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This paper examines how to build a portfolio and assess the impact of the COVID-19 on portfolio performance using the Sharpe single index model. The research sample consists of ten high market capitalization stocks representing five price fractions of the population listed stocks on the Indonesia Stock Exchange during the COVID-19 outbreak from March 1 to May 31, 2020. The results show that there are four stocks that are included in the portfolio formation, namely CASA with a proportion of 50%, BNLI with a proportion of 26 %, UNVR with a proportion of 15%, and HMSP with a proportion of 9%. Based on portfolio performance testing using the Sharpe single index model, it is known that the portfolio during the COVID-19 has a negative Sharpe ratio, meaning that portfolio performance is underperforming. The findings provide evidence that COVID-19 has had a negative impact on the stock market so that many investors have suffered losses on their portfolios. The implications of findings are that investors must evaluate portfolio performance and restructure the formation of new portfolios by considering the COVID-19 pandemic outbreak as a systematic risk factor that can determine the expected returns.

This paper empirically investigates the performance of portfolio screening strategies based on ESG (Environmental, Social, Governance) scores, by testing three main hypotheses motivated by the introduction of sustainability considerations within portfolio theory: i) ESG screened portfolios overperform the benchmark in the long term only if the exclusion threshold is low; ii) ESG screened portfolios do not overperform the benchmark in the short term independently of the exclusion threshold; iii) ESG screened portfolios overperform the benchmark in terms of systemic risk in periods of financial distress. To this end, negative and positive screening strategies based on Bloomberg ESG disclosure scores and different screening thresholds are set up from the 559 stocks belonging to the EURO STOXX index in the period 2007–2021. The risk-adjusted performance of the ESG screened portfolios is compared with the benchmark-passive one based on Sharpe Ratio (SR) and alphas (from both a one-factor model and the Carhart four-factor model) so as to test performance over total and systemic risk respectively. Two main results emerge. First, we prove overperformance of screened portfolios only in the long run and in the presence of negative screening strategies with a low screening threshold. Second, we do not find clear evidence of over-compensation for systemic risk in all periods of financial distress, thus the alleged safe-haven property of ESG portfolios is not always there. Given the increasing attention for sustainability, our results have relevant implications for both individual investors and the asset management industry.

This study investigates the application of large language models (LLMs) in sentiment analysis of financial news and their use in developing effective investment strategies. We conducted sentiment analysis on news articles related to the top 30 companies listed on Nasdaq using both discriminative models such as BERT and FinBERT, and generative models including Llama 3.1, Mistral, and Gemma 2. To enhance the robustness of the analysis, advanced prompting techniques—such as Chain of Thought (CoT), Super In-Context Learning (SuperICL), and Bootstrapping—were applied to generative LLMs. The results demonstrate that long strategies generally yield superior portfolio performance compared to short and long–short strategies. Notably, generative LLMs outperformed discriminative models in this context. We also found that the application of SuperICL to generative LLMs led to significant performance improvements, with further enhancements noted when both SuperICL and Bootstrapping were applied together. These findings highlight the profitability and stability of the proposed approach. Additionally, this study examines the explainability of LLMs by identifying critical data considerations and potential risks associated with their use. The research highlights the potential of integrating LLMs into financial strategy development to provide a data-driven foundation for informed decision-making in financial markets.

In this article, we studied the Corporate Sustainability Index (ISE) of the Brazilian Mercantile, Futures and Stock Exchange (BM&FBOVESPA), with the main objective of analyzing the performance of sustainable investments in the Brazilian stock market, during the period from December 2005 to December 2010. To achieve this aim, we characterized ISE portfolios and we compared its performance with the IBOVESPA (representing the market portfolio) and other BM&FBOVESPA sectoral indices. In the performance comparison, we used level of liquidity, return and risk indicators, as well as the following measures: Sharpe, Treynor, Sortino, and Omega. Our results show that although sustainable investments have presented some interesting characteristics, such as increasing liquidity and low diversifiable risk, they did not achieve satisfactory financial performance in the analysis period. This indicates that the constraints imposed by this type of investment in capital allocation in Brazil may be harming their return and risk attractiveness.

The objective of this study is to evaluate assets’ performance by considering the exit time within the risk measurement framework alongside Shannon entropy and, alternatively, excluding these factors, which can be used to create a portfolio aligned with short- or long-term objectives. This portfolio effectively balances the potential risks and returns, guiding investors to make decisions that are in line with their financial goals. To assess the performance, we used data envelopment analysis (DEA), whereby we utilized the risk measure as an input and the mean return as an output. The stop point probability–CVaR (SPP-CVaR) was the risk measurement used when considering the exit time. We calculated the SPP-CVaR by converting the risk-neutral density to the real-world density, calibrating the parameters, running simulations for price paths, setting the stop-profit points, determining the exit times, and calculating the SPP-CVaR for each stop-profit point. To account for negative data and to incorporate the exit time, we have proposed a model that integrates the mean return and SPP-CVaR, utilizing DEA. The resulting inefficiency scores of this model were compared with those of the mean-CVaR model, which calculates the risk across the entire time horizon and does not take the exit time and Shannon entropy into account. To accomplish this, an analysis was conducted on a portfolio that included a variety of stocks, cryptocurrencies, commodities, and precious metals. The empirical application demonstrated the enhancement of asset selection for both short-term and long-term investments through the combined use of Shannon entropy and the exit time.

