Period-aggregated transformer for learning latent seasonalities in long-horizon financial time series.

Fluctuations in the financial market are influenced by various driving forces and numerous factors. Traditional financial research aims to identify the factors influencing stock prices, and existing works construct a common neural network learning framework that learns temporal dependency using a fixed time window of historical information, such as RNN and LSTM models. However, these models only consider the short-term and point-to-point relationships within stock series. The financial market is a complex and dynamic system with many unobservable temporal patterns. Therefore, we propose an adaptive period-aggregation model called the Latent Period-Aggregated Stock Transformer (LPAST). The model integrates a variational autoencoder (VAE) with a period-to-period attention mechanism for multistep prediction in the financial time series. Additionally, we introduce a self-correlation learning method and routing mechanism to handle complex multi-period aggregations and information distribution. Main contributions include proposing a novel period-aggregation representation scheme, introducing a new attention mechanism, and validating the model's superiority in long-horizon prediction tasks. The LPAST model demonstrates its potential and effectiveness in financial market prediction, highlighting its relevance in financial research and predictive analytics.

Greenium of green securitization: Does external certification matter?

The financing costs of green asset-backed securities (ABS) are deeply affected by the increased information asymmetry and greenwashing risk resulting from risk transferring in securitization. To attract potential investors, many ABS issuers obtain external certifications, yet it is unclear whether they pay off financially. Based on a sample of 588 green ABS issued in China for 2016-2022, this paper examines the impact of external certification in the form of green certification and reputation of financial intermediaries involved in the issuance on the yield discount of green ABS over the paired non-green ABS. The empirical findings show that both external certifications lower the greenium of green ABS by serving as favorable signals and mitigating greenwashing concerns, especially in non-financial industry and the securities exchange market. Moreover, the information asymmetry and credit risk of issuers enhance the pricing effect of financial intermediary certification but undermine that of green certification. Our findings provide valuable implications to facilitate the financing efficiency of green financial markets and promote global low-carbon transition.

The impact of digital financial development on corporate leverage ratio: The case of a-share listed non-financial enterprises in China's Shanghai and Shenzhen stock exchanges.

This study investigates the impact of digital finance on corporate leverage ratios. The study employed a large sample of China's Shanghai and Shenzhen A-share non-financial listed enterprises from 2011-2020. The study's results depict that the development of digital finance can significantly reduce the leverage ratio of enterprises. We empirically identified that digital finance affects the difference in the term structure of the corporate leverage ratio. It was found that the development of digital finance has a significant negative impact on enterprises' short-term and long-term leverage ratios. Moreover, our heterogeneity analysis shows that the negative effect of digital financial development on corporate leverage ratios is different in state-owned and non-state-owned enterprises, large-scale and small-scale enterprises, and high-leverage and low-leverage enterprises. Mechanism analysis shows that the development of digital finance can reduce corporate leverage by lowering financing costs, alleviating financing constraints, and weakening non-systemic risks. Therefore, policymakers should focus on developing and adopting digital finance by creating a supportive regulatory environment, improving access to digital financial services, and encouraging innovation in the digital finance sector. Finally, our results remain robust after addressing endogeneity issues and conducting robustness checks.

The measurement of China's provincial physical capital stock based on the improved method and indicators.

Physical capital stock is the basic indicator of macroeconomic empirical studies and also an important manifestation of macroeconomic activity capacity. When measuring China's provincial physical capital stock by the perpetual inventory method, the treatment for the depreciation rate and the initial physical capital stock in the existing literature has some defects in reflecting the economic performance. The purpose of this paper is to build the most reliable and reasonable statistical series of China's provincial physical capital stock. We establish a measurement formula in form of the variable depreciation rate and improve the treatment for the depreciation rate and the initial physical capital stock. For the depreciation rate, we measure the benchmark depreciation rate of physical capital and determine the variable depreciation rate according to the economic growth rate, which makes the variable depreciation rate reflect the economic growth. For the initial physical capital stock, we measure the initial physical capital stock according to the capital output ratio, which makes the initial physical capital stock reflect the economic aggregate. Based on these improvements, we measure China's provincial physical capital stock from 1952 to 2022 by the perpetual inventory method. Through analyzing the measurement results, we have a more distinct insight into the temporal and spatial characteristics of China's provincial physical capital stock: The growth rate of physical capital stock at the provincial level has slowed in spite of remaining high; The eastern region is much higher than the central and western regions. According to these two characteristics, it is suggested that policymakers need to improve market mechanisms and adjust investment policies to promote a benign profile of economic growth and a regional optimization of capital formation.

