Representing Representative Streams

Tatiana Garanina^*
*Correspondence: Tatiana Garanina, Department of Accounting, University of School of Business and Marketing, Dammam, Saudi Arabia, Email:

Description

Representing representative streams is a concept that pertains to the visualization and analysis of data streams in order to capture their essential characteristics. In various domains, such as finance, social media, and environmental monitoring, data streams continuously generate vast amounts of information. However, due to their high volume, velocity, and variability, effectively analysing and understanding these streams pose significant challenges. Representative stream analysis focuses on extracting key patterns, trends, and outliers from data streams to provide meaningful insights. This abstract explores various approaches and techniques used to represent representative streams. It encompasses methods for dimensionality reduction, summarization, clustering, and anomaly detection. Dimensionality reduction techniques aim to reduce the complexity of data streams by transforming them into lower-dimensional representations while preserving important information [1].

This enables efficient storage, visualization, and processing of representative streams. Methods such as Principal Component Analysis (PCA) and t-Distributed stochastic neighbour embedding have been widely applied to achieve dimensionality reduction in streaming data. Human capital management is a key factor in environmental, social and governance ratings and plays a significant role in company operations. However, aside from employee counts, annual reports have traditionally not required businesses to disclose much information about their human capital. Investors have requested new regulations that require disclosure of human capital management practices as a result of this. Recent accounting studies on human capital have been sparked by the interest of investors and regulators, particularly in employee flows such as arrivals (attraction) and departures (retention) and their associated informational value regarding firm performance and capital market responses [2].

Regarding arrivals, Gutiérrez finds that increases in the number of job postings predict increases in employee numbers, revenues, expenses and earnings one year in advance, indicating that the intention to hire signals good performance. Regarding departures, we found that moderate or high levels of departure have a negative correlation with the 1-year-ahead return on assets. Human resource researchers, who have been studying employee flows longer than accounting researchers, tend to believe that arrivals are positive and departures are negative. Nonetheless, the observational proof is conflicting. A number of variables moderate the negative but insignificant correlation between departures and company performance. In previous studies, more than 25% of effect sizes are positive. This suggests that the relationship between employee flows and company performance probably varies from company to company. The little-studied interaction between inflows and outflows may also have an impact on the relationship between employee inflows and outflows and company performance [3].

We add to the existing body of knowledge in a number of ways by examining how employee flows can predict company performance. Instead of looking at each employee departure and arrival separately, we first examine them together. We anticipate that the inclusion of both measures will enhance performance predictions. Thusly, we exactly consider the degree to which inflows and surges bring about substitutions of representatives versus constriction or extension of the labour force and the differential impacts of take-offs versus appearances. Second, we look at whether the relationship between future execution and takeoffs and appearances vary with firms monetary circumstances. We hypothesize that when expenses exceed income; poorly performing businesses may benefit from downsizing or changing their operating setup by separating from employees responsible for poor performance, but may suffer from hiring. On the other hand, if successful operations are levered or downsized, good performers stand to benefit. Finally, we investigate whether employee flows influence lenders' required interest rates [4].

For firms showing unfortunate late monetary execution (earlier year misfortune firms), take-offs relate emphatically to 1-year-ahead income changes, while appearances are not related with profit changes. The outcomes are the opposite for businesses with strong recent financial performance (prior-year profit firms). Arrivals have a positive relationship with earnings fluctuations, while departures have a negative one. That is, when we condition departures on earnings from the previous year being below or above zero, the sign changes, while arrivals predict earnings increases only for profit-making firms from the previous year. In economic terms, departures and arrivals for prior-year profit firms range from 7% to 16% and departures for prior-year loss firms from a one standard deviation change in the statistically significant employee flow variables to a change in earnings relative to the sample mean level of earnings. When conditioning by the earnings of the previous year being either above or below zero, we are able to accurately predict earnings changes. Additionally, both incremental arrivals and departures aid in predicting earnings. This checks out in light of the fact that they anticipate profit changes diversely for earlier year misfortune and benefit firms [5].

Two sets of results suggest that departures have a greater impact than arrivals do. First, when estimating changes in gross profits and earnings, the absolute coefficients on departures are larger than the coefficients on arrivals. Second, we find that even when departures are replaced by new hires holding the same position within the company, there is a positive or negative relationship between departures and operating earnings for prior-year loss firms (profit firms). However, the effects are greater when they are not replaced.

Acknowledgement

None.

Conflict of Interest

No potential conflict of interest was reported by the authors.

References

1. Clarke, Charles. "The level, slope and curve factor model for stocks."J Financ Econ 143 (2022): 159-187.

Google Scholar, Crossref, Indexed at

2. Brooks, Gillian, Jenna Drenten and Mikolaj Jan Piskorski. "Influencer celebrification: How social media influencers acquire celebrity capital."J Advert 50 (2021): 528-547.

Google Scholar, Crossref

3. Cartwright, Severina, Iain Davies and Chris Archer-Brown. "Managing relationships on social media in business-to-business organisations."J Bus Res 125 (2021): 120-134.

Google Scholar, Crossref

4. Dean, Dwane Hal and Abhijit Biswas. "Third-party organization endorsement of products: An advertising cue affecting consumer prepurchase evaluation of goods and services."J Advert 30 (2001): 41-57.

Google Scholar, Crossref, Indexed at

5. Dechow, Patricia M. "Accounting earnings and cash flows as measures of firm performance: The role of accounting accruals."J Account Econ18 (1994): 3-42.

Google Scholar, Crossref