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on Financial Markets |
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Issue of 2022‒04‒04
fourteen papers chosen by |
| By: | Jordan Barone; Alain P. Chaboud; Adam Copeland; Cullen Kavoussi; Frank M. Keane; Seth Searls |
| Abstract: | As the economic disruptions associated with the COVID-19 pandemic increased in March 2020, there was a global dash-for-cash by investors. This selling pressure occurred across advanced sovereign bond markets and caused a deterioration in market functioning, leading to central bank interventions. We show that these market disruptions occurred disproportionately in the U.S. Treasury market and were due to investors’ selling pressures being far more pronounced and broad-based. Furthermore, we assess differences in key drivers of the market disruptions across sovereign bond markets, based on an analysis of the data as well as structured outreach to a range of market participants. |
| Keywords: | sovereign bond markets; financial crisis; COVID-19 |
| JEL: | G01 G12 E44 H63 |
| Date: | 2022–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:93852&r= |
| By: | Rian Dolphin; Barry Smyth; Ruihai Dong |
| Abstract: | Identifying meaningful relationships between the price movements of financial assets is a challenging but important problem in a variety of financial applications. However with recent research, particularly those using machine learning and deep learning techniques, focused mostly on price forecasting, the literature investigating the modelling of asset correlations has lagged somewhat. To address this, inspired by recent successes in natural language processing, we propose a neural model for training stock embeddings, which harnesses the dynamics of historical returns data in order to learn the nuanced relationships that exist between financial assets. We describe our approach in detail and discuss a number of ways that it can be used in the financial domain. Furthermore, we present the evaluation results to demonstrate the utility of this approach, compared to several important benchmarks, in two real-world financial analytics tasks. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.08968&r= |
| By: | Francesco A. Franzoni (Universita della Svizzera italiana (USI Lugano); USI Lugano; Centre for Economic Policy Research (CEPR); Swiss Finance Institute); Daniel Obrycki (The Applied Finance Group, Ltd.); Rafael Resendes (The Applied Finance Group, Ltd.) |
| Abstract: | In the asset pricing literature, higher investment is associated with lower expected stock returns. On the other hand, practitioners view investment as a value-creating activity when it generates payoffs above the cost of capital. The paper reconciles these views. Starting from a discounted cash-flow tautology, we argue that expected returns correlate positively with expected investment whenever the return on equity is large enough. We label this prediction the wealth creation effect. The empirical evidence supports this channel. The interaction of profitability and investment positively correlates with stock returns controlling for the usual characteristics. A wealth creation factor earns a premium of about 24bps per month leading to sizeable Sharpe ratio improvements relative to popular factor models. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2227&r= |
| By: | Andrea Antico; Giulio Bottazzi; Daniele Giachini |
| Abstract: | The behavioural finance literature attributes the persistent market misvaluation observed in real data to the presence of deviations from rational thinking of the actors involved. Cognitive biases and the use of simple heuristics can be described using expected utility maximising agents that adopt incorrect beliefs. Along these lines, Barberis et al. (1998) introduce a model which is able to replicate the behavior of both under-reaction and over-reaction to news. The representative agent they consider is characterized by an imperfect learning model. An interesting question that emerges is if, and to what degree, the heuristic mechanism they propose is evolutionary stable, that is how resilient is their representative agent to other agents possibly trading in the market. In fact, if the biased agent asymptotically disappears from the market, the misvaluation patters generated by its behavior does not survive in the long term. The present paper investigates this question comparing the performance of the agent described in Barberis et al. (1998) with the one of a pure Bayesian competitor. |
| Keywords: | Learning; Market Selection; Investor Sentiment; Model Misspecification; Financial Markets. |
| Date: | 2022–03–07 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2022/09&r= |
| By: | Tony Berrada (University of Geneva - Geneva Finance Research Institute (GFRI); Swiss Finance Institute); Leonie Engelhardt (University of Geneva); Rajna Gibson (University of Geneva - Geneva Finance Research Institute (GFRI); European Corporate Governance Institute (ECGI)); Philipp Krueger (University of Geneva - Geneva Finance Research Institute (GFRI); Swiss Finance Institute; European Corporate Governance Institute (ECGI); University of Geneva - Geneva School of Economics and Management) |
