776 research outputs found
Non-Parametric Causality Detection: An Application to Social Media and Financial Data
According to behavioral finance, stock market returns are influenced by
emotional, social and psychological factors. Several recent works support this
theory by providing evidence of correlation between stock market prices and
collective sentiment indexes measured using social media data. However, a pure
correlation analysis is not sufficient to prove that stock market returns are
influenced by such emotional factors since both stock market prices and
collective sentiment may be driven by a third unmeasured factor. Controlling
for factors that could influence the study by applying multivariate regression
models is challenging given the complexity of stock market data. False
assumptions about the linearity or non-linearity of the model and inaccuracies
on model specification may result in misleading conclusions.
In this work, we propose a novel framework for causal inference that does not
require any assumption about the statistical relationships among the variables
of the study and can effectively control a large number of factors. We apply
our method in order to estimate the causal impact that information posted in
social media may have on stock market returns of four big companies. Our
results indicate that social media data not only correlate with stock market
returns but also influence them.Comment: Physica A: Statistical Mechanics and its Applications 201
Market depth and order size: an analysis of permanent price effects of DAX futures' trades
In this paper we empirically analyze the permanent price impact of trades by investigating the relation between unexpected net order flow and price changes. We use intraday data on German index futures. Our analysis based on a neural network model suggests that the assumption of a linear impact of orders on prices (which is often used in theoretical papers) is highly questionable. Therefore, empirical studies, comparing the depth of different markets, should be based on the whole price impact function instead of a simple ratio. To allow the market depth to depend on trade volume could open promising avenues for further theoretical research. This could lead to quite different trading strategies as in traditional models. --
Modelling systemic risk using neural network quantile regression
We propose a novel approach to calibrate the conditional value-at-risk (CoVaR) of financial institutions based on neural network quantile regression. Building on the estimation results, we model systemic risk spillover effects in a network context across banks by considering the marginal effects of the quantile regression procedure. An out-of-sample analysis shows great performance compared to a linear baseline specification, signifying the importance that nonlinearity plays for modelling systemic risk. We then propose three network-based measures from our fitted results. First, we use the Systemic Network Risk Index (SNRI) as a measure for total systemic risk. A comparison to the existing network-based risk measures reveals that our approach offers a new perspective on systemic risk due to the focus on the lower tail and to the allowance for nonlinear effects. We also introduce the Systemic Fragility Index (SFI) and the Systemic Hazard Index (SHI) as firm-specific measures, which allow us to identify systemically relevant firms during the financial crisis.Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Peer Reviewe
Testing instantaneous causality in presence of non constant unconditional variance
The problem of testing instantaneous causality between variables with
time-varying unconditional variance is investigated. It is shown that the
classical tests based on the assumption of stationary processes must be avoided
in our non standard framework. More precisely we underline that the standard
test does not control the type I errors, while the tests with White (1980) and
Heteroscedastic Autocorrelation Consistent (HAC) corrections can suffer from a
severe loss of power when the variance is not constant. Consequently a modified
test based on a bootstrap procedure is proposed. The relevance of the modified
test is underlined through a simulation study. The tests considered in this
paper are also compared by investigating the instantaneous causality relations
between US macroeconomic variables.Comment: Keywords : VAR model, Unconditionally heteroscedastic errors,
instantaneous causalit
A Survey of Systemic Risk Analytics
We provide a survey of 31 quantitative measures of systemic risk in the economics and finance literature, chosen to span key themes and issues in systemic risk measurement and management. We motivate these measures from the supervisory, research, and data perspectives in the main text and present concise definitions of each risk measure—including required inputs, expected outputs, and data requirements—in an extensive Supplemental Appendix. To encourage experimentation and innovation among as broad an audience as possible, we have developed an open-source Matlab® library for most of the analytics surveyed, which, once tested, will be accessible through the Office of Financial Research (OFR) at http://www.treasury.gov/initiatives/wsr/ofr/Pages/default.aspx.United States. Dept. of the Treasury. Office of Financial ResearchMassachusetts Institute of Technology. Laboratory for Financial EngineeringNational Science Foundation (U.S.) (Grant ECCS-1027905
Understanding the Relationship between Online Discussions and Bitcoin Return and Volume: Topic Modeling and Sentiment Analysis
This thesis examines Bitcoin related discussions on Bitcointalk.com over the 2013-2022 period. Using Latent Dirichlet Allocation (LDA) topic modeling algorithm, we discover eight distinct topics: Mining, Regulation, Investment/trading, Public perception, Bitcoin’s nature, Wallet, Payment, and Other. Importantly, we find differences in relations between different topics’ sentiment, disagreement (proxy for uncertainty) and hype (proxy for attention) on one hand and Bitcoin return and trading volume on the other hand. Specifically, among all topics, only the sentiment and disagreement of Investment/trading topic have significant contemporaneous relation with Bitcoin return. In addition, sentiment and disagreement of several topics, such as Mining and Wallet, show significant relationships with Bitcoin return only on the tails of the return distribution (bullish and bearish markets). In contrast, sentiment, disagreement, and hype of each topic show significant relation with Bitcoin volume across the entire distribution. In addition, whereas hype has a positive relation with trading volume in a low-volume market, this relation becomes negative in a high-volume market
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