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on Financial Markets |
| By: | Miguel C. Herculano |
| Abstract: | Frazzini and Pedersen (2014) Betting Against Beta (BAB) factor is based on the idea that high beta assets trade at a premium and low beta assets trade at a discount due to investor funding constraints. However, as argued by Campbell and Vuolteenaho (2004), beta comes in "good" and "bad" varieties. While gaining exposure to low-beta, BAB factors fail to recognize that such a portfolio may tilt towards bad-beta. We propose a Betting Against Bad Beta factor, built by double-sorting on beta and bad-beta and find that it improves the overall performance of BAB strategies though its success relies on proper transaction cost mitigation. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.00416 |
| By: | Michael J. Fleming |
| Abstract: | Standard metrics point to an improvement in Treasury market liquidity in 2024 to levels last seen before the start of the current monetary policy tightening cycle. Volatility has also trended down, consistent with the improved liquidity. While at least one market functioning metric has worsened in recent months, that measure is an indirect gauge of market liquidity and suggests a level of current functioning that is far better than at the peak seen during the global financial crisis (GFC). |
| Keywords: | market liquidity; Treasury market; Treasury securities |
| JEL: | G12 |
| Date: | 2024–09–23 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:98808 |
| By: | Fang Wang (Florence Wong); Marko Gacesa |
| Abstract: | This study extends the examination of the Efficient-Market Hypothesis in Bitcoin market during a five year fluctuation period, from September 1 2017 to September 1 2022, by analyzing 28, 739, 514 qualified tweets containing the targeted topic "Bitcoin". Unlike previous studies, we extracted fundamental keywords as an informative proxy for carrying out the study of the EMH in the Bitcoin market rather than focusing on sentiment analysis, information volume, or price data. We tested market efficiency in hourly, 4-hourly, and daily time periods to understand the speed and accuracy of market reactions towards the information within different thresholds. A sequence of machine learning methods and textual analyses were used, including measurements of distances of semantic vector spaces of information, keywords extraction and encoding model, and Light Gradient Boosting Machine (LGBM) classifiers. Our results suggest that 78.06% (83.08%), 84.63% (87.77%), and 94.03% (94.60%) of hourly, 4-hourly, and daily bullish (bearish) market movements can be attributed to public information within organic tweets. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.15988 |
| By: | Pedro Bordalo; Nicola Gennaioli; Rafael La Porta; Andrei Shleifer |
| Abstract: | We address the joint hypothesis problem in cross-sectional asset pricing by using measured analyst expectations of earnings growth. We construct a firm-level measure of Expectations Based Returns (EBRs) that uses analyst forecast errors and revisions and shuts down any cross-sectional differences in required returns. We obtain three results. First, variation in EBRs accounts for a large chunk of cross-sectional return spreads in value, investment, size, and momentum factors. Second, time variation in these spreads is predictable, and proxied by predictable time variation in EBRs. This result holds even controlling for scaled price variables, which may capture time varying required return differentials. Third, firm characteristics typically viewed as capturing risk predict disappointment of expectations (and of EBRs). Overall, return spreads typically attributed to exotic risk factors are explained by predictable movements in non-rational expectations of firms’ earnings growth. |
| JEL: | G02 G1 G14 G4 G41 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33004 |
| By: | Henry Dyer; Michael J. Fleming; Or Shachar |
| Abstract: | Trading activity in benchmark U.S. Treasury securities now concentrates on the last trading day of the month. Moreover, this stepped-up activity is associated with lower transaction costs, as shown by a smaller price impact of trades. We conjecture that increased turn-of-month portfolio rebalancing by passive investment funds that manage relative to fixed-income indices helps explain these patterns. |
| Keywords: | end of month; Portfolio rebalancing; index; Treasury securities |
| JEL: | G12 |
| Date: | 2024–09–24 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:98822 |
| By: | Junhyeong Lee; Inwoo Tae; Yongjae Lee |
| Abstract: | Markowitz laid the foundation of portfolio theory through the mean-variance optimization (MVO) framework. However, the effectiveness of MVO is contingent on the precise estimation of expected returns, variances, and covariances of asset returns, which are typically uncertain. Machine learning models are becoming useful in estimating uncertain parameters, and such models are trained to minimize prediction errors, such as mean squared errors (MSE), which treat prediction errors uniformly across assets. Recent studies have pointed out that this approach would lead to suboptimal decisions and proposed Decision-Focused Learning (DFL) as a solution, integrating prediction and optimization to improve decision-making outcomes. While studies have shown DFL's potential to enhance portfolio performance, the detailed mechanisms of how DFL modifies prediction models for MVO remain unexplored. This study aims to investigate how DFL adjusts stock return prediction models to optimize decisions in MVO, addressing the question: "MSE treats the errors of all assets equally, but how does DFL reduce errors of different assets differently?" Answering this will provide crucial insights into optimal stock return prediction for constructing efficient portfolios. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.09684 |
| By: | Elie Bouri (School of Business, Lebanese American University, Lebanon); Oguzhan Cepni (Ostim Technical University, Ankara, Turkiye; University of Edinburgh Business School, Centre for Business, Climate Change, and Sustainability; Department of Economics, Copenhagen Business School, Denmark.); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Ruipeng Liu (Department of Finance, Deakin Business School, Deakin University, Melbourne, VIC 3125, Australia) |
| Abstract: | This paper analyses the effect of supply constraints on international stock market volatility and while also considering their effect on stock returns. Using higher-order nonparametric causality-in-quantiles tests and daily data for China, France, Germany, Italy, Spain, the United Kingdom, the United States, and overall Europe, we find strong evidence of Granger causality flowing from supply constraints to the entire conditional distribution of stock returns and volatility. Notably, supply constraints positively predict stock volatility. This positive predictability remains robust when using alternative measures, including monthly realized variance and different metrics of supply constraints. Our findings have implications for investors and policymakers. |
| Keywords: | Supply Constraints, Stock Markets Volatility, Higher-Order Nonparametric Causality-in-Quantiles Test |
| JEL: | C21 C22 E23 G15 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202441 |
| By: | Nicola Cetorelli; Saketh Prazad |
| Abstract: | U.S. bank holding companies (BHCs) have developed a very significant nonbank footprint over the years, adding thousands of specialty lenders, brokers and dealers, asset management, and insurance subsidiaries to their organizations. These nonbank subsidiaries represent a sizeable share of aggregate BHC assets and a significant component of the entire U.S. nonbank industry. We argue that liquidity management synergies are an important driver of the coexistence of commercial banks and nonbank subsidiaries within BHCs. Using unique data on BHC organizational structure and financial reports, we show that in the unrestricted pre-crisis regulatory environment, commercial banks within BHCs with a large nonbank footprint hold fewer liquid assets and more loans on their balance sheet. We show that our results are driven by explicit and implicit intracompany funding arrangements between affiliated banks and nonbanks. Post-GFC banking regulation, like resolution planning and liquidity regulation, has disrupted liquidity synergies and has caused BHCs to scale back their nonbank footprint. |
| Keywords: | banking firm; bank holding companies; firm boundaries; nonbank financial institutions; liquidity synergies; bank regulation |
| JEL: | G01 G21 G23 G28 |
| Date: | 2024–09–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednsr:98819 |