| Abstract: | We develop a conceptual framework to understand the incentive structure and pricing mechanisms of Sustainability-Linked-Bonds (SLBs). The model allows us to characterize the conditions under which an SLB is incentive compatible for a firm. We further derive a novel measure which identifies the extent of mispricing and potential wealth transfers between claim-holders at issuance. The model also allows us to compare the correct market yield of SLBs to the standard yield quoted by the industry. The comparison of the two yields suggests that the industry generally overstates the yield discount for firms that issue SLBs. The model generates several testable predictions. For instance, we provide evidence that SLBs which are overpriced according to our measure experience negative returns in the secondary market after issuance. We further show that for these overpriced bonds, the stock price reaction at issuance is significantly positive, which is consistent with a wealth transfer from bond - to shareholders Finally, we document a significant relationship between the mispricing measure and the issuing firms’ ESG ratings, a relationship that is complex and non-linear. |
| Keywords: | ESG investing, sustainability linked bonds, security design, managerial incentives, mispricing |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2226&r= |
| By: | Torsten Ehlers; Ulrike Elsenhuber; Anandakumar Jegarasasingam; Eric Jondeau |
| Abstract: | Environmental, Social, and Governance (ESG) scores are becoming an increasingly important tool for asset managers to design and implement ESG investment strategies. They amalgamate a broad range of fundamentally different factors, creating ambiguity for investors as to the signals of higher or lower ESG scores. We explore the feasibility and performance of more targeted investment strategies based on specific categories by deconstructing ESG scores into their granular components. First, we investigate the characteristics of the various categories underlying ESG scores. Not all types of ESG categories lend themselves to more targeted strategies, which is related to both limits to ESG data disclosure and the fundamental challenge of translating qualita-tive characteristics into quantitative measures. Second, we consider an investment scheme based on the exclusion of firms with the lowest scores in each category of interest. In most cases, this targeted strategy still allows investors to substantially improve the portfolio headline ESG score, with only a marginal impact on financial performance relative to a broad stock market benchmark. The exclusion results in regional and sectoral biases relative to the benchmark, which may be undesirable for some investors. We then implement a “best-in-class” strategy, based on exclud-ing firms with the lowest category scores and reinvesting the proceeds in firms with the highest scores maintaining the same regional and sectoral composition. This approach reduces the tracking error of the portfolio and slightly improves its risk-adjusted performance while still yielding a large gain in the headline ESG score. |
| Keywords: | sustainable investment, ESG ratings, ESG investing, negative screening, best-in-class screening. |
| JEL: | G11 G24 M14 Q01 |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1008&r= |
| By: | Kvam, Emilie (NTNU); Molnar, Peter (University of Stavanger); Wankel, Ingvild (NTNU); Odegaard, Bernt Arne (University of Stavanger) |
| Abstract: | We investigate the link between stock returns and ESG (Environmental, Social and Governance) concerns. The ESG concerns are measured by ESG-related sentiment extracted from Google Trends and Twitter, and also by the VIX index. We find that higher ESG scores are associated with lower stock returns on average. However, companies with high ESG scores deliver high returns in times of ESG concerns. Our results are consistent with the implications of equilibrium models of Pastor et al. (2021) and Pedersen et al. (2021) about the ESG score and changes in ESG concerns (preferences or news). |
| Keywords: | ESG investing; Social Media; Exclusion |
| JEL: | G10 G20 |
| Date: | 2022–03–15 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:stavef:2022_001&r= |
| By: | Antoine Bouveret; Antoine Martin; Patrick E. McCabe |
| Abstract: | Money market funds (MMFs) are popular around the world, with over $9 trillion in assets under management globally. From their origins in the 1970s, MMFs have operated in a niche between the capital markets and the banking system, as investment funds that offer private money-like assets with features similar to those of bank deposits. Hence, they are vulnerable to runs that arise from liquidity transformation and from sudden changes in investor perceptions of the funds’ ability to serve as money-like assets. Since 2000, MMF runs have occurred in many countries and under many regulatory regimes. The global pattern of runs and crises shows that MMF vulnerabilities are not unique to a particular set of governing arrangements, and that mitigating these vulnerabilities requires fundamental reforms that either place MMFs more clearly within the investment-fund sector or establish protections for MMFs similar to those for deposits. |
