Pub Date : 2026-01-28DOI: 10.1016/j.qref.2026.102121
Oliver Budras, Maik Dierkes, Sebastian Schroen
We investigate the role of text-implied uncertainty in the cross-section of stock returns. We employ word embeddings to derive an uncertainty dictionary from 10-K annual company filings. Stocks in the highest uncertainty quintile generate a 0.281% higher monthly return compared to stocks in the lowest quintile. This outperformance survives risk-adjustment. Furthermore, uncertainty is negatively related to filing period abnormal returns. This information is priced within one day after filing. High-uncertainty companies have lower return-on-assets, unexpected earnings, and are less likely to increase dividends in the subsequent fiscal year. Uncertainty-averse investors demand a premium for holding high uncertainty stocks.
{"title":"Text-implied uncertainty in 10-K filings: Do investors get the message?","authors":"Oliver Budras, Maik Dierkes, Sebastian Schroen","doi":"10.1016/j.qref.2026.102121","DOIUrl":"10.1016/j.qref.2026.102121","url":null,"abstract":"<div><div>We investigate the role of text-implied uncertainty in the cross-section of stock returns. We employ word embeddings to derive an uncertainty dictionary from 10-K annual company filings. Stocks in the highest uncertainty quintile generate a 0.281% higher monthly return compared to stocks in the lowest quintile. This outperformance survives risk-adjustment. Furthermore, uncertainty is negatively related to filing period abnormal returns. This information is priced within one day after filing. High-uncertainty companies have lower return-on-assets, unexpected earnings, and are less likely to increase dividends in the subsequent fiscal year. Uncertainty-averse investors demand a premium for holding high uncertainty stocks.</div></div>","PeriodicalId":47962,"journal":{"name":"Quarterly Review of Economics and Finance","volume":"106 ","pages":"Article 102121"},"PeriodicalIF":3.1,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-22DOI: 10.1016/j.qref.2026.102124
Huiqi Gan
This study examines whether and how revisions of analyst capital expenditure forecasts of peer firms’ investment affect focal firms’ investment level and efficiency. Analysts revise their forecasts when the new information accumulated since the original forecast warrants such revisions. If revisions of peer firms’ analyst capex forecasts bring new information about industry trends and the macro environment, focal firms, which operate in the same industry, should benefit from this information and incorporate it into their investment decisions. I find that focal firms’ investment level and efficiency are positively related to the analyst forecast revisions of peer firms’ investment. Moreover, these relations vary with firm characteristics such as firm age, levels of information asymmetry, board independence, and industry competition. These findings suggest that revisions of analyst capex forecasts provide valuable insight into industry growth opportunities that can be exploited by other firms in the same industry.
{"title":"Revisions of peer firms’ analyst forecasts and corporate investment","authors":"Huiqi Gan","doi":"10.1016/j.qref.2026.102124","DOIUrl":"10.1016/j.qref.2026.102124","url":null,"abstract":"<div><div>This study examines whether and how <em>revisions</em> of analyst capital expenditure forecasts of peer firms’ investment affect focal firms’ investment level and efficiency. Analysts revise their forecasts when the new information accumulated since the original forecast warrants such revisions. If <em>revisions</em> of peer firms’ analyst capex forecasts bring new information about industry trends and the macro environment, focal firms, which operate in the same industry, should benefit from this information and incorporate it into their investment decisions. I find that focal firms’ investment level and efficiency are positively related to the analyst forecast revisions of peer firms’ investment. Moreover, these relations vary with firm characteristics such as firm age, levels of information asymmetry, board independence, and industry competition. These findings suggest that revisions of analyst capex forecasts provide valuable insight into industry growth opportunities that can be exploited by other firms in the same industry.</div></div>","PeriodicalId":47962,"journal":{"name":"Quarterly Review of Economics and Finance","volume":"106 ","pages":"Article 102124"},"PeriodicalIF":3.1,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-22DOI: 10.1016/j.qref.2026.102125
Ming-Long Wang , Huai-Long Shi , Yu-Lei Wan , Jing-Jin Wang
