scholarly journals How Complexity and Uncertainty Grew with Algorithmic Trading

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 499
Author(s):  
Martin Hilbert ◽  
David Darmon
Keyword(s):  
Machine Learning ◽  
Macro Level ◽  
Algorithmic Trading ◽  
Theoretic Analysis ◽  
Learning Paradigm ◽  
Information Theoretic ◽  
Fourth Digit ◽  
Local Uncertainty ◽  
Micro Level ◽  
Bid Ask Spreads

The machine-learning paradigm promises traders to reduce uncertainty through better predictions done by ever more complex algorithms. We ask about detectable results of both uncertainty and complexity at the aggregated market level. We analyzed almost one billion trades of eight currency pairs (2007–2017) and show that increased algorithmic trading is associated with more complex subsequences and more predictable structures in bid-ask spreads. However, algorithmic involvement is also associated with more future uncertainty, which seems contradictory, at first sight. On the micro-level, traders employ algorithms to reduce their local uncertainty by creating more complex algorithmic patterns. This entails more predictable structure and more complexity. On the macro-level, the increased overall complexity implies more combinatorial possibilities, and therefore, more uncertainty about the future. The chain rule of entropy reveals that uncertainty has been reduced when trading on the level of the fourth digit behind the dollar, while new uncertainty started to arise at the fifth digit behind the dollar (aka ‘pip-trading’). In short, our information theoretic analysis helps us to clarify that the seeming contradiction between decreased uncertainty on the micro-level and increased uncertainty on the macro-level is the result of the inherent relationship between complexity and uncertainty.

scholarly journals Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247997
Author(s):  
Stephanie Josephine Eder ◽  
David Steyrl ◽  
Michal Mikolaj Stefanczyk ◽  
Michał Pieniak ◽  
Judit Martínez Molina ◽  
...  
Keyword(s):  
Machine Learning ◽  
Environmental Factors ◽  
Psychological Factors ◽  
Perceived Health ◽  
Macro Level ◽  
Perceived Vulnerability ◽  
Viral Spread ◽  
Starting Point ◽  
Micro Level ◽  
Perceived Vulnerability To Disease

During medical pandemics, protective behaviors need to be motivated by effective communication, where finding predictors of fear and perceived health is of critical importance. The varying trajectories of the COVID-19 pandemic in different countries afford the opportunity to assess the unique influence of ‘macro-level’ environmental factors and ‘micro-level’ psychological variables on both fear and perceived health. Here, we investigate predictors of fear and perceived health using machine learning as lockdown restrictions in response to the COVID-19 pandemic were introduced in Austria, Spain, Poland and Czech Republic. Over a seven-week period, 533 participants completed weekly self-report surveys which measured the target variables subjective fear of the virus and perceived health, in addition to potential predictive variables related to psychological factors, social factors, perceived vulnerability to disease (PVD), and economic circumstances. Viral spread, mortality and governmental responses were further included in the analysis as potential environmental predictors. Results revealed that our models could accurately predict fear of the virus (accounting for approximately 23% of the variance) using predictive factors such as worrying about shortages in food supplies and perceived vulnerability to disease (PVD), where interestingly, environmental factors such as spread of the virus and governmental restrictions did not contribute to this prediction. Furthermore, our results revealed that perceived health could be predicted using PVD, physical exercise, attachment anxiety and age as input features, albeit with smaller effect sizes. Taken together, our results emphasize the importance of ‘micro-level’ psychological factors, as opposed to ‘macro-level’ environmental factors, when predicting fear and perceived health, and offer a starting point for more extensive research on the influences of pathogen threat and governmental restrictions on the psychology of fear and health.

scholarly journals INFORMATION-THEORETIC ANALYSIS OF STOCHASTIC VOLATILITY MODELS

2019 ◽  
Vol 22 (01) ◽  
pp. 1850025
Author(s):  
OLIVER PFANTE ◽  
NILS BERTSCHINGER
Keyword(s):  
Machine Learning ◽  
Mutual Information ◽  
Stochastic Volatility ◽  
Asset Prices ◽  
Theoretic Analysis ◽  
Stochastic Volatility Models ◽  
Information Theoretic ◽  
Random Dynamics ◽  
Volatility Models ◽  
Heston's Model

Stochastic volatility models describe asset prices [Formula: see text] as driven by an unobserved process capturing the random dynamics of volatility [Formula: see text]. We quantify how much information about [Formula: see text] can be inferred from asset prices [Formula: see text] in terms of Shannon’s mutual information in a twofold way: theoretically, by means of a thorough study of Heston’s model; from a machine learning perspective, by means of investigating a family of exponential Ornstein–Uhlenbeck (OU) processes fitted on S&P 500 data.

