|
on Financial Markets |
| By: | Ruslan Goyenko; Bryan T. Kelly; Tobias J. Moskowitz; Yinan Su; Chao Zhang |
| Abstract: | Portfolio optimization focuses on risk and return prediction, yet implementation costs critically matter. Predicting trading costs is challenging because costs depend on trade size and trader identity, thus impeding a generic solution. We focus on a component of trading costs that applies universally – trading volume. Individual stock trading volume is highly predictable, especially with machine learning. We model the economic benefits of predicting volume through a portfolio framework that trades off tracking error versus net-of-cost performance – translating volume prediction into net-of-cost alpha. The economic benefits of predicting individual stock volume are as large as those from stock return predictability. |
| JEL: | C45 C53 C55 G00 G11 G12 G17 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33037 |
| By: | Simon Levy; Maxime L. D. Nicolas |
| Abstract: | This paper presents a novel approach to evaluating blue-chip art as a viable asset class for portfolio diversification. We present the Arte-Blue Chip Index, an index that tracks 100 top-performing artists based on 81, 891 public transactions from 157 artists across 584 auction houses over the period 1990 to 2024. By comparing blue-chip art price trends with stock market fluctuations, our index provides insights into the risk and return profile of blue-chip art investments. Our analysis demonstrates that a 20% allocation of blue-chip art in a diversified portfolio enhances risk-adjusted returns by around 20%, while maintaining volatility levels similar to the S&P 500. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.18816 |
| By: | Konark Jain; Jean-Fran\c{c}ois Muzy; Jonathan Kochems; Emmanuel Bacry |
| Abstract: | We investigate the disparity in the microstructural properties of the Limit Order Book (LOB) across different relative tick sizes. Tick sizes not only influence the granularity of the price formation process but also affect market agents' behavior. A key contribution of this study is the identification of several stylized facts, which are used to differentiate between large, medium, and small tick stocks, along with clear metrics for their measurement. We provide cross-asset visualizations to illustrate how these attributes vary with relative tick size. Further, we propose a Hawkes Process model that accounts for sparsity, multi-tick level price moves, and the shape of the book in small-tick stocks. Through simulation studies, we demonstrate the universality of the model and identify key variables that determine whether a simulated LOB resembles a large-tick or small-tick stock. Our tests show that stylized facts like sparsity, shape, and relative returns distribution can be smoothly transitioned from a large-tick to a small-tick asset using our model. We test this model's assumptions, showcase its challenges and propose questions for further directions in this area of research. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.08744 |
| By: | Xialu Liu; John Guerard; Rong Chen; Ruey Tsay |
| Abstract: | Searching for new effective risk factors on stock returns is an important research topic in asset pricing. Factor modeling is an active research topic in statistics and econometrics, with many new advances. However, these new methods have not been fully utilized in asset pricing application. In this paper, we adopt the factor models, especially matrix factor models in various forms, to construct new statistical factors that explain the variation of stock returns. Furthermore, we evaluate the contribution of these statistical factors beyond the existing factors available in the asset pricing literature. To demonstrate the power of the new factors, U.S. monthly stock data are analyzed and the partial F test and double selection LASSO method are conducted. The results show that the new statistical factors bring additional information and add explanatory power in asset pricing. Our method opens a new direction for portfolio managers to seek additional risk factors to improve the estimation of portfolio returns. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.17182 |
| By: | Klodiana Istrefi (BANQUE DE FRANCE); Florens Odendahl (BANCO DE ESPAÑA); Giulia Sestieri (BANQUE DE FRANCE) |
