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on Financial Markets |
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Issue of 2024‒03‒25
seven papers chosen by |
| By: | Matthijs Breugem; Raffaele Corvino; Roberto Marfe; Lorenzo Schonleber |
| Abstract: | This paper studies the measurement of forward-looking tail risk in US equity markets around the COVID-19 outbreak. We document that financial markets are informative about how pandemic risk has spread in the economy in advance of the actual outbreak. While the tail risk of the market index did not respond before the outbreak, investors identified less pandemic-resilient economic sectors whose tail risk boomed in advance of both the market drawdown and the implementation of social distancing provisions. This pattern is consistent across different methodologies for measuring forward-looking tail risk, using option contracts, and across various horizons. |
| Keywords: | G01, G10, G12, G14 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:cca:wpaper:714&r=fmk |
| By: | Daniel Celeny; Lo\"ic Mar\'echal |
| Abstract: | We extract firms' cyber risk with a machine learning algorithm measuring the proximity between their disclosures and a dedicated cyber corpus. Our approach outperforms dictionary methods, uses full disclosure and not devoted-only sections, and generates a cyber risk measure uncorrelated with other firms' characteristics. We find that a portfolio of US-listed stocks in the high cyber risk quantile generates an excess return of 18.72\% p.a. Moreover, a long-short cyber risk portfolio has a significant and positive risk premium of 6.93\% p.a., robust to all factors' benchmarks. Finally, using a Bayesian asset pricing method, we show that our cyber risk factor is the essential feature that allows any multi-factor model to price the cross-section of stock returns. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.04775&r=fmk |
| By: | Goncalves, Andrei S. (Ohio State U); Stathopoulos, Andreas (U of North Carolina at Chapel Hill) |
| Abstract: | Firms' payout decisions respond to expected returns: everything else equal, firms invest less and pay out more when their cost of capital increases. Given investors' demand for firm payout, market clearing implies that the dynamics of productivity and payout demand fully determine equilibrium asset prices and returns. We use this logic to propose a payout-based asset pricing framework and we illustrate the analogy between our approach and consumption-based asset pricing in a simple two-period model. Then, we introduce a quantitative payout-based asset pricing model and calibrate the productivity and payout demand processes to match aggregate U.S. corporate output and payout empirical moments. We find that model-implied payout yields and firm returns go a long way in reproducing key attributes of their empirical counterparts. |
| JEL: | E10 E13 G10 G11 G12 G35 |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:ohidic:2023-22&r=fmk |
| By: | Andrei Neagu; Fr\'ed\'eric Godin; Clarence Simard; Leila Kosseim |
| Abstract: | Dynamic hedging is the practice of periodically transacting financial instruments to offset the risk caused by an investment or a liability. Dynamic hedging optimization can be framed as a sequential decision problem; thus, Reinforcement Learning (RL) models were recently proposed to tackle this task. However, existing RL works for hedging do not consider market impact caused by the finite liquidity of traded instruments. Integrating such feature can be crucial to achieve optimal performance when hedging options on stocks with limited liquidity. In this paper, we propose a novel general market impact dynamic hedging model based on Deep Reinforcement Learning (DRL) that considers several realistic features such as convex market impacts, and impact persistence through time. The optimal policy obtained from the DRL model is analysed using several option hedging simulations and compared to commonly used procedures such as delta hedging. Results show our DRL model behaves better in contexts of low liquidity by, among others: 1) learning the extent to which portfolio rebalancing actions should be dampened or delayed to avoid high costs, 2) factoring in the impact of features not considered by conventional approaches, such as previous hedging errors through the portfolio value, and the underlying asset's drift (i.e. the magnitude of its expected return). |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.13326&r=fmk |
| By: | Lenhard, Gregor (University of Basel) |
| Abstract: | Recent survey evidence suggests that investors form beliefs about future stock returns by predominantly extrapolating their own experience: They overweight returns they have personally experienced while underweighting returns from earlier years and consequently expect high (low) stock market returns when they observe bullish (bearish) markets in their lifespan. Such events are difficult to reconcile with the existing models. This paper introduces a simple agent-based model for simulating artificial stock markets in which mean-variance optimizing investors have heterogeneous beliefs about future capital gains to form their expectations. Using this framework, I successfully reproduce various stylized facts from the empirical finance literature, such as under diversification, the predictive power of the price-dividend ratio, and the autocorrelation of price changes. The experimental findings show that the most realistic market scenarios are produced when agents have a bias for recent returns. The study also established a link between under diversification of investor portfolios and personal experiences. |
| JEL: | C63 G12 D84 |
| Date: | 2024–03–03 |
| URL: | http://d.repec.org/n?u=RePEc:bsl:wpaper:2024/01&r=fmk |
| By: | Hao Qian; Hongting Zhou; Qian Zhao; Hao Chen; Hongxiang Yao; Jingwei Wang; Ziqi Liu; Fei Yu; Zhiqiang Zhang; Jun Zhou |
| Abstract: | The stock market is a crucial component of the financial system, but predicting the movement of stock prices is challenging due to the dynamic and intricate relations arising from various aspects such as economic indicators, financial reports, global news, and investor sentiment. Traditional sequential methods and graph-based models have been applied in stock movement prediction, but they have limitations in capturing the multifaceted and temporal influences in stock price movements. To address these challenges, the Multi-relational Dynamic Graph Neural Network (MDGNN) framework is proposed, which utilizes a discrete dynamic graph to comprehensively capture multifaceted relations among stocks and their evolution over time. The representation generated from the graph offers a complete perspective on the interrelationships among stocks and associated entities. Additionally, the power of the Transformer structure is leveraged to encode the temporal evolution of multiplex relations, providing a dynamic and effective approach to predicting stock investment. Further, our proposed MDGNN framework achieves the best performance in public datasets compared with state-of-the-art (SOTA) stock investment methods. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.06633&r=fmk |
| By: | Colombo, Jéfferson Augusto; Yarovaya, Larisa |
| Abstract: | Cryptocurrencies and blockchain have become a global phenomenon, transforming people’s relationships with technology and offering innovative tools for businesses and individuals to strive in a digital age. However, little is still known about the main drivers of cryptocurrency ownership, especially in emerging markets. Based on a representative online survey among 573 Brazilian digital platform investors, we find that crypto investors tend to be young, male, more tolerant to risk, less optimistic in their economic views, and consider themselves as ‘better’ investors compared to non-crypto online traders. While crypto and non-crypto investors have similar educational backgrounds, our results show that cryptocurrency literacy positively and strongly relates to cryptocurrency ownership and intentions to invest in cryptocurrency. A gender gap among cryptocurrency investors has been confirmed. The findings further suggest that sophisticated investors are more likely to hedge pessimistic economic expectations using cryptocurrency than their unsophisticated peers. We also find significant heterogeneity among cryptocurrency investors (e.g., early x late adopters) on attitudes and beliefs. The insights into digital investors’ intentions to invest in cryptocurrency can be valuable for policymakers in designing strategies for the broader adoption of digital assets in the era of a decentralized economy, considering the planned adoption of CBDC in Brazil. |
| Date: | 2024–02–29 |
| URL: | http://d.repec.org/n?u=RePEc:fgv:eesptd:568&r=fmk |