Varieties of non-obvious meaning in CL and CADS: from ‘hindsight post-dictability’ to sweet serendipity

Corpora ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. 339-367 ◽  
Author(s):  
Alan Partington
Keyword(s):  
Case Studies ◽  
Corpus Linguistics ◽  
Macro Level ◽  
Discourse Studies ◽  
Micro Level ◽  
Special Relevance

In this paper, I want to examine the special relevance of (non)obviousness in corpus linguistics through drawing on case studies. The research discussion is divided into two parts. The first is an examination of (non)obviousness at the micro-level, that is, in lexico-grammatical analyses, whilst the second looks at the more macro-level of (non)obviousness on the plane of discourse. In the final sections, I will examine various types of non-obvious meaning one can come across in Corpus-assisted Discourse Studies (CADS), which range from: ‘I knew that all along (now)’ to ‘that's interesting’ to ‘I sensed that but didn't know why’ (intuitive impressions and corpus-assisted explanations) to ‘I never even knew I never knew that’ (serendipity or ‘non-obvious non-obviousness’, analogous to ‘unknown unknowns’).

Panpsychism versus Panprotopsychism and the Subject-Summing Problem

2017 ◽  
Author(s):  
Philip Goff
Keyword(s):  
Macro Level ◽  
Human Beings ◽  
Making Sense ◽  
Combination Problem ◽  
The Subject ◽  
Micro Level ◽  
Russellian Monism ◽  
Nature Of Matter

This is the first of two chapters discussing the most notorious problem facing Russellian monism: the combination problem. This is actually a family of difficulties, each reflecting the challenge of how to make sense of everyday human and animal experience intelligibly arising from more fundamental conscious or protoconscious features of reality. Key challenges facing panpsychist and panpsychist forms of Russellian monism are considered. With respect to panprotopsychism, there is the worry that it collapses into noumenalism: the view that human beings, by their very nature, are unable to understand the concrete, categorical nature of matter. With respect to panpsychism, there is the subject-summing problem: the difficulty making sense of how micro-level conscious subjects combine to produce macro-level conscious subjects. A solution to the subject-summing problem is proposed, and it is ultimately argued that panpsychist forms of the Russellian monism are to be preferred on grounds of simplicity and elegance.

Information Theoretic Analysis and Performance Gains of 3-Color Shift Keying

2021 ◽  
pp. 1-1
Author(s):  
Alexandros E. Tzikas ◽  
Panagiotis D. Diamantoulakis ◽  
George K. Karagiannidis
Keyword(s):  
Theoretic Analysis ◽  
Information Theoretic ◽  
Color Shift ◽  
Shift Keying ◽  
And Performance ◽  
Performance Gains

scholarly journals Young Children’s Belonging in Finnish Educational Settings: an Intersectional Analysis

2021 ◽  
Author(s):  
Anna-Maija Puroila ◽  
Jaana Juutinen ◽  
Elina Viljamaa ◽  
Riikka Sirkko ◽  
Taina Kyrönlampi ◽  
...  
Keyword(s):  
Educational Policy ◽  
Educational Programs ◽  
Macro Level ◽  
Educational Settings ◽  
Policy Documents ◽  
Meso Level ◽  
Continuity And Change ◽  
Positional Power ◽  
Micro Level ◽  
Similarities And Differences

AbstractThe study draws on a relational and intersectional approach to young children’s belonging in Finnish educational settings. Belonging is conceptualized as a multilevel, dynamic, and relationally constructed phenomenon. The aim of the study is to explore how children’s belonging is shaped in the intersections between macro-, meso-, and micro-levels of young children’s education in Finland. The data consist of educational policy documents and ethnographic material generated in educational programs for children aged birth to 8 years. A situational mapping framework is used to analyze and interpret the data across and within systems levels (macro-level; meso-level; and micro-level). The findings show that the landscape in which children’s belonging is shaped and the intersections across and within the levels are characterized by the tensions between similarities and differences, majority and minorities, continuity and change, authority and agency. Language used, practices enacted, and positional power emerge as the (re)sources through which children’s (un)belonging is actively produced.

scholarly journals Judgement of valence of musical sounds by hand and by heart, a machine learning paradigm for reading the heart

Heliyon ◽  
2021 ◽  
pp. e07565
Author(s):  
Ennio Idrobo-Ávila ◽  
Humberto Loaiza-Correa ◽  
Flavio Muñoz-Bolaños ◽  
Leon van Noorden ◽  
Rubiel Vargas-Cañas
Keyword(s):  
Machine Learning ◽  
Learning Paradigm ◽  
Musical Sounds
Sign in / Sign up

Export Citation Format

Share Document