was gathered from the State Bank of Pakistan and stock data from the Pakistan Stock Exchange. Ratio analysis, CAPM, Fama French-3 Factor, and Carhart-4 Factor models were used to analyze the data and determine which model was most suitable. The results indicate that the CAPM affects the market factors of the majority of portfolios. Conversely, the size factor, value factor, and momentum factor in the Fama French and Carhart models have an insignificant effect on the majority of portfolios. The Gibbon Rose Shanken test reveals that the CAPM model is the most suitable for evaluating Mutual Fund performance in Pakistan. The findings of this study have implications for asset management company managers and investors alike. It provides valuable insights into which funds perform better and which types of funds are ideal for investment.

Impact of Canopy Coverage and Morphological Characteristics of Trees in Urban Park on Summer Thermal Comfort Based on Orthogonal Experiment Design: A Case Study of Lvyin Park in Zhengzhou, China

As an integral part of urban forests, urban parks play a vital role in mitigating urban heat islands (UHI) and providing residents with comfortable outdoor recreational plots. For high-quality use of the trees in regulating the thermal comfort of urban parks, previous studies primarily focused on the microclimate variations caused by tree coverage and morphological features separately. However, there is still a lack of systematic understanding of how tree canopy coverage (TCC) and its morphological elements, including leaf area index (LAI), trunk height (TH), and crown diameter (CD), combined affect the thermal comfort in the urban park. This study employed an orthogonal experiment design and ENVI-met software to simulate the microclimate of various multi-factor combination models in the case of a typical urban park in a temperate continental climate zone in China, analyzing the simulated result through physiological equivalent temperature (PET). Results show that the contribution ratio of various elements to the thermal environment vary over time. In studied elements, the contribution ratio of TCC to PET is consistently higher than 50% during the morning, midday, and evening periods, reaching a peak of 67% in the evening. The maximum contribution ratios for CD, TH, and LAI occur during midday, morning, and midday, respectively, with corresponding contribution ratios of approximately 22%, 10%, and 9%, respectively. The ranking of elements affecting thermal comfort in the urban park generally is TCC, CD, LAI, TH throughout the day, apart from the morning, when the influence of TH is greater than LAI. The optimal combination of elements is 85% TCC, 4m TH, 3.9 LAI, and 7m CD, and thirteen combinations of element cases meet the thermal comfort requirements during summer. The research findings highlight the significance of optimizing the configuration of trees in creating a more comfortable and inviting space for human activities.

Competing-risks model for predicting the prognostic value of lymph nodes in medullary thyroid carcinoma.

Medullary thyroid carcinoma (MTC) is an infrequent form malignant tumor with a poor prognosis. Because of the influence of competitive risk, there may suffer from bias in the analysis of prognostic factors of MTC. By extracting the data of patients diagnosed with MTC registered in the Surveillance, Epidemiology, and End Results (SEER) database from 1998 to 2016, we established the Cox proportional-hazards and competing-risks model to retrospectively analyze the impact of related factors on lymph nodes statistically. A total of 2,435 patients were included in the analysis, of which 198 died of MTC. The results of the multifactor competing-risk model showed that the number of total lymph nodes (19-89), positive lymph nodes (1-10,11-75) and positive lymph node ratio (25%-53%,>54%), age (46-60,>61), chemotherapy, mode of radiotherapy (others), tumor size(2-4cm,>4cm), number of lesions greater than 1 were poor prognostic factors for MTC. For the number of total lymph nodes, unlike the multivariate Cox proportional-hazards model results, we found that it became an independent risk factor after excluding competitive risk factors. Competitive risk factors have little effect on the number of positive lymph nodes. For the proportion of positive lymph nodes, we found that after excluding competitive risk factors, the Cox proportional-hazards model overestimates its impact on prognosis. The competitive risk model is often more accurate in analyzing the effects of prognostic factors. After excluding the competitive risk, the number of lymph nodes, the number of positive and the positive proportion are the poor prognostic factors of medullary thyroid cancer, which can help clinicians more accurately evaluate the prognosis of patients with medullary thyroid cancer and provide a reference for treatment decision-making.

A stock multi-factor model based on slice inverse regression and the Bootstrap method

With the increasingly prominent position of machine learning algorithms in the field of financial quantification, the research on factor quantification and prediction models for high-frequency stock price time series has emerged as a prominent area of study. This paper presents a novel factor quantization prediction model based on slice inverse regression and Bootstrap. The proposed method effectively addresses the challenge of data dimensionality disaster and enables factor quantification while preserving the information of the original predictor variables. Additionally, the method incorporates Bootstrap technology and adopts the concept of model averaging instead of model selection, which enables the adaptive capturing of the unknown connection structure between factors and stock prices. Moreover, it effectively balances the bias and variance of individual prediction models. The empirical analysis using actual data demonstrates the superiority of the proposed method over the comparison method in terms of evaluation indicators such as mean square error, thus highlighting its robustness.

