No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Fundamental characteristics, machine learning, and stock price crash risk
... Machine learning algorithms repeatedly utilize statistical analysis and iterative training matched to search a, trying to detect patterns from massive data sets. Machine learning has three paradigms: supervised, unsupervised and reinforcement (Jiang, Lin, et al., 2024;Jiang, Ma, & Zhu, 2024). Supervised learning trains algorithms on labeled data with known outputs to learn input-output mappings. ...
... Gradient BoostingMachine is a repetitive process of optimizing decision trees to minimize a loss function. Good predictive models for carbon capture optimization GBM can help improve the input output relationships of complicated input parameter-capture performance metric relationships(Jiang, Lin, et al., 2024;Jiang, Ma, & Zhu, 2024). Gradient Boosting Machine models are ideal for large-and diversified-and datasets reflect the complex processes in carbon capture. ...
Artificial intelligence (AI) and environmental points are equally important components within the response to local weather change. Therefore, based on the efforts of reducing carbon emissions more efficiently and effectively, this study tries to focus on AI integration with carbon capture technology. The urgency of tackling climate change means we need more advanced carbon capture, and this is an area where AI can make a huge impact in how these technologies are operated and managed. It will minimize manufacturing emissions and improve both resource efficiency as well as our planet's environmental footprint by turning waste into something of value again. Artificial intelligence could be leveraged to analyze huge data sets from carbon capture plants, searching for optimal system settings and more efficient ways of identifying patterns in the available information at a larger scale than currently possible. In addition, AI incorporated sensors and monitoring mechanisms in the supply chain can identify any operational failure at reception itself allowing for timely action to protect those areas. Artificial intelligence also helps generative design for carbon capture materials, which allows researchers to explore new types of carbon‐absorbing material, including metal–organic frameworks and polymeric materials that are important in industrial CO2, such as moisture. In addition, it increases the accuracy of reservoir simulations and controls CO2 injection systems for storage or enhanced oil recovery. Through applying AI algorithms on reservoir geology, production performance and real‐time data this study would like to facilitate the optimization of injection processes as well as minimize CO2 emissions while assuring a maximum efficiency. Artificial intelligence integrates with renewable‐based carbon capture efforts that can be employed by AI‐driven smart grid systems to improve carbon capture methods.
... In recent years, significant improvements in data availability and rapid advancements in computational technologies have greatly facilitated the widespread application of artificial intelligence across various fields (Shi et al., 2021;Zeng et al., 2024). Notably, artificial intelligence has shown immense potential in the financial sector, offering novel perspectives for stock market analysis and helping uncover the dynamics of complex markets (Jiang et al., 2024;Ren et al., 2024;Szczygielski et al., 2024;Weng et al., 2022). Existing research primarily reveals the volatility and underlying mechanisms of the stock market through two main approaches. ...
Stock market volatility significantly impacts investors, policymakers, and industry development. While previous studies have identified key influencing factors, they have largely overlooked the unique volatility of the Chinese Baijiu stock market. This study adopts a complex network perspective, integrating transfer entropy, Peter and Clark momentary conditional independence (PCMCI), and interpretable machine learning methods to reveal the key drivers and mechanisms behind the market's volatility. The research identifies 10 critical factors spanning four dimensions: resources and agriculture, industry and manufacturing, services and consumption, and cross-domain sustainable development. Our findings indicate that the volatility of the Chinese Baijiu stock market is driven by a combination of agricultural, industrial, and sustainable development factors, highlighting the importance of industrial synergies. In addition to confirming the significance of traditional factors, this study also reveals the direct positive causal relationships of emerging industry variables, such as construction decoration, environmental protection, and energy, with the Chinese Baijiu stock market. These findings not only enhance the understanding of the dynamics of the Chinese Baijiu stock market but also provide a transferable research framework and methodology for other industries.
... The study contributes to the growing body of work that applies advanced machine learning techniques to financial markets. Previous research has demonstrated the efficacy of LSTMs in stock price forecasting Fischer & Krauss, 2018;Zhang et al., 2018), and more recent studies have highlighted the impact of Transformer models in financial text analysis (Pradeep et al., 2024;Münster et al., 2024;Jiang et al., 2024;Wen et al., 2022;Nguyen et al., 2015). However, the integration of these two powerful tools remains underutilized. ...