We investigate the cross-sectional pricing power of factors in the Chinese A-share market under a rigorous multiple-testing framework. We construct 139 characteristic-managed portfolios and 30 principal components (PCs) derived from instrumented PCA (IPCA), risk-premium PCA (RPPCA), and traditional PCA. Using individual stocks as test assets and a bootstrap-based panel regression approach, we evaluate these candidates’ incremental explanatory power while addressing data mining and -hacking concerns. We find that latent factors constructed via IPCA and RPPCA – notably IPC9 and RPPC1 – exhibit significant pricing power, whereas traditional PCs fail to provide incremental information. Among characteristic-based portfolios, three-year capital expenditure (CAPEX) growth, debt growth, and eight-quarter earnings consistency stand out by outperforming PCA-derived factors. Beyond statistical robustness, we provide new economic insights by linking factor efficacy to China-specific market frictions. Specifically, we show that IPC9 proxies for retail-driven noise trader risk and RPPC1 captures a flight-to-quality premium in large-cap stocks, yet the pricing power of both factors is strictly confined to market segments accessible to arbitrage capital. Furthermore, we document a structural migration in the CAPEX factor’s efficacy from state-owned to private enterprises, mirroring the secular evolution of China’s industrial policy. Further analysis under value-weighted schemes, small-cap exclusions, and sectoral filters underscores market heterogeneity: large-cap investors prioritize profitability and liquidity, mid-caps exhibit sensitivity to institutional trading dynamics, and non-financial stocks reward long-term investment efficiency. These findings contribute to a better understanding of conventional “factor zoo” paradigms and advocate for hybrid models that integrate standalone characteristics with synthetic risk proxies.
{"title":"Luck “duels” among factors in China","authors":"Ming-Long Wang , Huai-Long Shi , Yu-Lei Wan , Jing-Jin Wang","doi":"10.1016/j.qref.2026.102125","DOIUrl":"10.1016/j.qref.2026.102125","url":null,"abstract":"<div><div>We investigate the cross-sectional pricing power of factors in the Chinese A-share market under a rigorous multiple-testing framework. We construct 139 characteristic-managed portfolios and 30 principal components (PCs) derived from instrumented PCA (IPCA), risk-premium PCA (RPPCA), and traditional PCA. Using individual stocks as test assets and a bootstrap-based panel regression approach, we evaluate these candidates’ incremental explanatory power while addressing data mining and <span><math><mi>p</mi></math></span>-hacking concerns. We find that latent factors constructed via IPCA and RPPCA – notably IPC9 and RPPC1 – exhibit significant pricing power, whereas traditional PCs fail to provide incremental information. Among characteristic-based portfolios, three-year capital expenditure (CAPEX) growth, debt growth, and eight-quarter earnings consistency stand out by outperforming PCA-derived factors. Beyond statistical robustness, we provide new economic insights by linking factor efficacy to China-specific market frictions. Specifically, we show that IPC9 proxies for retail-driven noise trader risk and RPPC1 captures a flight-to-quality premium in large-cap stocks, yet the pricing power of both factors is strictly confined to market segments accessible to arbitrage capital. Furthermore, we document a structural migration in the CAPEX factor’s efficacy from state-owned to private enterprises, mirroring the secular evolution of China’s industrial policy. Further analysis under value-weighted schemes, small-cap exclusions, and sectoral filters underscores market heterogeneity: large-cap investors prioritize profitability and liquidity, mid-caps exhibit sensitivity to institutional trading dynamics, and non-financial stocks reward long-term investment efficiency. These findings contribute to a better understanding of conventional “factor zoo” paradigms and advocate for hybrid models that integrate standalone characteristics with synthetic risk proxies.</div></div>","PeriodicalId":47962,"journal":{"name":"Quarterly Review of Economics and Finance","volume":"106 ","pages":"Article 102125"},"PeriodicalIF":3.1,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.qref.2026.102115
Rui Cheng , Wenzhong Yue
This paper takes China as a case study to explore the relationship between digital finance and the labor income share. Based on the data of Ant Financial Services Group, a city-level digital finance index is constructed to identify its impact and mechanism. The results show that the development of digital finance significantly promotes the increase of the labor income share. Moreover, the possible channels lie in that digital finance improves the wage level and employment volume of workers by alleviating financing constraints and thereby increasing the share of labor income. Additionally, digital finance is more conducive to the increase of the income share of low-skilled workers. Meanwhile, the intensity of financial supervision is conducive to strengthening the role of digital finance in increasing the labor income share of enterprises.
