Limiting global warming to well below 2 °C implies that a substantial share of fossil-fuel reserves may ultimately remain unproduced. We examine whether equity valuations of oil and gas producers reflect this stranded-asset risk at the asset level. We show that developed reserves—assets that generate near-term production—remain positively valued, whereas growth in undeveloped reserves has become value-decreasing. Additions to undeveloped reserves, which require large capital commitments and long development horizons, are increasingly discounted by equity markets. Exploiting major transition-related shocks, including the COVID-19 demand collapse and the 2021 U.S. re-entry into the Paris Agreement, we find that firms with larger undeveloped-reserve shares and longer development horizons experience larger valuation declines when transition risk is repriced. Together, the evidence indicates that markets increasingly differentiate between near-term, cash-flow-producing assets and long-dated fossil-fuel investments whose extractability is uncertain.
This paper examines whether volatility management and factor-based mean–variance optimization improve out-of-sample portfolio performance relative to simple diversification benchmarks. Using nine equity factors from 1976 to 2025, the analysis compares recursive mean–variance portfolios, volatility-managed factors, equal-weight 1/N portfolios, and volatility-managed 1/N portfolios across multiple rolling windows, subperiods, and VIX regimes. Although volatility management improves the Sharpe ratios of several individual factors, these gains do not translate into robust portfolio-level outperformance once estimation risk and recursive implementation are considered. Optimized portfolios generally fail to be outperformed simple diversified benchmarks, while dynamic factor-selection strategies suffer from instability and look-ahead bias. Overall, the evidence suggests that simple diversification remains remarkably difficult to outperform in risk-adjusted terms, even when sophisticated volatility-management and optimization techniques are employed.
This paper proposes the Bubble Crash–GARCH model, a volatility-forecasting framework that incorporates tail price events into GARCH-type specifications for returns. Bubbles and crashes are first detected and date-stamped through the Phillips, Shi, and Yu real-time monitoring procedure applied to price series, and the resulting indicators are included as regressors in the conditional mean equation of returns. Unlike standard ARMA-type mean specifications, the conditional mean is directly driven by bubble and crash indicators generated in real time, avoiding the use of lead variables typically required by noncausal approaches, thus allowing us to disentangle and quantify the effects of both periodic bubble collapses and crashes. The model is evaluated on major cryptocurrencies and some of the Magnificent Seven stocks, over the period from January 1, 2018 to April 30, 2026, with December 31, 2023 used as the forecasting cutoff date. The benchmark model is selected through a systematic assessment of alternative volatility specifications, including asymmetric GARCH models and two-regime Markov switching GARCH models under different assumptions on the innovation distribution. In addition to own-asset bubble and crash effects, the analysis investigates contagion channels by incorporating episodes driven by Bitcoin and Nvidia for cryptocurrencies and the Magnificent Seven, respectively. Forecast accuracy is assessed through one-step-ahead volatility predictions and formally tested using the Clark–West test. The empirical results confirm that accounting for bubble and crash episodes improves volatility forecasts relative to the selected benchmark, both through idiosyncratic tail-event effects and through cross-asset contagion channels. These findings suggest that explicitly disentangling extreme price episodes from regular volatility dynamics enhances the informational content of GARCH-type models and provides useful evidence for risk management and asset allocation.
This paper critically examines UBS’s public pushback against higher capital requirements proposed in the aftermath of the 2023 Credit Suisse rescue. We challenge UBS’s claim that additional equity capital would substantially increase its cost of capital and impair firm value. Drawing on foundational corporate finance principles and recent empirical evidence, we demonstrate that higher equity buffers may reduce default risk and lower the bank’s cost of equity (CoE) and cost of debt (CoD). Increasing UBS’s capital buffers likely leads to lower funding costs and, in realistic scenarios, may affect its weighted average cost of capital (WACC) only marginally and by less than one basis point. Considering both effects, we estimate an almost complete M-M offset. Using UBS-specific data and a two-stage Gordon Growth model (GGM), we show that the valuation impact of increased capital buffers is economically negligible or even positive. Stronger capital buffers are unlikely to impair firm value and may enhance UBS’s long-term financial stability. Our findings support capital strengthening as a value-neutral or value-enhancing regulatory measure, providing evidence for calibrating capital buffer policies at systemically important banks.
