Organizations want to leverage AI systems by effective use of their data and reduction of risk espoused throughout the operation of AI systems. Given the aim for risk reduction, we see the need for highlighting several AI governance areas. In the paper we explore risks and limits connected with the application of AI models in financial modeling and its application in practice. We will present approaches to overcome limitations and potential biases. Based on an example model, we highlight certain issues and problems. We review current policies and present their requirements. Finally, we discuss how many policies are aligned with the technical possibilities and limits and based on that develop suggestions for changes.
Cryptocurrencies have become a very popular topic recently, primarily due to their disruptive potential and reports of unprecedented returns. In addition, academics increasingly acknowledge the predictive power of Twitter for a wide variety of events and more specifically for financial markets. This paper studies to what extent public Twitter sentiment can be used to predict price returns for the nine largest cryptocurrencies: Bitcoin, Ethereum, XRP, Bitcoin Cash, EOS, Litecoin, Cardano, Stellar and TRON. By using a cryptocurrency-specific lexicon-based sentiment analysis approach, financial data and bilateral Granger-causality testing, it was found that Twitter sentiment has predictive power for the returns of Bitcoin, Bitcoin Cash and Litecoin. Using a bullishness ratio, predictive power is found for EOS and TRON. Finally, a heuristic approach is developed to discover that at least 1–14% of the obtained Tweets were posted by Twitter “bot” accounts. This paper is the first to cover the predictive power of Twitter sentiment in the setting of multiple cryptocurrencies and to explore the presence of cryptocurrency-related Twitter bots.
This report investigates the development of the blockchain ecosystem in Italy, against the background of the SME and entrepreneurship structure and trends in the country. The report analyses in particular the characteristics and trends of companies introducing blockchain-based services in the Italian market, opportunities and challenges to their business development, sectors and firms being targeted, and relevance for enhancing digitalisation and productivity in the SME population at large. The report also illustrates recent trends in regulation and policy, and provides policy recommendations.
We present a new approach to identifying asset price bubbles based on options data. Given their forward-looking nature, options are ideal instruments with which to investigate market expectations about the future evolution of asset prices, which are key to understanding price bubbles. By exploiting the differential pricing between put and call options, we can detect and quantify bubbles in the prices of underlying asset. We apply our methodology to two stock market indexes, the S&P 500 and the Nasdaq-100, and two technology stocks, Amazon and Facebook, over the 2014-2018 sample period. We find that, while indexes exhibit rare and modest bubbles, Amazon and Facebook show more frequent and much larger bubbles. Since our approach can be implemented in real time, it is useful to both policy-makers and investors. As an illustration, our methodology applied to GameStop identifies a significant bubble between December 2020 and January 2021.
The advent of stablecoins offers new and innovative ways to improve financial inclusion, reduce transaction costs, and increase the efficiency of the global financial system. The following paper explores the assets and process necessary for creating a central bank digital currency (CBDC) on the Celo platform, as well as the potential impact on the financial system. Perhaps most importantly, the paper also introduces the idea that current technological advancements allow for a better understanding of the velocity of money, and may afford central banks the ability to influence money velocity, thus potentially creating a new transmission channel for monetary policy.
Two of the biggest barriers to the large-scale adoption of cryptocurrencies as a means of payment are ease-of-use and purchasing-power volatility. We introduce Celo, a protocol that addresses these issues with an address-based encryption scheme and a stable-value token. We show how these attributes together can be used to foster a monetary ecology that includes global reference currencies, local and regional stable-value currencies, and a social dividend. Our first application is a social-payments system centered around mobile phones.
We analyze banks’ abilities to achieve a sustainable business model. We first argue that assessment of the sustainability of a business model on the market requires consideration of the broad set of choices bank managers face, because such a set of business strategies and their adjustment affect performance in both the short and long-run. By measuring the variety of bank business strategies using a diversity index, we present a new framework to analyze the effect of a business model on bank performance (measured by a state-of-the-art stochastic frontier model). In particular, our method links the business model to performance by taking into account the long- and short-run effects. Using data that includes European commercial banks over the period 1993–2016, we find that a combination of (i) a persistent income business model together with the adjustment of an asset-focused business model in the long-run and (ii) diversification of the funding and income portfolios in the short run describes a sustainable cost-efficient business model. Our findings are robust to alternative specifications.
We investigate the relationship between the economic policy uncertainty index (EPU) and cryptocurrency volatility. We find that a change in EPU of China predicts cryptocurrency volatility, but a change in the EPU of the U.S., Japan, or Korea has no such effect. Moreover, changes in the China EPU are negatively associated with Bitcoin and Litecoin future volatility, which may imply that Bitcoin and Litecoin are hedging tools against the EPU risk. However, changes in China EPU may not affect the cryptocurrency volatility after the Chinese government's regulation of crypto-trading.
Several measures of credit-market booms are known to precede downturns in real economic activity. We offer an early indicator for all known measures of credit booms. Our measure is based on intra-family flow shifts towards high-yield bond mutual funds. It predicts indicators such as growth in financial intermediary balance sheets, increase in shares of high-yield bond issuers, and downturns of various measures of credit spreads. It also directly predicts the business cycle by positively predicting GDP growth and negatively predicting unemployment. Our results provide support for the investor demand-based narrative of credit cycles and can be useful for policymakers.