We propose a class of execution algorithms that consists of a strategic layer and a speculative layer. The strategic layer is an optimal trading schedule that encodes the trader's objective, her tolerance to risk, and the impact of her own trades in the market. The schedule of the strategic...

This paper examines the short-run weak-form efficiency of equities, using a proprietary data set permitting analysis of orderbook characteristics to measure short-term price predictability. The results show that high levels of algorithmic trader activity in a stock lowers the level of short-run...

We ask how machine learning can change a crucial step of the scientific process in economics: the advancement of theories through the discovery of "anomalies." Canonical examples of anomalies include the Allais Paradox and the Kahneman-Tversky choice experiments, which are concrete examples of...

We investigate trade-offs when trying to encourage adoption of stock-trading algorithms. We organize an artificial stock market experiment over three weeks where investors experience trading on their own and with the help of a financial algorithm.They then choose whether to adopt it. We vary the...

In this paper we combine a random effects model with different machine learning algorithms via an iterative process when predicting commercial real estate asset values. Using both random effects and machine learning allows us to combine the strengths of both approaches. The random effects will...

The Digital Age is speedily transforming into the Algorithmic Age, where algorithms carry on or contribute to a significant number of important social, economic, and technical decision-making. Apart from platform dominance, private governance, and surveillance capitalism, private and public use...

Online Appendix of “Steering via Algorithmic Recommendations” Chen and Tsai (2019)This paper studies whether self-preferencing affects algorithmic recommendations on dominant platforms. We focus on the dual role of Amazon.com as a platform owner and retailer. We find that products sold by...

This textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, deep neural networks, recurrent neural...

From the dawn of civilisation, automation and innovation have been at the forefront of society. At present, employers are eagerly looking for ways to optimise work and production processes. While AI has the potential to transform work and workplaces, displace jobs, create new jobs, and impact...

Covariates in regressions may be linked to each other on a network. Knowledge of the network structure can be incorporated into regularized regression settings via a network penalty term. However, when it is unknown whether the connection signs in the network are positive (connected covariates...