Quantifying the relationship between financial news and the stock market

The complex behavior of financial markets emerges from decisions made by many traders. Here, we exploit a large corpus of daily print issues of the Financial Times from 2nd January 2007 until 31st December 2012 to quantify the relationship between decisions taken in financial markets and developments in financial news. We find a positive correlation between the daily number of mentions of a company in the Financial Times and the daily transaction volume of a company's stock both on the day before the news is released, and on the same day as the news is released. Our results provide quantitative support for the suggestion that movements in financial markets and movements in financial news are intrinsically interlinked

Effect of network density and size on the short-term fairness performance of CSMA systems

As the penetration of wireless networks increase, number of neighboring networks contending for the limited unlicensed spectrum band increases. This interference between neighboring networks leads to large systems of locally interacting networks. We investigate whether the short-term fairness of this system of networks degrades with the system size and density if transmitters employ random spectrum access with carrier sensing (CSMA). Our results suggest that (a) short-term fair capacity, which is the throughput region that can be achieved within the acceptable limits of short-term fairness, reduces as the number of contending neighboring networks, i.e., degree of the conflict graph, increases for random regular conflict graphs where each vertex has the same number of neighbors, (b) short-term fair capacity weakly depends on the network size for a random regular conflict graph but a stronger dependence is observed for a grid deployment. We demonstrate the implications of this study on a city-wide Wi-Fi network deployment scenario by relating the short-term fairness to the density of deployment. We also present related results from the statistical physics literature on long-range correlations in large systems and point out the relation between these results and short-term fairness of CSMA systems. © 2012 Koseoglu et al; licensee Springer

The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

Searching choices : quantifying decision-making processes using search engine data

When making a decision, humans consider two types of information: information they have acquired through their prior experience of the world, and further information they gather to support the decision in question. Here, we present evidence that data from search engines such as Google can help us model both sources of information. We show that statistics from search engines on the frequency of content on the Internet can help us estimate the statistical structure of prior experience; and, specifically, we outline how such statistics can inform psychological theories concerning the valuation of human lives, or choices involving delayed outcomes. Turning to information gathering, we show that search query data might help measure human information gathering, and it may predict subsequent decisions. Such data enable us to compare information gathered across nations, where analyses suggest, for example, a greater focus on the future in countries with a higher per capita GDP. We conclude that search engine data constitute a valuable new resource for cognitive scientists, offering a fascinating new tool for understanding the human decision-making process

Quantifying human behaviour with online images

From online searches to social media posts, our everyday interactions with the Internet are creating vast amounts of data. Large volumes of this data can be accessed rapidly at low cost, opening up unprecedented possibilities to monitor and analyse social processes and measure human behaviour. As Internet connectivity has continued to improve, photo-sharing platforms such as Instagram and Flickr have gained widespread popularity. At the same time, considerable advances have been achieved in the power of computers to analyse the contents of images. In particular, deep learning based methods such as convolutional neural networks have radically transformed the performance of systems seeking to identify objects in images, or classify the contents of a scene. Here, we showcase a series of studies in which we seek to quantify various aspects of human behaviour by exploiting both the large quantities of photographic data shared online and recent developments in computer vision. Specifically, we investigate whether data extracted from photographs shared on Flickr and Instagram can help us track global protest outbreaks; estimate the income of inhabitants living in different areas of London and New York; and predict the occurrence of noise complaints in New York City. Our findings are in line with the striking hypothesis that information extracted through automatic analysis of photographs shared online may help us measure human behaviour, whether in individual cities or across the glob

Uniform weighted round robin scheduling algorithms for input queued switches

This paper concentrates on obtaining uniform weighted round robin schedules for input queued packet switches. The desired schedules are uniform in the sense that each connection is serviced at regularly spaced time slots, where the spacing is proportional to the inverse of the guaranteed data rate. Suitable applications include ATM networks as well as satellite switched TDMA systems that provide per packet delay guarantees. Three heuristic algorithms are proposed to obtain such schedules under the constraints imposed by the unit speedup of input queued switches. Numerical experiments indicate that the algorithms have remarkable performance in finding uniform schedules

Data Ethics Emergency Drill:A Toolbox for Discussing Responsible AI for Industry Teams

Researchers urge technology practitioners such as data scientists to consider the impacts and ethical implications of algorithmic decisions. However, unlike programming, statistics, and data management, discussion of ethical implications is rarely included in standard data science training. To begin to address this gap, we designed and tested a toolbox called the data ethics emergency drill (DEED) to help data science teams discuss and reflect on the ethical implications of their work. The DEED is a roleplay of a fictional ethical emergency scenario that is contextually situated in the team’s specific workplace and applications. This paper outlines the DEED toolbox and describes three studies carried out with two different data science teams that iteratively shaped its design. Our findings show that practitioners can apply lessons learnt from the roleplay to real-life situations, and how the DEED opened up conversations around ethics and values