Company Ranking Prediction Based on Network Big Data

Abstract

With the advent of the era of big data, it has become one of the important tasks of data mining to utilize the massive info on the internet to realize the forecast of relevant aspects. In recent years, some scholars have studied how to use company’s information for the revenue or stock forecasts, but there is very little research on inter-company ranking predictions. So from the perspective, this paper brings in the Learning-to-Rank (LtR) method into the corporation ranking prognostication, and proposes the Company Ranking Prediction Model (CRPM) grounded on LtR. First, obtain the information on the Fortune 500 official website and other correlative sites. Through data analysis, data cleaning, ranking processing, label division and data formatting, a brand-new corporation data-set containing four major categories of features (10 characteristics in total) is constructed. Then bring in four commonly utilized LtR approaches, employing the previously created data-set to train different models. Finally, each model is evaluated by the two information retrieval metrics, normalized discounted cumulative gain and mean average precision. The experimental results show that the assessment best-performing model CRPM-LambdaMART and CRPM-RandomForests can tellingly forecast the ranking of Fortune 500 companies in the next year, which has certain practical value for financiers to reasonably planning their investments and corporation managements to legitimately programming their strategic deployments.

KEYWORDS:

• Company ranking prediction
• Data mining
• Fortune 500
• Learning-to-rank
• Self-made data-set

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Qihong He

Qihong He was born in Yuanjiang, Hunan, China. He received the BS degree in electronic science and technology in 2018 from Hunan Institute of Engineering, China and received the MS degree in electronic engineering in 2021 from Xiangtan University. His research interests are data mining and learning-to-rank. Email: [email protected]

Xujun Li

Xujun Li was born in Anhua, Hunan, China. She received the BS and MS degrees in automatic control in 1996 and 2006 from Wuhan University of Technology. She received the PhD degree in materials science and engineering in 2016 from Xiangtan University. She is an associate professor in Xiangtan University. Her research interest includes artificial intelligence, virtual instrument and machine learning.

Yan Sun

Yan Sun was born in Lingbi, Anhui, China. She received the BS degree in electronic information engineering in 2018 from Anhui Science and Technology University, and received the MS degree in electronic science and technology in 2021 from Xiangtan University, China. Her research interests are machine learning and deep learning. Email: [email protected]