Small scale mining has always been part of life in Ghana, but its illegal form known as galamsey has grown into one of the country’s biggest problems. Galamsey gives work to many people, yet still it destroys rivers, forests and farms, leaving communities struggling to survive. This paper looks at the laws that guide small scale mining in Ghana, from the days of traditional mining, through the colonial period, the Small Scale Gold Mining Law of 1989, and the Minerals and Mining Act, 2006 (Act 703) as amended by Act 995. It explains how weak enforcement, corruption, political interference and the role of foreigners, especially some Chinese nationals, have made the laws less effective. The paper also shows, through recent court cases such as Republic v Fynn and Republic v Domotey and Others, that Ghana’s courts are ready to punish offenders when cases are well presented. The courts have given long prison terms and heavy fines, showing that they are not the weak link in the fight. The real problem is poor enforcement and selective justice, since many foreigners are deported instead of being put on trial. The paper argues that solving the galamsey problem needs strong enforcement, freedom from political interference, the setting up of special environmental courts, and better job opportunities for rural communities. With these steps, Ghana can protect the environment while also supporting its people to make a living in lawful ways.

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This paper investigates the weekly evolution of child skills as measured by unique data from a widely-emulated early childhood home-visiting program developed in Jamaica, adapted to rural China, and applied in different versions worldwide. The design of the study avoids problems of endogeneity of inputs and lack of truly comparable measures of skills across children that plague previous econometric studies of child development. Skills that are nominally classified as the same, in fact, do not appear to share a common unit scale across levels. They are produced by skill-specific, lifecycle-stage-specific technologies. We formulate and estimate a new dynamic stochastic skill production model for multiple skills that is consistent with the evidence. We quantify the dynamics of early life learning. The model explains the “fadeout” of measures of learning by the emergence of new skills not properly measured. We investigate the role of ability in learning. We find important differences in learning patterns between boys and girls.

Straddle Option is a financial trading tool that explores volatility premiums in high-volatility markets without predicting price direction. Although deep reinforcement learning has emerged as a powerful approach to trading automation in financial markets, existing work mostly focused on predicting price trends and making trading decisions by combining multidimensional datasets like blogs and videos, which led to high computational costs and unstable performance in high-volatility markets. To tackle this challenge, we develop automated straddle option trading based on reinforcement learning and attention mechanisms to handle unpredictability in high-volatility markets. Firstly, we leverage the attention mechanisms in Transformer DDQN through both self-attention with time series data and channel attention with multi-cycle information. Secondly, a novel reward function considering excess earnings is designed to focus on long-term profits and neglect short-term losses over a stop line. Thirdly, we identify the resistance levels to provide reference information when great uncertainty in price movements occurs with intensified battle between the buyers and sellers. Through extensive experiments on the Chinese stock, Brent crude oil, and Bitcoin markets, our attention-based Transformer-DDQN model exhibits the lowest maximum drawdown across all markets, and outperforms other models by 92.5% in terms of the average return excluding the crude oil market due to relatively low fluctuation.

As companies and investors globalize, we are increasingly faced with estimation questions about the risk associated with this globalization. When investors invest in China Mobile, Infosys or Vale, they may be rewarded with higher returns, but they are also exposed to additional risk. When Siemens and Apple push for growth in Asia and Latin America, they clearly are exposed to the political and economic turmoil that often characterize these markets. In practical terms, how, if at all, should we adjust for this additional risk? We will begin the paper with an overview of overall country risk, its sources and measures. We will continue with a discussion of sovereign default risk and examine sovereign ratings and credit default swaps (CDS) as measures of that risk. We will extend that discussion to look at country risk from the perspective of equity investors, by looking at equity risk premiums for different countries and consequences for valuation. In the fourth section, we argue that a company’s exposure to country risk should not be determined by where it is incorporated and traded. By that measure, neither Coca Cola nor Nestle are exposed to country risk. Exposure to country risk should come from a company’s operations, making country risk a critical component of the valuation of almost every large multinational corporation. In the final section, we will also look at how to move across currencies in valuation and capital budgeting, and how to avoid mismatching errors.

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Using a novel and dynamic measure of fund-level herding that captures the tendency of a fund manager to imitate the trading decisions of the institutional crowd based on a sample of 3490 mutual funds in China for 21 years between 2003 and 2023, we find that funds with higher herding tendencies face significantly elevated delisting risks. Additionally, herding behavior is associated with shorter fund lifespans, smaller asset bases, and higher portfolio manager turnover rates. These results remain robust after employing a battery of methods to address endogeneity concerns. Collectively, our study demonstrates that herding substantially amplifies funds’ running risks.

Mutual funds create liquidity for investors by issuing demandable equity shares while holding illiquid securities. We study the implications of this liquidity creation by examining frequent trading suspensions in China, which temporarily eliminate market liquidity in affected stocks. These suspensions cause significant mispricing of mutual funds due to inaccurate valuations of their illiquid holdings. We find that investors actively acquire information about suspended stocks held by mutual funds, driving flows into underpriced funds. This information is subsequently incorporated into stock prices when trading resumes. Our findings suggest that mutual fund liquidity creation stimulates information acquisition about illiquid, information-sensitive assets.

The Fibonacci sequence is created by the recurrence of Fn = Fn−1 + Fn−2 ( n ≥ 2; F0 = 0; F1=1) from which the nearly 38.2% or 61.8% is derived for revenue increase or decrease. It has been increasingly and widely studied in research on options market trading. The high volatility of the options market makes the option premium greatly affected by the growing emotional involvement of buyers and sellers before the position is closed. The efficient affective computing and measures may provide traders a rough guide to working out the route to a profit. Based on the practical application of Fibonacci-like sequence and affective computing of option trading data in China SSE (Shanghai Stock Exchange) 50ETF options, we concluded that profit statistically changes around 38.2% or 61.8% increase line once call options flood in the market and bring the rapid price acceleration. On the contrary, 38.2% or 61.8% is considered another temporary decrease line when the price quickly falls from the balance point of price under the influence of huge put options. The mixed emotions of greed and fear make the option premium commonly fluctuate in cycles. The Fibonacci-like wavelet analysis is only one of the options volatility strategies, and it does not change the nature of market uncertainty.

In highly competitive product markets, firms can internalize other firms’ resources through interfirm collaboration. Using a longitudinal dataset on strategic alliances among private and public firms in Europe, this study examines how industry competition induced by international trade inflows affects the interfirm competitive and cooperative dynamics. We document that industry-level competition shocks, caused by Chinese import penetration, are a key driver in shaping corporate alliances. Notably, firms with constrained cash flow but ample cash reserves are more likely to form alliances in industries experiencing competition shocks. After these alliances, we observe improvements in cash flow growth and investment, with this positive impact of interfirm collaboration being more pronounced among private firms. These findings suggest that strategic alliances are crucial tools for restructuring following international trade inflows, particularly among small, private enterprises.