Stock Characteristics Research Articles

Stock Characteristics and Individual Herding

Individuals who invest stocks in a market with excess volatility generally end up selling or holding the stocks at losses. The purpose of this study was to examine individual herding as it related to three comprehensible stock characteristics, market capitalization, price-to-book ratio, and industry affiliation. The target population was the individual investors who traded in Taiwan Stock Exchange in 2016. Data were collected through subscription. Based on Lakonishok, Shleifer, and Vishny's measure, individual herding was significant. The three stock characteristics were separately and as a whole related to individual herding. The findings confirmed sell-herding higher than buy-herding, more serious herding in high market capitalization stocks, and broad industry herding. The findings also extended knowledge to comparable herding levels with 8 to 10 years ago, more linearity between log market capitalization and log odds of herd occurrence, and less herding in P/B ratio stocks with other independent variables controlled.

Earnings management, policy uncertainty and firm life cycle stages: evidence from publicly traded companies in the USA and Brazil

PurposeThis paper aims to investigate the combined effect of economic policy uncertainty (EPU) and the firm life cycle on the degree of accrual-based earnings management of publicly traded companies in the USA and Brazilian stock markets.Design/methodology/approachThe EPU index used was the one developed by Baker et al. (2016), the Kothari et al. (2005) model was used in the main analysis to obtain the discretionary accruals and the classification of firms into different life cycles was based on the Dickinson (2011) approach, which relies on the sign of operating, investment and financing cash flows. The methodology includes correlation matrix and panel regression with fixed effects.FindingsThe overall results for the USA sample suggest that economic policy uncertainty does have a heterogeneous influence on the firms’ accrual earnings management conditional on their life cycle where firms in the introduction, growth and decline stages decrease this practice when policy uncertainty increases. For the Brazilian case, in general, there is no combined effect between these variables. These contrasting findings can be associated with either the different underlying characteristics of both stock markets or the reduced sample size for the emerging market analyzed.Originality/valueThis research contributes to the earnings management literature examining how policy uncertainty is related to accruals manipulation under different life cycle stages and institutional environments. It is also one of the first studies to explore this conditioning effect.

Spatio-Temporal Changes of Housing Features in Response to Urban Renewal Initiatives: The Case of Seoul

Over the past two decades, Seoul has been in a transitional period in terms of urban renewal approaches. Housing is a fundamental element of citizens’ lives and the built landscape, thus, it deserves thoughtful scrutiny. As such, this study empirically investigates the dynamics of the spatial and temporal characteristics of housing stock within the context of new urban renewal policies in Seoul. A fine-grained and multifaceted analysis shows that the supply of new apartments has decreased over time, revealing that denser housing redevelopment in the inner city has become more difficult. In addition, an exploratory spatial data analysis indicates that although spatial clustering of old housing units has been reduced, new housing units have become more spatially distributed and outwardly dispersed over time. Since the physical and locational changes of housing stock are closely related to urban renewal initiatives, this study suggests that the city government needs to incorporate the concept of sustainable urban growth management into its housing supply and renewal policies.

The Cross-Sectional Pricing of Corporate Bonds Using Big Data and Machine Learning

We investigate the return predictability across stocks and bonds using big data and machine learning. We find that machine learning models substantially improve the out-of-sample performance of stock and bond characteristics in predicting future stock and bond returns. Although both stock and bond characteristics provide strong forecasting power for both stock and bond returns, stock (bond) characteristics do not offer significant incremental predictive power above and beyond bond (stock) characteristics in predicting bond (stock) returns. The results also indicate that stock (bond) characteristics are cash flow (discount rate) predictors and stock (bond) return predictability is driven by mispricing (risk) phenomenon.

Double-Adjusted Mutual Fund Performance

Abstract Mutual fund returns are significantly related to stock characteristics in the cross-section after controlling for risk via factor models. We develop a new double-adjusted approach that controls for both factor model betas and stock characteristics in one performance measure. The new measure substantially affects performance rankings, with a quarter of funds experiencing a change in their percentile ranking greater than 10. Double-adjusted performance produces strong evidence of persistence in relative performance. Inference based on the new measure often differs, sometimes dramatically, from that based on traditional performance estimates. Received November 22, 2019; editorial decision June 28, 2020; Editor: Jeffrey Pontiff. 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.

