Securities selection attempts to distinguish prospective winners from losers conditional on beliefs and available information. This article surveys relevant academic research on this subject, including work about the combining of forecasts (Bates and Granger 1969), the Black-Litterman model...

A concise look in three unrelated, yet complex environments reveals a strong human dependence on the recognition heuristic. Its marketability to the mind as a good decision making tool (over other complex approaches), is shown to be almost innate and ultimately successful. The three...

Financial markets provide a natural quantitative lab for understanding some of the most advanced human behaviours. Among them is the invention and use of mathematical tools known as financial instruments. Besides money, the two most fundamental financial instruments are bonds and equities. More...

This paper introduces a new non-parametric approach to integrate empirical probability functions of the real return for different investment horizons for five portfolios of Swedish stocks and bonds. In our setting the problem reduces to generating new generalizations from an empirical Markov...

This paper introduces a new non-parametric approach to integrate empirical probability functions of the real return for different investment horizons for five portfolios of Swedish stocks and bonds. In our setting the problem reduces to generating new generalizations from an empirical Markov...

Standard portfolio construction models are based on the implicit assumption that all information relevant to making investment choices is probabilizable. However, both empirical behaviors of market participants and the increased success of approaches like risk parity underly an implicit...

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based...

Traditional machine learning methods have been widely studied in financial innovation. My study focuses on the application of deep learning methods on asset pricing.I investigate various deep learning methods for asset pricing, especially for risk premia measurement. All models take the same set...

Factor investing has gained widespread acceptance among institutional investors. Some investors believe it is preferable to stratify the investment universe into factors to manage portfolio risk more effectively, while other investors focus on factors because they believe they yield risk...

This paper investigates various machine learning trading and portfolio optimisation models and techniques. The notebooks to this paper are Python based. By last count there are about 15 distinct trading varieties and around 100 trading strategies. Code and data are made available where...