Graph and download economic data for Stock Market Capitalization to GDP for Russian Federation (DDDM01RUA156NWDB) from 2009 to 2020 about market cap, Russia, stock market, capital, and GDP.

Graph and download economic data for Financial Market: Share Prices for Russia (SPASTT01RUQ661N) from Q4 1997 to Q3 2025 about Russia and stock market.

Graph and download economic data for Financial Market: Share Prices for Russia (SPASTT01RUM661N) from Sep 1997 to Oct 2025 about Russia and stock market.

Stock market turnover ratio (%) in Russia was reported at 39.81 % in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Russia - Stock market turnover ratio - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

The paper examines the underpricing of initial public offerings of ordinary shares of Russian companies. It is based on the data of 102 IPOs of Russian issuers for 2002-2024. The econometric study using multiple regression models has shown that factors such as the sale of own shares by initial shareholders, high-tech nature of the company's activities, the period of a “hot” market, and the deviation of the actual share placement price from the expected one, increase the underpricing of the IPO. At the same time, it was found that the amount of capital raised, the prestige of the investment bank-underwriter and the auditor have a significant negative effect on IPO underpricing. The results of the study may be useful to investors when making decisions about investing in IPOs, to regulators when developing regulations, and to issuing companies when forming an IPO strategy.

We provide data and code to replicate the results presented in "ESG Performance and Stock Market Responses to Geopolitical Turmoil: evidence from the Russia-Ukraine War" (Boccaletti, Maranzano, Morelli & Ossola, 2025). The subfolders allow replicating the following: 1. Folder "Event Study - Synthetic" replicates the event study from Section 4 2. Folder "Regressions replication - Table 5 and Table 6" replicates the regression analysis from Section 5. For each subfolder a README file is provided. It contains information about the reproduction steps.

• VERSION 4 UPDATES * Compared to Version 3, Version 4 of the folder includes more detailed descriptions of the README files and methodological steps. Additionally, it includes the event study's aggregate results.

This dataset was used for training and evaluating the RNN-based autoencoder model. (CSV)

Details of the 24 countries and their stock indices.

In this study, we present a novel approach to analyzing financial crises of the global stock market by leveraging a modified Autoencoder model based on Recurrent Neural Network (RNN-AE). We analyze time series data from 24 global stock markets between 2007 and 2024, covering multiple financial crises, including the Global Financial Crisis (GFC), the European Sovereign Debt Crisis (ESD), and the COVID-19 pandemic. By training the RNN-AE with normalized stock returns, we derive correlations embedded in the model’s weight matrices. To explore the network structure, we construct threshold networks based on the middle-layer weights for each year and examine key topological metrics, such as entropy, average clustering coefficient, and average shortest path length, providing new insights into the dynamic evolution of global stock market interconnections. Our method effectively captures the major financial crises. Our analysis indicates that interactions among American indices were significantly higher during the GFC in 2008 and the COVID-19 pandemic in 2020. In contrast, interactions among European indices were more prominent during the 2022 Russia-Ukraine conflict. In examining net inter-continental interactions, the influence was stronger between Europe and America during the GFC and the ESD crisis while, the influence between America and Asia was more powerful during the COVID-19 pandemic. Finally, we determine the structural entropy of the constructed networks, which effectively monitors the states of the market. Overall, our RNN-AE based network construction method provides valuable insights into market dynamic and uncovering financial crises, offering a powerful tool for investors and policymakers.

and represent the interactions inside and between the continents (Asia-Pacific, Europe-Africa and America), respectively, for four major financial crises particularly in 2008, 2011, 2020, and 2022.