Owing to the availability and easy usability of bibliometric computer tools, the number of bibliometric literature reviews has soared, especially in active and dynamic research fields such as digital finance and fintech. This leads to the paradoxical situation of a high number of bibliometric literature reviews with diverging quantitative results, which ultimately results in confusion instead of consistency and clarity. To counter this trend and make the results comprehensible and reproducible, we propose a new methodology for systematic bibliometric literature reviews that relies on a robust two-stage approach to ensure a comprehensive identification of relevant literature, especially for developed research landscapes. We apply this 2-Stage Literature Search and Selection (2SLSS) approach to the literature on digital finance and fintech, present our findings, and discuss existing research gaps. Therefore, we contribute both to the analysis of the evolution of the digital finance literature and the fintech literature and to the methodological foundation of systematic bibliometric literature reviews.

We study how market makers price predictable, uninformed order flow, which allows the isolation of inventory costs from adverse selection. Retail savings plans (SPs) provide a unique laboratory for this analysis, as their pre-determined nature eliminates adverse selection risk. Using proprietary data from LS Exchange, a major European retail venue with a single market maker, we develop a novel methodology to identify SP trades and benchmark their execution prices against the reference market Xetra. While overall execution quality is high, we find that the market maker extracts small, systematic rents. For ETFs, the dominant SP asset class, execution is paradoxically worse than for similar in size non-SP trades, with the market maker charging a size-independent premium of approximately one basis point. Contrary to theory, this premium cannot be meaningfully explained by inventory risk. Our results show that even in the absence of adverse selection, market makers price predictable order flow distinctly worse than discretionary trades, challenging classic models of spread components and further motivating regulatory discussions regarding payment for order flow.

Exchanges worldwide are introducing on-exchange retail programs that segment retail order flow within lit markets and compete with off-exchange internalization and payment-for-order-flow models. Despite their rapid adoption, little causal evidence exists on whether such mechanisms can improve retail execution without harming market quality. We study the introduction of the Xetra Retail Service (XR), which enables retail orders to interact with competing Retail Liquidity Providers (RLPs). Retail orders are executed against RLPs that offer price improvements over displayed quotes or in the regular limit order book. We use granular ETF trading data in a difference-indifferences design (control: Euronext Milan) to assess execution outcomes and market quality effects. We find that 63% of retail ETF trades receive price improvement, averaging 0.93 bps (1.35 bps conditional). As expected, improvements are larger when spreads are wider and volatility is higher. Unexpectedly, price improvements are more likely for larger orders. At the overall market quality level, spreads, activity, and volatility remain stable, though displayed depth declines modestly. Overall, on-exchange retail segmentation can enhance retail execution without impairing market quality.

This replication study assesses the long-term effects of MiFID II’s research unbundling rules on investment research provision and stock market quality. We extend existing studies by utilizing a post-event period exceeding five years, by regional differentiation within Europe, and by incorporating the new option to rebundle payments for order execution and research services for SMEs. In line with existing studies, we find a decrease in analyst coverage after MiFID II, particularly for large caps, while SMEs remain unaffected. These findings are consistent across different European regions but not for the United Kingdom. Market quality experiences a decline, with lower trading volume and increased volatility, offset by liquidity improvements for larger firms. After the introduction of rebundling, which has not been analyzed by existing studies, SME research coverage declines, suggesting that investment firms are not utilizing this option. Our findings offer insights for evidence-based policy-making as regulatory discussions on research unbundling persist in different jurisdictions.

We analyze the determinants of the trading volumes of different trading mechanisms in equity markets using an extensive panel data set from European markets comprising public limit order books, call auctions, dark pools, internalization platforms, and the over-the-counter market. Market shares, resulting from investors’ order routing decisions, are driven by the degree of immediacy and anonymity offered by the venues, their ability to offer off-tick executions, as well as the informational environment and conditions in the market. Findings for small and large trades are distinctly different, likely because traders jointly choose trade size and venue type.

