Cathy Yi-Hsuan Chen

Distillation of News Flow Into Analysis of Stock Reactions

The gargantuan plethora of opinions, facts, and tweets on financial business offers the opportunity to test and analyze the influence of such text sources on future directions of stocks. It also creates though the necessity to distill via statistical technology the informative elements of this prodigious and indeed colossal data source. Using mixed text sources from professional platforms, blog fora, and stock message boards, we distill via different lexica sentiment variables. These are employed for an analysis of stock reactions: volatility, volume, and returns. An increased sentiment, especially for those with negative prospection, will influence volatility as well as volume. This influence is contingent on the lexical projection and different across Global Industry Classification Standard (GICS) sectors. Based on review articles on 100 S&P 500 constituents for the period of October 20, 2009, to October 13, 2014, we project into BL, MPQA, LM lexica and use the distilled sentiment variables to forecast individual stock indicators in a panel context. Exploiting different lexical projections to test different stock reaction indicators we aim at answering the following research questions: Are the lexica consistent in their analytic ability? To which degree is there an asymmetric response given the sentiment scales (positive v.s. negative)? Are the news of high attention firms diffusing faster and result in more timely and efficient stock reaction? Is there a sector specific reaction from the distilled sentiment measures? We find that there is significant incremental information in the distilled news flow and the sentiment effect is characterized as an asymmetric, attention-specific, and sector-specific response of stock reactions.

Empirical Analysis of the Intertemporal Relationship between Downside Risk and Expected Returns: Evidence from Time‐Varying Transition Probability Models

This paper examines the intertemporal relationship between downside risks and expected stock returns for five advanced markets. Using Value‐at‐Risk (VaR) as a measure of downside risk, we find a positive and significant relationship between VaR and the expected return before the world financial crisis (September 2008). However, when we estimate the model using a sample after this date, the results show a negative risk–return relationship. Evidence from a two‐state Markov regime‐switching model indicates that as uncertainty rises, the sign of the risk–return relationship turns negative. Evidence suggests that the Markov regime‐switching model helps to resolve the conflicting signs in the risk–return relationship.

Copula-Based Factor Model for Credit Risk Analysis

A standard quantitative method to assess credit risk employs a factor model based on joint multivariate normal distribution properties. By extending the one-factor Gaussian copula model to produce a more accurate default forecast, this paper proposes the incorporation of a state-dependent recovery rate into the conditional factor loading and to model them sharing a unique common factor. The common factor governs the default rate and recovery rate simultaneously, implicitly creating their association. In accordance with Basel III, this paper shows that the tendency toward default during a hectic period is governed more by systematic risk than by idiosyncratic risk. Among those considered, the model with random factor loading and a state-dependent recovery rate is shown to be superior in terms of default prediction.

A first econometric analysis of the CRIX family

The CRIX (CRyptocurrency IndeX) has been constructed based on approximately 30 cryptos and captures high coverage of available market capitalisation. The CRIX index family covers a range of cryptos based on di erent liquidity rules and various model selection criteria. Details of ECRIX (Exact CRIX), EFCRIX (Exact Full CRIX) and also intraday CRIX movements may be found on the webpage of hu.berlin/crix.

Dynamic Topic Modelling for Cryptocurrency Community Forums

Cryptocurrencies are more and more used in official cash flows and exchange of goods. Bitcoin and the underlying blockchain technology have been looked at by big companies that are adopting and investing in this technology. The CRIX Index of cryptocurrencies http://hu.berlin/CRIX indicates a wider acceptance of cryptos. One reason for its prosperity certainly being a security aspect, since the underlying network of cryptos is decentralized. It is also unregulated and highly volatile, making the risk assessment at any given moment difficult. In message boards one finds a huge source of information in the form of unstructured text written by e.g. Bitcoin developers and investors. We collect from a popular crypto currency message board texts, user information and associated time stamps. We then provide an indicator for fraudulent schemes. This indicator is constructed using dynamic topic modelling, text mining and unsupervised machine learning. We study how opinions and the evolution of topics are connected with big events in the cryptocurrency universe. Furthermore, the predictive power of these techniques are investigated, comparing the results to known events in the cryptocurrency space. We also test hypothesis of self-fulling prophecies and herding behaviour using the results.

Econometric Analysis of a Cryptocurrency Index for Portfolio Investment

Abstract The CRIX (CRyptocurrency IndeX) has been constructed based on approximately 30 cryptos and captures high coverage of available market capitalization. The CRIX index family covers a range of cryptos based on different liquidity rules and various model selection criteria. Details of ECRIX (Exact CRIX), EFCRIX (Exact Full CRIX) and also intraday CRIX movements may be found on the web page hu.berlin/crix.

Tail Event Driven Networks of SIFIs

The interdependence, dynamics and riskiness of financial institutions are the key features frequently tackled in financial econometrics. We propose a Tail Event driven Network Quantile Regression (TENQR) model which addresses these three aspects. More precisely, our framework captures the risk propagation and dynamics in terms of a quantile (or expectile) autoregression involving network effects quantified through an adjacency matrix. To reflect the nature and risk content of systemic risk, the construction of the adjacency matrix is suggested to include tail event covariates. The model is evaluated using the SIFIs (systemically important financial institutions) identified by the Financial Stability Board (FSB) as main players in the global financial system. The risk decomposition analysis of it identifies the systemic importance of SIFIs and thus provides measures for the required level of additional loss absorbency. It is discovered that the network effect, as a function of the tail probability, becomes more profound in stress situations and brings the various impacts to the SIFIs located in different geographic regions.

Data science & digital society

Data Science looks at raw numbers and informational objects created by different disciplines. The Digital Society creates information and numbers from many scientiHic disciplines. The amassment of data though makes is hard to Hind structures and requires a skill full analysis of this massive raw material. The thoughts presented here on DS2 - Data Science & Digital Society analyze these challenges and offers ways to handle the questions arising in this evolving context. We propose three levels of analysis and lay out how one can react to the challenges that come about. Concrete examples concern Credit default swaps, Dynamic Topic modeling, Crypto currencies and above all the quantitative analysis of real data in a DS2 context.

Adaptive weights clustering of research papers

The JEL classification system is a standard way of assigning key topics to economic articles in order to make them more easily retrievable in the bulk of nowadays massive literature. Usually the JEL (Journal of Economic Literature) is picked by the author(s) bearing the risk of suboptimal assignment. Using the database of a Collaborative Research Center from Humboldt-Universit¨at zu Berlin and Xiamen University, China we employ a new adaptive clustering technique to identify interpretable JEL (sub)clusters. The proposed Adaptive Weights Clustering (AWC) is available on www.quantlet.de and is based on the idea of locally weighting each point (document, abstract) in terms of cluster membership. Comparison with k-means or CLUTO reveals excellent performance of AWC.

Downside risk and stock returns: An empirical analysis of the long-run and short-run dynamics from the G-7 Countries

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