Stock price prediction is a critical yet challenging task in financial markets due to the complexity and volatility of asset movements. This paper presents a hybrid approach that combines Recurrent Neural Networks (RNN), particularly Long Short-Term Memory (LSTM) models, for time-series prediction with Transformer-based text analysis to capture sentiment from financial news. The study focuses on predicting Apple Inc.'s (AAPL) stock price, using three years of historical data alongside news sentiment analysis. The LSTM model captures temporal dependencies in the stock prices, while the Transformer model extracts relevant features from unstructured textual data, offering insights into market sentiment and external events. The results demonstrate that integrating sentiment data with stock price predictions significantly improves model accuracy, as reflected by a reduction in mean squared error (MSE) compared to models based solely on price data. This hybrid model offers a more holistic approach to financial forecasting, combining quantitative and qualitative data for enhanced prediction.The paper contributes to the field of machine learning in finance by highlighting the benefits of hybrid modelling approaches, and it opens avenues for future research on broader applications in other asset classes and more diverse data sources.Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
... We investigate the predictability of bank stock returns by combining advanced machine learning (ML) algorithms with textual analysis techniques. In recent years, interest in applying machine learning techniques to financial time series forecasting has surged, due to the ML models' ability to process large datasets and capture non-linear patterns in the data (Leippold et al., 2022;Christensen et al., 2023;Jiang et al., 2024). While most studies rely on traditional financial variables to train the ML models, we enhance the approach by incorporating both financial variables and textual features. ...
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. JEL classification: C63, E58, G17, G21, G40
... Recent studies also explore social and human factors, recognising their potential influence on crash risk (Liu and Liu, 2024;Si and Xia, 2023). Additionally, new strand of research investigates the impact of technological advancements in finance on crash risk (Jiang et al., 2024;Wang et al., 2023). The effects of crises on crash risk are also a subject of recent studies (Fiorillo et al., 2024;Tzomakas et al., 2023). ...
Purpose
Understanding and mitigating stock price crash risk is vital for investors and regulators to ensure financial market stability. This study aims to unveil significant research trends and opportunities.
Design/methodology/approach
This study adopts the bibliometric and systematic review approach to analyse 485 Scopus-indexed articles through citation, keyword co-occurrence, bibliographic coupling, and publication analyses and delve into the depth of crash risk literature.
Findings
This bibliometric review reveals not only a surge in crash risk publications over the last decade but also delineates several emerging thematic threads within this domain. We identify seven distinct themes that have gained prominence in recent literature: bad news hoarding, board characteristics, capital market factors, corporate policies, ownership impact, corporate governance, and external environmental influences on crash risk. This thematic analysis provides a comprehensive overview of the evolving landscape of crash risk research and underscores the multifaceted nature of factors contributing to market instability.
Practical implications
This study makes a substantial contribution by furnishing a thorough examination of existing studies, pinpointing areas where knowledge is lacking, and shedding light on emerging trends and debates within the crash risk literature.
Originality/value
This study identifies current research trajectories and propels future exploration into agency perspectives, audit quality, and corporate disclosures within crash risk literature.
... ing asset risk premiums (Gu et al., 2020), classifying venture capitalists (Bubna et al., 2020), hedge fund return prediction (Wu et al., 2020), latent factor modelling (Gu et al., 2021), return forecasting using news photos (Obaid and Pukthuanthong, 2021), image processing for return prediction (Jiang et al., 2023), bond return prediction (Bianchi et al., 2021;Guo et al., 2022), predicting future option returns (Bali et al., 2023), firm fundamentals for modelling of the equity option implied volatility curve (Chen et al., 2023), estimation of stochastic discount factor (Chen et al., 2024), and predicting stock price crash risks (Jiang et al., 2024), to name a few. In the context of realised volatility forecasting, Rahimikia and Poon (2020b) examined the performance of long short-term memory (LSTM) using LOB data and news sentiments. ...
This study develops a financial word embedding using 15 years of business news. Our results show that this specialised language model produces more accurate results than general word embeddings, based on a financial benchmark we established. As an application, we incorporate this word embedding into a simple machine learning model to enhance the HAR model for forecasting realised volatility. This approach statistically and economically outperforms established econometric models. Using an explainable AI method, we also identify key phrases in business news that contribute significantly to volatility, offering insights into language patterns tied to market dynamics.
Emerging technologies, such as artificial intelligence (AI), blockchain, and fintech, have profoundly reshaped the financial sector driving unprecedented innovation and creating transformative opportunities for development. However, they also pose significant challenges to long-term sustainability. While the existing literature provides valuable insights into their influence, a broader scope is necessary to reflect their role in advancing sustainable finance. This study conducts a bibliometric analysis of 2,446 publications from the Web of Science (1996–2024) to map the evolving nexus between emerging technologies and finance. Our findings reveal an expanding research landscape, with key themes including the application of emerging technologies in solving financial problems, the integration of technologies with behavioural and regulatory frameworks, financial innovation for promoting development, risk management and financial stability, digital currencies and blockchain, digital transformation challenges, and sustainable finance. The analysis highlights the dual nature of emerging technologies: while they enhance financial efficiency, transparency, and inclusion, and offer significant opportunities to advance sustainable finance, they also introduce risks such as cybersecurity threats, algorithmic bias, regulatory challenges, and critical barriers to long-term sustainability. To address these challenges, we propose a research agenda prioritizing ethical governance, stress-testing AI models under economic crises, securing decentralized systems, mitigating greenwashing risks, and fostering globally aligned regulatory standards. Interdisciplinary collaboration is essential to tackle ethical, security, and inclusivity concerns. It is imperative for policymakers, regulators, and financial institutions to align technological innovation with sustainability objectives to ensure that advancements contribute to the development of an equitable, resilient, and inclusive financial ecosystem.