Welfare costs of suboptimal retiree decisions.

We make a novel investigation of welfare costs associated with various suboptimal decisions made by retirees, both analytically and numerically. We utilize a unique framework that incorporates recursive utility with housing, and also encompasses expected utility and recursive utility without housing as special cases. Our findings indicate that under-investment in stocks incurs lower welfare costs compared to an equivalent over-investment. Suboptimal allocations in bond holdings result in higher costs than similar misallocations in stocks. Choosing not to participate in the stock market is less detrimental than avoiding the bond market. Should retirees opt to simplify their decision-making by investing solely in one type of asset, it is less costly for them to invest exclusively in bonds. Overconsumption of housing is less costly than an equivalent underconsumption. Suboptimal consumption imposes the highest welfare cost. Decisions regarding consumption, housing, and savings are found to be more crucial than the choice of how to distribute liquid savings between stocks and bonds. Additionally, recursive utility model better captures retirees' preference for bonds over stocks than expected utility model. Our results, which are consistent across various parameter settings, provide valuable insights for academics and policymakers aiming to enhance retiree welfare.

Do regional tax incentive policies improve productivity?

The difficulty in transforming old industrial areas constitutes a significant factor contributing to regional development imbalances. Can regional tax incentives, as a crucial component of regional policies, polish the "rust belt" regions? This study leverages the inaugural Value-Added Tax (VAT) reform in China as an opportunity to explore the potential of regional tax incentives in achieving sustainable development in traditional industrial areas. Drawing upon a comprehensive industrial enterprise database, we employ a Propensity Score Matching-Difference in Differences (PSM-DID) approach to examine the efficacy of these tax incentives. Our findings reveal that: (1) Regional tax incentives primarily enhance firms productivity by stimulating investment in enterprises, yet they do not contribute to improved investment efficiency or spur innovation within firms. (2) Regional tax incentives have alleviated financing constraints for enterprises in old industrial bases, significantly enhancing the Total Factor Productivity (TFP) of firms with higher financing constraints. This policy has had an even stronger impact on improving the TFP of state-owned and monopolistic enterprises. (3) Regional tax incentives have impeded productivity growth by preventing the exit of low-efficiency firms and the entry of high-efficiency ones. These incentives also increased the likelihood of "zombie firms" forming and failed to promote endogenous economic growth in the Northeast region. Additionally, they have distorted the allocation of resources towards capital and technology-intensive industries in that area. In China's old industrial bases, regional tax incentives should be coordinated with market-oriented reforms; these regional tax incentive policies should also be further enriched.

Examining public private partnership investment in energy towards achieving sustainable development goal 7 for ASEAN region.

The gradual progress in aligning financial flows with the adoption of clean technologies reveals a persistent funding gap, signaling a global misallocation of capital. Addressing this challenge necessitates political leadership and robust policies to counteract the insecurities impeding the redirection of financial flows. This study investigates into the impact of energy-related public-private partnership investments (PPPIE) and macro-environmental variables on the attainment of Sustainable Development Goal 7 (SDG7) across Association of Southeast Asian Nations (ASEAN) member countries from 1999 to 2021. Employing the Dynamac command technique, we conduct autoregressive distribution lag analysis and the Bounds Cointegration Test to evaluate ASEAN's efforts in achieving SDG7. Results indicate that a ten-year exogenous shock to the GDP growth rate initially causes a temporary decline in both GDP and PPPIE, albeit not statistically significant. However, in the long run, the shock becomes statistically significant, correlating with a negative decline in the GDP growth rate. This underscores the negative impact of external factors like the COVID-19 pandemic on the economic growth of ASEAN member countries. Specifically, a percentage increase in PPPIE leads to an 8.3% reduction in the GDP growth rate, revealing a detrimental and unsustainable impact on the economy. This signifies that energy investments in the ASEAN region, are predominantly unsustainable and adversely impact economic growth. Moreover, these energy investments contribute to a significant 52.6% increase in greenhouse gas emissions, indicating a substantial setback in the region's progress towards meeting SDG7's clean energy objectives by 2030. This suggests the present state of PPPIE does not align with sustainable clean energy goals of the region. Therefore, recommendations should include diversifying energy sources and investment strategies to enhance sustainable clean energy. Also, policymakers and researchers should reassess the terms and conditions of PPPIE, refining frameworks for private sector involvement to align with long-term economic sustainability goals.

Gender effects in crowdfunded business loan campaigns.

