Predicting standardized absolute returns using rolling-sample textual modelling

Understanding how textual information impacts financial market volatility has been one of the growing topics in financial econometric research. In this paper, we aim to examine the relationship between the volatility measure that is extracted from GARCH modelling and textual news information both publicly available and from subscription, and the performances of the two datasets are compared. We utilize a latent Dirichlet allocation method to capture the dynamic features of the textual data overtime by summarizing their statistical outputs, such as topic distributions in documents and word distributions in topics. In addition, we transform various measures representing the popularity and diversity of topics to form predictors for a rolling regression model to assess the usefulness of textual information. The proposed method captures the statistical properties of textual information over different time periods and its performance is evaluated in an out-of-sample analysis. Our results show that the topic measures are more useful for predicting our volatility proxy, the unexplained variance from the GARCH model than the simple moving average. The finding indicates that our method is helpful in extracting significant textual information to improve the prediction of stock market volatility.

Rolling regression technique and cross-sectional regression: A tool to analyze Capital Asset Pricing Model

The Capital Asset Pricing Model (henceforth, CAPM) is considered an extensively used technique to approximate asset pricing in the field of finance. The CAPM holds the power to explicate stock movements by means of its sole factor that is beta co-efficient. This study focuses on the application of rolling regression and cross-sectional regression techniques on Indian BSE 30 stocks. The study examines the risk-return analysis by using this modern technique. The applicability of these techniques is being viewed in changing business environments. These techniques help to find the effect of selected variables on average stock returns. A rolling regression study rolls the data for changing the windows for every 3-month period for three years. The study modifies the model with and without intercept values. This has been applied to the monthly prices of 30 BSE stocks. The study period is from January 2009 to December 2018. The study revealed that beta is a good predictor for analyzing stock returns, but not the intercept values in the developed model. On the other hand, applying cross-section regression accepts the null hypothesis. α, β, β2 ≠ 0. Therefore, a researcher is faced with the task of finding limitations of each methodology and bringing the best output in the model.

Implicitly learning when to be ready: From instances to categories

There is growing appreciation for the role of long-term memory in guiding temporal preparation in speeded reaction time tasks. In experiments with variable foreperiods between a warning stimulus (S1) and a target stimulus (S2), preparation is affected by foreperiod distributions experienced in the past, long after the distribution has changed. These effects from memory can shape preparation largely implicitly, outside of participants’ awareness. Recent studies have demonstrated the associative nature of memory-guided preparation. When distinct S1s predict different foreperiods, they can trigger differential preparation accordingly. Here, we propose that memory-guided preparation allows for another key feature of learning: the ability to generalize across acquired associations and apply them to novel situations. Participants completed a variable foreperiod task where S1 was a unique image of either a face or a scene on each trial. Images of either category were paired with different distributions with predominantly shorter versus predominantly longer foreperiods. Participants displayed differential preparation to never-before seen images of either category, without being aware of the predictive nature of these categories. They continued doing so in a subsequent Transfer phase, after they had been informed that these contingencies no longer held. A novel rolling regression analysis revealed at a fine timescale how category-guided preparation gradually developed throughout the task, and that explicit information about these contingencies only briefly disrupted memory-guided preparation. These results offer new insights into temporal preparation as the product of a largely implicit process governed by associative learning from past experiences.

Benefits on Sales Generated by Innovation

The object of the research is to assess whether organizations’ ability to innovate may be able to explain abnormal returns to firms by composing risk factor models. Using R&D investment indicators and published patents from a global sample of companies for the period 1992 to 2018, the 3-factor, 4-factor and 5-factor risk models were applied. Partly the case is that increased investment in innovation contributes to better sales performance and, consequently, excess returns. Regarding the rolling-regression method, the results show few scenarios in which the ability to innovate is an explanatory factor for financial performance.

Do hotel industry operations deteriorate air quality?

This study proposes and tests two hypotheses concerning the effects of hotel industry operations on air quality based on data of 26 major tourist cities in China from 2002 to 2017. The empirical analyses take two steps. In the first step, panel regression test results reveal that hotel industry operations (measured by hotel sales revenue) significantly raise the value of particulate matter (PM)2.5 (the key indicator of air quality), supporting the first hypothesis that hotel industry operations deteriorate air quality and providing empirical evidence of the adverse impact of the hotel industry on air quality. In the second step, subsample analyses support the second hypothesis that the impact of hotel sales revenue on air quality diminishes over time. The results from the rolling regression tests validate the existence of a diminishing effect of hotel industry operations on air quality.

Implicitly learning when to be ready: from instances to categories

[Manuscript submitted for review]There is growing appreciation for the role of long-term memory in guiding temporal preparation. In experiments with variable foreperiods between a warning stimulus (S1) and a target stimulus (S2), preparation is affected by foreperiod distributions experienced in the past, long after the distribution has changed. Such memory-guided preparation shapes preparation largely implicitly and outside of a participants’ control. Recent studies have demonstrated the associative nature of such memory-guided preparation. When distinct S1s predict different foreperiods, they can trigger dissociative preparation accordingly. Here, we demonstrate that memory-guided preparation allows for another key feature of learning: the ability to generalize across acquired associations and apply them to novel situations. Participants completed a foreperiod task where S1 was a unique image of either a face or a scene on each trial. Images of either category were paired with different distributions with predominantly shorter versus predominantly longer foreperiods. Participants displayed dissociative preparation to never-before seen images of either category, without being aware of the predictive nature of these categories. They continued doing so in a subsequent transfer phase, after they had been informed that these contingencies no longer held. A novel rolling regression analysis revealed at a fine timescale how category-guided preparation gradually developed throughout the task, and illustrated how instructions at the start of the transfer phase interacted with these influences from long-term memory. These results offer new insights into temporal preparation as the product of a largely implicit process governed by associative learning from past experiences.

Temporary contracts and Okun?s law in Poland

Research background: The share of temporary workers in Poland is one of the largest of any EU country, which may affect the output unemployment relationship. The Polish case seems to be a natural experiment. Contrary to many advanced European countries, the spread of temporary contracts in Poland was not caused by labor market reform but instead resulted mainly from spontaneous processes. Purpose of the article: This paper investigates the effect of the widespread use of temporary contracts on the relationship between output and unemployment in Poland. Methods: The analysis is based on the ?dynamic? version of Okun?s law and uses OLS regression, OLS split-sample regression and OLS rolling regression. The sample period is 1996?2018. Findings & Value added: The study found that unemployment?s sensitivity to output increased over time and was related to the greater use of temporary contracts, particularly among young people and women. Initially, at the turn of the 21st century, the expansion of temporary jobs changed the employment composition and had an insignificant effect on unemployment since firms mainly replaced permanent contracts with temporary contracts. Then, starting around 2006, temporary contracts began affecting unemployment levels and unemployment?s responsiveness to output. During this period, firms used temporary contracts as the main workforce adjustment device during the business cycle.