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on Financial Markets |
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Issue of 2024‒01‒08
twelve papers chosen by |
| By: | John Y. Campbell; Stefano Giglio; Christopher Polk |
| Abstract: | Value investing delivers volatile returns, with large drawdowns during both market booms and busts. This paper interprets these returns through an intertemporal CAPM, which predicts that aggregate cash flow, discount rate, and volatility news all move value returns. We document that indeed these shocks explain a large fraction of quarterly value returns over the last 60 years. We also distinguish between the intra-industry and inter-industry components of value, showing that the ICAPM explains the former better. Finally, we develop a novel methodology to perform this decomposition at the daily frequency, using it to interpret value returns during the Covid-19 pandemic. |
| JEL: | G12 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31859&r=fmk |
| By: | Kausik, B.N. |
| Abstract: | Equity premium, the surplus returns of stocks over bonds, has been an enduring puzzle. While numerous prior works approach the problem assuming the utility of money is invariant across contexts, our approach implies that in efficient markets the utility of money is polymorphic, with risk aversion dependent on the information available in each context, i.e. the discount on each future cash flow depends on all information available on that cash flow. Specifically, we prove that in efficient markets, informed investors maximize return on volatility by being risk-neutral with riskless bonds, and risk-averse with equities, thereby resolving the puzzle. We validate our results on historical data with surprising consistency. |
| Keywords: | Equity premium, efficient markets |
| JEL: | C58 G12 G17 |
| Date: | 2023–11–19 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119278&r=fmk |
| By: | HyeonJun Kim |
| Abstract: | We investigate the information content of 3PROTV, a south Korean financial youtube channel. In our sample we found evidence for the hypothesis that the channel have information content on stock selection, but only on negative sentiment. Positively mentioned stock had pre-announcement spike followed by steep fall in stock price around announcement period. Negatively mentioned stock started underperforming around the announcement period, with underreaction dynamics in post-announcement period. In the area of market timing, we found that change of sentimental tone of 3PROTV than its historical average predicts the lead value of Korean market portfolio return. Its predictive power cannot be explained by future change in news sentiment, future short term interest rate, and future liquidity risk. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.15247&r=fmk |
| By: | Ricardo Barahona (Banco de España); Stefano Cassella (Tilburg University); Kristy A. E. Jansen (USC Marshall School of Business and de Nederlandsche Bank) |
| Abstract: | We investigate how teams impact return extrapolation, a bias in belief formation which is pervasive at the individual level and crucial to behavioral asset-pricing models. Using a sample of US equity money managers and a within-subject design, we find that teams attenuate their own members’ extrapolation bias by 75%. This reduction is not due to learning or differences in compensation, workload, or investment objectives between solo-managed and team-managed funds. Rather, we provide supportive evidence that team members engaging in deeper cognitive reflection can explain the bias reduction. |
| Keywords: | expectation formation, extrapolation, heuristics, teams |
| JEL: | G23 G41 D91 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:2335&r=fmk |
| By: | Ummara Mumtaz; Summaya Mumtaz |
| Abstract: | The rise of ChatGPT has brought a notable shift to the AI sector, with its exceptional conversational skills and deep grasp of language. Recognizing its value across different areas, our study investigates ChatGPT's capacity to predict stock market movements using only social media tweets and sentiment analysis. We aim to see if ChatGPT can tap into the vast sentiment data on platforms like Twitter to offer insightful predictions about stock trends. We focus on determining if a tweet has a positive, negative, or neutral effect on two big tech giants Microsoft and Google's stock value. Our findings highlight a positive link between ChatGPT's evaluations and the following days stock results for both tech companies. This research enriches our view on ChatGPT's adaptability and emphasizes the growing importance of AI in shaping financial market forecasts. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.06273&r=fmk |
| By: | Fernando D. Chague; Bruno Giovannetti; Bernard Herskovic |
| Abstract: | Using granular data on the entire Brazilian securities lending market merged with all trades in the centralized stock exchange, we identify information leakage from short sellers. Our identification strategy explores trading execution mismatches between short sellers’ selling activity in the centralized exchange and borrowing activity in the over-the-counter securities lending market. We document that brokers learn about informed directional bets by intermediating securities lending agreements and leak that information to their clients. We find evidence that the information leakage is intentional and that brokers benefit from it. We also study leakage effects on stock prices. |
| JEL: | G12 G14 G23 G24 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31927&r=fmk |
| By: | Xihan Xiong; Zhipeng Wang; Tianxiang Cui; William Knottenbelt; Michael Huth |
