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on Financial Markets |
| By: | Lin Li (Audencia Business School) |
| Abstract: | Using an intangible intensity factor that is orthogonal to the Fama–French factors, we compare the role of intangible investment in predicting stock returns over the periods 1963–1992 and 1993–2022. For 1963–1992, intangible investment is weak in predicting stock returns, but for 1993–2022, the predictive power of intangible investment becomes very strong. Intangible investment has a significant impact not only on the MTB ratio (Fama–French high minus low [HML] factor) but also on operating profitability (OP) (Fama–French robust minus weak [RMW] factor) when forecasting stock returns from 1993 to 2022. For intangible asset‐intensive firms, intangible investment is the main predictor of stock returns, rather than MTB ratio and profitability. Our evidence suggests that intangible investment has become an important factor in explaining stock returns over time, independent of other factors such as profitability and MTB ratio. |
| Keywords: | Market-to-book ratio intangible investment profitability stock returns factor analysis, Market-to-book ratio, intangible investment, profitability, stock returns, factor analysis |
| Date: | 2025–05–19 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05074264 |
| By: | Ali Ozdagli; Dylan Ryfe |
| Abstract: | Asset managers are increasingly influential in financial markets. We use new regulatory as well as manually collected data on asset managers of life insurers, the largest institutional investors of corporate bonds, and find that insurers with the same asset managers have more similar portfolios and trades. This similarity increases further if the asset manager actively oversees the majority of both insurers’ assets. Moreover, the effect intensifies the longer insurers share the same asset manager. Nevertheless, the effect is primarily driven by purchases rather than sales and the resulting increase in correlation of portfolio returns is relatively small, alleviating associated financial stability concerns. |
| Keywords: | insurance companies; asset managers; portfolio similarity; financial stability; investment behavior |
| JEL: | G11 G18 G2 |
| Date: | 2025–04–25 |
| URL: | https://d.repec.org/n?u=RePEc:fip:feddwp:99954 |
| By: | Miguel Inverneiro; Tiago Pinheiro |
| Abstract: | Are lenders in the Portuguese financial system more likely to have large losses now than in the past? If a lender has large losses, is it more likely that another one will as well? How has that likelihood changed over time? We address these and other questions using granular credit exposure data in the period between 2009 and 2023. Our findings indicate that the risk of large losses is lower in 2023 than in 2012. Behind this result is a reduction in the borrowers’ default probabilities, a decline in the share of credit to firms accompanied by a rise in the share of credit to households and, to a more limited extent, an increase in loan recoveries. Additionally, we find that the risk of multiple lenders experiencing large losses simultaneously has decreased during the period of analysis. But, if one lender has large losses, the risk that another one will also face large losses has been rising since 2019. This result is driven by an increase in credit to sectors that have high default risk correlation. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ptu:wpaper:w202509 |
| By: | Ayush Jha; Abootaleb Shirvani; Ali Jaffri; Svetlozar T. Rachev; Frank J. Fabozzi |
| Abstract: | This paper introduces a state-dependent momentum framework that integrates ESG regime switching with tail-risk-aware reward-risk metrics. Using a dynamic programming approach and solving a finite-horizon Bellman equation, we construct long-short momentum portfolios that adjust to changing ESG sentiment regimes. Unlike traditional momentum strategies based on historical returns, our approach incorporates the Stable Tail Adjusted Return ratio and Rachev ratio to better capture downside risk in turbulent markets. We apply this framework across three asset classes, Russell 3000 equities, Dow Jones~30 stocks, and cryptocurrencies, under both pro- and anti-ESG market regimes. We find that ESG-loser portfolios significantly outperform ESG-winner portfolios in pro-ESG regimes, a counterintuitive result suggesting that market overreaction to ESG sentiment creates short-term pricing inefficiencies. This pattern is robust across tail-sensitive performance metrics and is most pronounced under a two-week formation and holding period. Our framework highlights how ESG considerations and sentiment regimes alter return dynamics, offering practical guidance for investors seeking to implement responsive momentum strategies under sustainability constraints. These findings challenge conventional assumptions about ESG investing and underscore the importance of dynamic, regime-aware portfolio construction in environments shaped by regulatory signals, investor flows, and behavioral biases. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.24250 |
| By: | Ahmad Haboub; Aris Kartsaklas; Vasilis Sarafidis |
| Abstract: | We hypothesize that portfolio sorts based on the V/P ratio generate excess returns and consist of companies that are undervalued for prolonged periods. Results, for the US market show that high V/P portfolios outperform low V/P portfolios across horizons extending from one to three years. The V/P ratio is positively correlated to future stock returns after controlling for firm characteristics, which are well known risk proxies. Findings also indicate that profitability and investment add explanatory power to the Fama and French three factor model and for stocks with V/P ratio close to 1. However, these factors cannot explain all variation in excess returns especially for years two and three and for stocks with high V/P ratio. Finally, portfolios with the highest V/P stocks select companies that are significantly mispriced relative to their equity (investment) and profitability growth persistence in the future. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.00206 |
| By: | Jiang Wang |
| Abstract: | Government intervention in the financial market through its own trading fundamentally changes the market's structure, function, behavior and outcome. We develop a general equilibrium framework to study the impact of government trading on market outcome and investor welfare. We show that with incompleteness and asymmetric information, the market equilibrium is in general sub-optimal and government intervention can improve investor welfare even without any additional information. However, the welfare impact of government intervention is sensitive to its policy design and the economy's structural details. In addition, popular policy goals such as informational efficiency, price stability and market liquidity can have different welfare implications. Performance measures for government trades based on market prices can be misleading since these trades also affect prices and welfare. |