We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best-performing methods (trees and neural networks) and trace their predictive gains to allowing nonlinear predictor interactions missed by other methods. All methods agree on the same set of dominant predictive signals, a set that includes variations on momentum, liquidity, and volatility.
Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
We construct size and value factors in China. The size factor excludes the smallest 30% of firms, which are companies valued significantly as potential shells in reverse mergers that circumvent tight IPO constraints. The value factor is based on the earnings-price ratio, which subsumes the book-to-market ratio in capturing all Chinese value effects. Our three-factor model strongly dominates a model formed by just replicating the Fama and French (1993) procedure in China. Unlike that model, which leaves a 17% annual alpha on the earnings-price factor, our model explains most reported Chinese anomalies, including profitability and volatility anomalies.
This paper presents a comprehensive study on predicting the cross section of Chinese stock market returns with a large panel of 75 individual firm characteristics. We use not only the traditional Fama-MacBeth regression, but also the “big-data” econometric methods: principal component analysis (PCA), partial least squares (PLS), and forecast combination to extract information from all the 75 firm characteristics. These characteristics are important return predictors, with statistical and economic significance. Furthermore, firm characteristics that are related to trading frictions, momentum, and profitability are the most effective predictors of future stock returns in the Chinese stock market. Keywords: Partial least squares, Machine learning, Firm characteristics, Chinese stock market, Return predictability
*
We are especially grateful to Vikrant Vig (Editor) and an anonymous referee for their constructive suggestions and helpful comments. We also thank Demetris Koursaros, Neophytos Lambertides, Hermes Niels, Panayiotis Theodossiou, Dimitris Tsouknidis, Photis Panayides, Christos Savva, and participants at the BI Norwegian Business School, Cyprus University of Technology, and 2016 FINEST Summer Workshop for useful comments and suggestions. Andreas Procopiou provided excellent research assistance. All remaining errors are our own.
We show that firms with younger CEOs are more likely to experience stock price crashes, including crashes caused by revelation of negative news in the form of breaks in strings of consecutive earnings increases. Such strings are accompanied by large increases in CEO compensation that do not dissipate with crashes. These findings suggest that CEOs have financial incentives to hoard bad news earlier in their career, which increases future crashes. This negative impact of CEO age effect is strongest in the presence of managerial discretion. Overall, the findings highlight the importance of CEO age for firm policies and outcomes.
Commonly used Jones-type discretionary accrual models applied in quarterly settings do not adequately control for nondiscretionary accruals that naturally occur due to firm growth. We show that the relation between quarterly accruals and backward-looking sales growth (measured over a rolling four-quarter window) and forward-looking firm growth (market-to-book ratio) is non-linear. Failure to control for the effects of firm growth and performance on innate accruals leads to excessive Type I error rates in tests of earnings management. We propose simple refinements to Jones-type models that deal with non-linear growth and performance effects and show that the expanded models are well-specified and exhibit high power in quarterly settings where one is testing for earnings management. The expanded models are able to identify the presence of earnings management in a sample of restatement firms. Our findings have important implications for the use of discretionary accrual models in earnings management research.
JEL Classifications: C15; M40; M41.
I investigate the relation between accruals and firm-level price crashes, representing extreme price decreases in weekly returns. I find that high accruals predict a higher price crash probability than low accruals. This finding can be explained by managers’ use of income-increasing accrual estimates to hoard bad news. Once accumulated bad news crosses a tipping point, it is released all at once and results in a price crash. Consistent with this explanation, I find the observed relation to be the strongest for operating assets (the least reliable accrual components). Cross-sectional analyses further support the bad news hoarding explanation.
This paper investigates the effects of having multiple large shareholders on the valuation of firms. Using data on Finnish listed firms, we show, consistent with our model, that a more equal distribution of votes among large blockholders has a positive effect on firm value. This result is particularly strong in family-controlled firms suggesting that families (which typically have managerial or board representation) are more prone to private benefit extraction if they are not monitored by another strong blockholder. We also show that the relation between multiple blockholders and firm value is significantly affected by the identity of these blockholders.
This paper documents pervasive evidence of intra-industry reversals in monthly
returns. Unlike the conventional reversal strategy based on stock returns
relative to the market portfolio, we document intra-industry return reversals
that are larger in magnitude, consistently present over time, and prevalent
across subgroups of stocks, including large and liquid stocks. These return
reversals are driven by order imbalances and noninformational shocks. Consistent
with reversals representing compensation for supplying liquidity, intra-industry
reversals are stronger following aggregate market declines and volatile times,
reflecting binding capital constraints and limited risk-bearing capacity of
liquidity providers.
