Abstract

This study examines the impact of disaggregated earnings on stock prices for listed shipping firms. It shows that operating and non-operating income from ships sales have higher power in explaining stock prices than operating earnings only as key information for future profitability, investment opportunities and firm valuation. The testing period is from 2000 to 2008. The methodologies are those of panel cointegration and panel causality tests. The empirical findings show that both types of earnings are positively related to stock prices, contrary to the belief that the non-operating component contains little information. The implications are very crucial, since managers may manipulate annual earnings by these non-operating activities.

Highlights

  • Operating income is considered one of the most important components of earnings, since it reflects the performance of a firm associated with its operational characteristics

  • Model 2: SPt = b0 + b1 OEt + b2 SHIPSNOEt + u2t where SP is the variable of stock prices, OE is the variable of operating income, SHIPSNOE is the variable of non-operating income associated with ship sales, while u’s are random variables, i.e. white noises with N(0, σ2)

  • With respect to Model 1, the results display that 1 percent increase in operating income leads to 0.50 percent in stock prices, while with respect to Model 2, the results indicate that a 1 percent increase in operating income increases stock prices by 0.45 percent

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Summary

Introduction

Operating income is considered one of the most important components of earnings (the other is operating cash flows), since it reflects the performance of a firm associated with its operational characteristics. Previous studies examined the association of total accounting earnings and financial aggregates, such as stock prices or returns (Ou and Penman, 1989; Lev and Thiagarajan, 1993; Fairfield et al, 1996; Dechow et al, 1999; Lee, 1999; Abarbanell and Bernard, 2000). These studies document that accounting earnings affect stock prices via the quality of information they reveal about the future of the firm.

Shipping Industry
Methodology
Dynamic Heterogeneity
Panel Unit Root Tests
Panel Cointegration Tests
Error Correction Estimates and Causality
Robustness Tests
Findings
Concluding Remarks and Implications
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