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Abstract Previous studies suggest that individuals sharing similar characteristics establish stronger social relationships. This motivates us to examine what combinations of socioeconomic investor attributes are more likely to be associated with joint trading behavior. We use a unique data set on actual social ties between investors and find that similarities in investors’ age, geographical location, or length of the co-employment can affect trade synchronization under certain circumstances. Our findings have implications for the analysis of investor networks.

Abstract In recent years there is a data surge of industrial and business data. This posses opportunities and challenges at the same time because the wealth of information is usually buried in complex and frequently disconnected data sets. Predictive maintenance utilizes such data for developing prognostic and diagnostic models that allow the optimization of the life cycle of machine components. In this paper, we address the modeling of the prognostics of machine components from mobile work equipment. Specifically, we are estimating survival curves and hazard rates using parametric and non-parametric models to characterize time dependent failure probabilities of machine components. As a result, we find the presence of different types of censoring masking the presence of different populations that can cause severe problems for statistical estimators and the interpretations of results. Furthermore, we show that the obtained hazard functions for different machine components are complex and versatile and are best modeled via non-parametric estimators. However, notable exceptions for individual machine components can be found amenable for a Generalized-gamma and Weibull model.

Forecasting the movements of stock prices is one of the most challenging problems in financial markets analysis. In this paper, we use machine learning (ML) algorithms for the prediction of future price movements using limit order book data. Two different sets of features are combined and evaluated: handcrafted features based on the raw order book data and features extracted by the ML algorithms, resulting in feature vectors with highly variant dimensionalities. Three classifiers are evaluated using combinations of these sets of features on two different evaluation setups and three prediction scenarios. Even though the large scale and high frequency nature of the limit order book poses several challenges, the scope of the conducted experiments and the significance of the experimental results indicate that the ML highly befits this task carving the path towards future research in this field.

Mid-price movement prediction based on the limit order book data is a challenging task due to the complexity and dynamics of the limit order book. So far, there have been very limited attempts for extracting relevant features based on the limit order book data. In this paper, we address this problem by designing a new set of handcrafted features and performing an extensive experimental evaluation on both liquid and illiquid stocks. More specifically, we present an extensive set of econometric features that capture the statistical properties of the underlying securities for the task of mid-price prediction. The experimental evaluation consists of a head-to-head comparison with other handcrafted features from the literature and with features extracted from a long short-term memory autoencoder by means of a fully automated process. Moreover, we develop a new experimental protocol for online learning that treats the task above as a multi-objective optimization problem and predicts: 1) the direction of the next price movement and; 2) the number of order book events that occur until the change takes place. In order to predict the mid-price movement, features are fed into nine different deep learning models based on multi-layer perceptrons, convolutional neural networks, and long short-term memory neural networks. The performance of the proposed method is then evaluated on liquid and illiquid stocks (i.e., TotalView-ITCH US and Nordic stocks). For some stocks, results suggest that the correct choice of a feature set and a model can lead to the successful prediction of how long it takes to have a stock price movement.

Stock price prediction is a challenging task, in which machine learning methods have recently been successfully used. In this paper, we extract over 270 hand-crafted features (factors) inspired by technical indicators and quantitative analysis and test their validity on short-term mid-price movement prediction for Nordic TotalView-ITCH stocks. The suggested feature list represents one of the most extensive studies in the field of financial feature engineering. We focus on a wrapper feature selection method using entropy, least-mean squares, and linear discriminant analysis. We also introduce a novel quantitative feature based on adaptive logistic regression for online learning. The proposed feature is consistently selected as the first feature among a large number of indicators used in this study. We further examine the best combinations of features using a high-frequency limit order book Nordic database. Our results suggest that sorting methods and classifiers can be used in such a way that one can reach the best classification performance with a combination of only a few advanced hand-crafted features.

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.

We identify temporal investor networks for Nokia stock by constructing networks from correlations between investor-specific net-volumes and analyze changes in the networks around dot-com bubble. The analysis is conducted separately for households, financial, and non-financial institutions. Our results indicate that spanning tree measures for households reflected the boom and crisis: the maximum spanning tree measures had a clear upward tendency in the bull markets when the bubble was building up, and, even more importantly, the minimum spanning tree measures pre-reacted the burst of the bubble. At the same time, we find less clear reactions in the minimal and maximal spanning trees of non-financial and financial institutions around the bubble, which suggests that household investors can have a greater herding tendency around bubbles.