Papers Read on Finance

Reading latest research papers on Finance. Everything ranging from social economic implications, fin-tech, to crypto currencies.

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Tuesday Nov 23, 2021

This discussion applies quantitative finance methods and economic arguments to cryptocurrencies in general and bitcoin in particular —as there are about 10, 000 cryptocurrencies, we focus (unless otherwise specified) on the most discussed crypto of those that claim to hew to the original protocol and the one with, by far, the largest market capitalisation.
2021: N. Taleb
https://arxiv.org/pdf/2106.14204.pdf

Tuesday Nov 16, 2021

In this paper we propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App. 2021: Jaime D. Acevedo-Viloria, Luisa Roa, Soji Adeshina, Cesar Charalla Olazo, Andr'es Rodr'iguez-Rey, Jose Alberto Ramos, Alejandro Correa-Bahnsenhttps://arxiv.org/pdf/2107.13673.pdf

Saturday Nov 13, 2021

In this work, we conduct the first comprehensive analysis of cryptographic libraries and the vulnerabilities affecting them. We collect data from the National Vulnerability Database, individual project repositories and mailing lists, and other relevant sources for eight widely used cryptographic libraries. 2021: Jenny Blessing, Michael A. Specter, D. Weitzner https://arxiv.org/pdf/2107.04940.pdf

Thursday Nov 11, 2021

We propose a new "sparsity algorithm" which solves the optimisation problem, while also maximising the sparsity of the counterfactual explanation. We apply the sparsity algorithm to provide a simple suggestion to publicly traded companies in order to improve their credit ratings. 2021: Dan Wang, Zhi Chen, I. Florescu https://arxiv.org/pdf/2107.10306.pdf

Thursday Nov 11, 2021

This paper discusses the use of Bayesian principles and simulation-techniques to estimate and calibrate the default probability of credit ratings. The methodology is a two-phase approach where, in the first phase, a posterior density of default rate parameter is estimated based the default history data. In the second phase of the approach, an estimate of true default rate parameter is obtained through simulations.
2021: Dominic Joseph
https://arxiv.org/pdf/2108.03389.pdf

Thursday Nov 11, 2021

We develop a dynamic covariate-assisted spectral clustering method to uniformly estimate the latent group membership of cryptocurrencies consistently. 2019: Li Guo, W. Härdle, Yubo Tao https://arxiv.org/pdf/1802.03708.pdf

Sunday Aug 22, 2021

Credit scoring is an essential tool used by global financial institutions and credit lenders for financial decision making. In this paper, we introduce a new method based on Gaussian Mixture Model (GMM) to forecast the probability of default for individual loan applicants. Clustering similar customers with each other, our model associates a probability of being healthy to each group.
2020: Hamidreza Arian, Seyed Mohammad Sina Seyfi, Azin Sharifi
https://arxiv.org/pdf/2011.07906.pdf

Friday Jul 30, 2021

Satoshi's blockchain was the first credible decentralized solution. And now, attention is rapidly starting to shift toward this second part of Bitcoin's technology, and how the blockchain concept can be used for more than just money. 2009: Vitalik Buterin

Thursday Jul 29, 2021

A purely peer-to-peer version of electronic cash would allow online
payments to be sent directly from one party to another without going through a
financial institution. Digital signatures provide part of the solution, but the main
benefits are lost if a trusted third party is still required to prevent double-spending.
Keywords: Crypto
2008: Satoshi Nakamoto
https://bitcoin.org/bitcoin.pdf

Wednesday Jul 28, 2021

We investigate the effectiveness of different machine learning methodologies in predicting economic cycles. We identify the deep learning methodology of Bi-LSTM with Autoencoder as the most accurate model to forecast the beginning and end of economic recessions in the U.S. It provided good out-of-sample predictions for the past two recessions and early warning about the COVID-19 recession.
2021: Zihao Wang, Kun Li, Steve Q. Xia, Hongfu Liu
https://arxiv.org/pdf/2107.10980.pdf

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