China is an important counterexample to the findings in the law, institutions, finance, and growth literature: Neither its legal nor financial system is well developed, yet it has one of the fastest growing economies. While the law–finance–growth nexus applies to the State Sector and the Listed Sector, with arguably poorer applicable legal and financial mechanisms, the Private Sector grows much faster than the others and provides most of the economy's growth. The imbalance among the three sectors suggests that alternative financing channels and governance mechanisms, such as those based on reputation and relationships, support the growth of the Private Sector.
An investment factor, long in low-investment stocks and short in high-investment stocks, helps explain the new issues puzzle.
Adding the investment factor into standard factor regressions reduces the SEO underperformance by about 75%, the IPO underperformance
by 80%, the underperformance following convertible debt offerings by 50%, and Daniel and Titman's (2006) composite issuance effect by 40%. The reason is that issuers invest more than nonissuers, and the investment factor earns
a significantly positive average return of 0.57% per month.
We find that measures of board and ownership structure explain a significant amount of cross-sectional variation in CEO compensation, after controlling for standard economic determinants of pay. Moreover, the signs of the coefficients on the board and ownership structure variables suggest that CEOs earn greater compensation when governance structures are less effective. We also find that the predicted component of compensation arising from these characteristics of board and ownership structure has a statistically significant negative relation with subsequent firm operating and stock return performance. Overall, our results suggest that firms with weaker governance structures have greater agency problems; that CEOs at firms with greater agency problems receive greater compensation; and that firms with greater agency problems perform worse.
We propose and evaluate explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts. In contrast to previously developed tests, a wide variety of accuracy measures can be used (in particular, the loss function need not be quadratic and need not even be symmetric), and forecast errors can be non-Gaussian, nonzero mean, serially correlated, and contemporaneously correlated. Asymptotic and exact finite-sample tests are proposed, evaluated, and illustrated.
The degree of state ownership remains significant in the Chinese economy despite more than two decades of economic reform since 1979. Most of the remaining state- owned enterprises (SOEs) are money losing, and the few exceptional ones tend to be sheltered by government protection in selected industries. Yet China has been enjoying one of the most spectacular growth experiences in world history, and much of the growth is driven by non-state-owned enterprises (non-SOEs). Given the poor financial performance of SOEs, the co-existence of state ownership and non-state ownership in China is a puzzling phenomenon. Does state ownership exist solely for the benefit of politicians, or has state ownership played any role in China's spectacular growth? Is there any rationale behind China's gradual and selective approach towards privatizing its SOEs? What types of SOEs does the Chinese government choose to privatize? What are the consequences of privatization? In this paper, we offer a multitask theory of SOE reform in the context of China, which has predictions about the types of SOEs to be chosen for privatization and the results of privatization. We then present empirical evidence supporting the basic premise of the theory and its predictions.
We provide measures of absolute and relative equity agency costs for corporations under different ownership and management structures. Our base case is Jensen and Meckling's (1976) zero agency-cost firm, where the manager is the firm's sole shareholder. Utilizing a sample of 1,708 small corporations from the FRB/NSSBF database, we find evidence supporting several predictions of agency cost theory. Agency costs are found to be: i) significantly higher when an outsider rather than an insider manages the firm; ii ) inversely related to the manager's ownership share; iii) increasing with the number of non-manager shareholders, and iv) to a lesser extent, lower with greater monitoring by banks. Agency Costs and Ownership Structure The seminal contributions of Jensen and Meckling (1976) on agency costs have called into attention the social and private costs of an agent's actions due to the incomplete alignment of the agent's and owner's interests. Agency theory has also brought the roles o...
We reconsider trend‐based predictability by employing flexible learning methods to identify price patterns that are highly predictive of returns, as opposed to testing predefined patterns like momentum or reversal. Our predictor data are stock‐level price charts, allowing us to extract the most predictive price patterns using machine learning image analysis techniques. These patterns differ significantly from commonly analyzed trend signals, yield more accurate return predictions, enable more profitable investment strategies, and demonstrate robustness across specifications. Remarkably, they exhibit context‐independence, as short‐term patterns perform well on longer time scales, and patterns learned from U.S. stocks prove effective in international markets.
This article is protected by copyright. All rights reserved
We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, keeps a fully flexible form, and accounts for time variation. The key innovations are to use the fundamental no-arbitrage condition as criterion function to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series. Our asset pricing model outperforms out-of-sample all benchmark approaches in terms of Sharpe ratio, explained variation, and pricing errors and identifies the key factors that drive asset prices.
This paper was accepted by Agostino Capponi, finance.
Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4695 .
