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We assess the accuracy of the popular trade classification rules—the tick (T), the reverse tick (RT), the quote (Q), the Lee and Ready (LR) and the Ellis, Michaely and O’Hara (EMO) rules—for the selected Central and Eastern Europe stock exchanges: the Warsaw Stock Exchange (WSE), the Prague Stock Exchange (PSE) and the Budapest Stock Exchange (BSE). We employ the transaction data on the most liquid stocks included in the large cap indices, namely WIG20, PX and BUX, from 9th of January till 14th of March, 2019, and discover the Q rule to be the most successful. It correctly classifies 100% of trades initiated both by the buyers and the sellers in the case of the BSE and 84.35% (82.78%) in the case of the WSE (the PSE). The results obtained for the LR rule are similarly good (96.32% for the BSE, 84.34% for the WSE and 82.86% for the PSE). The third one is the EMO rule with the rate of success of 83.69% for the BSE, 80.40% for the WSE and 67.86% for the PSE. The T and the RT rules are characterized by a considerably low level of accuracy (ranging from 19.91 to 40.65% for the T rule and from 13.04 to 38.37% for the RT rule). The modifications of the T and the RT rules that take into account the preceding and the following transaction price changes allow to obtain the distinctively higher estimates of accuracy statistics.

We use a VEC DCC M-GARCH model to investigate the daily co-movement of the Czech Republic, Hungary and Poland one-, five- and ten-year sovereign credit default swap (CDS) spreads in the period Jan 2009–May 2018. To control for a systemic risk stemming from the EU and other international markets, we nest the analysis within a four-variate system including the Germany CDS spread and the CBOE VIX. The latter serves us as a proxy for the exogenous driver of spreads. The analysis shows that the long-run dependence among the logs of CDS spreads is rare. It is only the Czech Republic and Germany one-year CDS spreads that exhibit a common stochastic trend. The remaining spreads do not co-integrate. Each country maturity time t log change in the spread depends upon that of time \(t - 1\) and earlier. The dynamics of spreads are country specific. The Hungary CDS spreads Granger cause almost all their counterparts. The causality running other way round is incidental. The median of pairwise conditional correlation estimates among the countries of interest differs across the maturities in the way indicating that the Czech, Hungarian and Polish markets are better integrated among one another than any single with the German market.

We estimate linear regressions with dummy variables for the rates of return, spreads and volumes of stocks included in the main Warsaw Stock Exchange index WIG 20 to reveal the intraday trading patterns after the Universal Trading Platform was introduced in April 2013. In doing so we use the data rounded to nearest second and aggregated into that of 1 h frequency. The analysis shows that the spreads and volumes exhibit either the day of the week or the hour of the day effect or both. The spreads resemble the reversed J and the volumes are U-shaped. The rates of return are mostly positive but eventually decline at the end of the trading day. Some of them exhibit the hour of the day but not the day of the week effect.

We examine the dividend smoothing behaviour of companies listed on the Istanbul Stock Exchange (ISE) in the period of 1996–2015. On the basis of Lintner’s dividend partial adjustment model, we calculate the speed of dividend adjustment (SOA) for individual companies from the research sample. As we find only 8% of the SOA results lower than 0.50 and almost 37% higher than 1.00, we ascertain that the Turkish stock market companies do not smooth the dividends. Nevertheless, we proceed to uncover the determinants of the level of the speed of adjustment coefficient. We find that the leverage, debt to equity ratio, current- and quick liquidity ratios, earnings retention ratio, payout and standard deviation of quick ratio statistically significantly affect the SOA level of individual companies in Turkey. In analysing the Istanbul Stock Exchange one should note that it has been operating in the current structure since April 2013 after the merger of all the exchanges functioning in the Turkish capital markets. Currently, the ISE is one of the largest regional stock exchanges, but despite its relatively huge liquidity, it is also risky because of the political pressure. In 2013–2016, the ISE greatly deteriorated in its capitalisation and turnover because of political events. These circumstances may have affected the obtained results.

The paper is devoted to the description of rates of return for stocks listed on the Warsaw Stock Exchange (WSE) basing on the information of order imbalance. The model employed in the research is a modified version of Fama and French (J Financ Econ 33(1):3–56, 1993) asset pricing model including additionally the ‘order imbalance factor’ built on the basis of the original imbalance indicators proposed by Nowak (Order imbalance on the Warsaw Stock Exchange, 2000–2012. Paper presented at the International Conference Financial Investments and Insurance – Global Trends and Polish Market, Wroclaw University of Economics, Wroclaw, 17–19 September 2014). The order imbalance is assumed as the temporary imbalance between buy and sell orders. Its estimation is preceded by an indication which side of the market was initiating the transaction, and a distinction between the so-called buyer- and seller-initiated trades [Lee and Ready (J Financ 46(2):73–746, 1991), Ellis, Michaely and O'Hara (J Financ Quant Anal 35(4):529–551, 2000)]. The imbalance indicators are calculated using the high frequency intraday data. The research hypothesis states that the proposed asset pricing model has good descriptive properties. The analysis is conducted for the selected stocks—index WIG20 constituents listed on the WSE over the period of 2000 to 2016. The model is validated using i.a. the underidentification test and the weak identification test [Kleibergen and Paap (J Econom 133(1):97–126, 2006)], overidentification test of all instruments [Hansen (Econometrica 50(4):1029–1054, 1982)] and endogeneity test of endogenous regressors.