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on Financial Markets |
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Issue of 2024‒04‒08
nine papers chosen by |
| By: | Vuko Vukcevic; Robert Keser |
| Abstract: | The paper provides a mathematical model and a tool for the focused investing strategy as advocated by Buffett, Munger, and others from this investment community. The approach presented here assumes that the investor's role is to think about probabilities of different outcomes for a set of businesses. Based on these assumptions, the tool calculates the optimal allocation of capital for each of the investment candidates. The model is based on a generalized Kelly Criterion with options to provide constraints that ensure: no shorting, limited use of leverage, providing a maximum limit to the risk of permanent loss of capital, and maximum individual allocation. The software is applied to an example portfolio from which certain observations about excessive diversification are obtained. In addition, the software is made available for public use. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.15588&r=fmk |
| By: | Algieri, Bernardina; Lawuobahsumo, Kokulo; Leccadito, Arturo (Université catholique de Louvain, LIDAM/LFIN, Belgium) |
| Abstract: | This study aims to investigate calendar effects in the cryptocurrency market. We consider the day-of-the-week, the month-of-the-year, quarter-of-the-year, the US Holidays, and Weekend calendar anomalies for the leading cryptocurrencies: Bitcoin, Dash, Dogecoin, Litecoin, Ripple, and Stellar. Our study employs the Autoregressive Conditional Density model with dummy variables to scrutinize these calendar effects. We find anomalies in the mean, variance, skewness, and kurtosis for these cryptocurrencies’ returns. Our result suggests that the cryptocurrency market in some periods tends to violate the Efficient Market Hypothesis. |
| Keywords: | Calendar effects ; Higher Moments ; Cryptocurrencies |
| JEL: | C58 E44 G15 |
| Date: | 2024–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:ajf:louvlf:2024001&r=fmk |
| By: | Lioba Heimbach; Wenqian Huang |
| Abstract: | In decentralized finance (DeFi), lending protocols are governed by predefined algorithms that facilitate automatic loans – allowing users to take on leverage. This paper examines DeFi leverage – ie the asset-to-equity ratio at the wallet level in major lending platforms. The overall leverage typically ranges between 1.4 and 1.9, while the largest and most active users consistently exhibit higher leverage than the rest. Leverage is mainly driven by loan-to-value requirements and borrowing costs, as well as crypto market price movements and sentiments. Higher wallet leverage generally undermines lending resilience, particularly increasing the share of outstanding debt close to being liquidated. Borrowers with high leverage are more likely to tilt towards volatile collateral when their debt positions are about to be liquidated. |
| Keywords: | leverage, collateralised borrowing, decentralised finance, automated algorithm |
| JEL: | G12 G23 O36 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1171&r=fmk |
| By: | Dalderop, J.; Linton, O. B. |
| Abstract: | Option-implied risk-neutral densities are widely used for constructing forward-looking risk measures. Meanwhile, investor risk aversion introduces a multiplicative pricing kernel between the risk-neutral and true conditional densities of the underlying asset’s return. This paper proposes a simple local estimator of the pricing kernel based on inverse density weighting, and characterizes its asymptotic bias and variance. The estimator can be used to correct biased density forecasts, and performs well in a simulation study. A local exponential linear variant of the estimator is proposed to include conditioning variables. In an application, we estimate a demand-based model for S&P 500 index options using net positions data, and attribute the U-shaped pricing kernel to heterogeneous beliefs about conditional volatility. |
| Keywords: | Density Forecasting, Nonparametric Estimation, Option Pricing, Trade Data |
| JEL: | C14 G13 |
| Date: | 2024–03–05 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:2411&r=fmk |
| By: | Hafner, C. M.; Linton, O. B.; Wang, L. |
| Abstract: | We develop a dynamic framework to detect the occurrence of permanent and transitory breaks in the illiquidity process. We propose various tests that can be applied separately to individual events and can be aggregated across different events over time for a given firm or across different firms. In an empirical study, we use this methodology to study the impact of stock splits on the illiquidity dynamics of the Dow Jones index constituents and the effects of reverse splits using stocks from the S&P 500, S&P 400 and S&P 600 indices. Our empirical results show that stock splits have a positive and significant effect on the permanent component of the illiquidity process while a majority of the stocks engaging in reverse splits experience an improvement in liquidity conditions. |
| Keywords: | Amihud illiquidity, Difference in Difference, Event Study, Nonparametric Estimation, Reverse Split, Structural Change |
| JEL: | C12 C14 G14 G32 |
| Date: | 2024–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:2410&r=fmk |
| By: | Rambod Rahmani; Marco Parola; Mario G. C. A. Cimino |
