David Toscano

Purpose: Standard Gaussian risk frameworks underestimate extreme joint losses in large equity universes, exposing investors to unbudgeted drawdowns. This article develops and tests a portfolio-construction pipeline that disciplines tail risk while exploiting forward-looking return signals. Design/methodology/approach: The four-stage pipeline integrates a rolling tail-index (Hill) filter, cluster pre-selection, a Conditional Value-at-Risk (CVaR) risk-parity optimizer with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and extreme value theory (EVT) marginals and a Student-t copula, and an eight-classifier machine-learning ensemble that reallocates weights within the risk budget. It is tested on S&P 500 constituents across 75 monthly walk-forward windows (January 2020–January 2026), net of transaction costs. Findings: Among twelve strategies, the pipeline delivers the highest Sharpe ratio (1.106) and monthly win rate (76.0%), surpassing risk parity, Hierarchical Risk Parity and the Gaussian CVaR specification it generalizes; block-bootstrap inference confirms its advantage over passive exposure (resampled mean Sharpe 1.316). Risk parity accumulates greater terminal wealth, a trade-off, since tail constraints curb high-momentum exposure in bull markets. Research limitations/implications: The analysis is limited to S&P 500 constituents and a January 2020–January 2026 evaluation period. Results may differ in other asset classes, international markets or under alternative transaction-cost assumptions. Future research could examine adaptive clustering schemes, alternative dependence structures and real-time implementation constraints. Practical implications: For investors prioritizing risk-adjusted efficiency over cumulative wealth, the framework is a transparent capital-allocation tool whose tail discipline holds at roughly 97 basis points annually. Originality/value: To the authors' knowledge, this is the first to combine tail-index screening, clustering, GARCH-EVT-copula CVaR optimization and a machine-learning overlay within one framework, jointly addressing the dimensionality, distributional misspecification and information-neglect problems that defeat these tools in isolation.

Whether richer information systematically improves portfolio allocation remains an open question in extreme-risk digital asset markets. This paper examines whether combining machine-learning-based return forecasts with intraday-informed volatility estimation enhances portfolio performance in NFT-ecosystem tokens. While prior evidence in traditional equity markets suggests that integrating predictive models with OHLC-based modified mean–variance (MMV) optimization can improve risk-adjusted returns, it is unclear whether such gains survive in highly volatile and non-stationary environments. Using a two-stage framework that separates return prediction from allocation, we compare classical mean–variance (MV), OHLC-based MMV, and naïve diversification strategies for portfolios of NFT-ecosystem tokens. Our results show that machine learning forecasts generate substantial economic value; however, incorporating intraday volatility into the optimization stage does not yield systematic improvements in risk-adjusted performance. Instead, the benefits of refined risk estimation are conditional on the portfolio’s correlation structure and the forecasting architecture employed. Overall, portfolio performance in NFT-ecosystem token markets is driven primarily by return predictability rather than increasingly sophisticated volatility modeling. The findings highlight a fundamental distinction between statistical improvements in risk measurement and their economic usefulness in allocation decisions in highly unstable financial markets.

In the valuation of forest resources, the alternative use of the land is one of the central themes. In most cases it is made without taking into account the uncertainty and the possible flexibility of the alternative use. Within these alternatives, the strategy of shifting to a more profitable and sustainable crop is a well-studied topic in forest research. Although the transformation opportunity could add great value to the project, the valuation of this flexibility is obviated by traditional discounted cashflow criteria (NPV). The application of real options theory (ROT) makes it possible to assess this flexibility based on the uncertainty that the transformation entails. However, the hypotheses that are made about the future evolution of the underlying asset, in this case the value of the new crop, may condition the precision of the result. Usually some researchers model these conversions under the hypothesis of geometric Brownian motion (GBM), hypotheses that are not plausible when the new crop has a strong seasonal component. In this work, an adapted model framework is proposed to evaluate forest transformation opportunity into another crop when land use has both high agronomic potential and high seasonal component, a context in which classic real options framework is not applicable. As a work based on a theoretical model, after methodological motivation, the strawberry crop is chosen as alternative due to its seasonal component. Using private data for this crop, we model through the Ornstein–Uhlenbeck process, with mean-reversion (MR) to a seasonal component, and then we use of Longstaff and Schwartz’s algorithm to calculate the option value. The results show that when considering flexibility in option valuation it leads to an increase on the return of more than 4%. Furthermore, robustness analysis evidence shows that option value is very sensitive to seasonal component, reinforcing previous evidence that suggests that the MR process offers a more accurate and appropriate valuation over the traditional GBM in the arena of agronomic potential valuation. Specifically, the result of valuing this transformation through the MR process is between 1.5 and 1.7 times the value of the NPV, which results in approximately a 13% annual return. If GBM had been used, the valuation would have been a 72% annual return, an unrealistic result in this context, due to the non-consideration of the seasonal mean-reverting prices process.