{"title":"How does digital finance influence the share of labor income? Evidence from China","authors":"Rui Cheng , Wenzhong Yue","doi":"10.1016/j.qref.2026.102115","DOIUrl":"10.1016/j.qref.2026.102115","url":null,"abstract":"<div><div>This paper takes China as a case study to explore the relationship between digital finance and the labor income share. Based on the data of Ant Financial Services Group, a city-level digital finance index is constructed to identify its impact and mechanism. The results show that the development of digital finance significantly promotes the increase of the labor income share. Moreover, the possible channels lie in that digital finance improves the wage level and employment volume of workers by alleviating financing constraints and thereby increasing the share of labor income. Additionally, digital finance is more conducive to the increase of the income share of low-skilled workers. Meanwhile, the intensity of financial supervision is conducive to strengthening the role of digital finance in increasing the labor income share of enterprises.</div></div>","PeriodicalId":47962,"journal":{"name":"Quarterly Review of Economics and Finance","volume":"106 ","pages":"Article 102115"},"PeriodicalIF":3.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.qref.2026.102116
Yue Liu , Meng Ju , Qilong Cao
This paper examines how perceived uncertainty affects corporate financial asset allocation using the China Stock Market & Accounting Research Database (CSMAR). It constructs an indicator measuring firms' perception of policy uncertainty and analyzes impact mechanisms through three pathways: risk-taking behaviour, strategic aggressiveness, and executive overconfidence. Findings show heightened uncertainty perceptions stimulate financial asset allocation by reducing risk-taking, strategic aggressiveness, and executive overconfidence. This relationship proves robust across stability and endogeneity tests. Heterogeneity analysis reveals non-state-owned enterprises, manufacturing firms, and growth-stage companies demonstrate greater sensitivity to perceived uncertainty.
{"title":"The impact of perceived uncertainty on corporate financial asset allocation: Evidence from China","authors":"Yue Liu , Meng Ju , Qilong Cao","doi":"10.1016/j.qref.2026.102116","DOIUrl":"10.1016/j.qref.2026.102116","url":null,"abstract":"<div><div>This paper examines how perceived uncertainty affects corporate financial asset allocation using the China Stock Market & Accounting Research Database (CSMAR). It constructs an indicator measuring firms' perception of policy uncertainty and analyzes impact mechanisms through three pathways: risk-taking behaviour, strategic aggressiveness, and executive overconfidence. Findings show heightened uncertainty perceptions stimulate financial asset allocation by reducing risk-taking, strategic aggressiveness, and executive overconfidence. This relationship proves robust across stability and endogeneity tests. Heterogeneity analysis reveals non-state-owned enterprises, manufacturing firms, and growth-stage companies demonstrate greater sensitivity to perceived uncertainty.</div></div>","PeriodicalId":47962,"journal":{"name":"Quarterly Review of Economics and Finance","volume":"106 ","pages":"Article 102116"},"PeriodicalIF":3.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.qref.2026.102117
Shu Ling Chiang , Ming Shann Tsai
The loss given default (LGD) of a defaulted mortgage is mainly determined by the following two variables: the foreclosure recovery rate (FRR) and foreclosure lag (FL). This study examines whether factors such as the FICO score, the LTV ratio, the interest rate, and the housing return influence these two variables. Furthermore, we use a threshold model to examine whether there is a threshold effect for the impact of the influential factors on the FRR and the FL when the housing return serves as the threshold variable. Our results reveal that all these four factors significantly influence the FRR and the FL. All factors, except for the LTV ratio, positively impact the FRR. In addition, the FL is negatively influenced by all factors, except the housing return at the selling date. Moreover, a structural change can be observed when the housing return exceeds a threshold level. The direction and magnitude of the impact of the influential factors on FRR and FL vary within the two distinct regimes, defined by housing return. Additionally, the explanatory power is enhanced when considering the threshold effect on the analyses. Our results are valuable for the mortgage insurance industry and financial institutions in effectively managing risks and minimizing potential losses.
