Machine Learning the Cryptocurrency Market

Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for $1,681$ cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that non-trivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market

Mapping the NFT revolution: market trends, trade networks and visual features

Non Fungible Tokens (NFTs) are digital assets that represent objects like art, videos, in-game items and music. They are traded online, often with cryptocurrency, and they are generally encoded as smart contracts on a blockchain. Media and public attention towards NFTs has exploded in 2021, when the NFT art market has experienced record sales while celebrated new star artists. However, little is known about the overall structure and evolution of the NFT market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs generating a total trading volume of 935 millions US dollars. Our data are obtained primarily from the Ethereum and WAX blockchains and cover the period between June 23, 2017 and April 27, 2021. First, we characterize the statistical properties of the market. Second, we build the network of interactions and show that traders have bursts of activity followed by inactive periods, and typically specialize on NFTs associated to similar objects. Third, we cluster objects associated to NFTs according to their visual features and show that NFTs within the same category tend to be visually homogeneous. Finally, we investigate the predictability of NFT sales. We use simple machine learning algorithms and find that prices can be best predicted by the sale history of the NFT collection, but also by some features describing the properties of the associated object (e.g., visual features of digital images). We anticipate that our analysis will be of interest to both researchers and practitioners and will spark further research on the NFT production, adoption and trading in different contexts

An Intelligent Safety System for Human-Centered Semi-Autonomous Vehicles

Nowadays, automobile manufacturers make efforts to develop ways to make cars fully safe. Monitoring driver's actions by computer vision techniques to detect driving mistakes in real-time and then planning for autonomous driving to avoid vehicle collisions is one of the most important issues that has been investigated in the machine vision and Intelligent Transportation Systems (ITS). The main goal of this study is to prevent accidents caused by fatigue, drowsiness, and driver distraction. To avoid these incidents, this paper proposes an integrated safety system that continuously monitors the driver's attention and vehicle surroundings, and finally decides whether the actual steering control status is safe or not. For this purpose, we equipped an ordinary car called FARAZ with a vision system consisting of four mounted cameras along with a universal car tool for communicating with surrounding factory-installed sensors and other car systems, and sending commands to actuators. The proposed system leverages a scene understanding pipeline using deep convolutional encoder-decoder networks and a driver state detection pipeline. We have been identifying and assessing domestic capabilities for the development of technologies specifically of the ordinary vehicles in order to manufacture smart cars and eke providing an intelligent system to increase safety and to assist the driver in various conditions/situations.Comment: 15 pages and 5 figures, Submitted to the international conference on Contemporary issues in Data Science (CiDaS 2019), Learn more about this project at https://iasbs.ac.ir/~ansari/fara

Heterogeneous rarity patterns drive price dynamics in NFT collections

We quantify Non Fungible Token (NFT) rarity and investigate how it impacts market behaviour by analysing a dataset of 3.7M transactions collected between January 2018 and June 2022, involving 1.4M NFTs distributed across 410 collections. First, we consider the rarity of an NFT based on the set of human-readable attributes it possesses and show that most collections present heterogeneous rarity patterns, with few rare NFTs and a large number of more common ones. Then, we analyze market performance and show that, on average, rarer NFTs: (i) sell for higher prices, (ii) are traded less frequently, (iii) guarantee higher returns on investment, and (iv) are less risky, i.e., less prone to yield negative returns. We anticipate that these findings will be of interest to researchers as well as NFT creators, collectors, and traders

From Reddit to Wall Street: the role of committed minorities in financial collective action

In January 2021, retail investors coordinated on Reddit to target short-selling activity by hedge funds on GameStop shares, causing a surge in the share price and triggering significant losses for the funds involved. Such an effective collective action was unprecedented in finance, and its dynamics remain unclear. Here, we analyse Reddit and financial data and rationalize the events based on recent findings describing how a small fraction of committed individuals may trigger behavioural cascades. First, we operationalize the concept of individual commitment in financial discussions. Second, we show that the increase of commitment within Reddit pre-dated the initial surge in price. Third, we reveal that initial committed users occupied a central position in the network of Reddit conversations. Finally, we show that the social identity of the broader Reddit community grew as the collective action unfolded. These findings shed light on financial collective action, as several observers anticipate it will grow in importance

Multi-scale spatio-temporal analysis of human mobility

The recent availability of digital traces generated by phone calls and online logins has significantly increased the scientific understanding of human mobility. Until now, however, limited data resolution and coverage have hindered a coherent description of human displacements across different spatial and temporal scales. Here, we characterise mobility behaviour across several orders of magnitude by analysing ∼850 individuals' digital traces sampled every ∼16 seconds for 25 months with ∼10 meters spatial resolution. We show that the distributions of distances and waiting times between consecutive locations are best described by log-normal and gamma distributions, respectively, and that natural time-scales emerge from the regularity of human mobility. We point out that log-normal distributions also characterise the patterns of discovery of new places, implying that they are not a simple consequence of the routine of modern life

