arXiv · arXiv q-fin · 2004
We describe and document three mechanisms by which corporations can influence or even control stock prices. (i) Parent and holding companies wield control over other publicly traded companies. (ii) Through clever management of treasury stock based on buyback programs and stock issuance, stock price fluctuations can be amplified or curbed. (iii) Finally, history shows a close interdependance between the level of stock…
Bertrand M. Roehner
OpenAlex · The Journal of Finance · 2004 · cites 390
ABSTRACT We examine the role of price discovery in the U.S. Treasury market through the empirical relationship between orderflow, liquidity, and the yield curve. We find that orderflow imbalances (excess buying or selling pressure) account for up to 26% of the day‐to‐day variation in yields on days without major macroeconomic announcements. The effect of orderflow on yields is permanent and strongest when liquidity i…
Michael W. Brandt, Kenneth A. Kavajecz
OpenAlex · European Finance Review · 2005 · cites 189
Abstract This paper examines the price differences between very liquid on-the-run U.S. Treasury securities and less liquid off-the-run securities over the on/off cycle. Comparing pairs of securities in time-series regressions allows us to disregard any fixed cross-sectional differences between securities. Also, since the liquidity of Treasury notes varies predictably over time, we can distinguish between current and …
David Goldreich, Bernd Hanke, Purnendu Nath
OpenAlex · Journal of Financial and Quantitative Analysis · 2010 · cites 174
Abstract In this paper, we identify jumps in U.S. Treasury-bond (T-bond) prices and investigate what causes such unexpected large price changes. In particular, we examine the relative importance of macroeconomic news announcements versus variation in market liquidity in explaining the observed jumps in the U.S. Treasury market. We show that while jumps occur mostly at prescheduled macroeconomic announcement times, an…
George J. Jiang, Ingrid Lo, Adrien Verdelhan
OpenAlex · Federal Reserve Bank of New York Economic policy review · 2001 · cites 125
This paper was presented at the conference \\"Economic Statistics: New Needs for the Twenty-First Century, \\" cosponsored by the Federal Reserve Bank of New York, the Conference on Research in Income and Wealth, and the National Association for Business Economics, July 11, 2002. Securities liquidity is important to those who transact in markets, those who monitor market conditions, and those who analyze market devel…
Michael J. Fleming
arXiv · arXiv · 2025
In this study, MLP models with dynamic structure are applied to factor models for asset pricing tasks. Concretely, the MLP pyramid model structure was employed on firm characteristic-sorted portfolio factors for modelling the large-cap US stocks. It was further developed as a practical factor investing strategy based on the predictions. The main findings were evaluated from 2 angles: model predictive power and backte…
Shanyan Lai
arXiv · arXiv · 2024
We develop a liquidity-sensitive multivariate volatility framework to improve the estimation of time-varying covariance structures under market frictions. We introduce two novel portfolio-level liquidity measures, liquidity jump and liquidity diffusion, which capture magnitude and volatility of liquidity fluctuation, respectively, and construct liquidity-adjusted return and volatility that reflect real-time liquidity…
Qi Deng
arXiv · arXiv · 2023
Portfolio management (PM) is a fundamental financial trading task, which explores the optimal periodical reallocation of capitals into different stocks to pursue long-term profits. Reinforcement learning (RL) has recently shown its potential to train profitable agents for PM through interacting with financial markets. However, existing work mostly focuses on fixed stock pools, which is inconsistent with investors' pr…
Wentao Zhang, Yilei Zhao, Shuo Sun, Jie Ying, Yonggang Xie
arXiv · arXiv · 2022
This paper considers liquidity as an explanation for the positive association between expected idiosyncratic volatility (IV) and expected stock returns. Liquidity costs may affect the stock returns, through bid-ask bounce and other microstructure-induced noise, which will affect the estimation of IV. We use a novel method (developed by Weaver, 1991) to eliminate microstructure influences from stock closing price-base…
M. Reza Bradrania, Maurice Peat, Stephen Satchell
arXiv · arXiv · 2022
Based on the commentary data of the Shenzhen Stock Index bar on the EastMoney website from January 1, 2018 to December 31, 2019. This paper extracts the embedded investor sentiment by using a deep learning BERT model and investigates the time-varying linkage between investment sentiment, stock market liquidity and volatility using a TVP-VAR model. The results show that the impact of investor sentiment on stock market…
Chenrui Zhang, Xinyi Wu, Hailu Deng, Huiwei Zhang
arXiv · arXiv · 2021
With the improvement of computer performance and the development of GPU-accelerated technology, trading with machine learning algorithms has attracted the attention of many researchers and practitioners. In this research, we propose a novel portfolio management strategy based on the framework of Deep Deterministic Policy Gradient, a policy-based reinforcement learning framework, and compare its performance to that of…
Huanming Zhang, Zhengyong Jiang, Jionglong Su
arXiv · arXiv · 2020
Our main task is to study the effect of corporate governance on the market liquidity of listed companies' stocks. We establish a theoretical model that contains the heterogeneity of investors' beliefs to explain the mechanisms by which corporate governance improves liquidity of the corporate stocks. In this process we found that the existence of noise traders who are semi-informed in the market is an important condit…
Jianhao Su
arXiv · arXiv · 2019
Predicting the intraday stock jumps is a significant but challenging problem in finance. Due to the instantaneity and imperceptibility characteristics of intraday stock jumps, relevant studies on their predictability remain limited. This paper proposes a data-driven approach to predict intraday stock jumps using the information embedded in liquidity measures and technical indicators. Specifically, a trading day is di…
Ao Kong, Hongliang Zhu, Robert Azencott
arXiv · arXiv · 2019
Quantitative finance has had a long tradition of a bottom-up approach to complex systems inference via multi-agent systems (MAS). These statistical tools are based on modelling agents trading via a centralised order book, in order to emulate complex and diverse market phenomena. These past financial models have all relied on so-called zero-intelligence agents, so that the crucial issues of agent information and learn…
J. Lussange, I. Lazarevich, S. Bourgeois-Gironde, S. Palminteri, B. Gutkin
arXiv · arXiv · 2010
This paper examines the validity of the Capital Asset Pricing Model (CAPM) on the Ugandan stock market using monthly stock returns from 10 of the 11 companies listed on the Uganda Stock Exchange (USE), for the period 1st March 2007 to 10th November 2009. Due to the absence of readily available Uganda Stock Exchange(USE) data, and the placement of daily price lists in pdf only, on the USE website: http://www.use.or.ug…
David Wakyiku
arXiv · arXiv · 2021
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the asset-liability management of the liquidity gap risk (or asset-liability…
Thierry Roncalli, Amina Cherief, Fatma Karray-Meziou, Margaux Regnault
OpenAlex · Speech · 2020 · cites 31
Remarks at Brookings-Chicago Booth Task Force on Financial Stability (TFFS) meeting, panel on market liquidity (delivered via videoconference).
Lorie Logan
OpenAlex · The Journal of Finance · 1999 · cites 731
The arrival of public information in the U.S. Treasury market sets off a two‐stage adjustment process for prices, trading volume, and bid‐ask spreads. In a brief first stage, the release of a major macroeconomic announcement induces a sharp and nearly instantaneous price change with a reduction in trading volume, demonstrating that price reactions to public information do not require trading. The spread widens dramat…
Michael J. Fleming, Eli M. Remolona