Data frequency and forecast performance for stock markets: A deep learning approach for DAX index

Due to non-stationary, high volatility, and complex nonlinear patterns of stock market fluctuation, it is demanding to predict the stock price accurately. Nowadays, hybrid and ensemble models based on machine learning and economics replicate several patterns learned from the time series. This paper analyses the SARIMAX models in a classical approach and using AutoML algorithms from the Darts library. Second, a deep learning procedure predicts the DAX index stock prices. In particular, LSTM (Long Short-Term Memory) and BiLSTM recurrent neural networks (with and without stacking), with optimised hyperparameters architecture by KerasTuner, in the context of different time-frequency data (with and without mixed frequencies) are implemented. Nowadays great interest in multi-step-ahead stock price index forecasting by using different time frequencies (daily, one-minute, five-minute, and tenminute granularity), focusing on raising intraday stock market prices. The results show that the BiLSTM model forecast outperforms the benchmark models –the random walk and SARIMAX - and slightly improves LSTM. More specifically, the average reduction error rate by BiLSTM is 14-17 per cent compared to SARIMAX. According to the scientific literature, we also obtained that high-frequency data improve the forecast accuracy by 3-4% compared with daily data since we have some insights about volatility driving forces.info:eu-repo/semantics/publishedVersio

k-core (bootstrap) percolation on complex networks: Critical phenomena and nonlocal effects

We develop the theory of the k-core (bootstrap) percolation on uncorrelated random networks with arbitrary degree distributions. We show that the k-core percolation is an unusual, hybrid phase transition with a jump emergence of the k-core as at a first order phase transition but also with a critical singularity as at a continuous transition. We describe the properties of the k-core, explain the meaning of the order parameter for the k-core percolation, and reveal the origin of the specific critical phenomena. We demonstrate that a so-called ``corona'' of the k-core plays a crucial role (corona is a subset of vertices in the k-core which have exactly k neighbors in the k-core). It turns out that the k-core percolation threshold is at the same time the percolation threshold of finite corona clusters. The mean separation of vertices in corona clusters plays the role of the correlation length and diverges at the critical point. We show that a random removal of even one vertex from the k-core may result in the collapse of a vast region of the k-core around the removed vertex. The mean size of this region diverges at the critical point. We find an exact mapping of the k-core percolation to a model of cooperative relaxation. This model undergoes critical relaxation with a divergent rate at some critical moment.Comment: 11 pages, 8 figure

k-core organization of complex networks

We analytically describe the architecture of randomly damaged uncorrelated networks as a set of successively enclosed substructures -- k-cores. The k-core is the largest subgraph where vertices have at least k interconnections. We find the structure of k-cores, their sizes, and their birth points -- the bootstrap percolation thresholds. We show that in networks with a finite mean number z_2 of the second-nearest neighbors, the emergence of a k-core is a hybrid phase transition. In contrast, if z_2 diverges, the networks contain an infinite sequence of k-cores which are ultra-robust against random damage.Comment: 5 pages, 3 figure

Controlling the uncontrolled: Are there incidental experimenter effects on physiologic responding?

The degree to which experimenters shape participant behavior has long been of interest in experimental social science research. Here, we extend this question to the domain of peripheral psychophysiology, where experimenters often have direct, physical contact with participants, yet researchers do not consistently test for their influence. We describe analytic tools for examining experimenter effects in peripheral physiology. Using these tools, we investigate nine data sets totaling 1,341 participants and 160 experimenters across different roles (e.g., lead research assistants, evaluators, confederates) to demonstrate how researchers can test for experimenter effects in participant autonomic nervous system activity during baseline recordings and reactivity to study tasks. Our results showed (a) little to no significant variance in participants' physiological reactivity due to their experimenters, and (b) little to no evidence that three characteristics of experimenters that are well known to shape interpersonal interactions-status (using five studies with 682 total participants), gender (using two studies with 359 total participants), and race (in two studies with 554 total participants)-influenced participants' physiology. We highlight several reasons that experimenter effects in physiological data are still cause for concern, including the fact that experimenters in these studies were already restricted on a number of characteristics (e.g., age, education). We present recommendations for examining and reducing experimenter effects in physiological data and discuss implications for replication

Bootstrap Percolation on Complex Networks

We consider bootstrap percolation on uncorrelated complex networks. We obtain the phase diagram for this process with respect to two parameters: $f$, the fraction of vertices initially activated, and $p$, the fraction of undamaged vertices in the graph. We observe two transitions: the giant active component appears continuously at a first threshold. There may also be a second, discontinuous, hybrid transition at a higher threshold. Avalanches of activations increase in size as this second critical point is approached, finally diverging at this threshold. We describe the existence of a special critical point at which this second transition first appears. In networks with degree distributions whose second moment diverges (but whose first moment does not), we find a qualitatively different behavior. In this case the giant active component appears for any $f>0$ and $p>0$, and the discontinuous transition is absent. This means that the giant active component is robust to damage, and also is very easily activated. We also formulate a generalized bootstrap process in which each vertex can have an arbitrary threshold.Comment: 9 pages, 3 figure

The Near Infrared NaI Doublet Feature in M Stars

The NaI near-infrared doublet has been used to indicate the dwarf/giant population in composite systems, but its interpretation is still a contentious issue. In order to understand the behaviour of this controversial feature, we study the observed and synthetic spectra of cool stars. We conclude that the NaI infrared feature can be used as a dwarf/giant discriminator. We propose a modified definition of the NaI index by locating the red continuum at 8234 angstrons and by measuring the equivalent width in the range 8172-8197 angstrons, avoiding the region at lambda > 8197 angstrons, which contains VI, ZrI, FeI and TiO lines. We also study the dependence of this feature on stellar atmospheric parameters.Comment: 9 pages, (TeX file) + 7 Figures in Postscript format. Accepted for publication in The Astrophysical Journa