| Abstract: | Due to the recent increase in interest in Financial Technology (FinTech), applications like credit default prediction (CDP) are gaining significant industrial and academic attention. In this regard, CDP plays a crucial role in assessing the creditworthiness of individuals and businesses, enabling lenders to make informed decisions regarding loan approvals and risk management. In this paper, we propose a workflow-based approach to improve CDP, which refers to the task of assessing the probability that a borrower will default on his or her credit obligations. The workflow consists of multiple steps, each designed to leverage the strengths of different techniques featured in machine learning pipelines and, thus best solve the CDP task. We employ a comprehensive and systematic approach starting with data preprocessing using Weight of Evidence encoding, a technique that ensures in a single-shot data scaling by removing outliers, handling missing values, and making data uniform for models working with different data types. Next, we train several families of learning models, introducing ensemble techniques to build more robust models and hyperparameter optimization via multi-objective genetic algorithms to consider both predictive accuracy and financial aspects. Our research aims at contributing to the FinTech industry in providing a tool to move toward more accurate and reliable credit risk assessment, benefiting both lenders and borrowers. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.03785&r=fmk |
| By: | Novan Fauzi Al Giffary; Feri Sulianta |
| Abstract: | The rapid development of information technology, especially the Internet, has facilitated users with a quick and easy way to seek information. With these convenience offered by internet services, many individuals who initially invested in gold and precious metals are now shifting into digital investments in form of cryptocurrencies. However, investments in crypto coins are filled with uncertainties and fluctuation in daily basis. This risk posed as significant challenges for coin investors that could result in substantial investment losses. The uncertainty of the value of these crypto coins is a critical issue in the field of coin investment. Forecasting, is one of the methods used to predict the future value of these crypto coins. By utilizing the models of Long Short Term Memory, Support Vector Machine, and Polynomial Regression algorithm for forecasting, a performance comparison is conducted to determine which algorithm model is most suitable for predicting crypto currency prices. The mean square error is employed as a benchmark for the comparison. By applying those three constructed algorithm models, the Support Vector Machine uses a linear kernel to produce the smallest mean square error compared to the Long Short Term Memory and Polynomial Regression algorithm models, with a mean square error value of 0.02. Keywords: Cryptocurrency, Forecasting, Long Short Term Memory, Mean Square Error, Polynomial Regression, Support Vector Machine |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.03410&r=fmk |
| By: | Massimiliano Caporin (Department of Statistical Sciences, University of Padova, Via Cesare Battisti 241, 35121 Padova, Italy); Petre Caraiani (Institute for Economic Forecasting, Romanian Academy, Romania; Bucharest University of Economic Studies, Romania); Oguzhan Cepni (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Ostim Technical University, Ankara, Turkiye); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | This paper explores how climate risks impact the overall systemic stress levels in the United States (US). We initially apply the TrAffic Light System for Systemic Stress (TALIS) approach that classifies the stock markets across all 50 states based on their stress levels, to create an aggregate stress measure called ATALIS. Then, we utilize a nonparametric causality-in-quantiles approach to thoroughly assess the predictive power of climate risks across the entire conditional distribution of ATALIS, accounting for any data nonlinearity and structural changes. Our analysis covers daily data from July 1996 to March 2023, revealing that various climate risk indicators can predict the entire conditional distribution of ATALIS3, particularly around its median. The full-sample result also carries over time, when the nonparametric causality-in-quantiles test is conducted based on a rolling-window. Our findings, showing that climate risks are positively associated with ATALIS over its entire conditional distribution, provide crucial insights for investors and policymakers regarding the economic impact of environmental changes. |
| Keywords: | State stock markets, Systemic stress, Climate risks, Quantile predictions |
| JEL: | C21 C32 C53 G10 Q54 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202407&r=fmk |
| By: | Gan Jin; Md Rafiul Karim; Günther G. Schulze |
| Abstract: | We are the first to analyze the effect of terror on stock markets by terror ideology. Surprisingly, we find that Islamist terror attacks created significant negative abnormal returns in American and European markets, but the stock market effects of other terror attacks were almost nil. For our sample of all 124 terrorist attacks in the US and Europe in the period 1994 to 2018 that caused at least five fatalities or ten injured people, we show that Islamist terror attacks are given significantly more air time (also after controlling for attack characteristics and the media pressure of competing news stories). This, however, explains only part of the differential effect of Islamist attacks on the stock markets. |
| Keywords: | terror, stock market, event studies, Islamist terror, media |
| JEL: | D74 F52 G10 G40 H56 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10960&r=fmk |