Search Results (10)

In this study, we introduce a detailed framework for predicting market conditions and selecting stocks by integrating machine learning techniques with ontological financial analysis. The process starts with ontology-based stock selection, categorizing companies using fundamental financial indicators such as liquidity, profitability, debt ratios, and growth metrics. For instance, firms showcasing favorable debt-to-equity ratios along with robust revenue growth are identified as high-performing entities. This classification facilitates targeted analyses of market dynamics. To predict market states—categorizing them into bull, bear, or neutral phases—the framework utilizes a Non-Stationary Markov Chain (NMC), BERT, to assess sentiment in financial news articles and Long Short-Term Memory (LSTM) networks to identify temporal patterns. Key inputs like the Sentiment Index (SI) and Illiquidity Index (ILLIQ) play essential roles in dynamically influencing regime predictions within the NMC model; these inputs are supplemented by variables including GARCH volatility and VIX to enhance predictive precision further still. Empirical findings demonstrate that our approach achieves an impressive 97.20% accuracy rate for classifying market states, significantly surpassing traditional methods like Naive Bayes, Logistic Regression, KNN, Decision Tree, ANN, Random Forest, and XGBoost. The state-predicted strategy leverages this framework to dynamically adjust portfolio positions based on projected market conditions. It prioritizes growth-oriented assets during bull markets, defensive assets in bear markets, and maintains balanced portfolios in neutral states. Comparative testing showed that this approach achieved an average cumulative return of 13.67%, outperforming the Buy and Hold method’s return of 8.62%. Specifically, for the S&P 500 index, returns were recorded at 6.36% compared with just a 1.08% gain from Buy and Hold strategies alone. These results underscore the robustness of our framework and its potential advantages for improving decision-making within quantitative trading environments as well as asset selection processes. Full article

This paper demonstrates whether the impact of agency costs on firm value depends on the level of managerial power using the fraction of capital held by the manager, as well as their level of voting rights. Focusing on a sample of 120 non-financial French firms incorporated in the CAC All-Tradable Index for the period 2008–2022, the first empirical analysis provides strong evidence that agency costs of equity, as measured in terms of operating expenses, administrative expenses and the agency cost of free cash flow, exert a negative impact on the firm’s market value. In a second empirical analysis, we split our sample into three sub-samples with the aim of capturing the effect of managerial power. The findings lead us to believe that the association between the agency cost measurement and the firm’s market value depend on the level of managerial power. This paper challenges prior studies by strengthening our understanding of managerial behavior (incentive, neutral, and entrenchment) in relation to shareholder wealth. Furthermore, it contributes to the recent literature by enabling a better knowledge of the disparity related to studies conducted in other countries with different governance models. Full article

We examined the impact of financing decisions on a firm’s sustainability in China as it aspires to achieve carbon neutrality. To proxy firms’ sustainability performance, we proposed an index for environmental, social, and governance (ESG) performance. The financing decision was proxied by debt funding and equity funding. Using secondary data from China Stock Market Accounting Data from 2016 to 2022, we utilize the fixed effect and fully modified ordinary least squares estimators for the empirical analysis. The analysis indicated a favorable link between debt funding and ESG performance. We uncovered an inconsistent association between equity funding and ESG performance. Moreover, ownership concentration revealed a significant role in moderating the impact of debt financing and ESG performance in China. The findings affirm that firms should rely on debt funding rather than equity funding to enhance their ESG performance. Hence, policymakers should enact laws allowing easy access to debt funding for companies to ensure higher ESG performance. This, in the long term, will contribute to the Chinese dream of carbon neutrality. Full article

Using a standard event study methodology and the EGARCH model, this study examined the depth of market anomaly at the onset of the Russia–Ukraine conflict in 2022. Equity markets in Africa and G7 nations were analyzed for their varied political and economic connections to the conflict. While the G7 nations were strongly opposed to Russia, African countries remained neutral. This study shows that abnormal losses in the initial period of the conflict were larger and more persistent in the G7 markets, contradicting the widely held notion that more developed equity markets are more efficient than the less developed markets. EGARCH results revealed that volatility persistence was widely present, although the leverage effect was only confirmed for U.S. and Canada. Throughout the period, commodity prices rose sharply, producing significant abnormal gains in the futures market. Unfortunately, this had a deleterious effect on African economies due to their heavy reliance on grain and fuel imports, all of which are priced in U.S. dollars, and which also rose sharply during the period. This study concludes with suggestions on how to mitigate currency and commodity price shocks to dollar-reliant and import-dependent economies. Full article

In recent years, a great deal of attention has been devoted to the use of neural networks in portfolio management, particularly in the prediction of stock prices. Building a more profitable portfolio with less risk has always been a challenging task. In this study, we propose a model to build a portfolio according to an equity-market-neutral (EMN) investment strategy. In this portfolio, the selection of stocks comprises two steps: a prediction of the individual returns of stocks using LSTM neural network, followed by a ranking of these stocks according to their predicted returns. The stocks with the best predicted returns and those with the worst predicted returns constitute, respectively, the long side and the short side of the portfolio to be built. The proposed model has two key benefits. First, data from historical quotes and technical and fundamental indicators are used in the LSTM network to provide good predictions. Second, the EMN strategy allows for the funding of long-position stocks by short-sell-position stocks, thus hedging the market risk. The results show that the built portfolios performed better compared to the benchmarks. Nonetheless, performance slowed down during the COVID-19 pandemic. Full article

