State Street Global Advisors

Despite the advocacy of leveraging data analytics to improve operational efficiency, there is a paucity of research on how analytical technologies afford professional service innovation and enhancement. We propose a data-driven decision support framework for civil litigation negotiation, which is a routine business activity in legal service firms. It is typically conducted in a traditional manner with the conflicting parties drawing on their past experiences and prior knowledge to guide decision-making. This model predicts human negotiation behavior based on historical records and incorporates the behavioral insights into the decision-making process. We introduce a sequential directed acyclic graph to characterize the causal relationships between offers and employ different approaches to predicting the opponent's next moves. By integrating utility analysis, each player can decide whether to accept the opponent's offer or counter back. The proposed framework is illustrated through a field experiment based on the UK MoJ Portal for handling low-cost injury claims and 88 cases with complete negotiation history. We find that better outcomes for both parties can be delivered by implementing the proposed model. The analysis result also represents convincing evidence that low-cost cases should ideally be settled out of the court via negotiation to maximize shared benefits. This paradigm could be easily generalized to other types of civil dispute resolutions negotiation to enhance both operational efficiency and service quality.

This article provides applications of derivatives to asset allocation and multi-asset management. The four applications include using futures for top-down asset allocation, deploying portable alpha strategies using derivatives to achieve desired convexity in payoff profiles, developing effective hedging strategies, and using derivatives for active speculative views by proprietary traders.

Leveraging Artificial Intelligence (AI) techniques to empower decision-making can promote social welfare by generating significant cost savings and promoting efficient utilization of public resources, besides revolutionizing commercial operations. This study investigates how AI can expedite dispute resolution in road traffic accident (RTA) insurance claims, benefiting all parties involved. Specifically, we devise and implement a disciplined AI-driven approach to derive the cost estimates and inform negotiation decision-making, compared to conventional practices that draw upon official guidance and lawyer experience. We build the investigation on 88 real-life RTA cases and detect an asymptotic relationship between the final judicial cost and the duration of the most severe injury, marked by a notable predicted R^2 value of 0.527. Further, we illustrate how various AI-powered toolkits can facilitate information processing and outcome prediction: (1) how regular expression (RegEx) collates precise injury information for subsequent predictive analysis; (2) how alternative natural language processing (NLP) techniques construct predictions directly from narratives. Our proposed RegEx framework enables automated information extraction that accommodates diverse report formats; different NLP methods deliver comparable plausible performance. This research unleashes AI’s untapped potential for social good to reinvent legal-related decision-making processes, support litigation efforts, and aid in the optimization of legal resource consumption.

In the 1980s there was a famous TV ad for Wendy’s with the tagline “Where’s the beef?”1 Many investors in today’s so-called smart beta strategies may well be asking a similar question, “Where’s the alpha?” Investors frequently buy into historical simulations or backtests, often supported by compelling studies by respected academics, suggesting wonderful performance with remarkable consistency, only to earn no alpha once they invest. The only winners typically are the asset managers and brokers through their fees and commissions. The problem is data mining and performance chasing, the nemeses of all investors. Yes, academics, “quants,” and investment professionals are all subject to those same temptations, very nearly to the same extent as retail investors. This article explores the ways seasoned professionals fall prey to these simple blunders and suggests the three lessons that could perhaps allow us to better meet client expectations, both by delivering improved outcomes and by encouraging more sensible expectations.

This article examines the characteristics and performance of active and smart beta equity exchange traded funds (ETFs) listed in the United States since 2000. Using a sample of 95 active equity ETFs and 376 smart beta equity ETFs, the author found that as of October 30, 2020, only 20% of active equity ETFs and 15% of smart beta equity ETFs performed better (in regard to return) than the S&P500 index (market) during the past five-year period. Using Fama–French–Carhart six-factor return attribution analysis, the author finds that more than 20% of smart beta equity ETFs and 10% of active equity ETFs have significant alpha at the 10% level of confidence after controlling for all Fama–French–Carhart factor returns. The excess market return factor is significant in all variants of return attribution analysis. All return attribution analyses reveal that the value investment category and the small-cap size category of both active and smart beta equity ETFs have 100% exposure to respective factor returns. There is significant scope for active and smart beta equity ETF fund managers to enhance the security selection process and create a better factor tilting strategy, respectively. TOPICS:Exchange-traded funds and applications, factor-based models, statistical methods, performance measurement Key Findings ▪ Twenty percent of smart beta equity exchange traded funds (ETFs) and 10% of active equity ETFs have significant alpha at the 10% level of confidence after controlling for all Fama–French–Carhart factor returns. The excess market return factor is significant in all variants of return attribution analysis. ▪ Fund managers of smart beta equity ETFs need to create a better factor tilted strategy to gain maximum exposure to intended factors. ▪ Fund managers of active equity ETFs should focus on a better security selection process to maximize alpha, that is, minimize market and other known factor exposures.