The optimal use of return predictability : an empirical study

In this paper we study the economic value and statistical significance of asset return predictability, based on a wide range of commonly used predictive variables. We assess the performance of dynamic, unconditionally efficient strategies, first studied by Hansen and Richard (1987) and Ferson and Siegel (2001), using a test that has both an intuitive economic interpretation and known statistical properties. We find that using the lagged term spread, credit spread, and inflation significantly improves the risk-return trade-off. Our strategies consistently outperform efficient buy-and-hold strategies, both in and out of sample, and they also incur lower transactions costs than traditional conditionally efficient strategies

An examination of the benefits of dynamic trading strategies in U.K. closed-end funds

We examine the after-cost out-of-sample performance of the unconditional mean-variance (UMV) strategy in the presence of conditioning information (Ferson and Siegel(2001)) using portfolios of U.K. equity closed-end funds. We find that the performance of the UMV strategy significantly improves when using lagged information variables with the highest persistence (first-order autocorrelation) levels and reduces turnover. This strategy is able to outperform alternative dynamic trading strategies and performs well across different subperiods. At low levels of trading costs, the UMV strategy is able to deliver significant value added to investors

CAY revisited: can optimal scaling resurrect the (C)CAPM?

In this paper, we evaluate specification and pricing error for the Consumption (C-) CAPM in the case where the model is optimally scaled by consumption-wealth ratio (CAY). Lettau and Ludvigson (2001b) show that the C-CAPM successfully explains a large portion (about 70%) of the cross-section of expected returns on Fama and French’s size and book-to-market portfolios, when the model is scaled linearly by CAY. In contrast, we use the methodology developed in Basu and Stremme (2005) to construct the optimal factor scaling as a (possibly non-linear) function of the conditioning variable (CAY), designed to minimize the model’s pricing error. We use a new measure of specification error, also developed in Basu and Stremme (2005), which allows us to analyze the performance of the model both in and out-of-sample. We find that the optimal factor loadings are indeed non-linear in the instrument, in contrast to the linear specification prevalent in the literature. While our optimally scaled C-CAPM explains about 80% of the cross-section of expected returns on the size and book-to-market portfolios (thus in fact out-performing the linearly scaled model of Lettau and Ludvigson (2001b)), it fails to explain the returns on portfolios sorted by industry. Moreover, although the optimal use of CAY does dramatically improve the performance of the model, even the scaled model fails our specification test (for either set of base assets), implying that the model still has large pricing errors. Out-of-sample, the performance of the model deteriorates further, failing even to explain any significant portion of the cross-section of expected returns. For comparison, we also test a scaled version of the classic CAPM and find that it has in fact smaller pricing errors than the scaled C-CAPM

Portfolio efficiency and discount factor bounds with conditioning information: a unified approach

In this paper, we develop a unified framework for the study of mean-variance efficiency and discount factor bounds in the presence of conditioning information. We extend the framework of Hansen and Richard (1987) to obtain new characterizations of the efficient portfolio frontier and variance bounds on discount factors, as functions of the conditioning information. We introduce a covariance-orthogonal representation of the asset return space, which allows us to derive several new results, and provide a portfolio-based interpretation of existing results. Our analysis is inspired by, and extends the recent work of Ferson and Siegel (2001,2002), and Bekaert and Liu (2004). Our results have several important applications in empirical asset pricing, such as the construction of portfolio-based tests of asset pricing models, conditional measures of portfolio performance, and tests of return predictability

The role of precision timing in stock market price discovery when trading through distributed ledgers

This paper investigates the importance of “time of execution” and the relevance of “precision time” in order driven transactions done over distributed ledgers. We created a distributed market place using stock market price data from the TMX exchange. We then proceeded to test and measure the impact of timing of orders at the nanosecond level. Whilst price discovery in order driven markets is done instantaneously, with distributed markets, it is necessary to know which order to process first to avoid “frount-running”. We argue that a protocol for the time of order of receipt and execution should be subject to nanosecond stacking. Our approach incorporates both transitory and permanent price discovery components. It allows for the efficient processing of transactions and the order they are received by a market clearing distributed ledger

Enhancing Financial Crime Detection by Implementing End to End AI Frameworks

Economic crime, encompassing money laundering, fraud, scams, and various other illegal financial activities, continues to evolve with the emergence of sophisticated Artificial Intelligence (AI) technologies. This white paper explores the dual-edged nature of AI in the financial sector. While AI tools are increasingly being exploited by criminals to commit financial crimes, they also hold the key to more robust and effective detection and prevention strategies. This paper delves into the array of AI techniques currently leveraged by malicious criminals, including deepfake technologies, phishing and spear phishing, automated social engineering, credential stuffing, synthetic identity fraud and others. Furthermore, it provides a comprehensive analysis of AI techniques capable of countering these threats. Key focus areas include Neural Networks for unusual patterns and behaviours, gradient boosting algorithms for risk assessment, reinforcement learning for optimisation of decision making, Markov chains for temporal patterns and anomalies over time, Naïve Bayes for real-time classification and decision trees for interpretable detection. The culmination of this paper is the presentation of a state-of-the-art end-to-end AI-driven solution that integrates AI technology to offer a holistic and dynamically adaptable approach to financial crime detection and prevention. By implementing this framework, financial institutions can significantly enhance their capabilities to identify, mitigate, and prevent financial crimes, ensuring a more secure financial ecosystem

Is tail risk the missing link between institutions and risk?

This paper examines the link between risk and institutional quality, an unresolved issue in finance. Our hypothesis is that institutions affect risk through extreme events and not through volatility. We focus on relative tail risk with an original approach that is able to estimate historical tail risk with greater precision. Using international stock market data, we show that tail risk is stable over time, unlike volatility. We find that tail risk captures the relation between risk and institutional quality better than volatility. Better governance substantially reduces the probability of extreme events

Stem-Like Cells and Therapy Resistance in Squamous Cell Carcinomas

Abstract Cancer stem cells (CSCs) within squamous cell carcinomas (SCCs) are hypothesized to contribute to chemotherapy and radiation resistance and represent potentially useful pharmacologic targets. Hallmarks of the stem cell phenotype that may contribute to therapy resistance of CSCs include quiescence, evasion of apoptosis, resistance to DNA damage, and expression of drug transporter pumps. A variety of CSC populations within SCCs of the head and neck and esophagus have been defined tentatively, based on diverse surface markers and functional assays. Stem-like self-renewal and differentiation capacities of these SCC subpopulations are supported by sphere formation and clonogenicity assays in vitro as well as limiting dilution studies in xenograft models. Early evidence supports a role for SCC CSCs in intrinsic therapy resistance, while detailed mechanisms by which these subpopulations evade treatment remain to be defined. Development of novel SCC therapies will be aided by pursuing such mechanisms as well as refining current definitions for CSCs and clarifying their relevance to hierarchical versus dynamic models of stemness