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In this paper, we introduce a completely new and unique historical dataset of Belgian stock returns during the nineteenth and the beginning of the twentieth century. This high-quality database comprises stock price and company related information on more than 1500 companies. Given the extensive use of CRSP return data and the data mining risks involved it provides an interesting out-of-sample dataset with which to test the robustness of ‘prevailing’ asset pricing anomalies. We re-examine mean reversals in long-horizon returns using the framework of Hodrick (1992) and Jegadeesh (1991). Our simulation experiments demonstrate that it has considerably better small sample properties than the traditional regression framework of Fama and French (1988a). In the short run, returns exhibit strong persistence, which is partially induced by infrequent trading. Contrary to Fama and French (1988a) and Poterba and Summers (1988), our results suggest that, in the long run, there is little to no evidence of stock prices containing autoregressive stationary components but instead resemble a random walk. Capital appreciation returns exhibit stronger time-varying behavior than total returns. Belgian stock returns demonstrate significant seasonality in January notwithstanding the absence of taxes. In addition, in contrast to other months, January months do show some evidence of mean reversion. |