Due to the availability and easy usability of bibliometric computer tools, the number of bibliometric literature reviews has soared – especially in active and dynamic research fields like Digital Finance and FinTech. This leads to the paradox situation of a high number of bibliometric literature reviews with diverging quantitative results – which ultimately results in confusion instead of conformity and clarity. To counter this trend and in order make results comprehensible and reproducible, we propose a methodology for systematic bibliometric literature reviews to trigger a discussion in the academic community of how to conduct bibliometric literature reviews systematically. We apply this methodology to the literature on Digital Finance and FinTech, present our findings, and discuss existing research gaps. Thereby, we contribute both to the analysis of the evolution of Digital Finance and FinTech literature and to the methodological foundation of systematic bibliometric literature reviews.

The amendment of existing and the passing of new regulations keep the corpus of regulation changing and growing dynamically. Against this background, companies face increasing costs to comply with existing and upcoming regulation. However, the high amount of regulatory texts makes it difficult for companies to identify which regulations apply to them. While regulatory technology, so-called RegTech, enables companies to comply with regulatory requirements or serves supervisory authorities to check compliance, there are no tools that enable companies to efficiently determine the relevance of a regulation in an automated manner. Therefore, this paper develops a decision support framework that makes use of techniques from natural language processing. We apply our approach to the Code of Federal Regulations in the U.S and discuss the results. As a key practical implication, our framework enables companies to retrieve regulations that speak to their business activities and may require compliance actions.

In 2018, the European financial regulation MiFID II introduced research unbundling rules that banned the bundling of research payments with execution costs. The aim of research unbundling is to increase transparency for investors and to avoid agency conflicts. Opponents argue that research unbundling reduces small and medium-sized enterprise (SME) research and, thereby, SMEs’ financing opportunities because this research can no longer be cross-subsidized by research fees paid for larger companies. The outbreak of COVID-19 and its impact on financial markets fueled intense discussions on rebundling for SMEs. Consequently, in February 2021, the European Commission adopted a Capital Markets Recovery Package that allows bundled research for SMEs below a market capitalization of EUR 1 billion. Against this backdrop, the authors conducted a survey among European market participants to investigate changes in research services due to research unbundling and the COVID-19 pandemic. Moreover, they examine market participants’ views on the expected effects and improvements of the option to rebundle SME research as provided by the recovery package.

Regulators conduct regulatory impact analyses (RIA) to evaluate whether regulatory actions fulfill the desired goals. Although there are different frameworks for conducting RIA, they are only applicable to regulations whose impact can be measured with structured data. Yet, a significant and increasing number of regulations require firms to comply by specifying and communicating textual data to consumers and supervisors. Therefore, we develop a methodological framework for RIA in case of unstructured data following the design science research paradigm. The framework enables the application of textual analysis and natural language processing to assess the impact of regulatory actions that result in unstructured data and offers guidance on how to map suitable methods to the dimensions impacted by the regulation. We evaluate the framework by applying it to the European financial market regulation MiFID II, specifically the recent regulatory changes regarding best execution. Thereby, we show that MiFID II failed to improve informativeness and comprehensibility of best execution policies.

We analyze how market fragmentation affects market quality of SME and other less actively traded stocks. Compared to large stocks, they are less likely to be traded on multiple venues and show, if at all, low levels of fragmentation. Concerning the impact of fragmentation on market quality, we find evidence for a hockey stick effect: Fragmentation has no effect for infrequently traded stocks, a negative effect on liquidity of slightly more active stocks, and increasing benefits for liquidity of large and actively traded stocks. Consequently, being traded on multiple venues is not necessarily harmful for SME stock market quality.

Brokerage houses historically have provided research and related services together with order execution without separate fees. This practice of research bundling through so-called soft commissions has triggered an intense and ongoing debate considering that research bundling leads to nontransparent pricing and, therefore, can induce agency conflicts. A new European regulation has banned the use of soft commissions by requiring fee separation for execution and research services. Against this backdrop, we provide a systematic review of the literature on soft commissions to build a profound basis for further regulatory discussions and to uncover future research opportunities.

