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on Financial Markets |
| By: | Winkler, Julian |
| Abstract: | What can granular data on investors' asset demand tell us about stock return variation? Motivated by the recent literature on demand-based asset pricing, I model the growth rate of portfolio holdings based on evolving asset fundamentals by including demand for asset-specific characteristics in a portfolio optimisation function. Alongside changes in asset characteristics, investors re-allocate wealth according to their evolving demand elasticities. Using the model, I decompose the growth rate of mutual fund holdings by the effect of i) changing stock characteristics, ii) new preferences, and iii) latent demand. I aggregate these components to reconstruct the historical impact of mutual fund investments on stock returns, and find that changing preferences explain at least as much variation in stock prices as changes in fundamentals. This demonstrates the importance of studying heterogeneity in investor preferences, and their evolution, in furthering our understanding of stock market phenomena. |
| Keywords: | Asset demand; Investor preferences; Institutional investors |
| JEL: | G02 G11 G23 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122802 |
| By: | Katsafados, Apostolos G.; Leledakis, George N.; Panagiotou, Nikolaos P.; Pyrgiotakis, Emmanouil G. |
| Abstract: | We combine machine learning algorithms (ML) with textual analysis techniques to forecast bank stock returns. Our textual features are derived from press releases of the Federal Open Market Committee (FOMC). We show that ML models produce more accurate out-of-sample predictions than OLS regressions, and that textual features can be more informative inputs than traditional financial variables. However, we achieve the highest predictive accuracy by training ML models on a combination of both financial variables and textual data. Importantly, portfolios constructed using the predictions of our best performing ML model consistently outperform their benchmarks. Our findings add to the scarce literature on bank return predictability and have important implications for investors. |
| Keywords: | Bank stock prediction; Trading strategies; Machine learning; Press conferences; Natural language processing; Banks |
| JEL: | C53 C88 G00 G11 G12 G14 G17 G21 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122899 |
| By: | Xavier Gabaix; Ralph S. J. Koijen; Federico Mainardi; Sangmin Simon Oh; Motohiro Yogo |
| Abstract: | We define risk transfer as the percent change in the market risk exposure for a group of investors over a given period. We estimate risk transfer using novel data on U.S. investors' portfolio holdings, flows, and returns at the security level with comprehensive coverage across asset classes and broad coverage across the wealth distribution (including 400 billionaires). Our key finding is that risk transfer is small with a mean absolute value of 0.65% per quarter. Leading macro-finance models with heterogeneous investors predict risk transfer that exceeds our estimate by a factor greater than ten because investors react too much to the time-varying equity premium. Thus, the small risk transfer is a new moment to evaluate macro-finance models. We develop a model with inelastic demand, calibrated to the standard asset pricing moments on realized and expected stock returns, that explains the observed risk transfer. The model is adaptable to other macro-finance applications with heterogeneous households. |
| JEL: | E7 G1 G4 G5 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33336 |
| By: | Hong, Jifeng; Kazakis, Pantelis; Strieborny, Martin |
| Abstract: | Utilizing a staggered Difference-in-Differences (DID) approach, we investigate the impact of green bond issuance on the probability of default among Chinese firms from 2016 to 2022. We find that issuing a green bond significantly reduces the firm’s default probability, highlighting the joint advantage of financial stability and environmental sustainability. The effect is particularly strong for firms that lack strong external monitoring by financial analysts and media, for high-polluting firms, and for firms facing a high level of competition. Our results also suggest that the transmission from green bond issuance to improved financial resiliency works both through alleviating financial constraints and through increasing stock liquidity. |
| Keywords: | green bond issuance; default probability; analyst and media coverage; financial constraints; stock liquidity |
| JEL: | G14 G32 G33 |
| Date: | 2024–12–19 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:123049 |
| By: | Wojciech Grabowski; Jakub Janus |
| Abstract: | This study investigates the impact of the Russo-Ukrainian war on stock market connectedness in 24 European economies. Using a framework based on Clayton copulas, we identify changes in the left-tail dependence of stock market returns between the war and pre-war periods and explore their determinants through limited dependent variable models. We find that the war-induced shifts in the market connectedness are significant but not uniform, involving both elevated left-tail linkages (financial contagion) and instances of diminished connectedness and increased market resilience. Such diverse changes can be attributed not only to cross-country differences in stock market volatilities and trade dynamics but also to countries' proximity to the warzone and their reliance on fossil-fuel imports, particularly their pre-war energy dependence on Russia. Our results highlight the need to consider these vulnerabilities in portfolio diversification strategies of international investors, as well as in financial stability policies. |
| Keywords: | stock markets; tail dependence; international market connectedness; financial contagion; Russia-Ukraine war. |
| JEL: | E44 F36 G11 G15 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp03602024 |
| By: | Yue Cai (Faculty of Economics, Gakushuin University) |
| Abstract: | Many mutual fund investors rely primarily on past performance and likely do not engage in sophisticated analysis of managers' alpha when making investment decisions. This paper explores how investors' misperception of managerial skill affects mutual funds' market power and investors' welfare, using data from China's mutual fund market. Our findings indicate that investors often confuse the effects of fund exposures to common systematic factors with genuine managerial skill, thereby increasing the market power of funds. Market power of funds are higher when investor demand arises from factor-related returns. Counterfactual experiments suggest that employing more sophisticated asset pricing models to assess fund managerial skills can enhance investor welfare. For instance, basing investment decisions on performance adjusted by a 4-factor model could increase investor welfare by $203 to $674 per year for each investor |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:wap:wpaper:2411 |