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on Financial Markets |
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Issue of 2023‒10‒23
ten papers chosen by |
| By: | Renee van Eyden (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Xin Sheng (Lord Ashcroft International Business School, Anglia Ruskin University, Chelmsford, United Kingdom); Joshua Nielsen (Boulder Investment Technologies, LLC, 1942 Broadway Suite 314C, Boulder, CO, 80302, USA) |
| Abstract: | In this paper, as a first step, we use the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to detect both positive and negative bubbles in the short-, medium- and long-term in the stock markets of the G7 countries. While detecting major crashes and booms in the seven stock markets over the monthly period of 1973:02 to 2020:05, we also observe similar timing of strong (positive and negative) LPPLS-CIs across the G7, suggesting synchronized boom-bust cycles. Given this, in the second step, we apply dynamic heterogeneous coefficients panel databased regressions to analyze the predictive impact of a model-free robust metric of oil price uncertainty on the bubbles indicators. After controlling for the impacts of output growth, inflation, and monetary policy, we find that oil price uncertainty predicts a decrease in all the time scales and countries of the positive bubbles and increases strongly the medium term for five countries (and weakly the short-term) negative LPPLS-CIs. The aggregate findings continue to hold with the inclusion of investor sentiment indicators. Our results have important implications for both investors and policymakers, as the higher (lower) oil price uncertainty can lead to a crash (recovery) in a bullish (bearish) market. |
| Keywords: | Multi-Scale Bubbles, Oil Price Uncertainty, Panel Data Regressions, G7 Stock Markets |
| JEL: | C22 C32 C33 G15 Q02 |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202332&r=fmk |
| By: | Mihir Gandhi; Niels Joachim Gormsen; Eben Lazarus |
| Abstract: | We measure investors’ short- and long-term stock-return expectations using both options and survey data. These expectations at different horizons reveal what investors think their own short-term expectations will be in the future, or forward return expectations. While contemporaneous short-term expectations are not countercyclical across all data sources, we find that forward expectations are consistently countercyclical, and excessively so: in bad times, forward expectations are higher than justified by investors’ own subsequent short-term return expectations. This excess volatility in forward expectations helps account for excess volatility in prices, inelastic demand for equities, and stylized facts about the equity term structure. |
| JEL: | G0 G01 G1 G10 G11 G12 G13 G14 G17 G4 |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31687&r=fmk |
| By: | Gong Cheng; Eric Jondeau; Benoit Mojon; Dimitri Vayanos |
| Abstract: | We study the impact of green investors on stock prices in a dynamic equilibrium asset pricing model where investors are green, passive or active. Green investors track an index that excludes progressively the firms with the highest greenhouse gas emissions. Active investors maximize expected returns and can buy stocks of brown firms whereas passive investors hold an index of the entire market. Contrary to the literature, we find a large fall in the stock prices of the high-emitting firms that are excluded and in turn an increase in stock prices of greener firms when the exclusion strategy is announced and during the transition process. The immediate and large effects at the announcement date yield a first-mover advantage to green investors that adopt the decarbonization strategy early. This large price impact comes from the imperfect substitution of stocks among investor populations. A smaller size of active investors relative to green investors amplifies the price impact of green investment. |
| Keywords: | asset pricing, green investing, passive investing, portfolio rebalancing |
| JEL: | G12 G23 Q54 |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1127&r=fmk |
| By: | Puru Gupta; Saul D. Jacka |
| Abstract: | We consider an augmented version of Merton's portfolio choice problem, where trading by large investors influences the price of underlying financial asset leading to strategic interaction among investors, with investors deciding their trading rates independently and simultaneously at each instant, in the spirit of dynamic Cournot competition, modelled here as a non-zero sum singular stochastic differential game. We establish an equivalence result for the value functions of an investor's best-response problem, which is a singular stochastic optimal control problem, and an auxiliary classical stochastic optimal control problem by exploiting the invariance of the value functions with respect to a diffeomorphic integral flow associated with the drift coefficient of the best-response problem. Under certain regularity conditions, we show that the optimal trajectories of the two control problems coincide, which permits analytical characterization of Markov-Nash equilibrium portfolios. For the special case when asset price volatility is constant, we show that the unique Nash equilibrium is deterministic, and provide a closed-form solution which illuminates the role of imperfect competition in explaining the excessive trade puzzle. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2309.16047&r=fmk |
| By: | Wenting Liu; Zhaozhong Gui; Guilin Jiang; Lihua Tang; Lichun Zhou; Wan Leng; Xulong Zhang; Yujiang Liu |