{"title":"Analyzing foreclosure recovery rates and lags in defaulted mortgages: Insights from the threshold model","authors":"Shu Ling Chiang , Ming Shann Tsai","doi":"10.1016/j.qref.2026.102117","DOIUrl":"10.1016/j.qref.2026.102117","url":null,"abstract":"<div><div>The loss given default (LGD) of a defaulted mortgage is mainly determined by the following two variables: the foreclosure recovery rate (FRR) and foreclosure lag (FL). This study examines whether factors such as the FICO score, the LTV ratio, the interest rate, and the housing return influence these two variables. Furthermore, we use a threshold model to examine whether there is a threshold effect for the impact of the influential factors on the FRR and the FL when the housing return serves as the threshold variable. Our results reveal that all these four factors significantly influence the FRR and the FL. All factors, except for the LTV ratio, positively impact the FRR. In addition, the FL is negatively influenced by all factors, except the housing return at the selling date. Moreover, a structural change can be observed when the housing return exceeds a threshold level. The direction and magnitude of the impact of the influential factors on FRR and FL vary within the two distinct regimes, defined by housing return. Additionally, the explanatory power is enhanced when considering the threshold effect on the analyses. Our results are valuable for the mortgage insurance industry and financial institutions in effectively managing risks and minimizing potential losses.</div></div>","PeriodicalId":47962,"journal":{"name":"Quarterly Review of Economics and Finance","volume":"106 ","pages":"Article 102117"},"PeriodicalIF":3.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.qref.2026.102123
Che-Chun Lin , Han-Bo Chen , I-Chun Tsai
This paper analyzes whether gold and Bitcoin serve as hedges against US stocks by exploring the connection between gold, Bitcoin, and US dollar liquidity. It discusses the characteristics of the gold standard, which is a currency that serves as a replacement for the US dollar. It is inferred that abnormal changes in US dollar liquidity (shocks from money supply) bring about short-term negative impact and immediate changes in gold and Bitcoin prices. Also, such changes may contribute to the inverse relationship between these asset types and the US stock market during certain periods. Moreover, this paper infers that other precious metals and virtual currencies with similar characteristics also provide a hedging option when a major shock occurs in the US stock market, and therefore, function as safe-haven assets as well. To verify the inference, this paper employs a time-varying parameter-vector autoregressive model and a quantile-on-quantile regression to estimate dynamic correlations. This paper takes the three years following the COVID-19 outbreak as the study period. It examines whether precious metals (gold and silver) and virtual currencies (Bitcoin and the Crypto Currency index 30) can act as safe-haven assets, allowing investors to navigate extreme events in the stock market. The results confirm the theory presented in this paper. It also shows that when there is an extreme shortage of US dollar liquidity, a highly negative correlation exists between asset returns and money supply growth for precious metals and virtual currencies. This implies that these assets can hedge extreme risks in US stocks caused by the shortage of US dollar liquidity.