Recently, many papers have shown evidence of a positive association between financial market participation and wealth holdings. It is often claimed that individuals with a higher level of financial market participation exhibit a higher propensity for planning retirement. In their planning process, individuals seek to achieve an optimal wealth level before their retirement by considering both their average saving rate and their retirement age. In this paper, we tested whether UK individuals with a higher level of financial market participation and, therefore, with a higher propensity for planning retirement were more likely to delay their retirement age than individuals with lower financial participation. On the basis of regression analyses using the English Longitudinal Study of Aging (ELSA) database for waves 1–6, our results support the hypothesis of a positive relationship between financial market participation and retirement age, reinforcing previous results.

In this paper, we test whether the short-run econometric conditions for the basic assumptions of the Ohlson valuation model hold, and then we relate these results with the fulfillment of the short-run econometric conditions for this model to be effective. Better future modeling motivated us to analyze to what extent the assumptions involved in this seminal model are not good enough approximations to solve the firm valuation problem, causing poor model performance. The model is based on the well-known dividend discount model and the residual income valuation model, and it adds a linear information model, which is a time series model by nature. Therefore, we adopt the time series approach. In the presence of non-stationary variables, we focus our research on US-listed firms for which more than forty years of data with the required cointegration properties to use error correction models are available. The results show that the clean surplus relation assumption has no impact on model performance, while the unbiased accounting property assumption has an important effect on it. The results also emphasize the uselessness of forcing valuation models to match the value displacement property of dividends.

In this paper, we provide evidence on the role of conventional monetary policy in the dynamics of stock market bubbles. We analyze the response of stock market returns to monetary policy shocks but condition the analysis on both the direction of monetary policy surprises and business conditions. Following a two-step approach, we first use a structural vector autoregressive (SVAR) model to identify a proxy variable of monetary policy shocks, and then we apply a conditional regression to contemporary stock market returns and these monetary policy shocks to extract the implicit relationship between these variables in different scenarios. Our results show that monetary policy does not impact on stock market returns in a significant form in the scenario defined by positive shocks and expansion periods, i.e. the lower effectiveness of restrictive monetary policy shocks coincides with the phase of the business cycle in which bubbles arise.

This paper is focused on the measurement of interest rate risk of nonfinancial firms. The measurement is the initial step in the risk management, which, in the context of financial risks, it is expected to lead to better levels of enterprises’ financial sustainability. Concretely, we checked the performance of alternative estimation procedures of the implied equity duration as a measure of the exposure to interest rate risk of firms listed on a small stock market. Previous evidence in the US stock market shows that when the implied equity duration is computed using industry-specific parameters instead of market parameters, significant differences arise in their absolute and relative values and even in their ranking. In this paper, we checked the robustness of these results when we moved to a smaller stock market. To do so, we replicated previous analyses carried out in the Spanish stock market but using alternative estimation procedures. We conclude that significant differences arise in the implied equity duration estimations when we consider industry-specific parameters instead of market parameters. This finding in a small stock market is in line with previous evidence found for the US stock market.

This paper analyses the effects of the mandatory adoption of International Financial Reporting Standards (IFRS) by Spanish listed companies in January 2005 on unconditional conservatism. The lack of robustness in previous evidence justifies analysis of this issue from a different perspective. To this end, we use, for the first time in this context, Ahmed and Duellman's (2007) methodology. These authors consider the impact of growth options and other future income, controlling for the idiosyncratic factors that the literature has found condition this type of conservatism. Additionally, beyond the pooled regression techniques usually used, we use econometric panel data techniques, which minimise the possible effect of endogeneity in the estimation of the proposed models. The results provide new evidence which supports our hypothesis that the adoption of IFRS has reduced the unconditional conservatism of Spanish listed companies.