Structure of Investor Networks and Financial Crises

In this paper, we ask whether the structure of investor networks, estimated using shareholder registration data, is abnormal during a financial crises. We answer this question by analyzing the structure of investor networks through several most prominent global network features. The networks are estimated from data on marketplace transactions of all publicly traded securities executed in the Helsinki Stock Exchange by Finnish stock shareholders between 1995 and 2016. We observe that most of the feature distributions were abnormal during the 2008–2009 financial crisis, with statistical significance. This paper provides evidence that the financial crisis was associated with a structural change in investors’ trade time synchronization. This indicates that the way how investors use their private information channels changes depending on the market conditions.

Helsinki Stock Exchange Index during World War One: Statistical Test of Hypotheses on the Basis of Counter-Factual Modeling

The article proposes an improved model of St. Petersburg Stock Exchange index dynamics and constructs a similar model of Helsinki Stock Exchange index on the basis of published results of a counterfactual model predicting the hypothetical dynamics of St. Petersburg Stock Exchange index after July 1914 under the assumption that there is no war. The author hypothesizes that internal economic factors that determined the downward trend of St. Petersburg Stock Exchange index also influenced the dynamics of Helsinki Stock Exchange index under the assumption that there was no war. To test this hypothesis the author has constructed (in the R software environment) the ARIMA statistical model that is an integrated autoregressive-moving average model which extends the ARMA model for non-stationary time series. The constructed counterfactual models proved that while the influence of pre-war factors remained, the dynamics of both indices did not show similar trends thus suggesting that the Finnish stock market was developing without any noticeable look at St. Petersburg Stock Exchange and inner economic factors of the Russian Empire.

Clusters of investors around initial public offering

The complex networks approach has been gaining popularity in analysing investor behaviour and stock markets, but within this approach, initial public offerings (IPOs) have barely been explored. We fill this gap in the literature by analysing investor clusters in the first two years after the IPO filing in the Helsinki Stock Exchange by using a statistically validated network method to infer investor links based on the co-occurrences of investors’ trade timing for 69 IPO stocks. Our findings show that a rather large part of statistically similar network structures form in different securities and persist in time for mature and IPO companies. We also find evidence of institutional herding.

News Sentiment Incorporation in Real-Time Trading

This article describes how machines are the new breed of traders as news sentiment arrivals drive the stock price change. Strategies are the technical approach to search for profit from event-based speculations. This paper revisits these topics in a novel way and first uncovers distinctive characteristics of high frequency trading in Helsinki stock exchange insinuating the impression on positive recovers of event trading. Here is a better prediction by the incorporation of news on returns that proposed event trading strategy has significant effects on Finnish stock. This article contributes to the con temporarily embarked, upgrading form of practical paperwork on the take of news events in high economic science.

Trading, Profits, and Volatility in a Dynamic Information Network Model

We introduce a dynamic noisy rational expectations model in which information diffuses through a general network of agents. In equilibrium, agents who are more closely connected have more similar period-by-period trades, and an agent’s profitability is determined by a centrality measure that is related to Katz centrality. Volatility after an information shock is more persistent in less central networks, and volatility and trading volume are also influenced by the network’s asymmetry and irregularity. Using account-level data of all portfolio holdings and trades on the Helsinki Stock Exchange between 1997 and 2003, we find support for the aggregate predictions, altogether suggesting that the market’s network structure is important for these dynamics.