{"title":"Hedging extreme risks in US stocks caused by the shortage of US dollar liquidity: Evidence from the COVID-19 outbreak","authors":"Che-Chun Lin , Han-Bo Chen , I-Chun Tsai","doi":"10.1016/j.qref.2026.102123","DOIUrl":"10.1016/j.qref.2026.102123","url":null,"abstract":"<div><div>This paper analyzes whether gold and Bitcoin serve as hedges against US stocks by exploring the connection between gold, Bitcoin, and US dollar liquidity. It discusses the characteristics of the gold standard, which is a currency that serves as a replacement for the US dollar. It is inferred that abnormal changes in US dollar liquidity (shocks from money supply) bring about short-term negative impact and immediate changes in gold and Bitcoin prices. Also, such changes may contribute to the inverse relationship between these asset types and the US stock market during certain periods. Moreover, this paper infers that other precious metals and virtual currencies with similar characteristics also provide a hedging option when a major shock occurs in the US stock market, and therefore, function as safe-haven assets as well. To verify the inference, this paper employs a time-varying parameter-vector autoregressive model and a quantile-on-quantile regression to estimate dynamic correlations. This paper takes the three years following the COVID-19 outbreak as the study period. It examines whether precious metals (gold and silver) and virtual currencies (Bitcoin and the Crypto Currency index 30) can act as safe-haven assets, allowing investors to navigate extreme events in the stock market. The results confirm the theory presented in this paper. It also shows that when there is an extreme shortage of US dollar liquidity, a highly negative correlation exists between asset returns and money supply growth for precious metals and virtual currencies. This implies that these assets can hedge extreme risks in US stocks caused by the shortage of US dollar liquidity.</div></div>","PeriodicalId":47962,"journal":{"name":"Quarterly Review of Economics and Finance","volume":"106 ","pages":"Article 102123"},"PeriodicalIF":3.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.qref.2026.102120
Rui Ke , Man Yin , Jing Jia
This paper extends the time-varying higher-order moment model to high-frequency data using the multiplicative component GARCH (MC-GARCH) framework of Engle and Sokalska (2012). We propose two multiplicative component time-varying higher-order moment models for intraday returns: the MC-GJRSK and MC-ARCD models. Empirical analysis based on the Shanghai composite and Shenzhen component indices reveals that intraday returns exhibit significant and persistent higher-order moment dynamics. Compared to the benchmark MC-GARCH model, the two proposed models, which incorporate higher-order moment information not only achieve superior in-sample fit, but also produce more accurate out-of-sample forecasts of Value-at-Risk (VaR) and Expected Shortfall (ES). Further analysis demonstrates that the superior forecasting performance of the proposed models remains robust across both high and low volatility periods. Moreover, the proposed models offer more accurate forecasts of tail conditional densities, thereby enhancing their effectiveness in intraday risk forecasting.
{"title":"Forecasting intraday risk incorporating the higher-order moments","authors":"Rui Ke , Man Yin , Jing Jia","doi":"10.1016/j.qref.2026.102120","DOIUrl":"10.1016/j.qref.2026.102120","url":null,"abstract":"<div><div>This paper extends the time-varying higher-order moment model to high-frequency data using the multiplicative component GARCH (MC-GARCH) framework of Engle and Sokalska (2012). We propose two multiplicative component time-varying higher-order moment models for intraday returns: the MC-GJRSK and MC-ARCD models. Empirical analysis based on the Shanghai composite and Shenzhen component indices reveals that intraday returns exhibit significant and persistent higher-order moment dynamics. Compared to the benchmark MC-GARCH model, the two proposed models, which incorporate higher-order moment information not only achieve superior in-sample fit, but also produce more accurate out-of-sample forecasts of Value-at-Risk (VaR) and Expected Shortfall (ES). Further analysis demonstrates that the superior forecasting performance of the proposed models remains robust across both high and low volatility periods. Moreover, the proposed models offer more accurate forecasts of tail conditional densities, thereby enhancing their effectiveness in intraday risk forecasting.</div></div>","PeriodicalId":47962,"journal":{"name":"Quarterly Review of Economics and Finance","volume":"106 ","pages":"Article 102120"},"PeriodicalIF":3.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.qref.2026.102122
Charilaos Mertzanis , Asma Houcine
This study investigates the impact of macroeconomic stability on the growth of fintech markets across 92 countries, emphasizing the importance of stable economic conditions in fostering fintech development. Using newly released cross-country data from the Bank for International Settlements and a comprehensive measure of macroeconomic stability, the findings reveal a robust, positive association between macroeconomic stability and FinTech credit. This indicates that stability reduces economic risk and attracts investment, creating an enabling environment for fintech growth. Interaction analyses demonstrate that this relationship is amplified in societies with advanced digital infrastructure and high levels of financial literacy, underscoring the complementary roles of technological and educational ecosystems. To enhance reliability, the study addresses endogeneity concerns and conducts comprehensive robustness checks, including sensitivity analyses and the inclusion of additional controls. Furthermore, the results highlight the mitigating effects of financial inclusion policies, regulatory adaptability to digital business models, and the scale of digital services trade in shaping the relationship between macroeconomic stability and fintech market expansion.
