The University of Buckingham Press Journals
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Order Imbalances and Market Efficiency: Evidence from a Pure Order-Driven Market
No full textUsing a sample of 196 stocks, this study investigates the intraday market efficiency of the National Stock Exchange of India (NSE), a market that is entirely order-driven. The return autocorrelation and variance ratio tests suggest that the hourly returns of stocks at NSE are not serially correlated. Hourly order imbalances (OIBs) are highly persistent up to four lags, and can help in return prediction. Investors appear to follow short-horizon OIBs to conduct counter-vailing trades, and remove serial dependence in short-term returns. A simple order imbalance based trading strategy appears to offer abnormal returns; however, these returns vanish once the trading costs are factored in. Overall, the results indicate that the de-facto market making at NSE is effective
Impacts of the 2023 NCAA Football Rules Changes on Scoring and the Totals Market
No full textThe National Collegiate Athletics Association (NCAA) introduced rule changes prior to the 2023–24 season. According to the Football Foundation Rules Committee, “…the most significant 2023 football rule changes involve adjustments to the timing and clock rules,” with the purpose to shorten the length of the game and to “…moderately reduce the number of plays per game.” We examine the impact of these changes on actual and expected scoring during the season. The rule changes led to lower average scoring, which was not fully encompassed at the start of the season in the financial (betting) market. The early season volatility in the market subsided as the season progressed resulting in a generally efficient market
From Russia with Influence? An AI-Driven Probabilistic Framework for Assessing Foreign Electoral Interference in U.S. Elections (2016–2036)
Full text linkConcerns over foreign electoral interference have grown since the 2016 U.S. presidential election, yet public-facing intelligence assessments continue to rely on vague probabilistic language that limits clarity, consistency, and operational insight. This study introduces an exploratory AI-facilitated framework designed to systematically quantify the likelihood of foreign election interference across U.S. elections from 2016 to 2036. Drawing on declassified intelligence assessments from the ODNI, NIC, and CISA, corroborated by open-source intelligence (OSINT), we applied a three-phase natural language processing (NLP) protocol using OpenAI’s tools to extract, classify, and scale linguistic indicators of confidence. These were then mapped to probabilistic values based on Sherman Kent’s CIA estimative language and modeled using Monte Carlo simulations to account for uncertainty. Named Entity Recognition and sentiment analysis identified country-specific attribution patterns, while lexical scaling translated narrative judgments into quantifiable interference probabilities. Results revealed persistently high likelihoods of Russian interference, alongside growing probabilistic signals from China and Iran over time. A hierarchical linear model confirmed significant variation by election year and actor, and simulation-based forecasts suggest increasing probabilistic risk through 2036. This framework offers a replicable, data-driven model for transforming qualitative intelligence into structured probability distributions, providing analysts and policymakers with an evidence-based tool to track, compare, and forecast adversarial influence strategies with greater transparency and granularity
Courts As Key Architects in the Recognition and Enforcement of Foreign Judgments with aFocus on the Turkish Experience
Full text linkABSTRACT
This study examines the Court of Cassation’s landmark decisions on the recognition and enforcement of foreign judgments, which have collectively shaped the development of Turkish private international law. In selecting the rulings analysed in this study, special attention is given to their significant impact on doctrine, their role in prompting complaints to the Constitutional Court, or European Court of Human Rights, and their influence on Parliament to undertake reforms in private international law. The first section of this study begins with decisions on the entitlement to initiate enforcement proceedings, where the Court of Cassation initially required identity of parties in both the foreign and enforcement proceedings, despite the absence of such a requirement in the 1982 Private International Law and Procedural Law Act (PILA). This interpretation ultimately led to the inclusion of an explicit provision in the 2007 PILA. It then examines decisions where foreign joint custody judgments were deemed contrary to public order. These decisions sparked debate among academics and practitioners, which influenced the Court of Cassation’s perspective and ultimately led to the acceptance of the enforcement of foreign joint custody judgments. The third section highlights the Court’s introduction of a new and controversial ground for refusal of enforcement based on the plaintiff’s bad faith. This reasoning is not among the statutory grounds listed in the 2007 Act. The study also explores the Court’s inconsistent approach to enforcing unreasoned foreign judgments, particularly those issued by German courts under the Mahnverfahren procedure. Although the study may appear to focus primarily on a single country, it incorporates comparative analysis of private international law, and the cited rulings of the Court of Cassation offer valuable insights into four issues that have been insufficiently addressed in the legal literature
Racial Biases in Wagering Markets – Betting Volume and Percentages in the WNBA
No full textThe 2024 WNBA season saw heightened engagement and betting interest, driven by the high-profile debuts of Caitlin Clark and Angel Reese. This study utilizes Action Network data to examine how race, star players, and scheduling influence betting volume. Findings reveal strategic scheduling opportunities based on weekday preferences but also indicate a sharp betting decline with the onset of NCAA Football and NFL seasons. Evidence points to racial biases in betting patterns, as games featuring more white players receive increased wagers. However, superstar effects, irrespective of race, appear to mitigate some biases, suggesting unique opportunities to expand audience engagement
Price Prediction of S&P 500 Using Machine Learning
No full textForecasting trends in stock indices is considered a difficult task in financial time series forecasting. Accurate forecasts of stock price trends can generate profits for investors. Due to the complexity of stock market data, developing effective forecasting models is very challenging. We are trying to predict stock prices for the next few days. This will become the basis for knowing the right time to invest or exit positions and generate profits.
