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The purpose of this paper is to evaluate and compare performances of three Nordic (Sweden, Denmark, Finland) and three Baltic (Lithuania, Latvia, Estonia) exchanges.

Portfolio performance is estimated using two different approaches: traditional measures – Sharpe, Sortino and Treynor ratios; and alternative measures – reward to value‐at‐risk and reward to expected tail loss (RETL).

The findings highlight the differences and similarities in Nordic and Baltic stock exchanges and their performance trends after creation of common marketplace OMX. Returns of Baltic, like Nordic, exchanges are normally distributed. During the period of 2000‐2006, Baltic exchanges outperformed Nordic exchanges.

The research is limited to six stock exchanges that are members of common marketplace OMX. Proposed alternative performance measures did not diverge from traditional approaches, because, apparently, Baltic exchanges offer normally distributed returns and should not be considered emerging markets. These measures should be further tested in developing and emerging markets.

The findings have both theoretical and practical implications. To the authors' best knowledge, it is the first public attempt to estimate performance of Baltic and Nordic exchanges in the context of modern portfolio theory and, alternatively, new science of risk management (value at risk and expected tail loss). The paper argues for the usage of an alternative measure for performance valuation – RETL. Furthermore, the paper discuses merits and limitations of different approaches to risk and performance measurement.

The purpose of this paper is to propose predictive models of speculative revaluation attacks, which would facilitate currency risk hedging in emerging and developed countries.

The purpose of this paper is achieved using the methodology of multiple triangulation. Paper combines different theoretical perspectives (three generations of speculative attack models), two sources of data (emerging countries and developed countries) and three methods (logit regression, probit regression and artificial neural networks, ANN) for identification of leading indicators and forecasting of speculative attacks. Combination of multiple observations (data), underlying theories and methods allowed achieving least biased results.

A list of leading indicators of speculative revaluation attacks was generated based on previous researches and three generations of speculative attacks' models. Qualitative and quantitative differences of speculative revaluation attacks in emerging and developed countries were identified. The decision matrix of currency risk hedging in the context of speculative devaluation and revaluation attacks was proposed.

Although the sample of this researcher includes a wide range of countries (65 in total), their separation into developed and emerging countries is arbitrary (in the course of 35 years some countries have changed the status from emerging towards developed). The initial list of leading indicators is limited, includes mostly economic variables. It could be improved by encompassing political variables, credit ratings, consumer and business confidence indices.

Developed predictive models of speculative revaluation attacks may significantly reduce important element of risk – uncertainty – and, consequently, the cost of financial hedging.

This paper is one of the first public attempts to apply alternative methodology of ANN for forecasting speculative attacks. The results showed that latter method is more accurate than probit and logit regressions. Also, to the author's best knowledge, this is a first public attempt to separately analyse the phenomenon of speculative revaluation attacks.