Crowdfunding is a growing source of finance for entrepreneurs. In this paper, we investigate the existence of a gender effect in the time needed to obtain a business loan through crowdfunding. Using data from three Dutch crowdfunding platforms, survival analysis of the time to completion for 934 business loan campaigns shows that female entrepreneurs have a 20% shorter campaign completion time compared to male entrepreneurs, whereas couples do not differ from males. This effect persists across the different platforms. Subsequent analysis shows that female entrepreneurs do not have the disadvantage they face in traditional lending channels when requesting funds through crowdfunding, and that herding behavior by investors benefits female entrepreneurs most.

Uncertainty in banking and debt financing of firms in Vietnam.

This study explores the effects of banking uncertainty on firms' debt financing. Employing data from 2007 to 2022 of Vietnam-a bank-based economy, we document that banking uncertainty negatively impacts corporate debt. The impact firmly holds across various debt maturities and sources, with the most predominant driver witnessed in bank debt. We also investigate the potential underlying mechanism linking banking uncertainty to debt financing, thereby validating the working of three crucial channels, including increased costs of debt, substitution of trade credit, and contractions in firm investment. Furthermore, conducting extended analysis, we find that debt financing exhibits more pronounced reactions to banking uncertainty for firms with closer ties to banks or during macroeconomic shocks, as captured by the financial crisis and the COVID-19 pandemic. Our findings survive after robustness checks by alternative measurement, static and dynamic econometric models, and endogeneity controls.

[Market research of russian medical construction facilities: cost estimation and investments in construction programs].

Within the framework of the study, conditions are analyzed, cost estimates and other characteristics of large construction projects in the medical field are presented. The article analyzes investment projects of construction medical facilities in the Russian Federation. Plans for the development of some large medical networks are also briefly highlighted. A review of literature sources on health development programs of the subjects of the Russian Federation was conducted, federal target programs were considered, investment projects for the construction of medical facilities planned for completion in 2023-2025 were studied. Some analytical results of monitoring for 2020-2023 on the activity of the investment market in the direction of the Russian strategic and institutional design of the construction of medical facilities in the Russian Federation are presented.

Quantifying economic vulnerabilities induced by interdependent networks.

Under economic globalization, countries are linked through trade and investments. This economic interdependence creates vulnerabilities. The indirect vulnerability induced by interdependent networks of trade and investments can put a country's economy at risk, but this risk has yet to be systematically quantified and investigated. In this paper, we developed the novel Potential Indirect Vulnerability Index (PIVI) to capture how interdependencies between networks of trade and foreign direct investment (FDI) may induce economic vulnerabilities. The model consisted of three main components: a target country (the importer of goods), an investing country (the exporter of FDI), and the intermediary countries that export commodities to the target country and receive FDI from the investing country, serving as conduits of the vulnerabilities caused indirectly by the investing country. The PIVI quantifies the indirect vulnerabilities based on the product of two fractions: 1) the dependency of the target country on commodities from each intermediary country; and 2) the dependency of each intermediary country on FDI from the investing country. We demonstrated the utility of PIVI by examining the US economy's vulnerability to China using 2019 trade and FDI data. Several Asian countries and a mix of agricultural products and raw materials were identified as conduits through which China could potentially influence the US economy. Vietnam was a sizeable risk because, while it has been a primary source of many US imports, it also received about 30% of its FDI from China. The US policy makers might opt to increase diversity in trade partners or to promote investment in countries such as Vietnam. We also applied the PIVI analysis to critical minerals, identifying cobalt, tungsten, and copper as the most vulnerability-inducing among them. PIVI is a flexible metric than can be aggregated and modified to provide a more nuanced and focused assessment of an economy's vulnerability.

Mining the relationship between COVID-19 sentiment and market performance.

In March 2020, the outbreak of COVID-19 precipitated one of the most significant stock market downturns in recent history. This paper explores the relationship between public sentiment related to COVID-19 and stock market fluctuations during the different phases of the pandemic. Utilizing natural language processing and sentiment analysis, we examine Twitter data for pandemic-related keywords to assess whether these sentiments can predict changes in stock market trends. Our analysis extends to additional datasets: one annotated by market experts to integrate professional financial sentiment with market dynamics, and another comprising long-term social media sentiment data to observe changes in public sentiment from the pandemic phase to the endemic phase. Our findings indicate a strong correlation between the sentiments expressed on social media and market volatility, particularly sentiments directly associated with stocks. These insights validate the effectiveness of our Sentiment(S)-LSTM model, which helps to understand the evolving dynamics between public sentiment and stock market trends from 2020 through 2023, as the situation shifts from pandemic to endemic and approaches new normalcy.

Systemic risk spillover between the stock market and banking deposits: Evidence from a sustainability perspective in the South Asian countries.

