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Long-term forecasts about commodity market indicators play an important role in informing policy and investment decisions by governments and market participants. Our study examines whether the accuracy of the multi-step forecasts can be improved using deep learning methods.

We first formulate a supervised learning problem and set benchmarks for forecast accuracy using traditional econometric models. We then train a set of deep neural networks and measure their performance against the benchmark.

We find that while the United States Department of Agriculture (USDA) baseline projections perform better for shorter forecast horizons, the performance of the deep neural networks improves for longer horizons. The findings may inform future revisions of the forecasting process.

This study demonstrates an application of deep learning methods to multi-horizon forecasts of agri-cultural commodities, which is a departure from the current methods used in producing these types of forecasts.

Debt, dividend and investment policy constitutes a company's important financial decisions to determine firm performance. The research emphasizes on the problem of overinvestment, a phenomenon that worsens firm operation. Furthermore, it clarifies the moderation role of debt and dividend policy in mitigating the negative effect of overinvestment on firm performance in the case of Vietnamese listed companies.

The research uses all financial statement of non-financial Vietnamese listed companies on Ho Chi Minh and Hanoi Stock Exchange in the period of 2008–2018. The data are collected from Thomson Reuters Eikon. The final data set is comprised of 669 listed companies. The study measures overinvestment though investment demand function and HP filter. Moreover, the research employs the dynamic model, so it has to apply the SGMM method to deal with the problem of endogeneity caused by the lagged dependent variable.

The research finds that overinvestment is negatively associated with firm performance. Debt or dividend policy separately can moderate the negative effect of overinvestment on firm performance. However, when these two policies are combined, they lessen the positive interaction impact of each policy due to the substitution between debt and dividend policy.

The research may have two limitations. Firstly, the research measures overinvestment indirectly through investment demand function and HP filter. These two measures only help identify the sign that companies may have the problem of overinvestment because we cannot determine whether they overinvest or not in reality. Secondly, when using interaction variables, the problem of multicollinearity may be higher, and this may adjust the signs and significance level of variables in the models.

Practically, the research proposes three policy recommendations. Firstly, a company can exploit debt or dividend policy to limit excessive free cash flow in order to constrain the problem of overinvestment. Secondly, a company should enhance its corporate governance to resolve agency problems. Thirdly, the government should make the financial sector more transparent and effective to improve monitoring functions of various parties in the capital market.

Overinvestment sometimes can cause social issues. Overinvestment means that companies make ineffective investment. If they continue this situation over a long time, companies may have financial distress or even go bankruptcy. As a result, it will slow down economic growth and increase unemployment in the economy.

The research is supposed to make two great contributions to the existing empirical studies in two aspects. Firstly, it is the first attempt to take into consideration the interaction between overinvestment and financial policies. Secondly, it helps enhance the fundamental stance of the agency theory, which supports the interdependence of debt, dividend and investment policy.

The objective of this paper is twofold. First, to study the safe-haven characteristic of the Islamic stock indexes and Ṣukūk during the crises time. Second, to evaluate this property in the last pandemic. This study employs the daily dataset from June 15, 2015, to June 15, 2020, for the most affected countries by the earlier disease.

This study uses the Markov-switching Capital Asset Pricing Model (CAPM) approach and the basic CAPM for the main analysis and the safe haven index (SHI) recently developed by Baur and Dimpfl (2021) for the robustness test.

Based on Baur and Lucey's (2010) definition, empirical findings indicate that Islamic stock indexes cannot be a refuge throughout the crisis regime for all selected conventional markets. However, Ṣukūk are a strong refuge in Brazilian, Russian and Malaysian markets. For the remainder countries, except Italy, the USA and Spain, the Ṣukūk index offers weak protection against serious conventional market downturns. Similar conclusions are obtained during the COVID-19 global crisis period. Finally, results are confirmed by using the SHI.

To the best of the authors’ knowledge, this paper is the first study that evaluates the safe haven effectiveness of the Islamic index and Ṣukūk using the SHI in the most impacted countries by the COVID-19 outbreak.