With the introduction of artificial intelligence and the increase in computing capacity, programmed forecasting methods have proven to be more effective at predicting stock prices. In this work, we used supervised machine learning algorithms such as linear regression model, SVR, XGBoost, and random forest.
Thus, these models are evaluated using standard strategic indicators such as the EMR. A low value of the indicator shows that the models are effective in predicting stock prices
Implementing Alternative Sentencing in the Criminal Justice System: Implications for Human Rights
Full text linkPrisons operate with varying aims, including the infliction of punishment, individualdeterrence and general deterrence, even retribution or revenge. One aim is to enable offenders to become aware of their mistakes and be accepted back into the community. A problem with implementing this aim is, however, that imprisonment may generate greater capacity for offending in the convict, who may emerge from prison ‘worse’ than when entering it. This article briefly reviews the philosophy that lies behind alternatives to imprisonment by reference to a small slice of the literature and concludes that more research is required to draw firm conclusions about their efficacy. Research is needed on the domesticlevel and internationally to determine how alternative punishments may result in upholding the human rights of offenders and improving outcomes for society as a whole
OECD BEPS from an IP Management Perspective
Full text linkOECD BEPS has been described as “the most significant re-write of international tax rules in a century”. The essence of OECD BEPS is the switch away from a focus on the legal ownership of the intangible assets (like patents, trademarks, trade secrets, etc.) within a corporate group to a focus on the economic ownership and usage of these intangible assets by the group members. This switch I suggest will have a major impact on IP management and the associated IP policies, IP processes, IP systems, IP data, IP governance, etc. within organizations. These OECD BEPS guidelines are not just about tax, they can be seen as an IP management handbook, dictating how companies should behave when managing their intangible assets
Predictive Dynamics in Cryptocurrency Trading: Unraveling Behavioral and Psychological Influences
No full textThe rapid expansion of cryptocurrency trading has become a defining feature of contemporary financial markets, attracting a constantly growing group of participants, now surpassing 106 million worldwide. This research focuses on the psychological and behavioral foundations of trading behaviors, investigating how individual psychological states and lifestyle choices impact cryptocurrency trading activities. Using Ordinary Least Squares (OLS) regression, we examine the influence of various factors such as Loneliness, Negative Emotions, Fear of Missing Out (FOMO), Socialization, Healthy Lifestyle Habits, Entertainment Spending, and Sense of Achievement on the frequency of cryptocurrency trades. Our study also includes an analysis of gender differences through Levene’s T-test, thereby increasing the depth of our predictive model. The results of this study aim to fill a gap in existing literature by quantifying the degree to which individual psychological profiles and behaviors can predict trading activities, thereby providing detailed insights into the emotional and cognitive dimensions of the digital trading world. This research not only contributes to the field of behavioral finance but also provides a foundation for developing strategic interventions tailored to various trader segments, ultimately fostering a deeper understanding of the complex dynamics that characterize the crypto market’s volatile landscape
The Legal Relationship between Universities and their Students – How Accountable and to what Extent in Law are Universities Liable for Student Suicides?
Full text linkThis article addresses how the relationship in law of universities and their students is to be characterised. Its main purpose is to establish where legal liability lies in the case of a student committing suicide, whilst undergoing an educational qualification. Characterisation of the relationship between universities and their students has been included within a number of models which are set out and assessed, the aim being to show the range of characterisation and at the same time point out the defect(s) in each characterisation. This analysis is undertaken in searching for a more meaningful and coherent model of the legal relationship between the university and the student. A legal framework of accountability is essential. Exploration of more conventional ways of addressing this, and alternatives, is required. The article therefore includes for consideration a less fashionable potential classification of a fiduciary relationship between universities and their students. It explores, too, the possibility in public law of founding a breach of a substantive legitimate expectation challenge where promises of safeguarding have been made to the now deceased student. This phenomenon goes beyond the United Kingdom, so reference is made to case law in other jurisdictions, such as Canada and USA