Evidence for a Conserved Quantity in Human Mobility

Recent seminal works on human mobility have shown that individuals constantly exploit a small set of repeatedly visited locations. A concurrent study has emphasized the explorative nature of human behaviour, showing that the number of visited places grows steadily over time. How to reconcile these seemingly contradicting facts remains an open question. Here, we analyse high-resolution multi-year traces of ~40,000 individuals from 4 datasets and show that this tension vanishes when the long-term evolution of mobility patterns is considered. We reveal that mobility patterns evolve significantly yet smoothly, and that the number of familiar locations an individual visits at any point is a conserved quantity with a typical size of ~25. We use this finding to improve state-of-the-art modelling of human mobility. Furthermore, shifting the attention from aggregated quantities to individual behaviour, we show that the size of an individual’s set of preferred locations correlates with their number of social interactions. This result suggests a connection between the conserved quantity we identify, which as we show cannot be understood purely on the basis of time constraints, and the ‘Dunbar number’ describing a cognitive upper limit to an individual’s number of social relations. We anticipate that our work will spark further research linking the study of human mobility and the cognitive and behavioural sciences

The effects of local and global link creation mechanisms on contagion processes unfolding on time-varying networks

Social closeness and popularity are key ingredients that shape the emergence and evolution of social connections over time. Social closeness captures local reinforcement mechanisms which are behind the formation of strong ties and communities. Popularity, on the other hand, describes global link formation dynamics which drive, among other things, hubs, weak ties and bridges between groups. In this chapter, we characterize how these mechanisms affect spreading processes taking place on time-varying networks. We study contagion phenomena unfolding on a family of artificial temporal networks. In particular, we revise four different variations of activity-driven networks that capture i) heterogeneity of activation patterns ii) popularity iii) the emergence of strong and weak ties iv) community structure. By means of analytical and numerical analyses we uncover a rich and process dependent phenomenology where the interplay between spreading phenomena and link formation mechanisms might either speed up or slow down the spreadin

Exposure to urban and rural contexts shapes smartphone usage behavior

Smartphones have profoundly changed human life. Nevertheless, the factors that shape how we use our smartphones remain unclear, in part due to limited availability of usage-data. Here, we investigate the impact of a key environmental factor: users' exposure to urban and rural contexts. Our analysis is based on a global dataset describing mobile app usage and location for similar to 500,000 individuals. We uncover strong and nontrivial patterns. First, we confirm that rural users tend to spend less time on their phone than their urban counterparts. We find, however, that individuals in rural areas tend to use their smartphones for activities such as gaming and social media. In cities, individuals preferentially use their phone for activities such as navigation and business. Are these effects (1) driven by differences between individuals who choose to live in urban vs. rural environments or do they (2) emerge because the environment itself affects online behavior? Using a quasi-experimental design based on individuals that move from the city to the countryside-or vice versa-we confirm hypothesis (2) and find that smartphone use changes according to users's environment. This work presents a quantitative step forward towards understanding how the interplay between environment and smartphones impacts human lives. As such, our findings could provide information to better regulate persuasive technologies embedded in smartphone apps. Further, our work opens the door to understanding new mechanisms leading to urban/rural divides in political and socioeconomic attitudes

A Non-Optimization-Based Dynamic Path Planning for Autonomous Obstacle Avoidance

This paper presents a non-optimization-based frame- work for path planning and tracking for evasive maneuvers in autonomous cars. The framework exploits a two-layer approach where a path planner generates a reference trajectory that is then tracked by a path tracking controller. A nested curvature preview controller implements path tracking. In the paper, we show how to describe the closed-loop performance of the controller. The quantification of the closed-loop performance in the frequency domain guides the generation of the evasive path. In this way, the algorithm generates a path that avoids the obstacle (if possible) accounting for both static and dynamic constraints. The proposed framework thus provides a non-optimization-based way to integrate the characteristics of the path tracker in the path planner algorithm, thus avoiding the need to define cost functions and use third party optimizers. The paper validates the proposed evasive maneuver strategy in simulation and on an instrumented vehicle. First, we test the trajectory tracker, showing that it tracks aggressive trajectories (with lateral acceleration close to 1 g) with an error smaller than 30 cm. Subsequently, we integrate the curvature preview with the path generator and show the joint generation-tracking performance in two different scenarios