Search Results (121)

Short-term trading presents a high-dimensional prediction problem, where the profitability of trading signals depends not only on model accuracy but also on how financial labels are defined and aligned with market dynamics. Traditional approaches often apply uniform modeling choices across assets, overlooking the asset-specific nature of volatility, liquidity, and market response. In this work, we introduce a structured, label-aware machine learning pipeline aimed at maximizing short-term trading profitability across four major benchmarks: S&P 500 (SPX), NASDAQ-100 (NDX), Dow Jones Industrial Average (DJI), and the Tadāwul All-Share Index (TASI and twelve of their most actively traded constituents). Our solution systematically evaluates all combinations of six model types (logistic regression, support vector machines, random forest, XGBoost, 1-D CNN, and LSTM), eight look-ahead labeling windows (3 to 10 days), and four feature subset sizes (44, 26, 17, 8 variables) derived through Random Forest permutation-importance ranking. Backtests are conducted using realistic long/flat simulations with zero commission, optimizing for Percentage Profit and Profit Factor on a 2005–2021 train/2022–2024 test split. The central contribution of the framework is a labeling-aware search mechanism that assigns to each asset its optimal combination of model type, look-ahead horizon, and feature subset based on out-of-sample profitability. Empirical results show that while XGBoost performs best on average, CNN and LSTM achieve standout gains on highly volatile tech stocks. The optimal look-ahead window varies by market from 3-day signals on liquid U.S. shares to 6–10-day signals on the less-liquid TASI universe. This joint model–label–feature optimization avoids one-size-fits-all assumptions and yields transferable configurations that cut grid-search cost when deploying from index level to constituent stocks, improving data efficiency, enhancing robustness, and supporting more adaptive portfolio construction in short-horizon trading strategies. Full article

The shift towards renewable energy demands decision-making tools that unite economic performance with environmental stewardship and social equity. The conventional evaluation methods fail to consider these interconnected factors, which results in substandard investment results. The paper establishes a sustainability accounting system that uses the Elimination and Choice Expressing Reality (ELECTRE) method to optimize investment distribution between solar power, wind power, and bioenergy systems. The evaluation framework uses six performance indicators, which include cost efficiency and return on investment, together with CO₂ emissions intensity, job creation, energy output, and financial sustainability indicators, like Net Present Value (NPV) and payback period. The barrier optimization algorithm solved the model in 10 iterations, which took 0.10 s to achieve an optimal objective value of 1.6929. The wind energy source demonstrated superior performance in every evaluation criterion because it achieved the highest concordance scores, lowest discordance levels, best payback period, and strongest NPV. The maximum allocation went to wind at 53.3%, while bioenergy received 31.0%, and solar received 16.7%. The optimized portfolio reached a total sustainability index (SI) of 1.70, which validates the method’s strength. The research shows that using ELECTRE with sustainability accounting creates an exact and open system for renewable energy investment planning. The framework reveals wind as the core alternative yet demonstrates how bioenergy and solar work together to support sustainable development across environmental and economic and social dimensions. Full article

In light of the growing interest in sustainable finance among investors and academics, in this study, we present an empirical analysis designed to understand whether sustainable investments outperform, underperform, or perform neutrally relative to conventional investments. The literature presents a spectrum of often-opposed conclusions, precluding the establishment of a definitive, consensus-driven judgment. Therefore, our analysis examines the behavior of sustainable investments within the Eurozone equity market from January 2019 to December 2023. Twenty portfolios are constructed to simulate sustainable investment strategies differentiated by environmental, social, and governance (ESG) strategy; stock inclusion/exclusion thresholds; and the type of ESG rating employed in the selection process. The analysis reveals that sustainable investments do not statistically significantly outperform or underperform traditional investments. This finding is significant for investors committed to ESG principles, as it suggests that they can align their investment choices with their ethical convictions without sacrificing performance. Full article

This paper proposes a dynamic portfolio allocation framework that integrates deep reinforcement learning (DRL) with classical portfolio optimization to enhance rebalancing strategies and risk–return management. Within a unified reinforcement-learning environment for portfolio reallocation, we train actor–critic agents (Proximal Policy Optimization (PPO) and Advantage Actor–Critic (A2C)). These agents learn to select both the risk-aversion level—positioning the portfolio along the efficient frontier defined by expected return and a chosen risk measure (variance, Semivariance, or CVaR)—and the rebalancing horizon. An ensemble procedure, which selects the most effective agent–utility combination based on the Sharpe ratio, provides additional robustness. Unlike approaches that directly estimate portfolio weights, our framework retains the optimization structure while delegating the choice of risk level and rebalancing interval to the AI agent, thereby improving stability and incorporating a market-timing component. Empirical analysis on daily data for 12 U.S. sector ETFs (2003–2023) and 28 Dow Jones Industrial Average components (2005–2023) demonstrates that DRL-guided strategies consistently outperform static tangency portfolios and market benchmarks in annualized return, volatility, and Sharpe ratio. These findings underscore the potential of DRL-driven rebalancing for adaptive portfolio management. Full article