Sentiment stocks

To study how investor sentiment at the firm level affects stock returns, we match more than 58 million social media messages in China with listed firms and construct a measure of individual stock sentiment based on the tone of those messages. We document that positive investor sentiment predicts higher stock risk-adjusted returns in the very short term followed by price reversals. This association between stock sentiment and stock returns is not explained by observable stock characteristics, unobservable time-invariant characteristics, market-wide sentiment, overreaction to news, or changing investor attention. Consistent with theories of investor sentiment, we find that the link between sentiment and stock returns is mainly driven by positive sentiment and non-professional investors. Finally, exploiting a unique feature of the Chinese stock market, we are able to isolate the causal effect of sentiment on stock returns from confounding factors.

Empirical Regularities in Stock Market Crashes

When a stock market crash is defined as the period from an index's prior peak until its recovery, crashes demonstrate empirical regularities in their scale and timing. For instance, measures of the duration, maximum decline, and lost value of crashes are very highly correlated. These correlations suggest that crashes belong to well-defined categories based on their size and become increasingly predictable as they progress. Accordingly, I advance four stock market crash categories, which are logarithmic in size. Crashes then range from small scale market disturbances like 'flash crashes' in Category 1 to the Wall Street Crash of 1929, America's sole Category 4. Furthermore, I find that U.S. stock markets are bimodal, switching between crashes and booms, and that this switching is regular. Specifically, I find that either a Category 2 or 3 crash occurs every four years, with a variance of just two years. Moreover, by definition, growth during a crash is close to zero. During boom periods, however, the average annual growth rate is 21.5%. Together, these results suggest a new foundation for examining patterns of returns and other characteristics of stock markets.

Bitcoin and sentiment

AbstractBaker and Wurgler identify high sentiment betas with small startup firms that have great growth potential. On the surface, cryptocurrencies share important features in common with high sentiment beta stocks. This paper investigates the degree to which, during the period July 18, 2010–February 26, 2018, the return to bitcoin displayed the characteristics of a high sentiment beta stock. Using a sentiment‐dependent factor model, the analysis indicates that in large measure, bitcoin returns resembled returns to high sentiment beta stocks. Additionally, we show that bitcoin's expected returns are low when sentiment measured by Volatility Index is high while expected returns are high when sentiment is low.

Evaluating the Significance of Financial Characteristics on Energy Consumption of Urban Building Stock using Principal Component Analysis and Logistic Regression

The increased population and the rapid urbanization seek our attention towards sustainable production and consumption in cities. In assessing the factors affecting the energy consumption characteristics of the buildings, it is crucial that we consider the user behavior along with the design characteristics of the buildings. One significant factor that influence the user behavior is the financial characteristics. We use non-parametric machine learning algorithms and econometric models to assess the influence of the user behavior characteristics in the urban building stock in New York City. The analysis was conducted on the open-data assessable, which is mandated by the Local Law 84. In our analysis we concluded that the financial characteristics have a significant effect in the energy consumption of the residential buildings, however, is not that significant in deciding the energy consumption of the commercial buildings.

Stocks delineation of bull’s eye fish (Priacanthus hamrur) in Indian water using morphometric measurements and meristic counts

The Moontail bull’s eye (Priacanthus hamrur) is one of the commonly available fish species found in the deep sea and under ledges or hovering next to coral heads during the day. In the present study, around 300 specimens were collected from different location in east and west coast of India including Kakinada, Kolkata, Cochin and Mumbai to investigate the stock differentiation among the populations. A total of 14 morphometric traits and 10 meristic counts were studied. The descriptive statistics of morphometric traits indicated the much larger growth in populations of west coast compares to the east coast. The meristic traits were not much efficient in identifying the stocks. Pre pelvic fin length, post anal fin length, post dorsal length, pre dorsal fin length, head length, eye diameter, body depth, caudal peduncle depth and dorsal fin base helped in the separation of stocks. All the four stocks have separate morphometric features. The present study will provide the baseline information on the stock characteristics of P. hamrur from Indian water and management measures of the resources for sustainable utilization.

Investigating Market Potentials and Operational Scenarios of Virtual Coupling Railway Signaling

The new concept of virtual coupling (VC) envisages autonomous trains running in radio-connected platoons to significantly improve railway capacity and address the forecasted increase in railway demand. Such a concept will introduce radical changes to current train services, technologies, and procedures, which calls for a deeper understanding of the possible modes of operation and the impacts on the entire railway business. This paper investigates market potentials and operational scenarios of VC for different segments of the railway market: high-speed, main-line, regional, urban, and freight trains. The research builds on the Delphi method, with an extensive survey to collect expert opinions about benefits and challenges of VC as well as stated travel preferences in futuristic VC applications. Survey outcomes show that VC train operations can be very attractive to customers of the high-speed, main-line, and regional market segments, with benefits that are especially relevant for freight railways. In particular, customers of regional and freight railways are observed to be unsatisfied with current train services and willing to pay higher fares to avail of a more frequent and flexible service enabled by VC. Operational scenarios for VC are then defined by setting market-attractive service headways and by characteristics of the rolling stock, infrastructure, and traffic management. An analysis of strengths and weaknesses of such a concept together with business opportunities and threats is carried out. The defined VC future scenario is set to induce a sustainable shift of customers from other travel modes to the railways.

