Can customer sentiment impact firm value? An integrated text mining approach

Developing measures to capture customer sentiment and securing a positive customer experience is a strategic necessity to improve firm profitability and shareholder value. The paper considers the relationship between customer satisfaction, earnings, and firm value as these drives change in stock prices, customer, and investor sentiment. The present study investigates the impact of customer sentiment polarity on stock prices based on Indian automobile sector databased such as the Indian Nifty Auto SNE (Maruti Suzuki, Tata Motors, and Eicher). A top-down approach is adopted to construct a financial proxy-based sentiment index completed with sentiment extracted from automobile news and customer reviews. The paper uses a text mining approach to holistically measure customer sentiment’s impact on investor sentiment and stock prices. The study was initially performed at the overall individual stock from the Nifty Auto NSE but focused on the top three passenger vehicle manufacturing companies i.e., Maruti Suzuki, Tata Motors, and Eicher. It was found that the sentiment index was augmented with news and customer reviews allows predicting more accurately NIFTY AUTO stock price movements. This implies that customer sentiment is a major driver of investor sentiment which in turn impacts the stock market and the firm value. Thus, the present study is an integrated approach to holistically measure customer sentiment’s impact on investor sentiment and stock prices.WOS:00071138140002

Forecasting Accessory Demand in the Automotive Industry

The automotive industry seeks effective ways to forecast consumer demand to avoid overstocking, waste, underproduction, and employee underperformance. Modeling future demand for vehicles is standard, however parts & accessories are a significant subset of overall automotive revenue. There is no industry standard for predicting the quantity of accessories sold or revenue. This paper seeks to use the best industry forecasting methods and research practices to build a predictive model that forecasts vehicle accessory sales. The time-series forecasting model utilizes Toyota Motor Corporation data in a first attempt to predict accessory sales

Predicting and Evaluating Software Model Growth in the Automotive Industry

The size of a software artifact influences the software quality and impacts the development process. In industry, when software size exceeds certain thresholds, memory errors accumulate and development tools might not be able to cope anymore, resulting in a lengthy program start up times, failing builds, or memory problems at unpredictable times. Thus, foreseeing critical growth in software modules meets a high demand in industrial practice. Predicting the time when the size grows to the level where maintenance is needed prevents unexpected efforts and helps to spot problematic artifacts before they become critical. Although the amount of prediction approaches in literature is vast, it is unclear how well they fit with prerequisites and expectations from practice. In this paper, we perform an industrial case study at an automotive manufacturer to explore applicability and usability of prediction approaches in practice. In a first step, we collect the most relevant prediction approaches from literature, including both, approaches using statistics and machine learning. Furthermore, we elicit expectations towards predictions from practitioners using a survey and stakeholder workshops. At the same time, we measure software size of 48 software artifacts by mining four years of revision history, resulting in 4,547 data points. In the last step, we assess the applicability of state-of-the-art prediction approaches using the collected data by systematically analyzing how well they fulfill the practitioners' expectations. Our main contribution is a comparison of commonly used prediction approaches in a real world industrial setting while considering stakeholder expectations. We show that the approaches provide significantly different results regarding prediction accuracy and that the statistical approaches fit our data best

A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis.

The main aim of this paper is to build a Real Time Leading Economic Indicator (RT-LEI) that improves Composite Leading Indicators (CLI)’s performance to anticipate GDP trends and turning points for the Spanish economy. The indicator has been constructed using a Factor Analysis and is composed of 21 variables concerning motor vehicle activity, financial activity, real estate activity, economic sentiment, and industrial sector. The data sources used are Google Trends and Thomson Reuters Eikon-Datastream. This work contributes to the literature, studying the dynamics of GDP, CLI and RT-LEI using Fractional Cointegration VAR (FCVAR model) and Continuous Wavelet Transform (CWT) for its resolution. The results show that the model does not present mean reversion and it is expected the RT-LEI reveals a bear trend in the next two years, alike IMF and Consensus FUNCAS′ forecasts. The reasons are mostly associated with escalating global protectionism, uncertainty related to Catalonia and faster monetary policy normalization.pre-print990 K

Energy demand models for policy formulation : a comparative study of energy demand models

This paper critically reviews existing energy demand forecasting methodologies highlighting the methodological diversities and developments over the past four decades in order to investigate whether the existing energy demand models are appropriate for capturing the specific features of developing countries. The study finds that two types of approaches, econometric and end-use accounting, are used in the existing energy demand models. Although energy demand models have greatly evolved since the early 1970s, key issues such as the poor-rich and urban-rural divides, traditional energy resources, and differentiation between commercial and non-commercial energy commodities are often poorly reflected in these models. While the end-use energy accounting models with detailed sector representations produce more realistic projections compared with the econometric models, they still suffer from huge data deficiencies especially in developing countries. Development and maintenance of more detailed energy databases, further development of models to better reflect developing country context, and institutionalizing the modeling capacity in developing countries are the key requirements for energy demand modeling to deliver richer and more reliable input to policy formulation in developing countries.Energy Production and Transportation,Energy Demand,Environment and Energy Efficiency,Energy and Environment,Economic Theory&Research