Matrix Differential Calculus with Applications in Statistics and Econometrics

International Momentum Strategies

Wisdom of Crowds: The Value of Stock Opinions Transmitted through Social Media

Testing the Wisdom of Crowds in the field: Transfermarkt valuations and international soccer results

This paper applies algorithmic analysis to large amounts of financial market text-based data to assess how narratives and sentiment play a role in driving developments in the financial system. We find that changes in the emotional content in market narratives are highly correlated across data sources. They show clearly the formation (and subsequent collapse) of very high levels of sentiment — high excitement relative to anxiety — prior to the global financial crisis. Our metrics also have predictive power for other commonly used measures of sentiment and volatility and appear to influence economic and financial variables. And we develop a new methodology that attempts to capture the emergence of narrative topic consensus which gives an intuitive representation of increasing homogeneity of beliefs prior to the crisis. With increasing consensus around narratives high in excitement and lacking anxiety likely to be an important warning sign of impending financial system distress, the quantitative metrics we develop may complement other indicators and analysis in helping to gauge systemic risk.

/pdf/news-and-narratives-in-financial-systems-exploiting-big-data-3zgj28ceuj.pdf

News and Narratives in Financial Systems: Exploiting Big Data for Systemic Risk Assessment

Social media is now used as a forecasting tool by a variety of firms and agencies. But how useful are such data in forecasting outcomes? Can social media add any information to that produced by a prediction/betting market? We source 13.8 million posts from Twitter, and combine them with contemporaneous Betfair betting prices, to forecast the outcomes of English Premier League soccer matches as they unfold. Using a microblogging dictionary to analyze the content of Tweets, we find that the aggregate tone of Tweets contains significant information not in betting prices, particularly in the immediate aftermath of goals and red cards. (JEL G14, G17)

/pdf/forecasting-with-social-media-evidence-from-tweets-on-soccer-3sgf9k6k4b.pdf

Forecasting with social media: evidence from tweets on soccer matches

Forecasting with news sentiment: Evidence with UK newspapers

This paper develops a new test of true versus spurious long memory, based on logperiodogram estimation of the long memory parameter using skip-sampled data. A correction factor is derived to overcome the bias in this estimator due to aliasing. The test is implemented using the bootstrap, with the distribution under the null hypothesis approximated using the sieve-autoregression to approximate short-run dependence following fractional differencing. The procedure is designed to be used in the context of rejection in a conventional test of signi…cance of the long memory parameter, and in this context the test is consistent. Its properties are investigated in a set of Monte Carlo experiments.

/pdf/a-test-of-the-long-memory-hypothesis-based-on-self-28539l4n4u.pdf

A test of the long memory hypothesis based on self-similarity

Information extracted from social media has been used by academics, and increasingly by practitioners, to predict stock returns. But to what extent does social media output predict asset fundamentals, and not simply short-term returns? In this paper we analyse 13.8m posts on Twitter, and high-frequency betting data from Betfair, concerning English Premier League soccer matches in 2013/14. Crucially, asset fundamentals are revealed at the end of play. We find that the Tweets of certain journalists, and the tone of all Tweets, contain fundamental information not revealed in betting prices. In particular, Tweets aid in the interpretation of news during matches.

Using Social Media to Identify Market Inefficiencies: Evidence from Twitter and Betfair

We test whether the selected cryptocurrencies exhibit long memory behavior in returns and volatility. We use data on five most traded cryptocurrencies: Bitcoin, Litecoin, Ethereum, Bitcoin Cash, and XRP. Using recent tests of long memory developed against persistent and nonlinear alternatives, this paper finds that long memory is mostly rejected in returns. The tests fail to reject the null hypothesis of long memory in most cases across different volatility proxies and cryptocurrencies. The estimated memory parameters show that volatility is persistent, and when volatility is measured by log range, it is borderline nonstationary.

https://www.mdpi.com/1911-8074/13/9/186/pdf

Dooruj Rambaccussing

Papers

Forecasting with social media: evidence from tweets on soccer matches

Forecasting with news sentiment: Evidence with UK newspapers

A test of the long memory hypothesis based on self-similarity

Using Social Media to Identify Market Inefficiencies: Evidence from Twitter and Betfair

True versus Spurious Long Memory in Cryptocurrencies