This is the first article that studies BitCoin price formation by considering both the traditional determinants of currency price, e.g., market forces of supply and demand, and digital currencies specific factors, e.g., BitCoin attractiveness for investors and users. The conceptual framework is based on the Barro (1979) model, from which we derive testable hypotheses. Using daily data for five years (2009–2015) and applying time-series analytical mechanisms, we find that market forces and BitCoin attractiveness for investors and users have a significant impact on BitCoin price but with variation over time. Our estimates do not support previous findings that macro-financial developments are driving BitCoin price in the long run.
Bitcoins have the potential to fundamentally change the way value is transferred globally. Their rapid adoption over the past four years has led many to consider the possible results of such a technology. To be a viable currency, however, it is imperative that the market for trading Bitcoins is efficient. By examining the changes in availability of predictable outsized returns and market liquidity over time, this paper examines historical Bitcoin market efficiency and establishes correlations between market liquidity, price predictability, and return data. The results provide insight into the turbulent nature of Bitcoin market efficiency over the past years, but cannot definitively measure the magnitude of the change due to the limitations in efficiency analysis. The most meaningful result of this study, however, is the statistically significant short-horizon price predictability that existed over the duration of the study, which has implications for Bitcoin market efficiency as well as for continued research in short-horizon Bitcoin price forecasting models.
The Bitcoin phenomenon—and the technological innovation that made it possible—is interesting, but for investors large and small, the more pertinent question is whether they should buy the digital currency or avoid it. Using both in-sample and out-of-sample settings, we analyze a Bitcoin investment from the standpoint of an investor with a diversified portfolio. Within the in-sample setting, Bitcoin does not yield added value to investors, with a utility function consistent with the mean-variance setting. On the other hand, Bitcoin does offer diversification benefits to investors with negative exponential and power utility functions. However, these benefits are not preserved in the out-of-sample framework. In most cases, the optimal portfolios that include only the traditional asset classes appear to have superior performance. <b>TOPICS:</b>Currency, portfolio construction, performance measurement
The present paper seeks to effectively address the following question: What Bitcoin looks like? To do so, we regress Bitcoin price on different variables (potential Bitcoin fundamentals recorded in the literature) by applying an ARDL Bounds Testing approach for daily data covering the period from December 2010 to June 2014. Our findings highlight the speculative behavior of Bitcoin. This virtual currency may be also used for economic reasons. However, there is any sign of being a safe haven. By considering the Chinese trading bankruptcy, the contribution of speculation (proxied by investors’ attractiveness to Bitcoin) remains dominant, indicating the robustness of our results.
Florian Glaser, Martin Haferkorn, Moritz Weber, Kai Zimmermann
Digital currencies are gaining more and more attention against the backdrop of recent events triggered by the ongoing economic crisis. While digital currencies face increasing popularity, the currencies' prices are free floating and subject to high volatility as a result of lacking fundamental valuation methods. On the basis of an overview over the most prominent currency -- Bitcoin -- and an economic literature review we propose an econometric model that incorporates the basic components of the current price discovery process of a digital currency's exchange rate. On the basis of our empirical validation we further show that, in the case of Bitcoin, price volatility is significantly influenced by the media coverage and positive sentiment.
OBJECTIVES OF THE STUDY:\n\nThis thesis has three objectives. First, the past development of monetary systems is studied to see how Bitcoin is positioned as the forerunner of a new category. Second, the attitudes and expecta-tions of Finnish stakeholders are studied to recognize the general perception and future outlook for Bitcoin. Third, bitcoins are examined as an investment instrument by studying price drivers and the degree of predictability of future returns.\n\nDATA AND METHODOLOGY:\n\nThe qualitative methods are based on a literature review and an interview study conducted with Finnish stakeholders from different financial institutions and Bitcoin start-ups. The quantitative methods consist of market sizing calculations, a regression analysis, and Granger tests. The regres-sions utilize novel variables network hashrate, network transactions, and bitcoin supply as explan-atory variables for bitcoin price. Also bitcoin price and Google Trends SVI are used as explanatory variables. The market sizing calculations are based on M2 monetary aggregates for USD, EUR, and JPY.\n\nFINDINGS OF THE STUDY:\n\nThe thesis develops a categorization for decentralized cpytocurrencies that takes into account the main developments of the past monetary systems. The interview study reveals optimism for the technology behind Bitcoin and other decentralized systems, while all interviewees accept the un-certainty of Bitcoin survival. The stakeholders perceive the main challenges of Bitcoin to be tech-nological weaknesses, trust, and reputational issues. In terms of market sizing, it is clear that Bitcoin is not currently a serious threat to fiat currencies. Price driver analysis revealed a momen-tum effect in price returns, as well as an inflationary effect caused by the increasing supply. Also the network hashrate was found to forecast future bitcoin returns. The results from the Granger tests challenge the causality assumed in the regressions.
