Focus 19 - décembre 2016

Depuis que les marchés financiers existent, les professionnels de la finance ont sans relâche exploité les évolutions technologiques, que ce soit le télégraphe, le téléphone, l’ordinateur, ou plus récemment l’Internet, visant sans cesse à diminuer la latence, autrement dit le temps qui s’écoule entre l’introduction d’un ordre et son exécution. Cette évolution a bénéficié aux investisseurs non-professionnels puisqu’ils peuvent désormais introduire leurs propres ordres d’achat ou de vente, en obtenir l’exécution en moins d’une seconde, et payer des coûts de transaction bien plus faibles que lorsqu’ils devaient attendre la confirmation de leur courtier durant d’interminables minutes passées au téléphone

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

The Challenge of Detecting High Frequency Trading

Master [120] ingénieur de gestion, Université catholique de Louvain, 201

Essays on technological innovation in finance

Innovations in financial markets have been numerous lately. Within two decades, most of the trading activity has been done electronically and trading rooms have become deserted as computers have been able to make autonomous trading decisions more rapidly than in the past; information is now spreading at the speed of light; and a plethora of electronic currencies, whose reliability is based on cryptographic proofs rather than trust, have been issued. There is no shortage of examples, from market finance to corporate finance. Every field is affected by the digitalization phenomenon: online investing and online financial advice, digital currencies and paperless payments, crowdfunding, or high-frequency trading, among others. In this thesis, we provide three empirical analyses of how some of these innovations affect financial markets.(ECGE - Sciences économiques et de gestion) -- UCL, 202

Googlization and retail trading activity

A large body of literature documents a positive relationship between the Google Search Volume Index (SVI) and market returns or volumes. Such findings are consistent with a buying pressure due to increased attention. Unlike most of the studies that use market data, we use the trading accounts for a sample of retail investors. The advantage is twofold; we are able to disentangle purchases from sales, and our results are not biased by any institutional trading. We find that the relationship between the SVI and retail trading activity is positive but not stronger for purchases than for sales. We also demonstrate evidence of a bidirectional causality between attention and trading activity, though contemporaneous effects predominate. Our results are robust to controls based on sociodemographics or subjective investor characteristics, as well as various specifications of the SVI and different measures of trading activity

Googlization and retail investors' trading activity

Building on Barber and Odean (2008), a growing body of papers document a positive relationship between Google Search Volume Index (SVI) and equity market returns. Such findings suggest that increased attention is combined with a buying pressure that subsequently results in positive returns. This relationship has been established at the market level. In this paper, we focus on a sample of retail investors and use SVI to test whether their aggregate (signed) trading activity is related to attention as well. We find that the relationship between SVI and our retail investors’ trading activity is positive, even when controlling for some socio-demographics or subjective investor characteristics. However, this relationship is not stronger for purchases than for sales, thereby providing no support for the buying pressure hypothesis. We also document a bi-directional causality between attention and trading activity, although the contemporaneous effects are economically stronger and predominate. Our results are robust to different measures of attention and trading activity

Googlization and retail investment decisions

Several papers have highlighted a positive link between the Google Search Volume Index (SVI) and financial markets. These studies focused mostly on market trading volume, returns, and volatility which are all publicly disclosed and financial measures. While the SVI has become an established proxy of investors’ attention, the behavioral finance literature asserts that retail investors are likely to buy stocks which grab their attention (Barber and Odean, 2008). In this paper, we test whether the relationship between investors’ attention and trading proxies remains unaffected when we consider a sample of retail investors who trade on an online brokerage platform between January 2004 and March 2012. Our results indicate that this relationship holds for varying socio-demographic characteristics but disappears when we consider more sophisticated trading proxies

Googlization and retail investment decisions

Several papers have highlighted a positive link between the Google Search Volume Index (SVI) and financial markets. These studies focused mostly on market trading volume, returns, and volatility which are all publicly disclosed measures. Building on Barber and Odean (2008)'s theory of limited attention, we test whether the relationship between the SVI and trading behavior holds once we consider a sample of individual investors' transactions. We also analyze whether that relationship is affected by investors' characteristics. Our results indicate that this relationship holds for varying socio-demographic characteristics but disappears when we consider more sophisticated trading proxies

Crypto market dynamics in stressful conditions

Understanding market liquidity and trading dynamics in one of the most innovative and volatile markets in the world, is crucial from the standpoint of both regulators and investors. In contrast to stocks, very little is known about the functioning of cryptos around extreme returns (ERs). Using high-frequency order-book and trade data for the 8 most widespread cryptos on 16 trading platforms over three years, we examine the contemporaneous and lagged influence of trading activity and liquidity on the occurrence of extreme returns (ERs) in a logistic regression framework adapted to rare events. Despite its huge volatility, we show that the trading and liquidity dynamics on the crypto market around ERs is not orthogonal to what traditional markets experience in stressful conditions. The number of trades is a particularly robust driver to explain the occurrence of ERs, followed by the relative spread. The same drivers are identified for traditional markets