The Evaluation and Obstacle Analysis of Urban Safety Resilience Based on Multi-Factor Perspective in Beijing

Urban resilience assessment is crucial to guarantee the safe and stable operation of our cities. Looking at the whole process of emergencies from the occurrence, development, causing disaster losses to taking emergency response measures, we combine the danger of urban accident and disaster, the capacity of disaster-bearing carriers, and the emergency management capacity as the influencing factors. Taking Beijing as an example, considering the completeness of the relevant indicators and the difficulty of obtaining data, TOPSIS is used to construct a multi-factor comprehensive assessment model of urban safety resilience; At the same time, the important factors constraining the improvement of safety resilience are further analyzed by using the obstacle degree model. The results show that: among the 16 districts in Beijing, Chaoyang and Haidian districts have the highest level of safety resilience; the level of the degree of disaster tolerance among the multi-factors has the greatest impact on the level of urban safety resilience in Beijing; and the multifactorial comprehensive assessment model of urban resilience is able to reflect the urban resilience distribution, discover the weaknesses that exist in the development of urban safety and provide decision-making aids for the relevant city managers to carry out urban resilience construction.

Mediating effects of obesity and metabolic factors in hyperuricemia and prehypertension

Objective: To assess the mediating effects of obesity and metabolic factors in the relationship between hyperuricemia (HUA) and prehypertension. Methods: A total of 9 399 individuals were selected using a multistage stratified whole-group random sampling method from 90 villages (neighborhood committees) in 30 towns (streets) of 5 districts (counties) in Fuzhou. A total of 4 754 study subjects were included. A linear regression model was used to analyze the association of HUA with obesity and metabolic factors. Single-factor and multi-factor logistic regression models were used to analyze the association of HUA, obesity, and metabolic factors with prehypertension. Mediating effects models were used to analyze the mediating effects of obesity and metabolic factors on the association between HUA and prehypertension. Results: After adjusting for confounders, the association between HUA and cholesterol, triglycerides, HDL-C, LDL-C, BMI, waist circumference, creatinine, and urea nitrogen were significantly correlated (P<0.001). HUA, waist circumference, BMI, and triglycerides were significantly associated with prehypertension (P<0.001). Waist circumference, BMI, and triglycerides mediated the relationship between HUA and prehypertension, with OR (95%CI) of 1.018 (1.007-1.027), 1.010 (1.002-1.018), and 1.010 (1.003-1.017) (P<0.001), with mediating proportions of 7.76%, 4.31%, and 4.31% respectively. No mediating effect of cholesterol, HDL-C, LDL-C, creatinine, and urea nitrogen was found on the relationship (P>0.05). Conclusions: Waist circumference, BMI, and triglycerides all had mediating effects in the association between HUA and prehypertension. For the general population, weight control, waist circumference, and a high-fat diet should be used to reduce the occurrence of prehypertension.

A - 3 Quantifying the Richness of the Boston Process Approach (BPA) Using a Novel Analytic Approach: the Framingham Heart Study.

Neuropsychological (NP) tests are often interpreted with a one test-one domain analytic approach in research, which misrepresents their clinical utility. The Boston Process Approach (BPA) allows for extraction of multi domain features within and across NP tests although its use is hampered by the data complexity and lack of objective quantification methods. In this study, we aim to develop a novel factor analytic approach that quantifies the richness of BPA data in the Framingham Heart Study (FHS). Data included BPA error scores (n = 172) derived from 10 baseline NP tests from 2238 Offspring participants. The outcome variable was dementia diagnosis. Factor analyses were conducted using Kemeny covariance matrix with maximum likelihood decomposition. Dwyer's (1937) method was used to estimate factor loadings for demographic variables (sex, education, and age) and dementia status. Participants' average age was 67.3 ± 9.3years, 54.5% were female, 41.3% had education ≥ college degree, and 92.5% were without dementia. A bifactor model demonstrated adequate fit (TLI = 0.95, RMSEA = 0.03), similar to the best multi-factor model (TLI = 0.95, RMSEA = 0.03), and improved over a single factor model (TLI = 0.87, RMSEA = 0.05). Omega estimates revealed saturation in general factor versus total factor loadings (ratio: 0.90). Dementia status loaded highly on the general factor (0.82, h2 = 0.81), as did sex (0.44, h2 = 0.27). BPA data fit a bifactor model, with most variation accounted for by the general factor. This study quantifies the richness of BPA measures into a single factor score that can be used as a predictor for several outcomes in clinical research.

