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on Financial Markets |
| By: | Avramov, D.; Ge, S.; Li, S.; Linton, O. B. |
| Abstract: | This paper introduces the Peer Index (PI), a measure capturing dual industry-related effects in cross-stock predictability: the overall strength of a firm’s peer group and its relative position within the peer group. PI robustly predicts future stock re-turns, earnings surprises, and earnings growth at both the industry and stock levels across short and longer horizons. Its predictive power persists even after controlling for expected returns derived from machine-learning models applied to firm-own characteristics. We provide evidence that markets underreact to peer-related information, with the PI effect stronger when information uncertainty is higher and investor attention lower, driving cross-stock predictability. |
| Keywords: | Cross-Stock Predictability, Industry Effect, Asset Pricing, Economic Links, Information Aggregation |
| JEL: | G11 G12 G14 |
| Date: | 2025–03–14 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camjip:2506 |
| By: | Massimiliano Caporin (Department of Statistical Sciences, University of Padova, Via Cesare Battisti 241, 35121 Padova, Italy); Oguzhan Cepni (Ostim Technical University, Ankara, Turkiye; University of Edinburgh Business School, Centre for Business, Climate Change, and Sustainability; Department of Economics, Copenhagen Business School, Denmark); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | The objective of this paper is to analyze the time-varying degree of interconnectedness of 50 state-level stock returns and their volatility of the United States (US) while filtering out common factors and insignificant coefficients using Least Absolute Shrinkage and Selection Operator (Lasso) regularization. Based on monthly data from February 1994 to November 2024, we find that not accounting for common factors is likely to result in relatively higher spillover indexes. Our findings, beyond their academic value, have important implications for investors and policymakers. |
| Keywords: | US state-level stock indexes, returns and volatility, common factors, Lasso, spillover indexes |
| JEL: | C32 G10 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202509 |
| By: | Camilla Skovbo Christensen (Department of Economics, University of Copenhagen); Isabel Skak Olufsen (Department of Economics, University of Copenhagen) |
| Abstract: | Does the presence of a partner affect individuals propensity to participate in the stock market? In this paper, we estimate the effect of cohabiting with a partner on stock market participation using rich administrative data from Denmark. It is a well-known puzzle that few eople participate in the stock market, and existing literature has pointed to multiple barriers for an individuals participation decision. These barriers likely change when individuals cohabit with a partner. For example, cohabiting with a partner can influence expenses, risk, and financial information that all affect the participation decision. We show that cohabiting with a partner impacts financial decisions as cohabitation increases both entry into and exit from the stock market. Those who enter the stock market are predominantly individuals who cohabit with a partner with stock market experience. Those who exit are predominantly individuals who cohabit with a partner while also becoming homeowners. Thus, our results suggest that information spill-over within couples can increase participation, and that couples who purchase a home at cohabitation face other barriers such as liquidity needs and additional risk that offset the positive effects of cohabitation. |
| Keywords: | Household Finance, Stock Market Participation, Intra-Household Decision-Making |
| JEL: | D14 J12 G51 G53 |
| Date: | 2025–03–26 |
| URL: | https://d.repec.org/n?u=RePEc:kud:kucebi:2505 |
| By: | Vasilios Plakandaras (Department of Economics, Democritus University of Thrace, Komotini, Greece); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Qiang Ji (Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, 100049, China) |
| Abstract: | The study investigates systemic financial risk in global markets, attributing it to geopolitical instability, climate risks, and economic uncertainties. Utilizing a state-of-the-art machine learning heterogeneous panel regression framework capable of capturing cross-sectional dependencies and nonlinear patterns, we examine financial stress across multiple economies, including China, the U.S., the U.K., and ten EU nations. Through extensive out-of-sample rolling window analysis, we show that while geopolitical uncertainty enhances short-term predictions, long-term risk forecasting is better achieved using financial and economic data. The study underscores the limitations of conventional regression models in capturing financial risk dynamics and suggests that machine learning-based panel regressions provide a more nuanced and accurate forecasting tool. The findings bear significant policy implications, highlighting the necessity for regulatory bodies to reassess risk frameworks and the role of climate-related disclosures in financial markets. |
| Keywords: | Systemic financial risk, machine learning, forecasting, climate risk, geopolitical risk |
| JEL: | C45 C58 G17 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202511 |