We test theoretical predictions of changes in make/take fees in a setting with isolated make rebates for liquidity providers on a single trading venue (Xetra) by examining the impact on both Xetra and the overall market. The rebates lead to higher quoted depth but do not change bid-ask spreads or trading volume on Xetra. For the overall market, no change in trading volume or liquidity is observable. This shows that market participants redistribute their orders to the venue offering fee rebates rather than providing additional liquidity to the overall market. Consequently, the impact of fee changes depends on the setting.

Financial intermediaries are essential for investors' participation in financial markets. Because of their position within the financial system, intermediaries who commit misconduct not only harm investors but also undermine trust in the financial system, which ultimately has a significant negative impact on the economy as a whole. Building upon information manipulation theory and warranting theory and making use of self-disclosed data with different levels of external verification, we propose different classifiers to automatically detect financial intermediary misconduct. In particular, we focus on self-disclosed information by financial intermediaries on the business network LinkedIn. We match user profiles with regulator-disclosed information and use these data for classifier training and evaluation. We find that self-disclosed information provides valuable input for detecting financial intermediary misconduct. In terms of external verification, our classifiers achieve the best predictive performance when also taking regulator-confirmed information into account. These results are supported by an economic evaluation. Our findings are highly relevant for both investors and regulators seeking to identify financial intermediary misconduct and thus contribute to the societal challenge of building and ensuring trust in the financial system.

Algorithmic decision-making plays an important role in financial markets. Current tools in trading focus on popular companies which are discussed in thousands of news items. However, it remains unclear whether methodologies from the field of data analytics relying on large samples can also be applied to small datasets of less popular companies or whether these methodologies lead to the discovery of meaningless patterns resulting in economic losses. We analyze whether the impact of media sentiment on financial markets is influenced by two levels of investor attention and whether this impacts algorithmic decision-making. We find that the influence differs substantially between news and companies with high and low investor attention. We apply a trading simulation to outline the practical consequences of these interrelations for decision support systems. Our results are of high importance for financial market participants, especially for algorithmic traders that consider sentiment for investment decision support.

The use of computer algorithms in securities trading, or algorithmic trading, has become a central factor in modern financial markets. The desire for cost and time savings within the trading industry spurred buy side as well as sell side institutions to implement algorithmic services along the entire securities trading value chain. This chapter encompasses this algorithmic evolution, highlighting key cornerstones in it development discussing main trading strategies, and summarizing implications for overall securities markets quality. In addition, it touches on the contribution of algorithmic trading to the recent market turmoil, the U.S. Flash Crash, including the discussions of potential solutions for assuring market reliability and integrity.

Circuit Breakers are widely implemented in 2016. Currently, the majority (86%) of the responding trading venues use circuit breakers to ensure investor protection and to increase market integrity and stability. Compared to the previous study (WFE, 2008), the proportion of exchanges using circuit breakers increased from 60% to 86%.

The new financial market regulation MiFID II/MiFIR will fundamentally change the trading and market infrastructure landscape in Europe. One key aspect is the trading obligation for shares that intends to restrict over-the-counter (OTC) trading to ensure that more trading takes place on regulated trading venues and on platforms of Systematic Internalisers (SIs). In this context, market observers often argue that SIs might have a competitive advantage due to the best execution concept in combination with the possible exemption of SIs from the tick size regime. Applying scenario analysis, we determine the likely migration of OTC trading volume to regulated trading venues and SIs. Based on our data set, covering intraday data including OTC trades as well as order book snapshots of EURO STOXX 50 constituents on major European venues, we investigate how changes in trading volume influence liquidity on lit markets. The results of our scenario analysis indicate that liquidity on lit markets might increase due to additional turnover formerly traded OTC. However, also a negative liquidity effect for lit markets and for the price discovery process is possible because of increased trading via SIs. According to this scenario, spreads might increase by 0.25%, round trip transaction costs of 50,000 € might increase by 0.92% and market depth 10 bps around the midpoint might decrease by 1.95% on lit venues. This effect on liquidity not only increases trading costs for investors in European equities trading, but also has a negative impact on issuers due to higher cost of capital and thereby on the real economy in Europe.

This Special Issue of the Journal of Management Information Systems was developed to build new foundations for research in the interdisciplinary space of the Fintech Revolution. Today, it seems tha...