This paper provides evidence on the outcomes of several different withdrawal policies for a long-term equity investor with a motive to preserve the real value of their assets and maximize withdrawals. Using data for the US and Finnish stock markets from 1913 to 2023, we find that historically, the maximum endowment-preserving withdrawal rates would have been 10.95
This study aims to address the criticisms of traditional mean–variance optimization (MVO), which suffers from dependence on historical data, infeasibility due to transaction costs, and the assumption of stable covariances. We introduce an alternative to both MVO and hierarchical risk parity (HRP) by proposing a method known as text-based hierarchical risk parity (TB-HRP). This innovative approach leverages financial text data, specifically from 10-K filings, to capture complex economic relationships, reduce dependence on historical prices, and adapt dynamically to the changing nature of businesses. Unlike traditional techniques that rely on past returns, TB-HRP offers several advantages, including the ability to reflect operational similarities, reduce noise in portfolio optimization, reveal unexpected relationships between companies, and provide a forward-looking perspective on risk factors. Using 10-K statements of firms in the S P 500, we apply the framework to construct a distance matrix and follow the approach similar to De Prado (J Portfolio Manag 42(4):59-69, 2016) for portfolio formation. TB-HRP can be used to enhance diversification, improve robustness, and dynamically adapt to market changes, making it a valuable tool for portfolio managers seeking to overcome the limitations of traditional optimization techniques.
This paper examines the impact of the Federal Reserve’s conventional and unconventional monetary policy on US stock prices using an event study with high-frequency data. Three indicators of monetary news are constructed to separately identify surprise changes in the current federal funds rate, forward guidance, and large-scale asset purchases (LSAPs). Estimation results show that all three types of monetary surprises have economically important and statistically significant effects on US stock prices. A modified version of Swanson’s (J Monet Econ 118:32–53, 2021) LSAP measure—which imposes a zero surprise constraint in the pre-LSAP period—confirms the significant effects of LSAP announcements. These findings differ from those of Swanson (2021), who reports statistically insignificant effects, and suggest that the modified LSAP measure may capture monetary surprises more precisely. The responses of stock prices to Target and Path surprises are persistent, while unanticipated LSAP announcements have only transitory effects. Although conventional policy was constrained by the zero-lower bound during the financial crisis and COVID-19, monetary policy remained effective. The financial market impact of monetary policy across US stock indexes is heterogeneous, with the effects of forward guidance and asset purchases weaker for growth and small-cap indexes than for large-cap stocks.
Within the industry of venture capital, I analyze what are the contributing factors that lead to venture capitalist investing outside of their preferred investment industry, despite documented subpar results when doing so. Initial tests indicate that the largest determinant of a non-preferred industry investment is if a VC firm’s past investments are concentrated in only a few industries. However, with the addition of VC firm fixed effects, I find the greatest contributing factor to be the VC’s preferred industry deal flow, where low deal flow significant increases the likelihood that a VC will invest in non-preferred industry investments. Furthermore, consistent with the notion that highly sought after VCs will have less exposure to fluctuations in deal flow, the deal flow effect is shown to be significantly more pronounced the less experience the VC has in their preferred industry.
Understanding the impact of cognitive biases on retail investor performance remains a critical challenge in behavioral finance. This study introduces the Behavioral Performance Attribution framework, a novel methodology for decomposing portfolio returns based on investor biases, providing an alternative to traditional return attribution models such as the Brinson, Hood, and Beebower (BHB) model. Using a large real-world trading dataset, we apply ordinary least squares (OLS) regression to quantify the explanatory power of behavioral biases across different investor groups. The empirical results demonstrate that biases such as Action Bias and Portfolio Concentration Bias significantly correlate with returns, with the Model Explainability Ratio (MER) ranging between 43.44% and 63.54%, confirming the framework's practical applicability. Notably, we estimate separate models for different investor subgroups, revealing that bias effects vary between investors outperforming and underperforming a benchmark. While excessive trading and portfolio concentration enhance outperformance among extreme outperformers, these same behaviors amplify losses among underperforming investors. Residual analysis highlights systematic over- and underestimation patterns, suggesting the need for further refinements incorporating additional behavioral and market-driven factors. These findings contribute to the growing literature on behavioral return attribution and offer a structured approach for integrating psychological factors into investment performance analysis.
We find empirical support that the availability of uncommitted funds (UCF), conditioned on the benefits to the insured, is a promising new measure of the financial quality of pension funds. Unlike funding ratios, UCF represent the surplus after fluctuation reserves for absorbing capital market risks are fully accumulated. Fund level data from Switzerland reveal that funds seem to be reluctant to build up UCF arbitrarily, but pass on part of it to the beneficiaries. This suggests that quality indicators such as UCF should not only be based on funding ratios, but incorporate the funds’ reserve and payout policy.
