Predicting financial performance with intellectual capital using machine learning

Abstract

Purpose

This study aims to apply machine learning techniques to efficiently predict leisure firms’ financial performance. Accurate financial forecasting is crucial in leisure and tourism, greatly affecting firms’ strategic decisions and competitive positioning. This study emphasizes the roles of intellectual capital to offer a nuanced understanding of how these types of capital influence firm success.

Design/methodology/approach

Using comprehensive firm-level data, this study examines several machine learning algorithms’ predictive capacity across a spectrum of industry sectors (general, manufacturing, service) to identify the most effective model and training dataset. These tools are used to evaluate financial metrics such as return on sales, return on assets and sales growth. A range of variables are incorporated into this process to enhance model accuracy and relevance.

Findings

Results demonstrate the support vector machine algorithm’s exceptional performance based on a training data set from the service sector in predicting leisure firms’ return on sales and sales growth. This algorithm is thus an efficacious strategic forecasting instrument. The variables significantly affecting firm performance include demand variation; organizational, product and technological innovation; synergistic innovation between multiple domains; salary levels; market strategy; and the number of employees.

Originality/value

By integrating advanced machine learning techniques with the strategic management of intellectual capital, this study presents a sophisticated approach to predicting leisure firms’ financial performance. Findings enrich the discourse on firm performance forecasting and offer actionable insights into strategic planning and resource allocation for practitioners in the leisure and tourism sectors.

研究目的

本研究应用机器学习技术来高效预测休闲企业的财务表现。准确的财务预测对于休闲和旅游业至关重要, 极大影响企业的战略决策和竞争定位。本研究强调智力资本的作用, 以深入理解这些资本类型如何影响企业的成功。

研究方法

本研究使用全面的企业层面数据, 考察多种行业领域（综合、制造、服务）中多个机器学习算法的预测能力, 识别出最有效的模型和训练数据集。这些工具用于评估财务指标, 如销售回报率、资产回报率和销售增长率。过程纳入多种变量, 以提高模型的准确性和相关性。

研究发现

结果表明, 基于服务行业训练数据集的支持向量机算法在预测休闲企业的销售回报率和销售增长率方面表现出色。因此, 该算法是一种有效的战略预测工具。影响企业绩效的显著变量包括需求变化、组织、产品和技术创新、多领域之间的协同创新、工资水平、市场战略和员工数量。

研究创新

通过将先进的机器学习技术与智力资本的战略管理相结合, 本研究提出了一种复杂的方法来预测休闲企业的财务表现。研究结果丰富了关于企业绩效预测的讨论, 并为休闲和旅游业的从业者提供了有关战略规划和资源分配的实用见解。

Citation

Lim, S.(E). (2025), "Predicting financial performance with intellectual capital using machine learning", Journal of Hospitality and Tourism Technology, Vol. 16 No. 2, pp. 369-388. https://doi.org/10.1108/JHTT-02-2024-0105