| Abstract: | This paper presents the Euro Area Communication Event-Study Database (EA-CED), a new dataset that tracks financial market movements around ECB Governing Council meetings (GC) and inter-meeting communication (IMC). Covering the period from 1999 to 2024, the EA-CED contains intraday changes in euro area financial variables around the time of 304 ECB GC policy announcements and 4, 400 IMC events, consisting mainly of speeches and interviews. We document several new empirical findings on the impact of IMC on financial markets. First, we show that many IMC events are associated with significant market movements, often of similar or larger magnitude than those associated with ECB policy announcements, particularly for yields at longer maturities. Significant effects are not limited to communication from the ECB’s President but extend to other members of the Governing Council. Second, the importance of IMC varies over time, peaking around tightening cycles, particularly in 2022-2023. Third, like ECB GC announcements, IMC events convey multi-dimensional information and lead to surprises regarding the path of monetary policy and the state of the economy. |
| Keywords: | monetary policy, ECB, communication, financial markets, euro area |
| JEL: | E03 E50 E61 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2431 |
| By: | Afees A. Salisu (Centre for Econometrics & Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Ahamuefula E. Ogbonna (Centre for Econometrics & Applied Research, Ibadan, Nigeria.); Elie Bouri (School of Business, Lebanese American University, Lebanon.); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | Using generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) model with monthly Economic Policy Uncertainty (EPU) index and daily stock volatility of 149 banks in the United States from August 2000 to August 2023, we show that EPU plays a significant role in predicting bank stock volatility. Across the groups of large, mid, and small cap banks, stock volatility tends to increase in response to EPU, suggesting that growing uncertainty induces higher volatility in bank stocks. EPU has a stronger impact on large-cap banks. The outperformance of the GARCH-MIDAS-EPU model holds in an out-of-sample analysis, regardless of market capitalization and forecast horizons. |
| Keywords: | Economic policy uncertainty (EPU), Bank-level stock returns volatility, GARCH-MIDAS model |
| JEL: | C32 C53 D80 G10 G21 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202444 |
| By: | Japheth Torsar Jev (Birmingham Newman University, Birmingham, UK) |
| Abstract: | The primary objective of this study was to examine the impact of the US sovereign credit rating downgrade on its equity market. Utilizing the event study methodology, a sample of three most capitalized listed companies -- Microsoft, Apple, and Amazon -- and the equity market index -- S&P500 -- were used as the proxy for the overall equity market. Three market models were constructed within the estimation window to determine the expected daily returns on the selected companies stocks. The result showed that the sovereign credit rating downgrade of the US government debt did not have any significant effects on the US equity market. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.18443 |
| By: | Sanjay Sathish; Charu C Sharma |
| Abstract: | Our research presents a new approach for forecasting the synchronization of stock prices using machine learning and non-linear time-series analysis. To capture the complex non-linear relationships between stock prices, we utilize recurrence plots (RP) and cross-recurrence quantification analysis (CRQA). By transforming Cross Recurrence Plot (CRP) data into a time-series format, we enable the use of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks for predicting stock price synchronization through both regression and classification. We apply this methodology to a dataset of 20 highly capitalized stocks from the Indian market over a 21-year period. The findings reveal that our approach can predict stock price synchronization, with an accuracy of 0.98 and F1 score of 0.83 offering valuable insights for developing effective trading strategies and risk management tools. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.06728 |
| By: | Berle, Erika (UiS); Jørgensen, Kjell (BI); Ødegaard, Bernt Arne (University of Stavanger) |
| Abstract: | We investigate whether the sustainability profile of a firm affects the terms at which the firm list on a stock market. Given the evidence that sustainable firms have a lower cost of capital, we expect this to also be reflected in the issue terms at an IPO. The laboratory for our investigation is stock listings (IPOs) at Euronext Oslo. We find that firms which emphasize environmental issues (ESG) in their prospectus have lower implied cost of capital. We find no link between the degree of underpricing and ESG issues. We also provide evidence on recent changes in the IPO landscape, where pure listings are becoming more common, and stock exchanges introduce tiered markets that attract younger and smaller companies. |
| Keywords: | IPO; Cost of Capital; Underpricing; ESG; Euronext Oslo |
| JEL: | G12 G24 G30 |
| Date: | 2024–10–12 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:stavef:2024_001 |