The excerpt appears to be a corrupted or scrambled text with no discernible mathematical formulas or quantitative analysis, and it presents no empirical data or backtesting procedures, focusing instead on conceptual discussions of behavioral finance.
The paper is primarily a conceptual overview and theoretical discussion of crowdfunding, with no mathematical models or formulas presented. Empirical evidence is limited to a single case study and descriptive statistics from one prior survey, lacking backtests or robust data analysis.
In 2013, a paper by Google DeepMind kicked off an explosion in Deep Reinforcement Learning (DRL), for games. In this talk, we show that DRL can also be applied
The excerpt is a book introduction discussing the conceptual foundations, problems, and potential of DeFi without presenting any mathematical models or empirical analysis, placing it firmly in the low-math, low-rigor category.
This paper uses new statistical techniques and two new databases to reassess the relationship between economic growth and FDI. After resolving biases plaguing
Prior research documents a carbon premium in realized returns, assuming they proxy for expected returns and thus the cost of capital. We find that the carbon pr
The paper is a theoretical survey that synthesizes existing literature with conceptual frameworks rather than presenting new mathematical models or empirical data analysis. It lacks the implementation-heavy elements of backtesting or data processing, focusing instead on integrating insights from acc
This chapter discusses how applied researchers in corporate finance can address endogeneity concerns. We begin by reviewing the sources of endogeneity - omitted
The paper employs sophisticated financial models (DCF, option pricing, risk decomposition) requiring advanced math, and its empirical component is data-heavy, featuring historical time-series analysis, implied premium calculations from market prices, and country-specific risk scores, making it backt
The paper centers on the estimation of the equity risk premium using established financial models (CAPM, Gordon Growth), involving algebraic and present value formulas, but focuses heavily on practical, data-driven applications like historical returns analysis, survey methods, and implied premium ca
The paper is a conceptual case study discussing potential applications of AI in academic research, lacking any mathematical formulas, derivations, or formal models. It also contains no backtesting, datasets, statistical metrics, or implementation details, focusing instead on high-level benefits and
This paper explores how entrepreneurs can use fungible tokens—whereby they issue digital assets and commit to only accept those tokens as payment for future pro
This handbook in Sustainable Finance corresponds to the lecture notes of the course given at University Paris-Saclay, ENSAE and Sorbonne University. It covers t
We develop a framework to theoretically and empirically analyze the fluctuations of the aggregate stock market. Households allocate capital to institutions, whi
This paper explores how entrepreneurs can use initial coin offerings — whereby they issue crypto tokens and commit to only accept those tokens as payment for th
In September 2022, James Simons spoke with members of the Journal of Investment Consulting editorial board about how his experience as a mathematician prepared
The title ‘Managing for Stakeholders’ suggests a discussion on corporate governance or business strategy rather than quantitative finance, with no mathematical formulas or empirical data evident in the excerpt.
This paper introduces MarketGAN, a factor-based generative framework for high-dimensional asset return generation under severe data scarcity. We embed an explicit asset-pricing factor structure as an economic inductive bias and generate returns as a single joint vector, thereby preserving cross-sect
These are the lecture notes for an advanced Ph.D. level course I taught in Spring ‘02 at the C.N. Yang Institute for Theoretical Physics at Stony Brook. The cou
This study evaluates the effectiveness of specialised large language models (LLMs) developed for accounting and finance. Empirical analysis demonstrates that th