Systemic failures and organizational risk management in algorithmic trading: Normal accidents and high reliability in financial markets.

This article examines algorithmic trading and some key failures and risks associated with it, including so-called algorithmic 'flash crashes'. Drawing on documentary sources, 189 interviews with market participants, and fieldwork conducted at an algorithmic trading firm, we argue that automated markets are characterized by tight coupling and complex interactions, which render them prone to large-scale technological accidents, according to Perrow's normal accident theory. We suggest that the implementation of ideas from research into high-reliability organizations offers a way for trading firms to curb some of the technological risk associated with algorithmic trading.

The absorption and multiplication of uncertainty in machine-learning-driven finance.

Uncertainty about market developments and their implications characterize financial markets. Increasingly, machine learning is deployed as a tool to absorb this uncertainty and transform it into manageable risk. This article analyses machine-learning-based uncertainty absorption in financial markets by drawing on 182 interviews in the finance industry, including 45 interviews with informants who were actively applying machine-learning techniques to investment management, trading, or risk management problems.

Training response of adolescent Kenyan town and village boys to endurance running.

To investigate the response to endurance training on physiological characteristics, 10 Nandi town boys and 14 Nandi village boys 16.5 and 16.6 years of age, respectively, from western Kenya performed 12 weeks of running training.