In this paper, we provide evidence on the role of conventional monetary policy in the dynamics of stock market bubbles. We analyze the response of stock market returns to monetary policy shocks but condition the analysis on both the direction of monetary policy surprises and business conditions. Following a two-step approach, we first use a structural vector autoregressive (SVAR) model to identify a proxy variable of monetary policy shocks, and then we apply a conditional regression to contemporary stock market returns and these monetary policy shocks to extract the implicit relationship between these variables in different scenarios. Our results show that monetary policy does not impact on stock market returns in a significant form in the scenario defined by positive shocks and expansion periods, i.e. the lower effectiveness of restrictive monetary policy shocks coincides with the phase of the business cycle in which bubbles arise.

Purpose: – In this paper, the authors aim to analyze the impact of International Financial Reporting Standards' (IFRS) mandatory adoption on the financial statements of Spanish listed companies.Design/methodology/approach: – The authors estimate a panel data model by generalized least squares' within-between in order to contrast the possible structural breaks in the relations between income statement items and balance sheet items, using data from the 35 largest listed companies.Findings: – The results show significant changes on these relations, but with different signs and degrees of intensity depending on the balance sheet item analyzed.Research limitations/implications: – The data choice introduces a size bias that could be taken into account in the generalization of the results to other listed companies.Originality/value: – This work is developed using a mandatory, local, accounting and panel data framework for first time using Spanish listed companies in order to measure the impact of the IFRS adoption.

In this paper, we test whether the short-run econometric conditions for the basic assumptions of the Ohlson valuation model hold, and then we relate these results with the fulfillment of the short-run econometric conditions for this model to be effective. Better future modeling motivated us to analyze to what extent the assumptions involved in this seminal model are not good enough approximations to solve the firm valuation problem, causing poor model performance. The model is based on the well-known dividend discount model and the residual income valuation model, and it adds a linear information model, which is a time series model by nature. Therefore, we adopt the time series approach. In the presence of non-stationary variables, we focus our research on US-listed firms for which more than forty years of data with the required cointegration properties to use error correction models are available. The results show that the clean surplus relation assumption has no impact on model performance, while the unbiased accounting property assumption has an important effect on it. The results also emphasize the uselessness of forcing valuation models to match the value displacement property of dividends.