Contributions published at Banking (Jean-Charles Rochet)

We explore the impact of investor attention on idiosyncratic risk in the cryptocurrency markets. Taking the Google Trends Index as the measure of investor attention, we find that investor attention can significantly reduce cryptocurrencies’ idiosyncratic risks by increasing the liquidity. We further study possible cross-sectional variations of the effect of investor attention on idiosyncratic risk. Evidence shows that the investor attention effect is more pronounced for smaller-cap and younger cryptocurrencies. Moreover, a relatively stable external market environment and rising market state are conducive to the further play of the attention effect.

Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long-term analysis (short-term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency.

In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil.

We compare the performance of various advanced forecasting techniques, namely artificial neural networks, k-nearest neighbors, logistic regression, Naïve Bayes, random forest classifier, support vector machine, and extreme gradient boosting classifier to predict stock price movements based on past prices. We apply these methods with the high frequency data of 27 blue-chip stocks traded in the Istanbul Stock Exchange. Our findings reveal that among the selected methodologies, random forest and support vector machine are able to capture both future price directions and percentage changes at a satisfactory level. Moreover, consistent ranking of the methodologies across different time frequencies and train/test set partitions prove the robustness of our empirical findings.

We prove the existence of statistical arbitrage opportunities for jump-diffusion models of stock prices when the jump-size distribution is assumed to have finite moments. We show that to obtain statistical arbitrage, the risky asset holding must go to zero in time. Existence of statistical arbitrage is demonstrated via ‘buy-and-hold until barrier’ and ‘short until barrier’ strategies with both single and double barrier. In order to exploit statistical arbitrage opportunities, the investor needs to have a good approximation of the physical probability measure and the drift of the stochastic process for a given asset.

Financial institutions have adopted big data to a considerable extent to provide better investment decisions. Consequently, high-frequency algorithmic traders use a vast amount of historical data with various statistical models to maximize their trading profits. Until recently, high-frequency algorithmic trading was the domain of institutional traders with access to supercomputers. Nowadays, any investor can potentially make high-frequency trades because of easy access to big data and software to analyze and execute trades. With that in mind, Borsa Istanbul introduced real time big data analytics as a product to its customers. These analytics are derived in real time from order book and trade data and aim to level the playing field between investment firms and retail traders. Using classical benchmark models in the literature, we show that Borsa Istanbul’s order imbalance-based data analytics are useful in predicting both time-series and cross-sectional intraday excess future returns, proving that this product is extremely beneficial to market participants, particularly day traders.

This paper studies the dynamic network connectedness between cryptocurrency returns and sentiments using the novel cryptocurrency-specific MarketPsych sentiment data for 13 cryptocurrencies with the highest market capitalization. The results indicate the dominance of cryptocurrencies with higher market capitalization and information transmission from cryptocurrency returns to sentiments. Our results also show that Bitcoin is losing its dominance to alt-coins in return spillovers while still dominant in sentiment spillovers.

Le modèle d’affaires traditionnel des banques commerciales exploite les économies d’envergure entre dépôts et crédits. Les nouvelles technologies de l’information (Fintechs) et les plateformes géantes sur Internet (Bigtechs) mettent ce modèle sérieusement en danger. Les Bigtechs menacent même la souveraineté monétaire des États en offrant potentiellement aux citoyens du monde de nouvelles devises qui échappent à leur contrôle, comme le projet Diem de Global Stablecoin élaboré par Facebook. Cette menace explique que de nombreuses juridictions envisagent sérieusement la création de monnaies digitales de banques centrales (MDBC ou CBDC en anglais), de façon à couper l’herbe sous le pied des Fintechs tentées par la création monétaire. Nous analysons ces bouleversements et leurs conséquences prévisibles et plaidons pour l’élaboration d’une véritable politique publique en matière de paiements. Classification JEL : D40, G01, G21, G23, O33 English Banking, Money and Credits The traditional business model of commercial banks exploits economies of scale between deposits and credit. New information technologies (Fintechs) and giant internet platforms (Bigtechs) are putting this model in serious jeopardy. Bigtechs even threaten the monetary sovereignty of states by potentially offering citizens of the world new currencies that are beyond the control of those states, such as Facebook’s Global Stablecoin project Diem. This threat explains why many jurisdictions are seriously considering the creation of central bank digital currencies (CBDCs), so as discourage Fintech firms from creating new currencies. We analyze these upheavals and their foreseeable consequences and advocate for the development of a real public policy on payments. Classification JEL : D40, G01, G21, G23, O33

