| Abstract : | The purpose of this paper is to investigate and present the impact of shipping market determinants on the stock prices of shipping companies. After collecting data for an 8-year period between August 2014 and September 2021, we are going to examine whether there is any kind of relation between stock prices returns with shipping freight rates, oil prices, vessels’ values as well as other economic and financial indicators, to provide further information to the existing bibliography of shipping stock prices forecasting as there is no other paper in the literature examining this impact simultaneously. Our findings indicated that Baltic Dirty Tanker Index (BDTI) and Baltic Clean Tanker Index (BCTI) have a positive correlation with the average share price of the 15 tanker companies that comprise our sample of companies however is rather mediocre both in levels and returns of the variables. In addition, BDTI’s and BCTI’s p-values showed statistical insignificance in regard to the Ordinary Least Squares (OLS) output where the returns of the data were regressed as well as the freight indices do not Granger cause the share prices neither in terms of the returns nor in terms of their volatilities. As for the tankers’ secondhand market and its effect on tankers’ companies share price, the correlation matrix reported a relatively positive correlation (0.55) in terms of levels however their correlation was very low in terms of their returns. Regarding OLS, secondhand market proved to be statistically insignificant in explaining shares’ prices. Furthermore, there is not pairwise causality between the two variables neither in terms of their returns nor in terms of their volatilities. Last but not least, oil price and shares’ price had a very low correlation both in their levels and returns. Although, as OLS output reports, Brent’s price is statistically significant at 90% significance level in explaining tanker companies’ share price and Brent’s price volatilities cause tanker companies’ share price volatilities. Concluding, the OLS regression model was statistically significant with high explanatory power (0.70) as well as all the selected independent variables jointly seem to cause shares’ prices at 90% significance level.
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