arXiv subjects

David Ardia

Publications and source records attributed to David Ardia.

14 recordsLinked to original sources

arXiv2024

Optimal Text-Based Time-Series Indices

We propose an approach to construct text-based time-series indices in an optimal way--typically, indices that maximize the contemporaneous relation or the predictive performance with respect to a target variable, such as inflation. We illustrate our methodology with a corpus of news articles from the Wall Street Journal by optimizing text-based indices focusing on tracking the VIX index and inflation expectations. Our results highlight the superior performance of our approach compared to existing indices.

econ.EM↗

arXiv2024

High-Dimensional Mean-Variance Spanning Tests

We introduce a new framework for the mean-variance spanning (MVS) hypothesis testing. The procedure can be applied to any test-asset dimension and only requires stationary asset returns and the number of benchmark assets to be smaller than the number of time periods. It involves individually testing moment conditions using a robust Student-t statistic based on the batch-mean method and combining the p-values using the Cauchy combination test. Simulations demonstrate the superior performance of the test compared to state-of-the-art approaches. For the empirical application, we look at the problem of domestic versus international diversification in equities. We find that the advantages of diversification are influenced by economic conditions and exhibit cross-country variation. We also highlight that the rejection of the MVS hypothesis originates from the potential to reduce variance within the domestic global minimum-variance portfolio.

stat.ME↗

arXiv2024

Revisiting Boehmer et al. (2021): Recent Period, Alternative Method, Different Conclusions

We reassess Boehmer et al. (2021, BJZZ)'s seminal work on the predictive power of retail order imbalance (ROI) for future stock returns. First, we replicate their 2010-2015 analysis in the more recent 2016-2021 period. We find that the ROI's predictive power weakens significantly. Specifically, past ROI can no longer predict weekly returns on large-cap stocks, and the long-short strategy based on past ROI is no longer profitable. Second, we analyze the effect of using the alternative quote midpoint (QMP) method to identify and sign retail trades on their main conclusions. While the results based on the QMP method align with BJZZ's findings in 2010-2015, the two methods provide different conclusions in 2016-2021. Our study shows that BJZZ's original findings are sensitive to the sample period and the approach to identify ROIs.

q-fin.TR↗

arXiv2023

Fast and Furious: A High-Frequency Analysis of Robinhood Users' Trading Behavior

We analyze Robinhood (RH) investors' trading reactions to intraday hourly and overnight price changes. Contrasting with recent studies focusing on daily behaviors, we find that RH users strongly favor big losers over big gainers. We also uncover that they react rapidly, typically within an hour, when acquiring stocks that exhibit extreme negative returns. Further analyses suggest greater (lower) attention to overnight (intraday) movements and exacerbated behaviors post-COVID-19 announcement. Moreover, trading attitudes significantly vary across firm size and industry, with a more contrarian strategy towards larger-cap firms and a heightened activity on energy and consumer discretionary stocks.

q-fin.TR↗

arXiv2023

Linking Frequentist and Bayesian Change-Point Methods

We show that the two-stage minimum description length (MDL) criterion widely used to estimate linear change-point (CP) models corresponds to the marginal likelihood of a Bayesian model with a specific class of prior distributions. This allows results from the frequentist and Bayesian paradigms to be bridged together. Thanks to this link, one can rely on the consistency of the number and locations of the estimated CPs and the computational efficiency of frequentist methods, and obtain a probability of observing a CP at a given time, compute model posterior probabilities, and select or combine CP methods via Bayesian posteriors. Furthermore, we adapt several CP methods to take advantage of the MDL probabilistic representation. Based on simulated data, we show that the adapted CP methods can improve structural break detection compared to state-of-the-art approaches. Finally, we empirically illustrate the usefulness of combining CP detection methods when dealing with long time series and forecasting.

stat.ME↗

arXiv2023

The Role of Twitter in Cryptocurrency Pump-and-Dumps

We examine the influence of Twitter promotion on cryptocurrency pump-and-dump events. By analyzing abnormal returns, trading volume, and tweet activity, we uncover that Twitter effectively garners attention for pump-and-dump schemes, leading to notable effects on abnormal returns before the event. Our results indicate that investors relying on Twitter information exhibit delayed selling behavior during the post-dump phase, resulting in significant losses compared to other participants. These findings shed light on the pivotal role of Twitter promotion in cryptocurrency manipulation, offering valuable insights into participant behavior and market dynamics.

