Rule Britannia!: British Stock Market Returns, 1825-1870

This article presents a new series of monthly equity returns for the British stock market for the period 1825-1870. In addition to calculating capital appreciation and dividend yields, the article also estimates the effect of survivorship bias on returns. Three notable findings emerge from this study. First, stock market returns in the 1825-1870 period are broadly similar for Britain and the United States, although the British market is less risky. Second, real returns in the 1825-1870 period are higher than in subsequent epochs of British history. Third, unlike the modern era, dividends are the most important component of returns.

The rate of return on equity across industrial sectors on the British stock market, 1825-70

This article assesses the contribution of the various industrial sectors to the growth of the British equity market in the 1825–70 period. It also provides estimates of the rates of return on these industrial sectors in this period. The article then proceeds to examine whether differences in rates of return across the various sectors can be explained by risk or other financial factors. One of the main findings is that the relatively high rates of return in the banking, insurance, and miscellaneous sectors appear to be in some measure explained by the presence of extended liability and uncalled capital.

Image segmentation based on multi-scan constraint satisfaction neural network

A novel image segmentation method based on a constraint satisfaction neural network (CSNN) is presented. The new method uses CSNN-based relaxation but with a modified scanning scheme of the image. The pixels are visited with more distant intervals and wider neighborhoods in the first level of the algorithm. The intervals between pixels and their neighborhoods are reduced in the following stages of the algorithm. This method contributes to the formation of more regular segments rapidly and consistently. A cluster validity index to determine the number of segments is also added to complete the proposed method into a fully automatic unsupervised segmentation scheme. The results are compared quantitatively by means of a novel segmentation evaluation criterion. The results are promising.