Most anomalies fail to hold up to currently acceptable standards for empirical finance. With microcaps mitigated via NYSE breakpoints and value-weighted returns, 65% of the 452 anomalies in our extensive data library, including 96% of the trading frictions category, cannot clear the single test hurdle of the absolute t-value of 1.96. Imposing the higher multiple test hurdle of 2.78 at the 5% significance level raises the failure rate to 82%. Even for replicated anomalies, their economic magnitudes are much smaller than originally reported. In all, capital markets are more efficient than previously recognized.
Received June 12, 2017; editorial decision October 29, 2018 by Editor Stijn Van Nieuwerburgh. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
The cross-section and time series of stock returns contains a wealth of information about the stochastic discount factor (SDF), the object that links cash flows to prices. A large empirical literature has uncovered many candidate factors—many more than seem plausible—to summarize the SDF. This special volume of the Review of Financial Studies presents recent advances in extracting information from both the cross-section and the time series, in dealing with issues of replication and false discoveries, and in applying innovative machine-learning techniques to identify the most relevant asset pricing factors. Our editorial summarizes what we learn and offers a few suggestions to guide future work in this exciting new era of big data and empirical asset pricing.
This article surveys corporate governance in China, as described in a growing literature published in top journals. Unlike the classical vertical agency problems in Western countries, the dominant agency problem in China is the horizontal agency conflict between controlling and minority shareholders arising from concentrated ownership structure; thus one cannot automatically apply what is known about the USA to China. As these features are also prevalent in many other countries, insights from this survey can also be applied to countries far beyond China. We start by describing controlling shareholder and agency problems in China, and then discuss how law and institutions are particularly important for China, where controlling shareholders have great power. As state-owned enterprises have their own features, we separately discuss their corporate governance. We also briefly discuss corporate social responsibility in China. Finally, we provide an agenda for future research.
We construct a new monthly index of Economic Policy Uncertainty for China in 2000–2018 based on Chinese newspapers. Different from the existing index, ours uses information from multiple local newspapers, and foreshadows declines in equity price, employment and output. Media censorship does not seem to have qualitative impact to our index. Moreover, we develop a daily uncertainty index and several policy-specific uncertainty indices for public use.
This study investigates the governance role of multiple large shareholders (MLS hereafter) in firms' investment decisions. Using a sample of 1640 Chinese firms listed on the Shanghai or Shenzhen stock markets, we compare the investment efficiency of firms having MLS with that of firms having a single large shareholder and find that the presence and power of MLS are associated with significantly higher investment efficiency. The results are robust after we address endogeneity and sample selection concerns. Further tests show that MLS exert governance mainly through “voice.” MLS tend to lower potential overinvestment and increase future investment performance. The impact of MLS on investment efficiency does not vary with a firm's access to resources and is less prominent in firms with stronger governance and less information asymmetry. Our results imply that MLS play a governance role and alleviate a firm's agency costs and information asymmetry manifested in a firm's investment efficiency.
This paper examines the impact of social trust on stock price crash risk. Social trust measures the level of mutual trust among the members of a society. Using a large sample of Chinese listed firms for the 2001-2015 period, we find that firms headquartered in regions of high social trust tend to have smaller crash risks. This result is robust to a battery of sensitivity tests and is more prominent for State-Owned Enterprises (SOEs), for firms with weak monitoring, and for firms with higher risk-taking. Moreover, we observe that firms in regions of high social trust are associated with higher accounting conservatism and fewer financial restatements. Our study suggests that social trust is an important variable that is omitted in the literature investigating the predictors of stock price crashes.
This study examines the association between chief executive officer (CEO) overconfidence and future stock price crash risk. Overconfident managers overestimate the returns to their investment projects and misperceive negative net present value (NPV) projects as value creating. They also tend to ignore or explain away privately observed negative feedback. As a result, negative NPV projects are kept for too long and their bad performance accumulates, which can lead to stock price crashes. Using a large sample of firms for the period 1993–2010, we find that firms with overconfident CEOs have higher stock price crash risk than firms with non-overconfident CEOs. The impact of managerial overconfidence on crash risk is more pronounced when the CEO is more dominant in the top management team and when there are greater differences of opinion among investors. Finally, it appears that the effect of CEO overconfidence on crash risk is less pronounced for firms with more conservative accounting policies.
We develop a new index of economic policy uncertainty (EPU) based on newspaper coverage frequency. Several types of evidence – including human readings of 12,000 newspaper articles – indicate that our index proxies for movements in policy-related economic uncertainty. Our US index spikes near tight presidential elections, Gulf Wars I and II, the 9/11 attacks, the failure of Lehman Brothers, the 2011 debt-ceiling dispute and other major battles over fiscal policy. Using firm-level data, we find that policy uncertainty is associated with greater stock price volatility and reduced investment and employment in policy-sensitive sectors like defense, healthcare, finance and infrastructure construction. At the macro level, innovations in policy uncertainty foreshadow declines in investment, output, and employment in the United States and, in a panel VAR setting, for 12 major economies. Extending our US index back to 1900, EPU rose dramatically in the 1930s (from late 1931) and has drifted upwards since the 1960s.