This research explores the link between stock markets and banking deposits in South Asian (Pakistan, India, Sri Lanka, Nepal) countries. This study empirically examines the systemic risk potential of financial institutions in South Asia using current systemic risk statistics. Yearly data on stock prices and banking deposits from January 2000 to December 2020 were analyzed using a two-stage process. In the first phase, we measure VaR (value at risk), and in the second step, we measure the DCC GARCH model for our empirical analysis. The study findings reveal systemic risk spillover between the stock markets of South Asian countries and the relevant country's banking system deposits. The policymakers can use our study findings to create a more sustainable financial sector.

Share pledge and accounting conservatism in share-pledging firms: Evidence from a natural experiment in China.

This paper focuses on firms in which insiders pledge their shares as collateral for loans. By investigating a natural experiment-China's enactment of provisions on share reductions that restrict pledge creditors' cashing-out behavior-we find that pledging firms exhibited more conservative financial reporting after the implementation than non-pledging firms. This effect was pronounced in firms with a higher ratio of pledged shares, a longer maturation period of the pledged shares, and more concentrated pledge creditors. Additionally, we show that pledging firms increased their accounting conservatism after the shock, leading to a lower risk of margin calls and stock price crashes. The effect on accounting conservatism was stronger in firms with controlling pledgers or when the pledge creditors were banks. Our results remained consistent after we performed several robustness tests. These behaviors are economically logical because the provisions heighten creditors' liquidity risk and the potential losses of loan default. Pledging shareholders embrace more accounting conservatism to mitigate creditors' concerns about agency costs and avoid triggering margin calls. Our findings provide direct support that creditors have a real demand for accounting conservatism and highlight the impact of shareholder-creditor conflicts on the financial reporting policies of pledging firms.

The time-varying effects of geopolitical risk on mutual fund risk taking.

Based on a time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV), this paper investigates the dynamic effects of geopolitical risk on mutual fund risk taking in China across three-time horizons and at three selected time points. Overall, the impulse responses are time-varying and we find a negative effect of geopolitical risk on mutual fund risk taking until 2015, with the short-term effect being the most pronounced, suggesting that when professional investors such as mutual fund managers are faced with the stock valuation uncertainty due to a geopolitical shock, they choose to reduce market risk exposures. After 2015, the short-term effect begins to diminish and gradually turns positive, which could be explained by the fact that with the increasing abundance and diversification of investment instruments, fund managers have more effective investment tools and more sophisticated trading strategies to hedge against geopolitical risk, rather than reducing market risk exposure. Further, we explore the heterogeneous effects of eight types of geopolitical risk and three types of mutual fund. The results indicate that the effect of geopolitical actions is stronger than that of geopolitical threats, while the effect of narrow geopolitical risk is stronger than that of broad geopolitical risk. Moreover, we find that the response of the risk taking of growth funds to the geopolitical risk is weaker than that of balanced and income funds.

The impact of macroeconomic policy uncertainty on micro-enterprise investment efficiency-Empirical evidence from China.

The impact of macroeconomic policy uncertainty (EPU) on micro-level entities has garnered increasing attention in economic circles. This study examines the influence of EPU on the efficiency of investments made by China's A-share listed companies between 2016 and 2021. Using a panel fixed effect model for analysis, the research reveals that EPU has a notable adverse effect on the investment efficiency of enterprises. Furthermore, it suggests that advancements in digital finance, strong ESG performance, and enhanced entrepreneurial confidence can mitigate this negative impact effectively. The study also highlights that enterprises with lower valuation, shareholder control, limited audit reputation, and no bank connections are more vulnerable to the impact of EPU on investment efficiency compared to those with higher valuation, manager control, strong audit reputation, and bank connections. Consequently, future efforts should be directed towards enhancing the stability and relevance of economic policies, promoting digital finance, and enhancing corporate governance structures.

Utilization of foreign investment in the productive service industry in Hubei province, China and its optimization counter-measures.