Stock Picking with Machine Learning

We combine insights from machine learning and finance research to build machine learn-ing algorithms for stock selection. Our study builds on weekly data for the historical constitu-ents of the S&P 500 over the period from January 1999 to March 2021 and includes typical equity factors as well as additional fundamental data, technical indicators, and historical re-turns. Deep neural networks (DNN), long short-term neural networks (LSTM), random forest, gradient boosting, and regularized logistic Regression models are trained on stock characteris-tics to predict whether a specific stock outperforms the market over the subsequent week. We analyze a trading strategy that picks stocks with the highest probability predictions to outper-form the market. Our empirical results show a substantial and significant outperformance of machine learning based stock selection models compared to a simple equally weighted bench-mark. Moreover, we find non-linear machine learning models such as neural networks and tree-based models to outperform more simple regularized logistic regression approaches. The re-sults are robust when applied to the STOXX Europe 600 as alternative asset universe. However, all analyzed machine learning strategies demonstrate a substantial portfolio turnover and trans-action costs have to be marginal to capitalize on the strategies.

Incorporating Research Reports and Market Sentiment for Stock Excess Return Prediction: A Case of Mainland China

The prediction of stock excess returns is an important research topic for quantitative trading, and stock price prediction based on machine learning is receiving more and more attention. This article takes the data of Chinese A-shares from July 2014 to September 2017 as the research object, and proposes a method of stock excess return forecasting that combines research reports and investor sentiment. The proposed method measures individual stocks released by analysts, separates the two indicators of research report attention and rating sentiment, calculates investor sentiment based on external market factors, and uses the LSTM model to represent the time series characteristics of stocks. The results show that (1) the accuracy and F1 evaluation indicators are used, and the proposed algorithm is better than the benchmark algorithm. (2) The performance of deep learning LSTM algorithm is better than traditional machine learning algorithm SVM. (3) Investor sentiment as the initial hidden state of the model can improve the accuracy of the algorithm. (4) The attention of the split research report takes the two indicators of investor sentiment and price as the input of the model, which can effectively improve the performance of the model.

Why Black‐Majority Neighbourhoods Are The Epicentre Of Population Shrinkage In The American Rust Belt

AbstractDespite a clear association between the most African‐American neighbourhoods and overall population shrinkage in Rust Belt cities, few have explored the theoretical challenges that this poses. First, if racial prejudice is the key ingredient for this relationship, why would this result in overall population loss and not simply a reduction of White residents? Second, why if stigmatisation generates the pre‐conditions for in‐migration and investment – as the gentrification and urban ecologies literatures suggest – why would Black neighbourhoods not be the epicentre of population growth? This paper explores these questions through a theoretical synthesis of the residential choice, shrinking cities, and racial prejudice literatures. I rely on an intra‐city examination of neighbourhood change in Cleveland, Detroit and Pittsburgh. I argue that three factors in shrinking Rust Belt cities limit the application of more general neighbourhood change paradigms: (i) the persistence of racial prejudice; (ii) abundant housing supply; and (iii) housing stock characteristics.

Individual analysts, stock return synchronicity and information efficiency

To shed light on whether and how effective are analysts as information intermediaries, we construct the information efficiency indicator and investigate the information performance of China's analysts from the perspective of heterogeneous individual analysts based on stock return synchronicity. Furthermore, we verify the rationality and generalization of information efficiency based on the regression of primary and expanded indicators on stock characteristics and analyst characteristics. Our indicator provides a novel method to accurately measure analysts' information performance from the level of individual analysts rather than from a homogeneous community or heterogeneous group and highlights the analysts' roles as the information intermediaries.

Intermediation Frictions in Equity Markets

Stocks with similar characteristics but different levels of ownership by financial institutions have returns and risk premia that comove very differently with shocks to the risk bearing capacity of financial intermediaries. After accounting for observable stock characteristics, excess returns on more intermediated stocks have higher betas on contemporaneous shocks to intermediary willingness to take risk and are more predictable by state variables that proxy for intermediary health. The empirical evidence suggests that asset pricing models featuring financial intermediaries as marginal investors and frictions that induce changes in intermediary risk bearing capacity are useful in explaining price movements even in asset classes with comparatively low barriers to household participation.