This paper tests the proposition that politicians and their affiliated firms (i.e., firms operating in their province) temporarily suppress negative information in response to political incentives. We examine the stock price behavior of Chinese listed firms around two visible political events – meetings of the National Congress of the Chinese Communist Party and promotions of high-level provincial politicians– that are expected to asymmetrically increase the costs of releasing bad news. The costs create an incentive for local politicians and their affiliated firms to temporarily restrict the flow of negative information about the companies. The result will be fewer stock price crashes for the affiliated firms during these event windows, followed by an increase in crashes after the event. Consistent with these predictions, we find that the affiliated firms experience a reduction (an increase) in negative stock return skewness before (after) the event. These effects are strongest in the three-month period directly preceding the event, among firms that are more politically connected, and when the province is dominated by faction politics and cronyism. Additional tests document a significant reduction in published newspaper articles about affected firms in advance of these political events, suggestive of a link between our observed stock price behavior and temporary shifts in the listed firms’ information environment.This article is protected by copyright. All rights reserved.
Uncertainty is an amorphous concept. It reflects uncertainty in the minds of consumers, managers, and policymakers about possible futures. It is also a broad concept, including uncertainty over the path of macro phenomena like GDP growth, micro phenomena like the growth rate of firms, and noneconomic events like war and climate change. In this essay, I address four questions about uncertainty. First, what are some facts and patterns about economic uncertainty? Both macro and micro uncertainty appear to rise sharply in recessions and fall in booms. Uncertainty also varies heavily across countries—developing countries appear to have about one-third more macro uncertainty than developed countries. Second, why does uncertainty vary during business cycles? Third, do fluctuations in uncertainty affect behavior? Fourth, has higher uncertainty worsened the Great Recession and slowed the recovery? Much of this discussion is based on research on uncertainty from the last five years, reflecting the recent growth of the literature.
We investigate the impact of excess perk consumption on crash risk in state-owned enterprises in China. To enjoy excess perks, executives in state-owned enterprises have an incentive to withhold bad news for extended periods, leading to higher future stock price crash risk. Consistent with this assertion, we find a positive correlation between excess perks and crash risk. The findings are robust to the inclusion of other determinants of crash risk identified in the literature, such as earnings management, conditional conservatism, and firm-level corporate governance mechanisms. The results still hold after accounting for possible endogeneity issues using a two-stage least squares estimation. Earnings management (conditional conservatism) helps amplify (lessen) this impact. Moreover, better external monitoring mitigates the impact of excess perks on firm crash risk. We further find that the impact of excess perks on crash risk is more pronounced in firms whose executives are approaching retirement and persists for at least two years.
This paper examines the effect of controlling shareholders on stock price synchronicity by focusing on two salient corporate governance features in a concentrated ownership setting, namely, ultimate cash flow rights and the separation of voting and cash flow rights (i.e., excess control). Using a unique dataset of 654 French listed firms spanning 1998–2007, this study provides evidence that stock price synchronicity increases with excess control, supporting the argument that controlling shareholders tend to disclose less firm-specific information to conceal opportunistic practices. Additionally, this study shows that firms with substantial excess control are more likely to experience stock price crashes, consistent with the conjecture that controlling shareholders are more likely to hoard bad information when their control rights exceed their cash flow rights. Another important finding is that firms’ stock prices are less synchronous and less likely to crash when controlling shareholders own a large fraction of cash flow rights. This is consistent with the argument that controlling shareholders have less incentive to adopt poor disclosure policies and to accumulate bad news, since high cash flow ownership aligns their interests with those of minority investors.
Prior research argues that a manager whose wealth is more sensitive to changes in the firm’s stock price has a greater incentive to misreport. However, if the manager is risk-averse and misreporting increases both equity values and equity risk, the sensitivity of the manager’s wealth to changes in stock price (portfolio delta) will have two countervailing incentive effects: a positive “reward effect” and a negative “risk effect.” In contrast, the sensitivity of the manager’s wealth to changes in risk (portfolio vega) will have an unambiguously positive incentive effect. We show that jointly considering the incentive effects of both portfolio delta and portfolio vega substantially alters inferences reported in prior literature. Using both regression and matching designs, and measuring misreporting using discretionary accruals, restatements, and SEC Accounting and Auditing Enforcement Releases, we find strong evidence of a positive relation between vega and misreporting and that the incentives provided by vega subsume those of delta. Collectively, our results suggest that equity portfolios provide managers with incentives to misreport when they make managers less averse to equity risk.