| Abstract: | Technological advancement drives financial innovation, reshaping the traditional finance landscape and redefining user-market interactions. The rise of blockchain and Decentralized Finance (DeFi) underscores this intertwined evolution of technology and finance. While DeFi has introduced exciting opportunities, it has also exposed the ecosystem to new forms of market misconduct. This paper aims to bridge the academic and regulatory gaps by addressing key research questions about market misconduct in DeFi. We begin by discussing how blockchain technology can potentially enable the emergence of novel forms of market misconduct. We then offer a comprehensive definition and taxonomy for understanding DeFi market misconduct. Through comparative analysis and empirical measurements, we examine the novel forms of misconduct in DeFi, shedding light on their characteristics and social impact. Subsequently, we investigate the challenges of building a tailored regulatory framework for DeFi. We identify key areas where existing regulatory frameworks may need enhancement. Finally, we discuss potential approaches that bring DeFi into the regulatory perimeter. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.17715&r=fmk |
| By: | Jean-Baptiste Hasse (Aix-Marseille Univ., CNRS, AMSE, Marseille, France and Université Catholique de Louvain, LFIN, Louvain-La-Neuve, Belgium); Christelle Lecourt (Aix-Marseille Univ., CNRS, AMSE, Marseille, France); Souhila Siagh (Aix-Marseille Univ., CERGAM, Marseille, France) |
| Abstract: | In this paper, we examine rebalancing strategies for long-term institutional investors. Specifically, we test the difference in risk-adjusted performances between stock-bond portfolios based on buy-and-hold, periodic and threshold rebalancing strategies. Using the Norwegian Sovereign Wealth Fund (SWF) as a benchmark and an econometric approach based on a bootstrap test of Sharpe ratios difference, we show that the optimal rebalancing differs across economic and financial cycles. Furthermore, we find that the optimal strategy is periodic rebalancing except during recessions and crises when the buy-and-hold approach is best, thus calling into question the hypothesis of the countercyclical behavior of SWFs. Our results are robust to alternative performance measures, asset allocations, investment horizons, rebalancing rule, nonnormal and non-iid returns, transaction costs and time sampling. Finally, our findings promote the consideration of macroprudential rules to improve the Santiago Principles and a specific monitoring framework targeted at SWFs. |
| Keywords: | Portfolio Rebalancing, Financial Stability, Bootstrap Test, Institutional Investors |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:aim:wpaimx:2322&r=fmk |
| By: | Mahyar Kargar; Benjamin Lester; Sébastien Plante; Pierre-Olivier Weill |
| Abstract: | In over-the-counter (OTC) markets, customers search for counterparties. Little is known about this process, however, because existing data is comprised of transaction records, which are only informative about the end of a successful search. Leveraging data from the leading trading platform for corporate bonds, we offer evidence about the search process: we analyze customers’ repeated attempts to trade (successful and unsuccessful). We estimate that it takes two to three days to complete a transaction after an unsuccessful attempt, with substantial variation depending on trade and customer characteristics. Our analysis offers insights into the sources of trading delays in OTC markets. |
| JEL: | D83 G0 G10 G12 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31904&r=fmk |
| By: | Rohan Kekre; Moritz Lenel |
| Abstract: | We study the effects of dollar swap lines using high frequency responses in asset prices around policy announcements. News about expanded dollar swap lines causes a reduction in liquidity premia, compression of deviations from covered interest parity (CIP), and depreciation of the dollar. Equity prices rise and the VIX falls, while the response of long-term government bond prices is mixed. The cross-section of high frequency responses implies that swap lines affect the dollar factor or the price of risk. Our findings are qualitatively consistent with models relating the supply of dollar liquidity to the broader economy. |
| JEL: | E44 F31 G15 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31901&r=fmk |
| By: | Ruslan Tepelyan; Achintya Gopal |
| Abstract: | The use of machine learning to generate synthetic data has grown in popularity with the proliferation of text-to-image models and especially large language models. The core methodology these models use is to learn the distribution of the underlying data, similar to the classical methods common in finance of fitting statistical models to data. In this work, we explore the efficacy of using modern machine learning methods, specifically conditional importance weighted autoencoders (a variant of variational autoencoders) and conditional normalizing flows, for the task of modeling the returns of equities. The main problem we work to address is modeling the joint distribution of all the members of the S&P 500, or, in other words, learning a 500-dimensional joint distribution. We show that this generative model has a broad range of applications in finance, including generating realistic synthetic data, volatility and correlation estimation, risk analysis (e.g., value at risk, or VaR, of portfolios), and portfolio optimization. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.14735&r=fmk |
| By: | Michele Costa |
| Abstract: | We aim to explore the interplay between ESG scores and assets characteristics, specifically focusing on volatility. We classify stocks on the basis of both high/low ESG and high/low ESG momentum and we evaluate ESG effects by measuring the distance between the 2 group distributions. The analysis of stocks within the STOXX Europe 600 Index from 2017 to 2022 suggests that companies with higher ESG tend to exhibit lower volatility. However, we haven’t observed a similar trend when examining ESG momentum. Furthermore, our findings enable us to highlight and compare the effects associated with the COVID pandemic and the conflict in Ukraine. |
| JEL: | G11 C40 Q56 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:bol:bodewp:wp1189&r=fmk |