This study explores the association of novel COVID-19 with the dominant financial assets, global uncertainty, commodity prices, and stock markets of the top ten corona-affected countries. We employ a wavelet coherence technique to unearth this linkage using daily data of COVID-19 deaths and reported cases from 1 January 2020 until 26 February 2021. The study finds a weak coherence between COVID-19 and global uncertainty variables in the short and medium term, while a strong positive correlation has been witnessed in the long run. The COVID-19 cases impact the stock markets in the short and medium term, while no significant impact is reported in the long run. On the other hand, a substantial impact of the COVID-19 outbreak has also been found on the exchange rate. In addition, the real asset market, such as gold, remains more stable during the COVID-19 outbreak. Thus, the study recommends that investors and portfolio managers should add such assets to their investment options to safeguard the excessive risk and downside momentum of the equity market. The study also has implications for regulators who are concerned with the neutrality of the COVID-19 effect and market stability. Full article

We empirically test predictability on asset price using stock selection rules based on maximum drawdown and its consecutive recovery. In various equity markets, monthly momentum- and weekly contrarian-style portfolios constructed from these alternative selection criteria are superior not only in forecasting directions of asset prices but also in capturing cross-sectional return differentials. In monthly periods, the alternative portfolios ranked by maximum drawdown measures exhibit outperformance over other alternative momentum portfolios including traditional cumulative return-based momentum portfolios. In weekly time scales, recovery-related stock selection rules are the best ranking criteria for detecting mean-reversion. For the alternative portfolios and their ranking baskets, improved risk profiles in various reward-risk measures also imply more consistent prediction on the direction of assets in future. Moreover, turnover rates of these momentum/contrarian portfolios are also reduced with respect to the benchmark portfolios. In the Carhart four-factor analysis, higher factor-neutral intercepts for the alternative strategies are another evidence for the robust prediction by the alternative stock selection rules. Full article

The world is fighting against the impacts of the climate crisis. Although the technical feasibility of 100% renewable energy systems was already verified by a variety of research studies, there were still more than 200 GW of unsustainable new coal power capacity under construction at a global level in 2018. To achieve the required carbon neutrality, current energy systems need to be transformed toward sustainable energy. The review of the literature has shown that several barriers for carbon-neutral technologies exist, which currently impede the sustainable transition. This paper focuses on the development of an enabling framework to overcome existing barriers to facilitate sustainable and carbon-neutral technologies at the national level. Additionally, it should support decision makers to consider all underlying criteria of this urgently needed energy transition. The criteria of such an enabling framework can be classified in 11 categories, which are (1) environmental and ecological protection; (2) society, culture, and behavior; (3) equity and justice; (4) knowledge; (5) energy markets; (6) energy policy; (7) legal requirements; (8) finance; (9) institutions; (10) infrastructure; and (11) clash of interests. Even though some criteria differ from country to country, a strong governmental support for the transition is always required to be successful. Full article

The process of reaching carbon neutrality by 2050 and cutting CO₂ emissions by 2030 by 55% compared to 1990 as per the EU Green Deal is highly complex. The energy mix must be changed to ensure long-term environmental sustainability, mainly by closing down coal sites, while preserving the energy-intensive short-term economic growth, ensuring social equity, and opening opportunities for regions diminishing in population and potential. Romania is currently in the position of deciding the optimal way forward in this challenging societal shift while morphing to evidence-based policy-making and anticipatory governance, mainly in its two coal-mining regions. This article provides possible future scenarios for tackling this complex issue in Romania through a three-pronged, staggered, methodology: (1) clustering Romania with other similar countries from the point of view of the Just Transition efforts (i.e., the energy mix and the socio-economic parameters), (2) analyzing Romania’s potential evolution of the energy mix from the point of the thermal efficiency of two major power plants (CEH and CEO) and the systemic energy losses, and (3) providing insights on the socio-economic context (economic development and labor market transformations, including the component on the effects on vulnerable consumers) of the central coal regions in Romania. Full article

The representation of women among STEM doctorates has grown over the past decades but the underrepresentation of women in the STEM labor force persists. This paper examines the immediate post-degree employment outcomes of nine cohorts of STEM doctorates who attained their degrees between 1995 and 2013. The results reveal both progress toward gender equity and persistent inequities. Contrary to historical gender disparities, a small female advantage has emerged in the attainment of tenure-track faculty positions, women are increasingly less likely than men to enter postdoctoral positions, and the flow of STEM doctorates into business and industry, which was once male dominated, is now gender neutral. Among the doctorates who do not follow the doctorate-to-faculty career path, women are as likely as men to “stay in STEM,” but less likely to attain research-oriented jobs. Gender segregation in occupational attainment and significant gender gaps in earnings, however, continue to be defining characteristics of the STEM labor force. The results show that the labor market disparities vary across STEM fields but are largely not attributable to the gendered impact of parenthood and dual-career marriage. Full article