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The history of online trading began in the 1960s with the emergence of electronic communication networks, which allowed the electronic execution of trades outside traditional exchanges. The internet revolution led to the development of online brokerage platforms such as E*Trade and Schwab, enabling non-institutional investors to participate in the digital trading revolution. These platforms have evolved to serve the retail investor market, eventually adapting to mobile-first and commission-free models, significantly lowering the barriers to entry for financial markets. Platforms like Robinhood and other fintech firms have rapidly gained market share by offering services and products previously unavailable, such as commission-free trades, mobile trading, and novel products such as fractional shares and cryptocurrency investing. This chapter provides an overview of the history of online trading. It also introduces several new developments in fintech and the online trading industry and discusses various controversies and future implications of new technologies.

In recent years, new and technologically innovative financial products and services, generally subsumed under the fintech umbrella, have permeated all areas of capital markets at an exponential rate. Primarily driven by developments in Web3 and advancements in artificial intelligence (AI), fintech solutions offer valuable benefits to all existing markets and participants and are the basis for introducing wholly new segments to classic capital market ecosystems. However, this increasing fintech adaptation does not come without challenges. Due to the technologies' nascent nature and often unregulated status, many products are susceptible to manipulation and fraud. The result can be sizable investor losses and excessive regulatory and public scrutiny. This chapter highlights the most essential and prominent fintech solutions used in capital markets today, along with their features, value additiveness, and degree of adaptation.

The purpose of this study is to conduct a comprehensive systematic literature review using bibliometric approach to investigate the academic structure of World Trade Organization Dispute Settlement research.

The study examines the bibliographic information for 1,858 articles from Scopus and the Australian Business Deans Council-indexed journals published between 1995 and 2024 using Dimensions.ai and Google Scholar search engines. Exploratory-cum-descriptive research design with bibliometric approach is used to answer the stated literature review research questions.

The data shows a gradual decline in WTO-Dispute Settlement System (WTO-DSS) research relative to the total international business area in the three decades. Developed countries appear as key contributors to the research, with the USA and the UK standing out as the most productive and influential research countries. The study shows a significant change in the focus of this research corpus from legalized to non-legalized approaches, with a greater emphasis on transparency and environmental sustainability. The research identifies global politics and international trade law as influential subjects in the discipline.

To the best of the authors’ knowledge, the study is a first of its kind where bibliometric approach has been used to study the evolution of WTO-DSS literature. The study adds to the understanding of WTO Dispute Settlement research patterns and recommends future research options.

This chapter explores the role of artificial intelligence (AI), particularly its subfield of machine learning (ML) methods, as a core technology of the fintech revolution in the financial services industry. It simplifies some of the complex concepts related to AI by introducing the main ML paradigms and related techno-methodic aspects. This chapter uses real-world examples to illustrate how next-generation AI powered by ML is transforming the financial services industry. Next, in illustrating the risks associated with AI adoption, this chapter discusses the need for regulation to address the essential facets of AI governance, including transparency, accountability, ethics, and responsible use. Lastly, it looks at emerging regulatory approaches across leading global jurisdictions. The primary goal is to give readers an initial understanding of AI's profound impact on the financial sector.

Preferential market access is a pillar of the enabling clause in international trade, and as such has received scholarly attention in the recent past. This study aims to empirically examine intensity and utilisation of Uganda’s preferential market access.

Secondary data at the six-digit level of the harmonised system was sourced from the International Trade Centre’s and UN COMTRADE’s Trade Map database on trade flows to compute; trade intensity indices, Balsa’s revealed comparative advantage (RCA) indices and the existing potential trade. The time period was 2013–2022.

It is evident that Uganda is not taking full advantage of its preferential market access. The findings of the trade intensity analysis revealed that the strength of trade relations between Uganda and its preferential markets has been consistently weak in the period 2013–2022, while the intensity of trade relations with its FTA partners has been strong. The RCA results revealed that all Uganda’s agricultural exports to its preferential markets have a comparative advantage, with exports of roses reporting an exceptionally high RCA relative to other agricultural exports. The RCA results also show that the comparative advantage of Uganda’s coffee exports recovered after a significant fall in the period 2014–2022. The analysis of the existing potential for trade reveals a disproportionally low market share held by Uganda across all product lines, notably, only 2.3% of the US$29bn coffee imports in its preferential markets.

One limitation of this study is that it primarily relied on quantitative data from the International Trade Centre and thus lacks an exhaustive understanding of the circumstances at the export firm level. Qualitative data, such as interviews with exporters and policymakers, could provide deeper insights into the specific challenges and opportunities faced by Ugandan businesses in these markets.

This paper highlights the need for additional trade facilitation measures to address preferential market access bottlenecks such as stringent rules of origin and call for an aggressive government intervention in enhancing the export readiness of SMEs in Uganda. In addition, the paper is informative to Uganda’s exporters regarding the existing and potential increase in their exports to international markets.

