arXiv research

AI predicts employee attrition to prevent turnover.

We aim to predict whether an employee of a company will leave or not, using the k-Nearest Neighbors algorithm. We use evaluation of employee performance, average monthly hours at work and number of years spent in the company, among others, as our features. Other approaches to this problem include the use of ANNs, decis…

Clustering is the problem of separating a set of objects into groups (called clusters) so that objects within the same cluster are more similar to each other than to those in different clusters. Spectral clustering is a now well-known method for clustering which utilizes the spectrum of the data similarity matrix to pe…

New findings show pure strategy equilibria are more robust in a war of attrition game.

Proposes a deep learning model to predict user attrition.

Science, technology, engineering, and math (STEM) fields play growing roles in national and international economies by driving innovation and generating high salary jobs. Yet, the US is lagging behind other highly industrialized nations in terms of STEM education and training. Furthermore, many economic forecasts predi…

This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design \& test employee retention policies. This type of retention dis…

Each year, roughly 30% of first-year students at US baccalaureate institutions do not return for their second year and over $9 billion is spent educating these students. Yet, little quantitative research has analyzed the causes and possible remedies for student attrition. Here, we describe initial efforts to model stud…

Fair market valuations ignore future worker profits in employee-owned firms.

Study examines downsizing impact on Indian construction firms' profitability.

We propose a discrete time algorithm for the valuation of employee stock options based on exponential indifference prices and taking into account both the possibility of partial exercise of a fraction of the options and the use of a correlated traded asset to hedge part of their risk. We determine the optimal exercise …

A game theory study examines gradual concessions in variable contribution games under uncertainty.

In this paper we consider three types of embedded options in pension benefit design. The first is the Florida second election (FSE) option, offered to public employees in the state of Florida in 2002. Employees were given the option to convert from a defined contribution (DC) plan to a defined benefit (DB) plan at a ti…

Mathematical models reveal key factors for engaging Gen Z at work.

This paper examines the economic benefits of monthly gratuity options.

We uncover a new anomaly in asset pricing that is linked to the remuneration: the more a company spends on salaries and benefits per employee, the better its stock performs, on average. Moreover, the companies adopting similar remuneration policies share a common risk, which is comparable to that of the value premium. …

New framework values ESOs with multiple exercises and job termination risk.

Study explores factors influencing saving behavior among Dhaka employees.

Study examines market risks on pension system sustainability.

Study improves pension scheme efficiency in Kenya through governance and risk management.

In many developing countries intellectual property infringement and the commerce of pirate goods is an entrepreneurial activity. Digital piracy is very often the only media for having access to music, cinema, books and software. At the same time, bio-prospecting and infringement of indigenous knowledge rights by intern…

Research finds investors may lose from more diverse workplaces.

Study predicts dropout and certification in MOOCs using student logs and forum data.

With pressure to increase graduation rates and reduce time to degree in higher education, it is important to identify at-risk students early. Automated early warning systems are therefore highly desirable. In this paper, we use unsupervised clustering techniques to predict the graduation status of declared majors in fi…

Proposes a variational autoencoder for long-term customer revenue forecasting.

We analyze the income distribution of employees for 9 consecutive years (2001-2009) using a complete social security database for an economically important district of Romania. The database contains detailed information on more than half million taxpayers, including their monthly salaries from all employers where they …

The paper develops methods to estimate treatment effects in sample selection models.

Firm size data usually do not show the normality that is often assumed in statistical analysis such as regression analysis. In this study we focus on two firm size data: the number of employees and sale. Those data deviate considerably from a normal distribution. To improve the normality of those data we transform them…

Cluster analysis reveals diverse performance outcomes from innovation during crises.

India introduces NPS to manage pension liabilities and promote savings.

The excessive compensation packages of CEOs of U.S. corporations in recent years have brought to the foreground the issue of fairness in economics. The conventional wisdom is that the free market for labor, which determines the pay packages, cares only about efficiency and not fairness. We present an alternative theory…

Study finds risk management significantly improves pension scheme efficiency in Kenya.

Turtle Score analyzes developer similarity to match high-performing candidates.

This study improves detection of insider threats using machine learning.

Statistical mechanics explains income and wealth distribution in developed economies.

Proposes new method to handle hidden confounders in causal mediation analysis.

To elucidate allometric scaling in complex systems, we investigated the underlying scaling relationships between typical three-scale indicators for approximately 500,000 Japanese firms; namely, annual sales, number of employees, and number of business partners. First, new scaling relations including the distributions o…

Survey examines machine learning for credit rating predictions.

Multimodal analysis assesses job interview performance and provides feedback.

In many professons employees are rewarded according to their relative performance. Corresponding economy can be modeled by taking $N$ independent agents who gain from the market with a rate which depends on their current gain. We argue that this simple realistic rate generates a scale free distribution even though intr…

Crowdsourced predictions from microservices improve supply chain efficiency.

Method estimates treatment effect bounds in sample selection models.

AI-driven framework improves enterprise financial audits and risk identification.

Study shows OAT decomposition generates unexplained profit and loss, while SU decompositions depend on risk factor order.

We analyse the optimal exercise of an executive stock option (ESO) written on a stock whose drift parameter falls to a lower value at a change point, an exponentially distributed random time independent of the Brownian motion driving the stock. Two agents, who do not trade the stock, have differing information on the c…

This work focuses on the indifference pricing of American call option underlying a non-traded stock, which may be partially hedgeable by another traded stock. Under the exponential forward measure, the indifference price is formulated as a stochastic singular control problem. The value function is characterized as the …

Classification is one of the widely used analytical techniques in data science domain across different business to associate a pattern which contribute to the occurrence of certain event which is predicted with some likelihood. This Paper address a lacuna of creating some time window before the prediction actually happ…

Improved antibody humanness prediction using patent data.