This paper examines whether a simple accounting-based fundamental analysis strategy, when applied to a broad portfolio of high book-to-market firms, can shift the distribution of returns earned by an investor. I show that the mean return earned by a high book-to-market investor can be increased by at least 7 percent annually through the selection of financially strong high BM firms while the entire distribution of realized returns is shifted to the right. In addition, an investment strategy that buys expected winners and shorts expected losers generates a 23 percent annual return between 1976 and 1996 and the strategy appears to be robust across time and to controls for alternative investment strategies. Within the portfolio of high BM firms, the benefits to financial statement analysis are concentrated in small and medium sized firms, companies with low share turnover and firms with no analyst following, yet this superior performance is not dependent on purchasing firms with low share prices. A positive relationship between the sign of the initial historical information and both future firm performance and subsequent quarterly earnings announcement reactions suggests that the market initially underreacts to the historical information. In particular, 1/6th of the annual return difference between ex ante strong and weak firms is earned over the four three-day periods surrounding these quarterly earnings announcements. Overall, the evidence suggests that the market does not fully incorporate historical financial information into prices in a timely manner.
This paper evaluates alternative models for detecting earnings management. The paper restricts itself to models that assume the construct being managed is discretionary accruals, since such models are commonly used in the extant accounting literature. Existing models range from simple models in which discretionary accruals are measured as total accruals, to more sophisticated models that separate total accruals into a discretionary and a non-discretionary component. Prior to this paper, there had been no systematic evidence bearing on the relative performance of these alternative models at detecting earnings management. This paper evaluates the relative performance of the competing models by comparing the specification and power of commonly used test statistics across the measures of discretionary accruals generated by each model. The specification of the test statistics is evaluated by examining the frequency with which they generate type I errors for a random sample of firm-years and for samples of firm-years with extreme financial performance. We focus on samples with extreme financial performance because the stimuli investigated in previous research are frequently correlated with financial performance. The first sample of firms are targeted by the Securities and Exchange Commission for allegedly overstating annual earnings and the second sample is created by artificially introducing earnings management into a random sample of firms.
Using a large sample of U.S. firms for the period 1993-2009, we provide evidence that the sensitivity of a chief financial officer's (CFO) option portfolio value to stock price is significantly and positively related to the firm's future stock price crash risk. In contrast, we find only weak evidence of the positive impact of chief executive officer option sensitivity on crash risk. Finally, we find that the link between CFO option sensitivity and crash risk is more pronounced for firms in non-competitive industries and those with a high level of financial leverage.
I find evidence consistent with managers manipulating real activities to avoid reporting annual losses. Specifically, I find evidence suggesting price discounts to temporarily increase sales, overproduction to report lower cost of goods sold, and reduction of discretionary expenditures to improve reported margins. Cross-sectional analysis reveals that these activities are less prevalent in the presence of sophisticated investors. Other factors that influence real activities manipulation include industry membership, the stock of inventories and receivables, and incentives to meet zero earnings. There is also some, though less robust, evidence of real activities manipulation to meet annual analyst forecasts.
We investigate the relation between the transparency of financial statements and the distribution of stock returns. Using earnings management as a measure of opacity, we find that opacity is associated with higher R2s, indicating less revelation of firm-specific information. Moreover, opaque firms are more prone to stock price crashes, consistent with the prediction of the Jin and Myers [2006. R2 around the world: new theory and new tests. Journal of Financial Economics 79, 257–292] model. However, these relations seem to have dissipated since the passage of the Sarbanes-Oxley Act, suggesting that earnings management has decreased or that firms can hide less information in the new regulatory environment.
This paper offers an explanation for stock market crashes which focuses on the role of rational but uninformed traders. We show that uninformed traders can precipitate a price crash because as prices decline, they surmise that informed traders received negative information, which leads them to reduce their demand for assets and drive the price of stocks even lower. The model yields several implications, such as that crashes can occur even when the fundamentals are strong, and that the magnitude of the crash depends on the fraction of uninformed investors and the amount of unsophisticated passive investing present in the market.
It has been previously documented that individual firms' stock return volatility rises after stock prices fall. This paper finds that this statistical relation is largely due to a positive contemporaneous relation between firm stock returns and firm stock return volatility. This positive relation is strongest for both small firms and firms with little financial leverage. At the aggregate level, the sign of this contemporaneous relation is reversed. The reasons for the difference between the aggregate- and firm-level relations are explored.
Morck, Yeung and Yu show that R2 is higher in countries with less developed financial systems and poorer corporate governance. We show how control rights and information affect the division of risk bearing between managers and investors. Lack of transparency increases R2 by shifting firm-specific risk to managers. Opaque stocks with high R2s are also more likely to crash, that is, to deliver large negative returns. Using stock returns from 40 stock markets from 1990 to 2001, we find strong positive relations between R2 and several measures of opaqueness. These measures also explain the frequency of crashes.