This paper investigates an infinite-horizon optimal consumption and investment problem for an agent who consumes two types of goods: necessities and luxuries. The agent derives utility from both goods but faces a ratcheting constraint on luxury consumption, which prohibits any decline in its level over time. This constraint captures the irreversible nature of high living standards or luxury habits often observed in real economies. We formulate the problem in a complete financial market with a risk-free asset and a risky stock and solve it analytically using the dual–martingale method. The dual problem is shown to reduce to a family of optimal stopping problems, from which we derive explicit closed-form solutions for the value function and optimal policies. Our results reveal that the ratcheting constraint generates asymmetric consumption dynamics: necessities adjust freely, whereas luxuries exhibit downward rigidity. As a consequence, the marginal propensity to consume necessities declines with wealth, while luxury consumption and portfolio risk exposure increase more sharply compared to the benchmark case without ratcheting. The model provides a continuous-time microfoundation for persistent high consumption levels and greater risk-taking among wealthy individuals. Full article

This work looks for the optimal allocation of different assets, namely, the G7 stock indices, commodities (gold and WTI crude oil), cryptocurrencies (Bitcoin and Ripple), and S&P Green Bond, over four periods: before the COVID-19 crisis, during the COVID-19 crisis and before the Russia–Ukraine war, during the COVID-19 crisis and Russia–Ukraine war, and after the COVID-19 pandemic and during the Russia–Ukraine war. Metaheuristics, Non-dominated Sorting Genetic Algorithm (NSGAII), Strength Pareto Evolutionary Algorithm (SPEA2), and Particle Swarm Optimization (PSO) are applied to find the best allocation. The results reveal that there a significant preference for the S&P Green Bond during the four periods of study according to three algorithms, thanks to its portfolio diversification abilities. During the COVID-19 pandemic and the geopolitical crisis, the most optimal portfolio was Nikkei 225 because of its quick recovery from the pandemic and poor reliance on the Russia–Ukraine markets, while WTI crude oil and both dirty and clean cryptocurrencies were poor contributors to the investment portfolio because these assets are sensitive to geopolitical problems. After the end of the pandemic and during the ongoing Russia–Ukraine war, the three algorithms obtained remarkably different results: the NSGAII portfolio was invested in various assets, 32% of the SPEA2 portfolio was allocated to the S&P Green Bond, and half of the PSO portfolio was allocated to the S&P Green Bond too. This may be due to changes in investors’ preferences to protect their fortune and to diversify their portfolio during the war. From a risk-averse perspective, NSGAII does not underestimate the risk, while in terms of forecasting accuracy, PSO is an adequate algorithm. In terms of time, NSGAII is the fastest algorithm, while SPEA2 requires more time than the NSGAII and PSO algorithms. Our results have important implications for both investors and risk managers in terms of portfolio and risk management decisions, and they highlight the factors that influence investment choices during health and geopolitical crises. Full article

This paper shows that a common form of external habit persistence, despite having much success in asset pricing, implies an extreme degree of conformity in investors’ portfolio choice. If an investor with this utility function uses US aggregate consumption as her external habit benchmark, she has to hold all non-redundant securities contained in the US aggregate wealth portfolio. Even for an investor who uses the average consumption of a more narrowly-defined community as her benchmark, she is still required to hold non-zero positions in all (non-redundant) individual stocks held by any other member of the community. If markets are incomplete, even if an individual investor holds a financial portfolio that conforms perfectly with that associated with the external habit benchmark, it is still impossible for the investor to ensure that consumption exceeds habit in all states of the world. Because of this implication, this form of external habit is unlikely to describe the preferences of individual investors—notwithstanding its success as a model for the representative agent in asset pricing. Full article