q-fin.TR↗

arXiv2023

Factor Exposure Heterogeneity in Green and Brown Stocks

Using the peer-exposure ratio, we explore the factor exposure heterogeneity in green and brown stocks. By looking at peer groups of S&P 500 index firms over 2014-2020 based on their greenhouse gas emission levels, we find that, on average, green stocks exhibit less factor exposure heterogeneity than brown stocks for most of the traditional equity factors but the value factor. Hence, investment managers shifting their investments from brown stocks to green stocks have less room to differentiate themselves regarding their factor exposures. Finally, we find that factor exposure heterogeneity has increased for green stocks compared to earlier periods.

econ.GN↗

arXiv2023

How easy is it for investment managers to deploy their talent in green and brown stocks?

We explore the realized alpha-performance heterogeneity in green and brown stocks' universes using the peer performance ratios of Ardia and Boudt (2018). Focusing on S&P 500 index firms over 2014-2020 and defining peer groups in terms of firms' greenhouse gas emission levels, we find that, on average, about 20% of the stocks differentiate themselves from their peers in terms of future performance. We see a much higher time-variation in this opportunity set within brown stocks. Furthermore, the performance heterogeneity has decreased over time, especially for green stocks, implying that it is now more difficult for investment managers to deploy their skills when choosing among low-GHG intensity stocks.

q-fin.PM↗

arXiv2022

Thirty Years of Academic Finance

We study how the financial literature has evolved in scale, research team composition, and article topicality across 32 finance-focused academic journals from 1992 to 2021. We document that the field has vastly expanded regarding outlets and published articles. Teams have become larger, and the proportion of women participating in research has increased significantly. Using the Structural Topic Model, we identify 45 topics discussed in the literature. We investigate the topic coverage of individual journals and can identify highly specialized and generalist outlets, but our analyses reveal that most journals have covered more topics over time, thus becoming more generalist. Finally, we find that articles with at least one woman author focus more on topics related to social and governance aspects of corporate finance. We also find that teams with at least one top-tier institution scholar tend to focus more on theoretical aspects of finance.

q-fin.GN↗

arXiv2021

Media abnormal tone, earnings announcements, and the stock market

We conduct a tone-based event study to examine the aggregate abnormal tone dynamics in media articles around earnings announcements. We test whether they convey incremental information that is useful for price discovery for nonfinancial S&P 500 firms. The relation we find between the abnormal tone and abnormal returns suggests that media articles provide incremental information relative to the information contained in earnings press releases and earnings calls.

q-fin.GN↗

arXiv2021

The R package sentometrics to compute, aggregate and predict with textual sentiment

We provide a hands-on introduction to optimized textual sentiment indexation using the R package sentometrics. Textual sentiment analysis is increasingly used to unlock the potential information value of textual data. The sentometrics package implements an intuitive framework to efficiently compute sentiment scores of numerous texts, to aggregate the scores into multiple time series, and to use these time series to predict other variables. The workflow of the package is illustrated with a built-in corpus of news articles from two major U.S. journals to forecast the CBOE Volatility Index.

stat.ML↗

arXiv2021

A Century of Economic Policy Uncertainty Through the French-Canadian Lens

A novel token-distance-based triple approach is proposed for identifying EPU mentions in textual documents. The method is applied to a corpus of French-language news to construct a century-long historical EPU index for the Canadian province of Quebec. The relevance of the index is shown in a macroeconomic nowcasting experiment.

econ.GN↗

arXiv2016

Value-at-Risk Prediction in R with the GAS Package

GAS models have been recently proposed in time-series econometrics as valuable tools for signal extraction and prediction. This paper details how financial risk managers can use GAS models for Value-at-Risk (VaR) prediction using the novel GAS package for R. Details and code snippets for prediction, comparison and backtesting with GAS models are presented. An empirical application considering Dow Jones Index constituents investigates the VaR forecasting performance of GAS models.

q-fin.RM↗

arXiv2016

Generalized Autoregressive Score Models in R: The GAS Package

This paper presents the R package GAS for the analysis of time series under the Generalized Autoregressive Score (GAS) framework of Creal et al. (2013) and Harvey (2013). The distinctive feature of the GAS approach is the use of the score function as the driver of time-variation in the parameters of nonlinear models. The GAS package provides functions to simulate univariate and multivariate GAS processes, estimate the GAS parameters and to make time series forecasts. We illustrate the use of the GAS package with a detailed case study on estimating the time-varying conditional densities of a set of financial assets.

stat.CO↗