We develop a series of cross-sectional regression specifications to forecast skewness in the daily returns of individual stocks. Negative skewness is most pronounced in stocks that have experienced (1) an increase in trading volume relative to trend over the prior six months, consistent with the model of Hong and Stein (NBER Working Paper, 1999), and (2) positive returns over the prior 36 months, which fits with a number of theories, most notably Blanchard and Watson's (Crises in Economic and Financial Structure. Lexington Books, Lexington, MA, 1982, pp. 295–315) rendition of stock-price bubbles. Analogous results also obtain when we attempt to forecast the skewness of the aggregate stock market, though our statistical power in this case is limited.
We develop a theory of market crashes based on differences of opinion among investors. Because of short-sales constraints,
bearish investors do not initially participate in the market and their information is not revealed in prices. However, if
other previously bullish investors bail out of the market, the originally bearish group may become the marginal “support buyers,”
and more will be learned about their signals. Thus accumulated hidden information comes out during market declines. The model
explains a variety of stylized facts about crashes and also makes a distinctive new prediction—that returns will be more negatively
skewed conditional on high trading volume.
Evidence suggests the volatility of stock prices cannot be accounted for by information about future dividends. The authors argue that some of the volatility of stock prices in excess of fundamentals results from fluctuations in the amount of public information over time. Their model assumes that dividends and consumption are constant in the aggregate but that there are good firms and bad firms whose identity may be unknown to the public, as in George Akerlof's (1970) 'lemons' problem. In that case, the collective valuation of the constant dividend stream depends on the degree of informational asymmetry. Copyright 1994 by Oxford University Press.
This paper defines the news impact curve that measures how new information is incorporated into volatility estimates. Various new and existing ARCH models, including a partially nonparametric one, are compared and estimated with daily Japanese stock return data. New diagnostic tests are presented that emphasize the asymmetry of the volatility response to news. The authors' results suggest that the model by L. Glosten, R. Jagannathan, and D. Runkle (1989) is the best parametric model. The EGARCH also can capture most of the asymmetry; however, there is evidence that the variability of the conditional variance implied by the EGARCH is too high. Copyright 1993 by American Finance Association.
This paper examines the relation between stock returns and stock market volatility. We find evidence that the expected market risk premium (the expected return on a stock portfolio minus the Treasury bill yield) is positively related to the predictable volatility of stock returns. There is also evidence that unexpected stock market returns are negatively related to the unexpected change in the volatility of stock returns. This negative relation provides indirect evidence of a positive relation between expected risk premiums and volatility.
This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction. It also establishes consistency of the estimated covariance matrix under fairly general conditions.
We introduce a new hybrid approach to joint estimation of Value at Risk (VaR) and Expected Shortfall (ES) for high quantiles of return distributions. We investigate the relative performance of VaR and ES models using daily returns for sixteen stock market indices (eight from developed and eight from emerging markets) prior to and during the 2008 financial crisis. In addition to widely used VaR and ES models, we also study the behavior of conditional and unconditional extreme value (EV) models to generate 99 percent confidence level estimates as well as developing a new loss function that relates tail losses to ES forecasts. Backtesting results show that only our proposed new hybrid and Extreme Value (EV)-based VaR models provide adequate protection in both developed and emerging markets, but that the hybrid approach does this at a significantly lower cost in capital reserves. In ES estimation the hybrid model yields the smallest error statistics surpassing even the EV models, especially in the developed markets.
If asset returns have systematic skewness, expected returns should include rewards for accepting this risk. We formalize this intuition with an asset pricing model that incorporates conditional skewness. Our results show that conditional skewness helps explain the cross-sectional variation of expected returns across assets and is significant even when factors based on size and book-to-market are included. Systematic skewness is economically important and commands a risk premium, on average, of 3.60 percent per year. Our results suggest that the momentum effect is related to systematic skewness. The low expected return momentum portfolios have higher skewness than high expected return portfolios. THE SINGLE FACTOR CAPITAL ASSET PRICING MODEL ~CAPM! of Sharpe ~1964! and Lintner ~1965! has come under recent scrutiny. Tests indicate that the crossasset variation in expected returns cannot be explained by the market beta alone. For example, a growing number of studies show that "fundamental" variables such as size, book-to-market value, and price to earnings ratios account for a sizeable portion of the cross-sectional variation in expected returns ~see, e.g., Chan, Hamao, and Lakonishok ~1991! and Fama and French ~1992!!. Fama and French ~1995! document the importance of SMB ~the difference between the return on a portfolio of small size stocks and the return on a portfolio of large size stocks! and HML ~the difference between the return on a portfolio of high book-to-market value stocks and the return on a portfolio of low book-to-market value stocks!. There are a number of responses to these empirical findings. First, the single-factor CAPM is rejected when the portfolio used to proxy for the market is inefficient ~see Roll ~1977! and Ross ~1977!!. Roll and Ross ~1994! an...
Out-of-sample equity premium prediction: combination forecasts and links to the real economy
- Rapach