This study examines the relationship between common ownership and the correlation of stock returns. Using S&P 500 data, we find that higher levels of common ownership at the individual investor level are associated with stronger pairwise correlations of stock returns in subsequent periods. This relationship holds across multiple industries and remains robust after controlling for factors such as market risks, stock prices, liquidity, financial leverage, index additions, geographic proximity, correlated trading, and the level of competition in the industry. Our findings indicate that these effects are not solely driven by large institutional investors. Another contribution of our study is distinguishing between the effects driven by the level and the similarity of these control variables. Moreover, since reduced competition is associated with higher stock return correlation, our results are consistent with the existence of anticompetitive effects of common ownership across several industries. We provide further support for this competition channel by demonstrating that this effect is mainly driven by longer-term investors. Our findings have important policy implications, suggesting that antitrust regulations should consider indirect competitive effects from common ownership, even without explicit coordination between companies.
Transaction costs are a major factor affecting portfolio returns in asset management. We propose an enhanced target volatility strategy to reduce the deleterious effect of transaction costs by adding rebalancing boundaries to the target volatility asset allocation mechanism. We formulate a constrained optimization problem to determine the optimal rebalancing boundary level. Based on simulations using an overlapping block bootstrap approach, we find that the extended target volatility portfolio with rebalancing boundary levels can provide better investment outcomes (higher portfolio returns and reduced transaction costs) without losing the ability to control portfolio risk under a pre-determined threshold. Further computational analysis on different real market scenarios confirms these findings and allows us to summarize insights on the appropriate boundaries to use under different market conditions and transaction cost magnitudes. Our findings have important practical implications given the popularity of the target volatility investment strategy as well as other asset management concepts with a dynamic asset allocation mechanism.
Based on an analysis of changes in the yields of German government bonds, we propose a simple model for the term structure of interest rates and show that this model with two parameters (relating to the interest level and slope of the term structure) fits empirically well the data for a change horizon of one year or longer, especially in the low-interest environment, and give examples for applications. In addition, we provide closed-form solutions for some interest bearing instruments and give a new interpretation for the convexity when this linear model for the term structure is used.
This study investigates the critical but often overlooked role of distance metric selection in classification models using mixed data, with a focus on P2P loan performance forecasting. Unlike previous studies that used standard distance metrics with minimal critical evaluation, we systematically evaluate 24 distance metrics across four diverse P2P lending platforms. Our results demonstrate that a simple mixed-data distance metric significantly improves prediction accuracy and computational efficiency, while effectively capturing loan dependencies. Furthermore, the appropriate choice of distance metric enables investors to filter underperforming loans more effectively, optimizing portfolio outcomes. This research provides a robust methodological framework for enhancing credit and profit scoring models in P2P lending, providing investors with a robust, scalable, and efficient analytical tool.
This study analyzes how financial sectors in 43 countries responded to the collapse of Silicon Valley Bank (SVB). The findings reveal significant adverse responses in Africa, Europe, and North America surrounding the event. In contrast, the equity market effects of SVB on financial sectors in the Asia–Pacific region (except Australia and Japan), Latin America, the Gulf Corporation Council, and Israel were limited. Additional tests reveal that financial sectors with high exchange rate sensitivity (local currency vis-a-vis US dollar) and those with strong equity correlations to the US financial sector experienced higher losses. Finally, greater investor attention to the SVB failure, as measured by Google Search Volume, is a significant determinant of pre-event market responses.
This paper investigates the cross-sectional relation between climate policy uncertainty and expected stock returns in China. We quantify climate policy uncertainty in China based on news from two leading mainland newspapers: the Renmin Daily and the Guangming Daily. Using the sample of Chinese A-share manufacturing companies from 2008 to 2019, we find that stocks with lower climate policy uncertainty beta generate about 4
Institutional investors have been shown to impact firm performance. We extend on the literature by documenting how the impact varies with a firm's life cycle. Utilizing a large sample of U.S. corporations, from 1990 to 2020, empirical findings suggest a positive association between institutional ownership and firm performance for firms in the introduction and decline phases of the life cycle. Firms in the intro or decline phases with high institutional ownership exhibit a positive association between asset turnover and operating performance and an inverse association between operating expenses and firm performance. In these phases the drivers of firm performance function better in the presence of institutional ownership. We conclude that institutional investors' ability to positively impact performance is most significant at firms that require their involvement the most. Our results are robust to alternative firm life-cycle measures, model specification, and potential endogeneity concerns.
