Publication Library
Seven Tools of Causal Inference with Reflections on Machine Learning
Description: Seven Tools of Causal Inference with Reflections on Machine Learning
Created At: 14 December 2024
Updated At: 14 December 2024
Evolutionary Finance Simulations and the Agent-Based Approach
Description: The only way to make a major advance in finance modeling is to explore entirely new approaches rather than make incremental modifications to existing models. The purpose of our research is precisely to build models and perform analysis of the economy as a complex system prone to sudden and major changes. This includes the collection of new methods of empirical analysis, and the development of new mathematical and computational tools. This effort will be guided by emerging new conceptual paradigms such as network theory, behavioral economics and agent-based modeling, and empirically grounded by laboratory experiments and micro data. By combining these ideas into a practical simulation and forecasting tool, we aim at building the foundations of a new paradigm within finance.
Created At: 14 December 2024
Updated At: 14 December 2024
Experiments in Finance A survey of historical trends
Description: Experiments complement other methods in identifying causal relationships and measuring behavioral deviations from theoretical predictions. While the experimental method has long been central in many scientific disciplines, it was almost nonexistent in finance until the 1980s. To survey the development of experiments in finance, we compiled a comprehensive account of experimental studies published in the Journal of Finance, Journal of Financial Economics, Review of Financial Studies, Review of Finance, Journal of Quantitative and Financial Analysis, and Journal of Banking and Finance, and experimental finance studies published in the top 5 journals in economics. With this novel dataset, we identified historical trends in experimental finance. Since the first experiments were published in finance journals in the 1980s, especially in the last 20 years, the share of experimental publications in these journals has markedly increased. In this article, we report trends toward descriptive, individual decision, and field experiments.
Created At: 14 December 2024
Updated At: 14 December 2024
Topological Data Analysis and Machine Learning Theory
Description: Topological Data Analysis and Machine Learning Theory
Created At: 14 December 2024
Updated At: 14 December 2024
Topological Data Analysis
Description: Scientific data is often in the form of a finite set of noisy points, sampled from an unknown space, and embedded in a high-dimensional space. Topological data analysis focuses on recovering the topology of the sampled space. In this chapter, we look at methods for constructing combinatorial representations of point sets, as well as theories and algorithms for effective computation of robust topological invariants. Throughout, we maintain a computational view by applying our techniques to a dataset representing the conformation space of a small molecule.
Created At: 14 December 2024
Updated At: 14 December 2024