The Global Investment Report 2023 revealed that after a sharp decline in 2020 and a strong rebound in 2021, global foreign direct investment (FDI) declined by 12 percent to $1.3 trillion in 2022. However, in developing countries, FDI increased by 4% to $916 billion, a record share of more than 70% of global flows. The number of greenfield investment projects in developing countries increased by 37 percent and international project finance transactions by 5 percent. Foreign investment from China, the second largest recipient of foreign investment globally, increased by 5 percent. The service industry has become the mainstream industry in the global FDI structure. The global industry is accelerating its transformation to a "service-based economy," international FDI in productive service industries has become an essential means of industrial transfer in developed countries and a meaningful way to upgrade the industrial structure and high-quality development in emerging economies. As a representative province in central China, Hubei Province has unique advantages in human capital, factor cost, and market potential, which provide preferential conditions to attract foreign investment. This paper first introduced the concept of the productive service industry, based on the relevant statistical data from 2011 to 2022, focused on the current situation of foreign investment utilization in five major sub-sectors of the productive service industry in Hubei Province in the past ten years, and empirically investigated the impact of foreign investment utilization in five major sub-sectors of the productive service industry on the economic growth of Hubei Province, and obtained that the level of foreign investment attraction varied significantly among the regions in Hubei Province. The three productive service industries, namely transportation, storage and postal services, information transmission, software and information technology services, and financial services, played a significant role in the active attraction and optimal utilization of foreign capital and the economic development of Hubei Province. Based on this, it was proposed to build a market-oriented rule of law and internationalized business environment, improve the infrastructure construction in different regions of the province, focus on the training of professional talents for the development of productive service industries, and pay attention to the improvement of independent innovation capacity.

Factor-GAN: Enhancing stock price prediction and factor investment with Generative Adversarial Networks.

Deep learning, a pivotal branch of artificial intelligence, has increasingly influenced the financial domain with its advanced data processing capabilities. This paper introduces Factor-GAN, an innovative framework that utilizes Generative Adversarial Networks (GAN) technology for factor investing. Leveraging a comprehensive factor database comprising 70 firm characteristics, Factor-GAN integrates deep learning techniques with the multi-factor pricing model, thereby elevating the precision and stability of investment strategies. To explain the economic mechanisms underlying deep learning, we conduct a subsample analysis of the Chinese stock market. The findings reveal that the deep learning-based pricing model significantly enhances return prediction accuracy and factor investment performance in comparison to linear models. Particularly noteworthy is the superior performance of the long-short portfolio under Factor-GAN, demonstrating an annualized return of 23.52% with a Sharpe ratio of 1.29. During the transition from state-owned enterprises (SOEs) to non-SOEs, our study discerns shifts in factor importance, with liquidity and volatility gaining significance while fundamental indicators diminish. Additionally, A-share listed companies display a heightened emphasis on momentum and growth indicators relative to their dual-listed counterparts. This research holds profound implications for the expansion of explainable artificial intelligence research and the exploration of financial technology applications.

How mixed ownership affects investment efficiency? evidence from state-owned enterprises in China.

The inefficiency observed in investment within state-owned enterprises presents a significant practical challenge that can affect the sustainable development of China's economy. To address this issue, this study comprehensively explores the intricate mechanisms underlying the governance implications of mixed ownership on the investment efficiency of listed companies. Drawing on unbalanced panel data encompassing Shanghai and Shenzhen Stock Exchange A-share listed companies in China spanning the period from 2008 to 2022, this study employs a fixed-effects model to unveil the nuanced ways in which mixed ownership influences investment efficiency through the lens of agency costs. This study transcends the boundaries of traditional agency conflicts between managers and shareholders. It delves deeper, illuminating the diverse effects of agency conflicts between significant controlling shareholders and minority shareholders. The results revealed a noteworthy positive correlation between mixed ownership and investment efficiency, and verified the intermediary role of agency costs between mixed ownership and investment efficiency, which is an important result of our research. Heterogeneity analysis indicates that the relationship between the two can be affected by external events, such as during the COVID-19 pandemic, investment efficiency is not the most concerned issue for enterprises. The findings have practical implications for practitioners and policymakers, as they offer avenues for optimizing investment strategies and fostering efficient and effective corporate governance practices.

Market liquidity and volatility: Does economic policy uncertainty matter? Evidence from Asian emerging economies.

This research investigates the complex interaction between liquidity and volatility while considering Economic Policy Uncertainty (EPU) as a moderating factor. Using a comprehensive dataset that incorporates various liquidity measures such as market resilience, depth, and breadth, the study examines how changes in liquidity impact volatility in four Asian incipient economies: China, Pakistan, India, and South Korea. By utilizing sophisticated econometric techniques, particularly the System Generalized Method of Moment (GMM), the findings demonstrate a statistically significant inverse relationship between liquidity and volatility. These findings imply that, within the Asian context, lower levels of volatility are correlated with higher market liquidity. By incorporating EPU into the model, the research acknowledges the significant role of economic factors in shaping market dynamics. Stakeholders, decision-makers, and investors can gain valuable insights from this analysis of variables influencing market stability in Asian emerging economies. The study's outcomes can guide policymakers in formulating strategies that promote market stability and improve market microstructure.