Another specification of Ohlson\u27s \u27other information\u27 term for the earnings/returns association: Theory and some evidence

Return-on-equity (ROE) is the correct profit metric to evaluate the performance of a business. However, the primary emphasis on financial ratio analysis must be on operating performance. The advanced version of the DuPont model remedies the original model\u27s failure to cleanly separate the effects of operating and financing decisions. It introduces the concept of return on net operating assets (RNOA) as the core measure of operating performance and clearly separates the effects of leverage and operating decisions. The advanced model does not change the result of the ROE calculation. However, the elements underlying the ROE ratio are different and provide a clean separation of operating and financing decisions. RNOA is effectively insulated from financing decisions. Changing the amount of debt does not affect the operating assets or the profit before interest expense and, therefore, does not affect RNOA. It also permits straightforward computation of the impact on ROE of alternative financing decisions. Changes in the interest rate affect the spread, while changes in the amount of debt affect financial leverage in a transparent manner

The persistence of the small firm/January effect: Is it consistent with investors\u27 learning and arbitrage efforts?

Monte Carlo simulation is a powerful spreadsheet-based tool that allows managers to better understand and visualize risk and uncertainty in discounted cash flow (DCF) analysis. The primary output, a histogram of net present values (NPV), maps the entire distribution of possible outcomes as a bell-shaped curve and therefore estimates the probability of success for the project (e.g., NPV \u3e zero). Although we use fictional names, we illustrate a real capital budgeting problem using Monte Carlo simulation to demonstrate how employing this tool can result in more-informed decision making

The persistence of the small firm/January effect: Is it consistent with investors' learning and arbitrage efforts?

Using improved methodology and an expanded research design, we examine whether the small firm/January effect (Keim, D. B. (1983). Size-related anomalies and stock return seasonality: further empirical evidence. Journal of Financial Economics 12:13-32), is declining over time due to market efficiency. First, we find that January returns are smaller after 1963-1979, but have simply reverted to levels that existed before that time. Second, we show that the January effect is not limited to mature markets but also appears in firms trading on the relatively new NASDAQ exchange in the 1970s. Third, trading volume for small firms in December and January is not different from other months, implying that traders are not actively arbitraging the anomaly. Together, our results suggest that this anomaly continues to defy rational explanation in an efficient market.January effect Market efficiency Arbitrage Trading volume