{"title":"Stable grounds, digital gains: The role of macroeconomic resilience in fintech market development","authors":"Charilaos Mertzanis , Asma Houcine","doi":"10.1016/j.qref.2026.102122","DOIUrl":"10.1016/j.qref.2026.102122","url":null,"abstract":"<div><div>This study investigates the impact of macroeconomic stability on the growth of fintech markets across 92 countries, emphasizing the importance of stable economic conditions in fostering fintech development. Using newly released cross-country data from the Bank for International Settlements and a comprehensive measure of macroeconomic stability, the findings reveal a robust, positive association between macroeconomic stability and FinTech credit. This indicates that stability reduces economic risk and attracts investment, creating an enabling environment for fintech growth. Interaction analyses demonstrate that this relationship is amplified in societies with advanced digital infrastructure and high levels of financial literacy, underscoring the complementary roles of technological and educational ecosystems. To enhance reliability, the study addresses endogeneity concerns and conducts comprehensive robustness checks, including sensitivity analyses and the inclusion of additional controls. Furthermore, the results highlight the mitigating effects of financial inclusion policies, regulatory adaptability to digital business models, and the scale of digital services trade in shaping the relationship between macroeconomic stability and fintech market expansion.</div></div>","PeriodicalId":47962,"journal":{"name":"Quarterly Review of Economics and Finance","volume":"106 ","pages":"Article 102122"},"PeriodicalIF":3.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1016/j.qref.2026.102114
Clemens Löffler, Thomas Kaufmann-Lerchl, Christopher Liska
This paper examines how a firm should finance multiple investment projects when credit markets are imperfect and banks possess market power. Using the Monti–Klein framework, we model the firm’s strategic choice between raising debt for projects separately (decentralized funding) or jointly (centralized funding) and its implications for project selection and capital allocation. Our results reveal that imperfect competition in the banking sector crucially shapes the firm’s optimal financing mode. Depending on equity levels and market competitiveness, under centralized funding the firm may optimally bundle strong and weak projects — a form of corporate socialism — to reduce overall borrowing costs. Contrary to conventional views, winner picking and corporate socialism can coexist: bundling weak projects can enhance financing terms for strong ones and enable more aggressive resource reallocation toward strong projects. The findings offer a novel rationale for cross-subsidization as an optimal response to imperfect credit markets rather than a symptom of inefficiency.
{"title":"(De)Centralized Debt Financing and Project Selection under imperfect Bank Competition","authors":"Clemens Löffler, Thomas Kaufmann-Lerchl, Christopher Liska","doi":"10.1016/j.qref.2026.102114","DOIUrl":"10.1016/j.qref.2026.102114","url":null,"abstract":"<div><div>This paper examines how a firm should finance multiple investment projects when credit markets are imperfect and banks possess market power. Using the Monti–Klein framework, we model the firm’s strategic choice between raising debt for projects separately (decentralized funding) or jointly (centralized funding) and its implications for project selection and capital allocation. Our results reveal that imperfect competition in the banking sector crucially shapes the firm’s optimal financing mode. Depending on equity levels and market competitiveness, under centralized funding the firm may optimally bundle strong and weak projects — a form of corporate socialism — to reduce overall borrowing costs. Contrary to conventional views, winner picking and corporate socialism can coexist: bundling weak projects can enhance financing terms for strong ones and enable more aggressive resource reallocation toward strong projects. The findings offer a novel rationale for cross-subsidization as an optimal response to imperfect credit markets rather than a symptom of inefficiency.</div></div>","PeriodicalId":47962,"journal":{"name":"Quarterly Review of Economics and Finance","volume":"106 ","pages":"Article 102114"},"PeriodicalIF":3.1,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号