Investment research forecasts of corporate financial indicators have a significant impact on capital markets and are an important decision support tools in financial sector. In contrast, previous research largely concludes that these forecasts are insufficiently accurate, and are not unbiased or rational. The findings are mostly based on composite market data for a single indicator and forecast horizon, mainly for the US market, and standard forecast error measures. In this study, instead of standard error measures, the concept of forecast linear trend accuracy is proposed as an acceptability criterion appropriate for long-term investors. The concepts of unbiasedness and rationality are considered according to the rational expectations theory. The existing idea of eliminating the estimated historical bias from future forecasts, in order to improve accuracy, is elaborated. The key terms are mathematically formalized, along with proofs of the required characterizations and properties, including the consistency of trend accuracy with certain standard error measures, as well as the hierarchical relation between trend accuracy, unbiasedness and rationality. The presence of hypothesised properties was tested on the most comprehensive sample of market consensus forecasts of European investment research to date, for 233 companies with the largest liquid market capitalizations, three main financial indicators and four forecast horizons, in the period 2005–2017. The results of tests, conducted at the level of individual companies, indicate that the prevailing share of forecasts exhibits accurate trend (64–67% in total), unbiasedness (75–77% in total) and weak rationality (67–71% in total). The bias elimination procedure is moderately successful for short-term horizons, where it reduces the average error MAPE by a total of 22%, with errors reduced in 63% of forecasts, while for long-term horizons the procedure is not successful. The explanation for the strong presence of desirable properties of accurate trend, unbiasedness and weak rationality in investment research forecasts is the predominant use of corporate financial models in the forecasting process, which are, in accordance with the complex systems theory, based on a large number of predictors and relations between variables as well as the expert judgment of analysts.