We provide the first systematic evidence on the link between long-short anomaly portfolio returns—a cornerstone of the cross-sectional literature—and the timeseries predictability of the aggregate market excess return. Using 100 representative anomalies from the literature, we employ a variety of shrinkage techniques (including machine learning, forecast combination, and dimension reduction) to efficiently extract predictive signals in a high-dimensional setting. We find that longshort anomaly portfolio returns evince statistically and economically significant out-of-sample predictive ability for the market excess return. The predictive ability of anomaly portfolio returns appears to stem from asymmetric limits of arbitrage and overpricing correction persistence.

The study addresses the growing popularity and need of green investing. Green investing have been shown to churn lesser yields and underperform general market portfolios. Rapid growth of green bonds, green funds and green theme indices worldwide indicate towards the growing segment within investment community. The ethical screens lead to crunching of investable universe as a result such funds are expected to lose on diversification benefits. The study attempts to investigate the performance of green and non-green portfolios during the crisis and validate the differential impact of crisis on their demand. It further examines the impact of market cycles on the returns of portfolios. The period is classified into pre-crisis, crisis and post-crisis period. Asset pricing models believed to explain the returns on well diversified market portfolio have been applied on constructed green and non-green portfolios to measure the abnormal return. Green portfolios are noticed to be picking pace and outperforming market after the crisis surpassed. Indian investors are not penalizing companies for their green initiatives and such initiatives are believed to drive demand for the stock.

Purpose: Using a portfolio comprising liquid global stocks and bonds, this study aims to limit absolute risk to that of a standardised benchmark and determine whether this has a significant impact on expected return in both high volatility period (HV) and low volatility period (LV). Design/methodology/approach: Using a traditional benchmark comprising 40% equity and 60% bonds, a constant tracking error (TE) frontier was constructed and implemented. Portfolio performance for different TE constraints and different economic periods (expansion and contraction) was explored. Findings: Results indicate that during HV, replicating benchmark portfolio risk produces portfolios that outperform both the maximum return (MR) portfolio and the benchmark. MR portfolios outperform those with the same risk as that of the benchmark in LV. The MR portfolio weights assets to obtain the highest return on the TE frontier. During HV, the benchmark replicated risk portfolio obtained a higher absolute risk value than that of the MR portfolio because of an inefficient benchmark. In HV, the benchmark replicated risk portfolio favoured intermediate maturity treasury bills. Originality/value: There is a dearth of literature exploring the performance of active portfolios subject to TE constraints. This work addresses this gap and demonstrates, for the first time, the relative portfolio performance of several standard portfolio choices on the frontier.

We provide a rationale for window dressing wherein investors respond to conflicting signals of managerial ability inferred from a fund's performance and disclosed portfolio holdings. We contend that window dressers make a risky bet on their performance during a reporting delay period, which affects investors' interpretation of the conflicting signals and hence their capital allocations. Conditional on good (bad) performance, window dressers benefit (suffer) from higher (lower) investor flows compared with non–window dressers. Window dressers also show poor past performance, possess little skill, and incur high portfolio turnover and trade costs, characteristics which in turn result in worse future performance.

Purpose – Extant literature has looked at the effect of alliance capability and organizational culture on alliance portfolio performance, but the relationship between the two has not been explored. The purpose of this paper is to explore the hypothesis that an alliance supportive culture is not only fostered by a firm’s alliance capabilities, but that it mediates the relationship between capabilities and performance. Design/methodology/approach – Survey responses from 190 alliance managers, collected using a two-stage process, were analyzed to investigate the interrelationship of firm-level alliance capability, alliance supportive culture and portfolio performance. Findings – Alliance supportive culture was found to mediate the relationship between alliance capability and alliance portfolio performance. This finding suggests that in order to effectively manage a firm’s portfolio of alliances, the benefits of alliance capability must be transferred broadly into the organization’s cultural orientation toward alliances. Research limitations/implications – Further research may extend this analysis to explore the effect of subcomponents of alliance capability and alliance culture to better understand fine-grained influences on alliance performance. The findings of this study also may be extended to inform how supportive culture orientation affects partner selection, negotiation and time to performance. Practical implications – Managers should utilize culture-building actions as a way of extending the value of their firms’ alliance capabilities in order to improve their effectiveness across the portfolio. Originality/value – Extant studies have considered the discrete effects of capability and cultural orientation on alliance portfolio success, but the mediation effect has not previously been investigated. The findings also identify a boundary condition for the benefit of alliance capabilities on portfolio performance.