|
on Financial Markets |
|
Issue of 2023‒11‒27
five papers chosen by |
| By: | Michael Pinelis; David Ruppert |
| Abstract: | We construct the maximally predictable portfolio (MPP) of stocks using machine learning. Solving for the optimal constrained weights in the multi-asset MPP gives portfolios with a high monthly coefficient of determination, given the sample covariance matrix of predicted return errors from a machine learning model. Various models for the covariance matrix are tested. The MPPs of S&P 500 index constituents with estimated returns from Elastic Net, Random Forest, and Support Vector Regression models can outperform or underperform the index depending on the time period. Portfolios that take advantage of the high predictability of the MPP's returns and employ a Kelly criterion style strategy consistently outperform the benchmark. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.01985&r=fmk |
| By: | Dörries, Julian; Korn, Olaf; Power, Gabriel J. |
| Abstract: | Derivatives strategies that aim to earn variance risk premiums are exposed to sharp price declines during market crises, calling into question their suitability for the longterm investor. Our paper defines, analyzes, and proposes potential solutions to three problems (payoff, leverage and finite maturity) linked to designing suitable variancebased investment strategies. We conduct an empirical study of such strategies for the S&P 500 index options market and find strong effects of certain design elements on risk and return. Overall, our results show that variance strategies can be attractive to the long-term investor if properly designed. |
| Keywords: | Variance Risk Premium, Variance Factor, Trading Strategies, Long-term Investor |
| JEL: | G10 G11 G23 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:279557&r=fmk |
| By: | Carolina Camassa |
| Abstract: | In the rapidly evolving field of crypto-assets, white papers are essential documents for investor guidance, and are now subject to unprecedented content requirements under the EU's Markets in Crypto-Assets Regulation (MiCAR). Natural Language Processing can serve as a powerful tool for both analyzing these documents and assisting in regulatory compliance. This paper delivers two contributions to the topic. First, we survey existing applications of textual analysis to unregulated crypto-asset white papers, uncovering a research gap that could be bridged with interdisciplinary collaboration. We then conduct an analysis of the changes introduced by MiCAR, highlighting the opportunities and challenges of integrating NLP within the new regulatory framework. The findings set the stage for further research, with the potential to benefit regulators, crypto-asset issuers, and investors. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.10333&r=fmk |
| By: | Emiel Sanders; Mathieu Simoens; Rudi Vander Vennet (-) |
| Abstract: | At the outbreak of the Covid-19 pandemic, the European Central Bank issued a strong recommendation towards banks to halt dividend payouts. The goal of this de facto dividend ban was to boost banks’ capital to ensure the supply of new credit. However, given the importance of dividends for stock market investors, this unprecedented measure is likely to have impacted bank valuations. Hence, banks may have chosen to preserve their higher capital buffers to boost payouts after the lifting of the ban, rendering the intended positive effect on credit supply a priori uncertain. We first investigate the effect of the dividend ban announcement on euro area banks’ valuations and find a significantly negative impact. Second, we assess the effect of the dividend ban on syndicated lending, including potential heterogeneity depending on the stock market reaction. We show that credit supply significantly increased, without counteracting effect of the negative stock market reaction. |
| Keywords: | Covid-19; dividend, euro area banks; market valuation; syndicated lending |
| JEL: | E51 G21 G28 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:rug:rugwps:23/1078&r=fmk |
| By: | Yi Jiang; Shohei Shimizu |
| Abstract: | While economic theory explains the linkages among the financial markets of different countries, empirical studies mainly verify the linkages through Granger causality, without considering latent variables or instantaneous effects. Their findings are inconsistent regarding the existence of causal linkages among financial markets, which might be attributed to differences in the focused markets, data periods, and methods applied. Our study adopts causal discovery methods including VAR-LiNGAM and LPCMCI with domain knowledge to explore the linkages among financial markets in Japan and the United States (US) for the post Covid-19 pandemic period under divergent monetary policy directions. The VAR-LiNGAM results reveal that the previous day's US market influences the following day's Japanese market for both stocks and bonds, and the bond markets of the previous day impact the following day's foreign exchange (FX) market directly and the following day's Japanese stock market indirectly. The LPCMCI results indicate the existence of potential latent confounders. Our results demonstrate that VAR-LiNGAM uniquely identifies the directed acyclic graph (DAG), and thus provides informative insight into the causal relationship when the assumptions are considered valid. Our study contributes to a better understanding of the linkages among financial markets in the analyzed data period by supporting the existence of linkages between Japan and the US for the same financial markets and among FX, stock, and bond markets, thus highlighting the importance of leveraging causal discovery methods in the financial domain. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.16841&r=fmk |