Estimation, Comparison, and Projection of Multifactor Age–Cohort Affine Mortality Models

Affine mortality models, developed in continuous time, are well suited to longevity risk applications including pricing and capital management. A major advantage of this mortality modeling approach is the availability of closed-form cohort survival curves, consistent with the assumed time dynamics of mortality rates. This article makes new contributions to the estimation of multifactor continuous-time affine models including the canonical Blackburn-Sherris, the arbitrage-free Nelson-Siegel (AFNS), and the Cox-Ingersoll-Ross (CIR) mortality models. We discuss and address numerical issues with model estimation. We apply the estimation methods to age–cohort mortality data from five different countries, providing insights into the dynamics of mortality rates and the fitting performance of the models. We show how the use of maximum likelihood with the univariate Kalman filter turns out to be faster and more robust compared to traditional estimation methods that heavily use large matrix multiplication and inversion. We present graphical and numerical goodness-of-fit results and assess model robustness. We project cohort survival curves and assess the out-of-sample performance of the models for the five countries. We confirm previous results by showing that, across these countries, although the CIR mortality model fits the historical mortality data well, particularly at older ages, the canonical and AFNS affine mortality models provide better out-of-sample performance. We also show how these affine mortality models are robust with respect to the set of age–cohort data used for parameter estimation. R code is provided.

Is downside risk priced in cryptocurrency market?

This paper explores the cryptocurrency market through the prism of traditional multifactor asset-pricing models with particular attention to the downside market risk. The analysis for about 2000 cryptocurrencies reveals that there is a significant heterogeneity in the exposure to the downside market risk, and that a higher downside risk exposure is associated with higher average returns. This finding is irrespective of whether we use cryptocurrency market or equity market as the benchmark. The extra downside risk is priced with a statistically significant premium in cross-sectional regressions. Adding the downside risk component to the CAPM and the 3-factor Liu, Tsyvinski, and Wu (2022) model for cryptocurrencies improves the explanatory power of the models significantly. The downside risk is orthogonal to the size and momentum risks and constitutes an important component in the multifactor cryptocurrency pricing model. The result is robust in sub-samples of cryptocurrencies with different market capitalizations.

Analysis of entrepreneurial activity in the regions of Russia: Configuration of factors of digital environment

Subject. The article investigates a set of factors of digital environment that have a favorable and constraining effect on the regional entrepreneurship development. Objectives. The aim is to analyze the influence of digital environment factors on complex and individuals ndicators of entrepreneurial activity in the region during the pandemic and post-pandemic periods. Methods. The study employs methods of generalization, classification, grouping and systematization, evaluation of correlation relationships and construction of regression models, analysis of panel data. The information base includes regional statistics, data on scientific and technical infrastructure, data from statistical books for the period of the national program "Digital Economy of the Russian Federation". Results. The study generalized and grouped factors of digital environment (financial, infrastructural, scientific and educational, institutional, information resource, and network), the proposed indicators for assessing the level of formation of digital environment and entrepreneurial activity of the region. The practical significance of the findings consists of the built econometric multifactor models of dependence of entrepreneurial activity indicators in Russian regions on complex and specific factors of digital environment. The findings are applicable in the development of regional policies in the area of entrepreneurship support, creation of special environmental conditions and measures on the side of regional authorities and business support institutions to adapt entrepreneurship to factors volatility. Conclusions. The correlation and regression analysis showed that the factors of digital environment mainly affect the growth of the number of fast-growing companies and concentration of business in the region. A greater number of factors with a high level of correlation relate to the development of digital infrastructure and ICT sector in the region.

A Semi-Static Replication Method for Bermudan Swaptions under an Affine Multi-Factor Model

We present a semi-static replication algorithm for Bermudan swaptions under an affine, multi-factor term structure model. In contrast to dynamic replication, which needs to be continuously updated as the market moves, a semi-static replication needs to be rebalanced on just a finite number of instances. We show that the exotic derivative can be decomposed into a portfolio of vanilla discount bond options, which mirrors its value as the market moves and can be priced in closed form. This paves the way toward the efficient numerical simulation of xVA, market, and credit risk metrics for which forward valuation is the key ingredient. The static portfolio composition is obtained by regressing the target option’s value using an interpretable, artificial neural network. Leveraging the universal approximation power of neural networks, we prove that the replication error can be arbitrarily small for a sufficiently large portfolio. A direct, a lower bound, and an upper bound estimator for the Bermudan swaption price are inferred from the replication algorithm. Additionally, closed-form error margins to the price statistics are determined. We practically study the accuracy and convergence of the method through several numerical experiments. The results indicate that the semi-static replication approaches the LSM benchmark with basis point accuracy and provides tight, efficient error bounds. For in-model simulations, the semi-static replication outperforms a traditional dynamic hedge.