One of the key sub-sectors in the aviation industry includes that of engine manufacturers, who have long led technological advancement and the battle to reduce airline carbon emissions. However, these same companies have been susceptible to a number of issues that have been central to international airlines due to higher costs and competition pressures. When an aviation disaster occurs, there is widespread allocation of blame and responsibility, which has left engine manufacturers exposed until the true cause is identified. This can generate many issues with regards to reputational damage and ability to generate finance. We set out to analyse such interactions over time and region. Our results indicate that engine manufacturers have had to contend with substantial income and financial leverage issues in the aftermath of a major aviation disaster, irrespective of whether they have been identified as a causation factor in the incident itself. Further, we clearly identify that there exists an average one day loss of 1.64% in the immediate aftermath of aviation incidents. Substantial corporate instability is found to persist without the company being in any way responsible for the incident. Shortly thereafter, contagion effects increase as speculation diminishes and more factual evidence arrives. The role of social media is examined as a potential contributory factor.

In this study, the predictability of the most liquid twelve cryptocurrencies are analyzed at the daily and minute level frequencies using the machine learning classification algorithms including the support vector machines, logistic regression, artificial neural networks, and random forests with the past price information and technical indicators as model features. The average classification accuracy of four algorithms are consistently all above the 50% threshold for all cryptocurrencies and for all the timescales showing that there exists predictability of trends in prices to a certain degree in the cryptocurrency markets. Machine learning classification algorithms reach about 55–65% predictive accuracy on average at the daily or minute level frequencies, while the support vector machines demonstrate the best and consistent results in terms of predictive accuracy compared to the logistic regression, artificial neural networks and random forest classification algorithms.

This paper examines the impact of blockchain and crypto-related name changes on corporate and financial performance of the corporations. We document several pieces of evidence suggesting that companies who partake in such “crypto-exuberant” naming practices become more volatile and offer substantial and persistent stock market premiums as a reward for their corporate identity change. However, the retroactive name changes harm firm's short-term profitability and have a dampening effect on financial leverage of the company. This paper advances the Dotcom effect literature by providing novel results on the changing traditional pathways of price discovery and information flows after the announcement of corporate name changes to blockchain-related names. The identified contagion channels display that crypto-exuberant companies become more susceptible to cryptocurrency markets, which should interest regulators and investors.

Cryptocurrencies have been broadly scrutinised in recent times for a host of concerning regulatory and cybercriminality issues. Although steps have been taken to promote regulatory sufficiency in the near future, we examine the avenues through which this extremely high-risk industry can derive potentially devastating contagion channels, influencing both unwilling and unsuspecting investors. We focus this research on the expressions of interest by publicly traded companies across the world to utilise cryptocurrency and blockchain projects. We find evidence that there exists a substantial stock price premium and sustained increase in volatility in the aftermath of blockchain announcements, with emphasis on highly-speculative motives such as coin creation and corporate name changes. Changes in price discovery and information flows are found to be largely determined from cryptocurrency-based pricing sources in the aftermath of speculative announcements. We discuss the inherent ethical and legal issues, considering as to whether such announcements are simply an attempt to artificially manipulate share prices and take part in the current phase of crypto-exuberance.

We estimate the time-varying long-run correlations of European sovereign bond markets to identify specific effects that are attributed to changing European regulatory and political dynamics over the last twenty years. Our empirical results from using the DCC-MIDAS methodology indicate that regulatory changes in Europe have created significant and negative impact on the long-run correlations within the month where the regulation is decided to be taken into action. This impact still remains in the following months and robust with respect to the trend component of the long-run correlations. A direct implication is that the more regulations the EU attempts to put in place, the lower the long-run convergence process of sovereign bond markets is. We then analyse the structural shifts in the long-run correlation dynamics with penalized contrasts methodology and try to find out the reasons of these severe changes. Accordingly, some of the structural shifts overlap with the dates of a limited number of regulatory changes, in addition to the major global economic and political events.

