This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an 'explosion' of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing.

This study presents an in-depth exploration of market dynamics and analyses potential drivers of trading volume. The study considers established facts from the literature, such as calendar anomalies, the correlation between volume and price change, and this relation's asymmetry, while proposing a variety of time series models. The results identified some key volume predictors, such as the lagged time series volume data and historical price indicators (e.g. intraday range, intraday return, and overnight return). Moreover, the study provides empirical evidence for the price-volume relation asymmetry, finding an overall price asymmetry in over 70% of the analysed stocks, which is observed in the form of a moderate overnight asymmetry and a more salient intraday asymmetry. We conclude that volatility features, more recent data, and day-of-the-week features, with a notable negative effect on Mondays and Fridays, improve the volume prediction model. | INTRODUCTIONThis study investigates the drivers affecting the trading volume with an in-sample analysis. We explore the interaction between truly exogenous determinants and trading volume. Several hypotheses are evaluated while looking at the previous literature, where various factors are discussed in isolation, and we propose a liquidity extraction model by placing these findings in a unified context.Identifying the drivers of trading volume is crucial in order to anticipate and minimize market impact, by accurately sizing and executing orders. Achieving optimal order sizing relies on precise volume prediction, that is, planning trades and deciding how much to trade given the current market context and the predicted volume levels. To better illustrate the importance of trading volume, some recent facts include the total turnover value, which was $63tn in 2011 (World Federation of Exchanges, 2012) and $49tn in 2012 (World Federation of Exchanges, 2013. The NYSE's turnover averaged more than 100% between 2004 and 2009, with 138% in 2008(NYSE Euronext, 2016, meaning that the entire market value has changed hands once a year, although it has decreased to significantly lower levels during the following years, averaging 72% for the 2010-2015 period.In order to better understand the factors affecting the trading volume, it is necessary to survey and combine apparently disjoint literature concepts. We start by reviewing the relevant areas of the behavioural finance literature. Here, a large amount of research has mainly investigated the calendar effects on price returns, and there is very little emphasis on the calendar effects on trading volume. We particularly focus on the day-of-theweek effect, which, once investigated, can formulate ------------------------------------------------------------------This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

This study investigates the effect of periodic events, such as the stock index futures and options expiration days and the Morgan Stanley Capital International (MSCI) quarterly index reviews, on the trading volume in the pan-European equity markets. The motivation of this study stems from anecdotal evidence of increased trading volume in the equity markets during the run-up to the index options and futures expiration days and MSCI rebalances. This study investigates this phenomenon in more detail and analyses the trading volumes of seven European stock indices and the MSCI International Pan-Euro Price Index. The analysis features a multi-step ahead volume forecast, which is important for practitioners in order to plan multi-day trades while looking to minimise the market impact. The results confirm higher trading activity on the futures and options expiration days, as well as on the MSCI rebalance day. We report a clear futures and options expiration day effect, which accounts for the Friday effect in terms of larger trading volumes. The MSCI rebalance trading volume is significantly different from the volume of the adjacent months with no MSCI reviews, but they cannot explain the end-of-month effect entirely.

There is anecdotal evidence of reduced trading volume in equity markets when other external markets are not trading. This phenomenon can be called the “cross‐market holiday effect,” and this study investigates it in detail, providing evidence for the existence of a strong cross‐market holiday effect in the pan‐European equity markets. The analysis provides an in‐depth examination of other aspects like lagged volumes, market capitalization, or multistep ahead modelling. The trading volumes on dates when there is at least one cross‐market holiday are on average 8.5% lower than the volumes of the previous period. There are salient effects when the holiday takes place in a dominant market or when most of the European markets are shut. We test whether the lower trading activity on Monday cross‐market holidays is a consequence of the weekend effect or whether the Monday bank holidays push down the Monday trading volume. We report a significantly lower volume associated with the Monday bank holidays, and we argue that the weekend effect has an insignificant impact on the Monday volumes where there is at least one regional cross‐market holiday.

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