| JEL: | A10 G0 G1 G18 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33827 |
| By: | Jiahao Yang; Ran Fang; Ming Zhang; Jun Zhou |
| Abstract: | In high-frequency trading (HFT), leveraging limit order books (LOB) to model stock price movements is crucial for achieving profitable outcomes. However, this task is challenging due to the high-dimensional and volatile nature of the original data. Even recent deep learning models often struggle to capture price movement patterns effectively, particularly without well-designed features. We observed that raw LOB data exhibits inherent symmetry between the ask and bid sides, and the bid-ask differences demonstrate greater stability and lower complexity compared to the original data. Building on this insight, we propose a novel approach in which leverages the Siamese architecture to enhance the performance of existing deep learning models. The core idea involves processing the ask and bid sides separately using the same module with shared parameters. We applied our Siamese-based methods to several widely used strong baselines and validated their effectiveness using data from 14 military industry stocks in the Chinese A-share market. Furthermore, we integrated multi-head attention (MHA) mechanisms with the Long Short-Term Memory (LSTM) module to investigate its role in modeling stock price movements. Our experiments used raw data and widely used Order Flow Imbalance (OFI) features as input with some strong baseline models. The results show that our method improves the performance of strong baselines in over 75$% of cases, excluding the Multi-Layer Perception (MLP) baseline, which performed poorly and is not considered practical. Furthermore, we found that Multi-Head Attention can enhance model performance, particularly over shorter forecasting horizons. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.22678 |
| By: | Abeeb Olaniran (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Elie Bouri (Corresponding author. School of Business, Lebanese American University, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | This study adopts a multilayer network approach to investigate the connectedness among clean, brown, and technology ETFs across four moments: returns, volatility, skewness, and kurtosis. Motivated by the non-normality of return distributions and energy transition under intensified climate risk, we demonstrate the importance of incorporating both lower- and higher-order moments to fully capture risk transmission dynamics. Within-layer, cross-layer, and total connectedness analysis reveals generally high interdependence, with notable exceptions during late 2024 (across all layers) and the 2008-2009 period (particularly for skewness and kurtosis). These episodes suggest that investor responses to extreme events differ across statistical moments, stressing the need for a multilayer framework in assessing market behaviour. While the return and volatility layers effectively capture major market shocks, skewness and kurtosis exhibit weaker spillovers, especially prior to the 2008 global financial crisis. Technology ETF plays a central role, exhibiting the highest overlap in both inflows and outflows during crisis periods, particularly between 2008 and 2014, and during COVID-19. Conversely, clean ETF shows limited vulnerability to systemic shocks, suggesting resiliency. Climate risks impact the spillovers across the within- and cross-layers. These findings are particularly relevant to investors, portfolio managers, and policymakers tasked with risk mitigation amid climate change concerns. |
| Keywords: | Clean energy, climate risk, exchange-traded funds (ETFs), spillover and multilayer network, higher-order moments, financial crises |
| JEL: | C32 G10 Q54 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202519 |
| By: | Masoud Ataei |
| Abstract: | This study evaluates the scale-dependent informational efficiency of stock markets using the Financial Chaos Index, a tensor-eigenvalue-based measure of realized volatility. Incorporating Granger causality and network-theoretic analysis across a range of economic, policy, and news-based uncertainty indices, we assess whether public information is efficiently incorporated into asset price fluctuations. Based on a 34-year time period from 1990 to 2023, at the daily frequency, the semi-strong form of the Efficient Market Hypothesis is rejected at the 1\% level of significance, indicating that asset price changes respond predictably to lagged news-based uncertainty. In contrast, at the monthly frequency, such predictive structure largely vanishes, supporting informational efficiency at coarser temporal resolutions. A structural analysis of the Granger causality network reveals that fiscal and monetary policy uncertainties act as core initiators of systemic volatility, while peripheral indices, such as those related to healthcare and consumer prices, serve as latent bridges that become activated under crisis conditions. These findings underscore the role of time-scale decomposition and structural asymmetries in diagnosing market inefficiencies and mapping the propagation of macro-financial uncertainty. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.01543 |
| By: | Guglielmo Maria Caporale; Luis Alberiko Gil-Alana |
| Abstract: | This paper examines persistence and nonlinearities in the US Federal Funds rate over the period from July 1954 to April 2025 by using fractional integration methods. More precisely, a general model including both deterministic and stochastic components is estimated under alternative assumptions concerning the error term (white noise and autocorrelation), and both linear and a nonlinear specification (the latter based on Chebyshev polynomials) are considered. The empirical results provide evidence of mean reversion but also of high persistence when allowing for autocorrelation in the errors. Moreover, they point towards significant nonlinearities in the stochastic behaviour of the series. Both are important properties of the Federal Funds rate, mainly reflecting underlying inflation persistence and policy shifts respectively. |
| Keywords: | US Federal Funds rate, fractional integration persistence, nonlinearities |
| JEL: | C22 E43 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11913 |