| Abstract: | With the increasing volume of high-frequency data in the information age, both challenges and opportunities arise in the prediction of stock volatility. On one hand, the outcome of prediction using tradition method combining stock technical and macroeconomic indicators still leaves room for improvement; on the other hand, macroeconomic indicators and peoples' search record on those search engines affecting their interested topics will intuitively have an impact on the stock volatility. For the convenience of assessment of the influence of these indicators, macroeconomic indicators and stock technical indicators are then grouped into objective factors, while Baidu search indices implying people's interested topics are defined as subjective factors. To align different frequency data, we introduce GARCH-MIDAS model. After mixing all the above data, we then feed them into Transformer model as part of the training data. Our experiments show that this model outperforms the baselines in terms of mean square error. The adaption of both types of data under Transformer model significantly reduces the mean square error from 1.00 to 0.86. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2309.16196&r=fmk |
| By: | Ayelen Banegas; Phillip J. Monin |
| Abstract: | Hedge funds have become among the most active participants in U.S. Treasury (UST) markets over the past decade. As a result, the financial stability vulnerabilities associated with their leveraged Treasury market exposures, which are facilitated by low or zero haircuts on their Treasury repo borrowing, have become more prominent. |
| Date: | 2023–09–08 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2023-09-08-3&r=fmk |
| By: | Elias Albagli (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Christian Hellwig (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Aleh Tsyvinski (Yale University [New Haven]) |
| Abstract: | We analyze the consequences of noisy information aggregation for investment. Market imperfections create endogenous rents that cause overinvestment in upside risks and underinvestment in downside risks. In partial equilibrium, these inefficiencies are particularly severe if upside risks are coupled with easy scalability of investment. In general equilibrium, the shareholders' collective attempts to boost value of individual firms leads to a novel externality operating through price that amplifies investment distortions with downside risks but offsets distortions with upside risks. |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04210328&r=fmk |
| By: | Paul Alexander Bilokon |
| Abstract: | In academic literature portfolio risk management and hedging are often versed in the language of stochastic control and Hamilton--Jacobi--Bellman~(HJB) equations in continuous time. In practice the continuous-time framework of stochastic control may be undesirable for various business reasons. In this work we present a straightforward approach for thinking of cross-asset portfolio risk management and hedging, providing some implementation details, while rarely venturing outside the convex optimisation setting of (approximate) quadratic programming~(QP). We pay particular attention to the correspondence between the economic concepts and their mathematical representations; the abstractions enabling us to handle multiple asset classes and risk models at once; the dimensional analysis of the resulting equations; and the assumptions inherent in our derivations. We demonstrate how to solve the resulting QPs with CVXOPT. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2309.15767&r=fmk |
| By: | Jochen Güntner; Benjamin Karner (Economics, Johannes Kepler University Linz) |
| Abstract: | Bonds issued in high and low interest-rate environments often list at different prices despite very similar characteristics. From a risk-neutral investor's perspective, higher current prices imply higher losses in case of default, which must be compensated, if markets are efficient. We call this the "bond agio premium" and use constituent-level bond index data for January 1997 through December 2022 to show that — holding issuer and maturity fixed — it is reflected by bond prices. Higher premia for lower rating buckets imply that different estimates for US dollar- and euro-denominated bonds are consistent with different fractions of sovereign and corporate debt. |
| Keywords: | Bond agio premium, Bond pricing, Empirical asset pricing, Fixed income factor investing |
| JEL: | G11 G12 G14 G33 |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:jku:econwp:2023-13&r=fmk |
| By: | Michael D. Plante |
| Abstract: | A large number of companies operating in the EV and battery supply chain have listed on a major U.S. stock exchange in recent years. This paper investigates 1) how these companies’ stock returns are related to systematic risk factors that can explain movements in the stock market and 2) how these companies’ idiosyncratic returns are related to one another. To do so, I compile a unique data set of intradaily stock returns that spans the supply chain, including companies focused on the mining of battery and EV-related critical minerals, advanced battery technology, lithium-ion battery production, EV original equipment manufacturers (EV OEMs) and EV charging companies. The returns are decomposed into a systematic and idiosyncratic component, with the systematic component given by latent factors extracted from a large panel of stock returns using high-frequency principal components. A key feature of the returns of interest is that they can be explained not only by a market factor but also by a second factor that loads on tech and consumer discretionary stocks. There is evidence for cross-sectional dependence in the idiosyncratic returns but correlations are generally low, except for some specific groups, e.g., lithium mining companies. The first principal component of the idiosyncratic returns, which can be viewed as an “EV” factor, explains only about 13 percent of their variation. |
| Keywords: | stock returns; principal components; electric vehicles; batteries; high-frequency data |
| JEL: | G10 Q40 C55 |
| Date: | 2023–09–26 |
| URL: | http://d.repec.org/n?u=RePEc:fip:feddwp:96951&r=fmk |