Financial time-series data often exhibit statistically significant skewness and heavy tails, and numerous flexible distributions have been proposed to model them. In the context of the Log-linear Realized GARCH model with Skew-t (ST) distributions, our objective is to explore how the choice of prior distributions in the Adaptive Random Walk Metropolis method and initial parameter values in the Generalized Reduced Gradient (GRG) Solver method affect ST parameter and log-likelihood estimates. An empirical study was conducted using the FTSE 100 index to evaluate model performance. We provide a comprehensive step-by-step tutorial demonstrating how to perform estimation and sensitivity analysis using data tables in Microsoft Excel. Among seven ST distributions—namely, the asymmetric, epsilon, exponentiated half-logistic, Hansen, Jones–Faddy, Mittnik–Paolella, and Rosco–Jones–Pewsey distributions—Hansen’s ST distribution is found to be superior. This study also applied the GRG method to estimate new approaches, including Realized Real-Time GARCH, Realized ASHARV, and GARCH@CARR models. An empirical study showed that the GARCH@CARR model with the feedback effect provides the best goodness of fit. Out-of-sample forecasting evaluations further confirm the predictive dominance of models incorporating real-time information, particularly Realized Real-Time GARCH for volatility forecasting and Realized ASHARV for 1% VaR estimation. The findings offer actionable insights for portfolio managers and risk analysts, particularly in improving volatility forecasts and tail-risk assessments during market crises, thereby enhancing risk-adjusted returns and regulatory compliance. Although the GRG method is sensitive to initial values, its presence in the spreadsheet method can be a powerful and promising tool in working with probability density functions that have explicit forms and are unimodal, high-dimensional, and complex, without the need for programming experience. Full article

Ensemble learning techniques continue to show greater interest in forecasting the volatility of cryptocurrency assets. In particular, XGBoost, an ensemble learning technique, has been shown in recent studies to provide the most accurate forecast of Bitcoin volatility. However, the performance of XGBoost largely depends on the tuning of its hyperparameters. In this study, we examine the effectiveness of the Bayesian optimization method for tuning the XGBoost hyperparameters for Bitcoin volatility forecasting. We chose to explore this method rather than the most commonly used manual, grid, and random hyperparameter choices due to its ability to predict the most promising areas of hyperparameter spaces through exploitation and exploration using acquisition functions, as well as its ability to minimize error with a reduced amount of time and resources required to find an optimal configuration. The obtained XGBoost configuration improves the forecast accuracy of Bitcoin volatility. Our empirical results, based on letting the data speak for itself, could be used for a comparative study on Bitcoin volatility forecasting. This would also be important for volatility trading, option pricing, and managing portfolios related to Bitcoin. Full article

We study a finite-horizon optimal job-switching and portfolio allocation problem where an agent faces a mandatory retirement date. The agent can freely switch between two jobs with differing levels of income and leisure. The financial market consists of a risk-free asset and a risky asset, with the agent making dynamic consumption, investment, and job-switching decisions to maximize lifetime utility. The utility function follows a Cobb–Douglas form, incorporating both consumption and leisure preferences. Using a dual-martingale approach, we derive the optimal policies and establish a verification theorem confirming their optimality. Our results provide insights into the trade-offs between labor income and leisure over a finite career horizon and their implications for retirement planning and investment behavior. Full article

Volatility modelling is a key feature of financial risk management, portfolio optimisation, and forecasting, particularly for market indices such as the JSE Top40 Index, which serves as a benchmark for the South African stock market. This study investigates volatility modelling of the JSE Top40 Index log-returns from 2011 to 2025 using a hybrid approach that integrates statistical and machine learning techniques through a two-step approach. The ARMA(3,2) model was chosen as the optimal mean model, using the auto.arima() function from the forecast package in R (version 4.4.0). Several alternative variants of GARCH models, including sGARCH(1,1), GJR-GARCH(1,1), and EGARCH(1,1), were fitted under various conditional error distributions (i.e., STD, SSTD, GED, SGED, and GHD). The choice of the model was based on AIC, BIC, HQIC, and LL evaluation criteria, and ARMA(3,2)-EGARCH(1,1) was the best model according to the lowest evaluation criteria. Residual diagnostic results indicated that the model adequately captured autocorrelation, conditional heteroskedasticity, and asymmetry in JSE Top40 log-returns. Volatility persistence was also detected, confirming the persistence attributes of financial volatility. Thereafter, the ARMA(3,2)-EGARCH(1,1) model was coupled with XGBoost using standardised residuals extracted from ARMA(3,2)-EGARCH(1,1) as lagged features. The data was split into training (60%), testing (20%), and calibration (20%) sets. Based on the lowest values of forecast accuracy measures (i.e., MASE, RMSE, MAE, MAPE, and sMAPE), along with prediction intervals and their evaluation metrics (i.e., PICP, PINAW, PICAW, and PINAD), the hybrid model captured residual nonlinearities left by the standalone ARMA(3,2)-EGARCH(1,1) and demonstrated improved forecasting accuracy. The hybrid ARMA(3,2)-EGARCH(1,1)-XGBoost model outperforms the standalone ARMA(3,2)-EGARCH(1,1) model across all forecast accuracy measures. This highlights the robustness and suitability of the hybrid ARMA(3,2)-EGARCH(1,1)-XGBoost model for financial risk management in emerging markets and signifies the strengths of integrating statistical and machine learning methods in financial time series modelling. Full article