| Keywords: | money market funds; liquidity transformation; runs; nonbank financial institutions; short-term funding markets; information-insensitive assets; financial stability |
| JEL: | G20 G23 G28 |
| Date: | 2022–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:93851&r= |
| By: | Bernhard K. Meister; Henry C. W. Price |
| Abstract: | In this chapter structures that generate yield in cryptofinance will be analyzed and related to leverage. While the majority of crypto-assets do not have intrinsic yields in and of themselves, similar to cash holdings of fiat currency, revolutionary innovation based on smart contracts, which enable decentralised finance, does generate return. Examples include lending or providing liquidity to an automated market maker on a decentralised exchange, as well as performing block formation in a proof of stake blockchain. On centralised exchanges, perpetual and finite duration futures can trade at a premium or discount to the spot market for extended periods with one side of the transaction earning a yield. Disparities in yield exist between products and venues as a result of market segmentation and risk profile differences. Cryptofinance was initially shunned by legacy finance and developed independently. This led to curious and imaginative adaptions, reminiscent of Darwin's finches, including stable coins for dollar transfers, perpetuals for leverage, and a new class of exchanges for trading and investment. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.10265&r= |
| By: | De Castro, Angelo |
| Abstract: | This paper examines various models and strategies for the adoption of cryptocurrencies, arguments on the stabilization of their value, and the relationship between artificial intelligence and blockchain technology. |
| Date: | 2022–02–16 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:trpwc&r= |
| By: | Chao Zhang; Yihuang Zhang; Mihai Cucuringu; Zhongmin Qian |
| Abstract: | We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree models in terms of performance, due to their ability to uncover and model complex latent interactions among variables. Our findings remain robust when we apply trained models to new stocks that have not been included in the training set, thus providing new empirical evidence for a universal volatility mechanism among stocks. Finally, we propose a new approach to forecasting one-day-ahead RVs using past intraday RVs as predictors, and highlight interesting diurnal effects that aid the forecasting mechanism. The results demonstrate that the proposed methodology yields superior out-of-sample forecasts over a strong set of traditional baselines that only rely on past daily RVs. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.08962&r= |
| By: | Whelsy BOUNGOU; Alhonita YATIE |
| Abstract: | As a topical topic, this paper studies the responses of world stock market indices to the ongoing war between Ukraine and Russia. The empirical analysis is based on daily stock market returns in a sample of 94 countries and covers the period from 22 January 2022 to 3 March 2022. We consistently document a negative relationship between the Ukraine-Russia war and world stock market returns. Furthermore, our results reveal that returns have been significantly lower since the invasion of Russian troops into Ukraine on 24 February 2022. Overall, we provide the first empirical evidence of the effect of the Ukraine-Russia war on international stock market returns.As a topical topic, this paper studies the responses of world stock market indices to the ongoing war between Ukraine and Russia. The empirical analysis is based on daily stock market returns in a sample of 94 countries and covers the period from 22 January 2022 to 3 March 2022. We consistently document a negative relationship between the Ukraine-Russia war and world stock market returns. Furthermore, our results reveal that returns have been significantly lower since the invasion of Russian troops into Ukraine on 24 February 2022. Overall, we provide the first empirical evidence of the effect of the Ukraine-Russia war on international stock market returns. |
| Keywords: | War, Russia, Ukraine, Stock index |
| JEL: | H56 G11 G14 G15 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:grt:bdxewp:2022-06&r= |
| By: | 子, 鬼谷 |
| Abstract: | This paper aims at answering the question whether the VN30 index futures introduction has an impact on stock market volatility in Vietnam. Apply GARCH model of volatility with additive dummy variable from 28/7/2000 to 10/9/2020, the result shows that when the first listed index futures contract appears, it makes the volatility of VNIndex increases. The result is still robust after excluding the turmoil period of Vietnam stock market. This paper implies that policy maker should be more careful in promoting derivatives market in Vietnam. |
| Date: | 2020–11–04 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:kvpnz&r= |
| By: | Roy Havemann; Henk Janse van Vuuren; Daan Steenkamp; Rossouw van Jaarsveld |
| Abstract: | ThebondmarketimpactoftheSouthAfricanRese rveBankbondpurchaseprogramme |
| Date: | 2022–03–09 |
| URL: | http://d.repec.org/n?u=RePEc:rbz:wpaper:11024&r= |