Stock Characteristics and Stock Returns: A Skeptic’s Look at the Cross Section of Expected Returns

The correlation between stock characteristics and the cross section of stock returns plays a central role in empirical implementations of modern asset pricing models and has important implications for investment management. This remains true whether the correlation is due to investor preferences regarding the characteristics directly or whether the characteristics are proxies for state variables, the risk of which investors are attempting to hedge. This article asks what we know about the relation between these characteristics and the cross section of returns. The skeptic’s answer is, not much. A combination of lack of persistence in the characteristics and problems caused by model uncertainty, data snooping, and nonstationarity means that our knowledge is sketchy at best. Investors should be forewarned when considering any strategies such as smart beta that are premised on the correlation between characteristics and the cross section of returns. <b>TOPICS:</b>Portfolio management/multiasset allocation, performance measurement <b>Key Findings</b> • Many investment strategies, including smart beta, are premised on the existence of predictable correlations between stock characteristics and expected returns. • The skeptic’s view is that those correlations are not predictable owing to a combination of lack of persistence, model uncertainty, data snooping, and nonstationarity. • Empirical results and a review of the published literature offer support for the skeptic’s view and serves as a warning for investors.

The Promises and Pitfalls of Machine Learning for Predicting Stock Returns

Recent research suggests that machine learning models dominate traditional linear models in predicting cross-sectional stock returns. We confirm this finding when predicting one-month forward-looking returns based on a set of common stock characteristics, including predictors such as short-term reversal. Despite the statistical advantage of machine learning model predictions, we demonstrate that the economic gains tend to be more limited, and critically dependent on the ability to take risk and implement trades efficiently. Unlike traditional models, machine learning models have been somewhat more effective over the past decade at discerning valuable predictions from cross-sectional equity characteristics.

Improving understanding of carbon stock characteristics of Eucalyptus and Acacia trees in southern China through litter layer and woody debris

Surveying the quality and quantity of carbon stock in litter layer and woody debris of Eucalyptus and Acacia plantations is critical in understanding their carbon pools. Here, the focus of the present study was on a number of Eucalyptus and Acacia plantations of different stand aged in the Pearl River Delta region of southern China. The plantation type proved to be a crucial driver of the carbon concentration in litter layer and woody debris, with Acacia exhibiting a superior ability to Eucalyptus to accumulate carbon with stand age in both these materials. The relative contribution of the litter layer and woody debris to the carbon stock of the ecosystem was also significantly higher under mature Acacia (8% and 7%, respectively) than that under mature Eucalyptus (4% and 1%, respectively). Most of the carbon stock within the litter layer was present in the leaf debris. The carbon stock in woody debris was mainly contained in the components within the 10–20 cm diameter class during the primary decay stage, represented as snags in middle-aged and mature Acacia, and as logs for mature Eucalyptus, respectively. The results indicate that both plantation type and stand age influence the characteristics of carbon stored in litter layer and woody debris significantly.

Delineating forest stands from grid data

BackgroundForest inventories are increasingly based on airborne laser scanning (ALS). In Finland, the results of these inventories are calculated for small grid cells, 16 m by 16 m in size. Use of grid data in forest planning results in the additional requirement of aggregating management prescriptions into large enough continuous treatment units. This can be done before the planning calculations, using various segmentation techniques, or during the planning calculations, using spatial optimization. Forestry practice usually prefers reasonably permanent segments created before planning. These segments are expected to be homogeneous in terms of site properties, growing stock characteristics and treatments. Recent research has developed methods for partitioning grids of ALS inventory results into segments that are homogeneous in terms of site and growing stock characteristics. The current study extended previous methods so that also the similarity of treatments was considered in the segmentation process. The study also proposed methods to deal with biases that are likely to appear in the results when grid data are aggregated into large segments.MethodsThe analyses were conducted for two datasets, one from southern and the other from northern Finland. Cellular automaton (CA) was used to aggregate the grid cells into segments using site characteristics with (1) growing stock attributes interpreted from ALS data, (2) predicted cutting prescriptions and (3) both stand attributes cutting prescriptions. The CA was optimized for each segmentation task. A method based on virtual stands was used to correct systematic errors in variable estimates calculated for segments.ResultsThe segmentation was rather similar in all cases. The result is not surprising since treatment prescriptions depend on stand attributes. The use of virtual stands decreased biases in growth prediction and in the areas of different fertility classes.ConclusionsAutomated stand delineation was not sensitive to the type of variables that were used in the process. Virtual stands are an easy method to decrease systematic errors in calculations.