A multi-factor forecasting model for carbon emissions based on decomposition and swarm intelligence optimization

With the growth of national economy, carbon emissions are increasing day by day, which has caused a series of adverse effects on people’s production, life and environment. Therefore, it is very important to establish an accurate carbon emissions forecasting system for developing a low-carbon economy. Aiming at the problems of many influencing factors and randomness of carbon emissions, a multi-factor forecasting model for carbon emissions with generalized phase permutation entropy (GPPE), singular spectrum decomposition (SSD), bidirectional long short-term memory (BiLSTM) optimized by artificial tasmanian devil optimizer (TDO), named GPPE-SSD-TDO-BiLSTM, is proposed. Firstly, Pearson correlation coefficient method is used to analyze the main influencing factors of carbon emissions. Secondly, carbon emissions is decomposed by GPPE-SSD to obtain different singular spectrum components (SSCs). Finally, each component and influencing factors are brought into the TDO-BiLSTM forecasting model, and each forecasting result is reconstructed to obtain the final forecasting result. To verify the validity of the proposed model, carbon emissions of China and India in recent five years are used as experimental data, compares eight different forecasting models, and evaluates the forecasting models with six evaluation indexes. Taking China’s carbon emissions as an example, the MAE, MAPE, RMSE, R2 and STD of the proposed model can reach 0.1593, 0.5226, 0.2131, 0.9973 and 3.1056 respectively, which shows that its forecasting effect is better than that of other comparative models.

Selection ability and market timing skills of mutual fund and unit trust managers in a developing economy: evidence from Ghana

PurposeThe study assess the selection ability and market timing skills of mutual fund and unit trust managers in Ghana.Design/methodology/approachThe study uses an improved survivorship bias-free dataset of yearly after-fee returns of all mutual funds and unit trusts operating in Ghana from January 2011 to December 2019, cumulating in nine years of quantitative fund data. The authors assess Mutual funds and Unit trusts that ever existed, “alive” or “dead,” over the sample period in the study. The authors construct factor loadings to enable the application of multifactor models in the analysis. The authors apply the unconditional versions of the Jensen alpha, Fama-French three-factor, and Carhart four-factor models to determine the selection ability and market timing skills of 32 mutual funds and 17 unit trusts. The authors deploy HAC-consistent robust standard errors to the OLS estimations to subdue the effect of heterogeneity and autocorrelation.FindingsThe results indicate that, on average, mutual funds and unit trust managers possess market timing skills but no selection ability. When the results are decomposed into fund types, fixed-income and balanced mutual fund managers possess selection ability and market timing skills.Originality/valueTo the authors' best knowledge, this study is the earliest to examine the selection ability and market timing skills of both mutual fund and unit trust managers in Sub-Saharan Africa (SSA). It is also the earliest to construct factor loadings for the Ghana stock market.

Multi-Factor Collaborative Analysis of Conservation Effectiveness of Nature Reserves Based on Remote Sensing Data and Google Earth Engine

Protected areas (PAs) play a crucial role in safeguarding biological resources and preserving ecosystems. However, the lack of standardized and highly operational criteria for evaluating their conservation effectiveness, particularly across different ecological types, remains a significant gap in the literature. This study aims to address this gap by constructing a conservation effectiveness evaluation model for two distinct types of PAs in Heilongjiang Province, China: the Zhalong National Nature Reserve (ZlNNR), a wetland ecological reserve; and the Mudanfeng National Nature Reserve (MdfNNR), a forest ecological reserve. We employed various methods, including land use dynamic index, visual analysis of landscape patterns, remote sensing inversion, and a multi-factor comprehensive assessment model, to assess changes in conservation effectiveness from 2000 to 2020. Our findings reveal a contrast between the two PAs. In the ZlNNR, croplands and water bodies increased significantly by 4069.4 ha (K = 1.5820%) and 2541.58 ha (K = 3.2692%). In the MdfNNR, impervious lands increased greatly by 65.35 ha (K = 7.4021%), whereas forest lands decreased by 125 ha (K = −0.067%). The core area of the two PAs displayed increased landscape regularity, whereas the experimental area showed heightened landscape diversity. In ZlNNR, the MPSL value increased by 134.91%, whereas the PDL value decreased by 57.43%, indicating a more regular landscape pattern. In MdfNNR, the SHDIL value decreased by 110.7%, whereas the PDL value increased by 52.55%, indicating a more fragmented landscape pattern. The area with improved vegetation trends in ZlNNR was 8.59% larger than in MdfNNR, whereas the area with degraded vegetation trends was 4.86% smaller than in MdfNNR. In all years, the high effectiveness area was larger in ZlNNR than in MdfNNR, whereas the medium and low effectiveness areas were smaller in ZlNNR compared to MdfNNR. This study provides a scientifically rigorous assessment method for evaluating the conservation effectiveness of different types of PAs, laying a solid theoretical foundation and practical guidance for future conservation strategies.