The strength of Uganda’s trade relations with its preferential markets is empirically examined in this paper and provides useful insights for enhancing utilisation of preferential market access by beneficiary countries.

This paper takes the manufacturing cluster supply chain as the research object and explores the evaluation and enhancement strategy of manufacturing cluster supply chain synergy. The purpose of this study was to (1) analyze the mechanism of manufacturing cluster supply chain synergy; (2) construct manufacturing cluster supply chain synergy evaluation model; (3) algorithm realization of manufacturing cluster supply chain synergy evaluation and (4) propose manufacturing cluster-based supply chain synergy enhancement strategy.

Breaking through the limitations of traditional manufacturing cluster supply chain synergy evaluation, we take horizontal synergy and vertical synergy as coupled synergy subsystems, use the complex system synergy model to explore the horizontal synergy between core enterprises and cluster enterprises and the vertical synergy of supply chain enterprises and use the coupling coordination model to construct the coupled synergy evaluation model of manufacturing cluster supply chain, which is an innovation of the evaluation perspective of previous cluster supply chain synergy and also an enrichment and supplementation of the evaluation methodology. This is not only the innovation of the evaluation perspective but also the enrichment and supplementation of the evaluation method.

Using Python software to conduct empirical analysis on the evaluation model, the research shows that the horizontal and vertical synergies of the manufacturing cluster supply chain interact with each other and jointly affect the coupling synergy. On this basis, targeted strategies are proposed to enhance the synergy of the manufacturing cluster supply chain.

This study takes manufacturers, suppliers and sellers in the three-level supply chain as the research object and does not consider the synergistic evaluation between distributors and consumers in the supply chain, which can be further explored in this direction in the future.

Advanced manufacturing clusters, as the main force of manufacturing development, and the synergistic development of supply chain are one of the important driving forces for the high-quality development of China’s manufacturing industry. As a new type of network organization coupling industrial clusters and supply chains, cluster supply chain is conducive not only to improving the competitiveness of cluster supply chains but also to upgrading cluster supply chains through horizontal synergy within the cluster and vertical synergy in the supply chain.

Research can help accelerate the transformation and upgrading of clustered supply chains in the manufacturing industry, promote high-quality development of the manufacturing industry and accelerate the rise of the global value chain position of the manufacturing industry.

(1) Innovation of research perspective. Starting from two perspectives of horizontal synergy and vertical synergy, we take a core enterprise in the cluster supply chain as the starting point, horizontally explore the main enterprises of the cluster as the research object of horizontal synergy, vertically explore the upstream and downstream enterprises of the supply chain as the research object of vertical synergy and explore the coupling synergy of cluster supply chain as two subsystems, which provides new perspectives of evaluation of the degree of synergy and synergy evaluation. (2) Innovation of research content. Nine manufacturing clusters are selected as research samples, and through data collection and model analysis, it is verified that the evaluation model and implementation algorithm designed in this paper have strong practicability, which not only provides methodological reference for the evaluation of manufacturing cluster-type supply chain synergy but also reduces the loss caused by the instability of clusters and supply chains and then provides a theoretical basis for improving the overall performance of cluster-type supply chains.

The purpose of this paper is to assess the latest directives of the European Parliament and the Council – MiFID II and MiFIR – on markets in financial instruments in response to the growth of dark pools in European equity markets.

This paper examines the impact of the new regulatory packages on European equity markets by identifying areas where the legislation is effective and comparing these changes in EU legislation with US legislation on dark pools.

This paper find that the MiFID II and MiFIR directives, implemented by the European Securities and Markets Authority to address these concerns, have reduced information asymmetry between market participants, thereby increasing competition between regulated markets and alternative trading facilities.

Increased competition can improve market quality, which has practical implications for financial market regulation and policy formulation.

These findings are novel in the existing literature on high frequency trading through dark pools. They improve the understanding of dark trading and its impact on competition and market efficiency. In addition, this research can assist policymakers in designing effective financial market regulation. The economic analysis of legislation also helps regulators assess the impact of new legal provisions on the functioning of capital markets.

This chapter discusses the evolution of online trading, its application in various market structures, and its benefits and potential concerns. Computers were first used in electronic communication networks among brokers and dealers to make trades and for informational purposes. Online brokers became popular with retail investors as the internet spread. Online trading comes with various trading protocols and order types. It enables traders to automate trading decisions and process data more easily using charting tools and customized programs connected to the broker's infrastructure. Electronic trading allows for greater centralization but can also be accompanied by market fragmentation. Market regulation has affected market structure and is still evolving. Centralization allows for more competitive prices and reduces search costs. Decentralized markets could cope better with asymmetric information.

Candlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.

The data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.

The inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.

To statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).

This study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.