A simple equilibrium model is presented which permits the joint study of optimal Central Bank prudential, monetary and balance sheet policies in the pre and post 2008 Crisis periods. It explains the new policies-the purchase of risky securities (QE), payment of interest on reserves (IR) and use of reverse repo (RRP)-as the response to the lack of safe assets in the economy, and shows why these policies were not needed to achieve optimality before 2008, but were needed for the 2008 Crisis and thereafter.

The spread of misinformation with regards to aviation disasters continues to be a point of concern for aviation companies. Much of this information usually surrounds speculation based on the cause and responsibility attributed to the incident, implicitly possessing the potential to generate significant financial market price volatility. In this paper, we investigate a number of stylised facts relating to the effects of airline disasters on aviation stocks, while considering contagion effects, information flows and the sources of price discovery within the broad sector. Results indicate a substantially elevated levels of share price volatility in the aftermath of aviation disasters, while cumulative abnormal returns present sharp under-performance of the analysed companies relative to international exchanges. When considering an EGARCH analysis, we observe that share price volatility appears to be significantly influenced by the scale of the disaster in terms of the fatalities generated. Significant contagion effects upon the broad aviation index along with substantial changes in traditional price discovery channels are also identified. The role that the spread of information on social media, whether it be correct or of malicious origins, cannot be eliminated as an explanatory factor of these changing dynamics over time and region.

We utilise a high-frequency analysis to investigate the period surrounding the establishment of two new futures contracts based on the performance of Bitcoin. Our analysis shows that there have been significant pricing effects sourced from both fraudulent and regulatory unease within the industry. While analysing breakpoints in efficiency, we verify the view that Bitcoin futures dominate price discovery relative to spot markets. However, we add to this research by finding that CBOE futures are found to be the leading source of informational flow when compared directly to their CME equivalent.

We analyse the relationship between the price volatility of a broad range of cryptocurrencies and that of implied volatility of both United States and European financial markets as measured by the VIX and VSTOXX respectively. Overall, our results indicate the existence of time-varying positive interrelationships between the conditional correlations of cryptocurrencies and financial market stress. Further, these correlations are found to increase substantially during periods of high financial market stress, indicating that the contagion of significant financial market fear influences these new financial products.

L’industrie financière a rendu la spéculation accessible à tous, comptant sur la «sagesse des foules» pour réguler les marchés. Mais cette sagesse n’est pas toujours au rendez-vous: de la folie des tulipes en Hollande en 1637 à la crise des subprimes de 2008, les exemples sont nombreux de bulles spéculatives qui éclatent! La passionnante histoire de la spéculation, relatée ici, n’est pas avare de surprises. Parmi elles, la plus paradoxale est que la spéculation financière peut aussi bien être bénéfique que néfaste. À petite dose, elle améliore le partage des risques dans l’économie et contribue au financement des innovations. À forte dose, elle provoque des crises financières très coûteuses pour la société. De même que l’on limite la vitesse sur les routes pour éviter les accidents, il faut limiter la spéculation financière, et pour cela il faut la taxer.

We study an optimal dividend problem under a bankruptcy constraint. Firms face a trade‐off between potential bankruptcy and extraction of profits. In contrast to previous works, general cash flow drifts, including Ornstein–Uhlenbeck and CIR processes, are considered. We provide rigorous proofs of continuity of the value function, whence dynamic programming, as well as comparison between discontinuous sub‐ and supersolutions of the Hamilton–Jacobi–Bellman equation, and we provide an efficient and convergent numerical scheme for finding the solution. The value function is given by a nonlinear partial differential equation (PDE) with a gradient constraint from below in one direction. We find that the optimal strategy is both a barrier and a band strategy and that it includes voluntary liquidation in parts of the state space. Finally, we present and numerically study extensions of the model, including equity issuance and gambling for resurrection.

We investigate the effects of macroeconomic announcements made in the United States on trading activity of stocks listed in Borsa Istanbul. The influence of these releases on the selected variables are an important source of information for market participants. Results show a clear negative impact on weighted bid, ask and mid-prices in the five-minute period post-release. Available liquidity measured by pending orders in limit order book decreases with the news arrival. These results present implications for market dynamics and signal that liquidity consumption (through market orders) largely dominates liquidity provision (through limit orders) in the five-minute period following the release.