nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒05‒30
seven papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Supervised machine learning classification for short straddles on the S&P500 By Alexander Brunhuemer; Lukas Larcher; Philipp Seidl; Sascha Desmettre; Johannes Kofler; Gerhard Larcher
  2. Improving Portfolio Liquidity with Cash-Value-at-Risk for Covariance Estimations in Quantitative Trading By Tuan Tran; Nhat Nguyen
  3. The return of (I)DeFiX By Florentina Șoiman; Jean-Guillaume Dumas; Sonia Jimenez-Garces
  4. Debt-Financed Collateral and Stability Risks in the DeFi Ecosystem By Michael Darlin; Leandros Tassiulas
  5. The Price and Cost of Bitcoin By John E. Marthinsen; Steven R. Gordon
  6. Climate change and credit risk: the effect of carbon taxes on Italian banks’ business loan default rates By Maria Alessia Aiello; Cristina Angelico
  7. Fuzzy Expert System for Stock Portfolio Selection: An Application to Bombay Stock Exchange By Gour Sundar Mitra Thakur; Rupak Bhattacharyyab; Seema Sarkar

  1. By: Alexander Brunhuemer; Lukas Larcher; Philipp Seidl; Sascha Desmettre; Johannes Kofler; Gerhard Larcher
    Abstract: In this working paper we present our current progress in the training of machine learning models to execute short option strategies on the S&P500. As a first step, this paper is breaking this problem down to a supervised classification task to decide if a short straddle on the S&P500 should be executed or not on a daily basis. We describe our used framework and present an overview over our evaluation metrics on different classification models. In this preliminary work, using standard machine learning techniques and without hyperparameter search, we find no statistically significant outperformance to a simple "trade always" strategy, but gain additional insights on how we could proceed in further experiments.
    Date: 2022–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2204.13587&r=
  2. By: Tuan Tran (EPHE - École pratique des hautes études - PSL - Université Paris sciences et lettres); Nhat Nguyen
    Abstract: Understanding characteristics of covariance matrix is an important research topic. In quantitative trading, portfolio liquidity is a hidden dimension and important as others such as portfolio volatility. In this paper, we propose a liquidity impact measure to improve the portfolio liquidity and also a novel Cash Value at Risk to evaluate the liquidity risk from portfolio cash perspective. Experimental results on various scenarios show that our approach improve a portfolio turnover significantly and also better than others on Cash Value at Risk in almost all cases. An interesting finding is that linear shrinkage covariance estimations not only improve the covariance structure but also resolve a large partial of liquidity.
    Keywords: portfolio liquidity,shrinkage estimation,covariance matrix
    Date: 2022–04–21
    URL: http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03647881&r=
  3. By: Florentina Șoiman (CASC - Calcul Algébrique et Symbolique, Sécurité, Systèmes Complexes, Codes et Cryptologie - LJK - Laboratoire Jean Kuntzmann - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes, CERAG - Centre d'études et de recherches appliquées à la gestion - UGA - Université Grenoble Alpes); Jean-Guillaume Dumas (CASC - Calcul Algébrique et Symbolique, Sécurité, Systèmes Complexes, Codes et Cryptologie - LJK - Laboratoire Jean Kuntzmann - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes); Sonia Jimenez-Garces (CERAG - Centre d'études et de recherches appliquées à la gestion - UGA - Université Grenoble Alpes)
    Abstract: Decentralized Finance (DeFi) is a nascent set of financial services, using tokens, smart contracts, and blockchain technology as financial instruments. We investigate four possible drivers of DeFi returns: exposure to cryptocurrency market, the network effect, the investor's attention, and the valuation ratio. As DeFi tokens are distinct from classical cryptocurrencies, we design a new dedicated market index, denoted DeFiX. First, we show that DeFi tokens returns are driven by the investor's attention on technical terms such as "decentralized finance" or "DeFi", and are exposed to their own network variables and cryptocurrency market. We construct a valuation ratio for the DeFi market by dividing the Total Value Locked (TVL) by the Market Capitalization (MC). Our findings do not support the TVL/MC predictive power assumption. Overall, our empirical study shows that the impact of the cryptocurrency market on DeFi returns is stronger than any other considered driver and provides superior explanatory power.
