The informational content of prices hypothesis in Modigliani and Miller (and Fisher before them) advocates that organizations’ market prices could somehow estimate their growth prospects and intangible assets. For this estimation, discounted cash flow models are frequently employed. However,...

Bitcoin Pricing Kernels (PK) are estimated using a novel data set from Deribit, one of the largest Bitcoin derivatives exchanges. The PKs improve the understanding of investor sentiment and risk premia. Bootstrap-based confidence bands are estimated in order to validate the results. Investors...

In establishing the foundation for their investment process, investors typically set up the investment framework first by dividing their investment universes into different buckets along the combinations of multiple sensible dimensions such as geography and industry. As the framework is applied...

We introduce a new deep learning architecture for predicting price movements from limit order books. This architecture uses a causal convolutional network for feature extraction in combination with masked self-attention to update features based on relevant contextual information. This...

This paper presents an analytically tractable and practically-oriented model of non-linear dynamics of a multi-asset market in the limit of a large number of assets. The asset price dynamics are driven by money flows into the market from external investors, and their price impact. This leads to...

The aim of this study is to investigate the volatility spillover connectedness between NFTs attention and financial markets. This paper firstly proposes a new direct proxy for the public's attention in the NFT market: the non-fungible tokens attention index (NFTsAI), based on 590m news stories...

This paper examines return and volatility connectedness between Bitcoin, traditional financial assets (Crude Oil, Gold, Stocks, Bonds, and the United States Dollar-USD), and major global uncertainty measures (the Economic Policy Uncertainty-EPU, the Twitter-based Economic Uncertainty-TEU, and...

Machine Learning algorithms have been widely used and proven effective in financial markets. In this paper, we introduced a Machine Learning model set trained on the residual factors from the Fama-French three-factor model (Fama and French, 1992) to find significant alpha factors. To include...

The Fundamental Review of the Trading Book (FRTB) presents greater challenges than the current Basel 2.5 Framework in calculating and managing market risk. Financial institutions often struggle to comply with regulatory timelines due to the complexity, difficulty, and expense of FRTB...

This study empirically evaluates the impact of the Private Securities Litigation Reform Act of 1995 (PSLRA) and the Sarbanes Oxley Act of 2002 upon the (equity) risk of the largest US firms, the backbone of the US economy. Drawing from the literature, hypotheses are developed and empirically...