We investigate whether a belief in trend continuation, often associated with the so-called “hot-hand effect,” can be endogenously triggered by personal performance feedback in a controlled financial experiment. Participants allocated funds across assets with randomly generated prices, under conditions of known probabilities and varying levels of risk. In a two-stage setup, participants were first exposed to random price sequences to learn the task and potentially develop perceptions of personal success. They then faced additional price paths under incentivized conditions. Our findings show that participants initially increased purchases following gains—consistent with a feedback-driven belief in momentum—but this pattern faded over time. When facing sustained losses, loss aversion dominated decision-making, overriding early optimism. These results highlight how cognitive heuristics and emotional biases interact dynamically, suggesting that belief in trend continuation is context-sensitive and constrained by the reluctance to realize losses. Full article

Dietary choices and patterns have enormous consequences along the lines of individual, community, and planetary health. Excess meat consumption has been linked to chronic disease risk, and at large scales, the underlying industries maintain a massive environmental footprint. For these reasons, public and planetary health experts are unified in emphasizing a whole or minimally processed plant-based diet. In response, the purveyors of ultra-processed foods have added “meat alternatives” to their ultra-processed commercial portfolios; multinational corporations have been joined by “start-ups” with new ultra-processed meat analogues. Here, in our Viewpoint, we revisit the 1990s food industry rhetoric and product innovation, a time in which multinational corporations pushed a great “low-fat transition.” We focus on the McLean Deluxe burger, a carrageenan-rich product introduced by the McDonald’s Corporation in 1991. Propelled by a marketing and media-driven fear of dietary fats, the lower-fat burger was presented with great fanfare. We reflect this history off the current “great protein transition,” a period once again rich in rhetoric, with similar displays of industry detachment from concerns about the health consequences of innovation. We scrutinize the safety of carrageenan and argue that the McLean burger should serve as a cautionary tale for planetary health and 21st century food innovation. Full article

This study explores modernization strategies for existing district heating (DH) networks to enhance their efficiency and sustainability, focusing on achieving net-zero emissions in urban heating systems. Building upon a literature review and expert interviews, we developed a strategic decision-support framework that outlines distinct strategies for retrofitting district heating grids and includes a portfolio analysis. This framework serves as a tool to guide DH operators and stakeholders in selecting well-founded modernization pathways by considering technical, economic, and social dimensions. The review identifies several promising measures, such as reducing operational temperatures at substations, implementing optimized substations, integrating renewable and waste heat sources, implementing thermal energy storage (TES), deploying smart metering and monitoring infrastructure, and expanding networks while addressing public concerns. Additionally, the review highlights the importance of stakeholder engagement and policy support in successfully implementing these strategies. The developed strategic decision-support framework helps practitioners select a tailored modernization strategy aligned with the local context. Furthermore, the findings show the necessity of adopting a comprehensive approach that combines technical upgrades with robust stakeholder involvement and supportive policy measures to facilitate the transition to sustainable urban heating solutions. For example, the development of decision-support tools enables stakeholders to systematically evaluate and select grid modernization strategies, directly helping to reduce transmission losses and lower greenhouse gas (GHG) emissions contributing to climate goals and enhancing energy security. Indeed, as shown in the reviewed literature, retrofitting high-temperature district heating networks with low-temperature distribution and integrating renewables can lead to near-complete decarbonization of the supplied heat. Additionally, integrating advanced digital technologies, such as smart grid systems, can enhance grid efficiency and enable a greater share of variable renewable energy thus supporting national decarbonization targets. Further investigation could point to the most determining context factors for best choices to improve the sustainability and efficiency of existing DH systems. Full article

Recent developments in dynamic portfolio optimization have focused on the role played by portfolio frictions. Portfolio frictions make the portfolio’s response to financial shocks weaker and more gradual than in a model without frictions. At the same time, institutional investors are prevented from short-selling, a situation in which investors are restricted from taking negative positions in an asset, while other types of investors can short-sell. However, the literature has not yet discussed the implication of a short-selling constraint in a model of optimal portfolio choice with frictions. We solve a general equilibrium model of portfolio choice with frictions and a short-selling constraint. The model features investors who own firms and allocate capital across firms, households who work in the firms and earn revenues, and firms that produce the final good using capital and labor and redistribute profits to investors. We show the conditions under which negative financial conditions reduce the optimal share invested in a firm to zero. Finally, we simulate the model to show that the short-selling constraint prevents investors from amplifying financial shocks, which leads to a more stable business cycle. Our results are important for financial regulators as they suggest forbidding short-selling. Full article