    Abstract: La finance décentralisée (DeFi) est un ensemble naissant de services financiers, utilisant les jetons, les contrats intelligents et la technologie blockchain comme instruments financiers. Nous étudions quatre moteurs possibles des rendements de DeFi : l'exposition au marché des crypto-monnaies, l'effet de réseau, l'attention de l'investisseur et le ratio de valorisation. Les jetons DeFi étant distincts des crypto-monnaies classiques, nous concevons un nouvel indice de marché dédié, dénommé DeFiX. Tout d'abord, nous montrons que les rendements des jetons DeFi sont déterminés par l'attention de l'investisseur sur des termes techniques tels que "finance décentralisée" ou "DeFi", et sont exposés à leurs propres variables de réseau et au marché des crypto-monnaies. Nous construisons un ratio de valorisation pour le marché DeFi en divisant la valeur totale bloquée (TVL) par la capitalisation boursière (MC). Nos résultats ne confirment pas l'hypothèse du pouvoir prédictif de la TVL/MC. Dans l'ensemble, notre étude empirique montre que l'impact du marché des crypto-monnaies sur les rendements du DeFi est plus fort que tout autre facteur considéré et fournit un pouvoir explicatif supérieur.
    Keywords: Blockchain,financial return,asset pricing,index,DeFi token
    Date: 2022–03–31
    URL: http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03625891&r=
  4. By: Michael Darlin; Leandros Tassiulas
    Abstract: The rise of Decentralized Finance ("DeFi") on the Ethereum blockchain has enabled the creation of lending platforms, which serve as marketplaces to lend and borrow digital currencies. We first categorize the activity of lending platforms within a standard regulatory framework. We then employ a novel grouping and classification algorithm to calculate the percentage of fund flows into DeFi lending platforms that can be attributed to debt created elsewhere in the system ("debt-financed collateral"). Based on our results, we conclude that the wide-spread use of stablecoins as debt-financed collateral increases financial stability risks in the DeFi ecosystem.
    Date: 2022–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2204.11107&r=
  5. By: John E. Marthinsen; Steven R. Gordon
    Abstract: Explaining changes in bitcoin's price and predicting its future have been the foci of many research studies. In contrast, far less attention has been paid to the relationship between bitcoin's mining costs and its price. One popular notion is the cost of bitcoin creation provides a support level below which this cryptocurrency's price should never fall because if it did, mining would become unprofitable and threaten the maintenance of bitcoin's public ledger. Other research has used mining costs to explain or forecast bitcoin's price movements. Competing econometric analyses have debunked this idea, showing that changes in mining costs follow changes in bitcoin's price rather than preceding them, but the reason for this behavior remains unexplained in these analyses. This research aims to employ economic theory to explain why econometric studies have failed to predict bitcoin prices and why mining costs follow movements in bitcoin prices rather than precede them. We do so by explaining the chain of causality connecting a bitcoin's price to its mining costs.
    Date: 2022–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2204.13102&r=
  6. By: Maria Alessia Aiello (Bank of Italy); Cristina Angelico (Bank of Italy)
    Abstract: Climate change poses severe systemic risks to the financial sector through multiple transmission channels. In this paper, we estimate the potential impact of different carbon taxes (€50, €100, €200 and €800 per ton of CO2) on the Italian banks’ default rates at the sector level in the short term using a counterfactual analysis. We build on the micro-founded climate stress test approach proposed by Faiella et al. (2021), which estimates the energy demand of Italian firms using granular data and simulates the effects of the alternative taxes on the share of financially vulnerable agents (and their debt). Credit risks stemming from introducing a carbon tax – during periods of low default rates – are modest on banks: on average, in a one-year horizon, the default rates of firms increase but remain below their historical averages. The effect is heterogeneous across different sectors and rises with the tax value; however, even assuming a tax of €800 per ton of CO2, the default rates are lower than the historical peaks.
    Keywords: climate change, carbon tax, climate stress test, banks’ credit risk
    JEL: Q43 Q48 Q58 G21
    Date: 2022–04
    URL: http://d.repec.org/n?u=RePEc:bdi:opques:qef_688_22&r=
  7. By: Gour Sundar Mitra Thakur (Mondal); Rupak Bhattacharyyab (Mondal); Seema Sarkar (Mondal)
    Abstract: Selection of proper stocks, before allocating investment ratios, is always a crucial task for the investors. Presence of many influencing factors in stock performance have motivated researchers to adopt various Artificial Intelligence (AI) techniques to make this challenging task easier. In this paper a novel fuzzy expert system model is proposed to evaluate and rank the stocks under Bombay Stock Exchange (BSE). Dempster-Shafer (DS) evidence theory is used for the first time to automatically generate the consequents of the fuzzy rule base to reduce the effort in knowledge base development of the expert system. Later a portfolio optimization model is constructed where the objective function is considered as the ratio of the difference of fuzzy portfolio return and the risk free return to the weighted mean semi-variance of the assets that has been used. The model is solved by applying Ant Colony Optimization (ACO) algorithm by giving preference to the top ranked stocks. The performance of the model proved to be satisfactory for short-term investment period when compared with the recent performance of the stocks.
    Date: 2022–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2204.13385&r=

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