We present a thorough empirical analysis of market impact on the Bitcoin/USD exchange market using a complete dataset that allows us to reconstruct more than one million metaorders. We empirically confirm the "square-root law'' for market impact, which holds on four decades in spite of the quasi-absence of statistical arbitrage and market marking strategies. We show that the square-root impact holds during the whole trajectory of a metaorder and not only for the final execution price. We also attempt to decompose the order flow into an "informed'' and "uninformed'' component, the latter leading to an almost complete long-term decay of impact. This study sheds light on the hypotheses and predictions of several market impact models recently proposed in the literature and promotes heterogeneous agent models as promising candidates to explain price impact on the Bitcoin market -- and, we believe, on other markets as well.
Bitcoin is an unregulated digital currency originally introduced in 2008 without legal tender status. Based on a decentralized peer-to-peer network to confirm transactions and generate a limited amount of new bitcoins, it functions without the backing of a central bank or any other monitoring authority. In recent years, Bitcoin has seen increasing media coverage and trading volume, as well as major capital gains and losses in a high volatility environment. Interestingly, an analysis of Bitcoin returns shows remarkably low correlations with traditional investment assets such as other currencies, stocks, bonds or commodities such as gold or oil. In this paper, we shed light on the impact an investment in Bitcoin can have on an already well-diversified investment portfolio. Due to the non-normal nature of Bitcoin returns, we do not propose the classic mean-variance approach, but adopt at Conditional Value-at-Risk framework that does not require asset returns to be normally distributed. Our results indicate that Bitcoin should be included in optimal portfolios. Even though an investment in Bitcoin increases the CVaR of a portfolio, this additional risk is overcompensated by high returns leading to better risk-return ratios.
To understand the relationship between experimental and behavioral economics, we need to go back to the late 1970s and early 1980s. In the 1970s, psychologists began conducting new kinds of experiments, the results of which seemed to falsify the assumption of rational individual behavior. This compelled experimental economists to stake out a position for the economics discipline regarding the results. Much to their surprise, their experiments corroborated the results of the psychologists. This led them to completely discard preference theory but at the same time to emphasize the role of the market as the mechanism that rationalizes individual behavior. An initially diverse and unorganized group of financial and other economists drew very different conclusions from these same experimental results. They saw them as proof of observed anomalies in financial markets and hailed Daniel Kahneman and Amos Tversky's prospect theory as the most important candidate for replacing the traditional microeconomic model of human behavior.
Recent work in the corporate governance literature stresses the need to provide boards of directors (BoDs) with explicit incentives to safeguard shareholder welfare (Bebchuk, Fried, and Walker 2002; Bebchuk and Fried 2004). Jensen (1993) observes that “encouraging outside board members to hold substantial equity interests would provide better incentives.” In a similar spirit, the National Association of Corporate Directors (National Association of Corporate Directors 1995) proposed that “boards should pay directors solely in the form of stock and cash — with equity representing a substantial portion of the total up to 100 percent.” Indeed, equity-based BoD compensation has been on the rise in recent years (Bhagat and Black 2002; Conference Board 2006; Pearl Meyer & Partners 2007).1 The underlying premise is that equity awards help align BoD incentives with shareholder interests and enhance long-term firm value (Byrne 1996; Gabrielle 2001). However, to the extent that directors hold both vested and unvested (restricted) equity-based instruments, their actions are likely influenced by a combination of short-term and long-term incentives. The literature has focused mainly on the beneficial long-term incentive effects of equity awards. However, the effects of accompanying short-term incentives are not clear. Are they non–value adding, or do they in fact affect BoD behavior in a way that benefits shareholders? We address these questions in this paper. In particular, our purpose is to jointly examine the short-term and long-term incentive effects of equity-based BoD compensation on the BoD’s corporate governance (contracting and monitoring) and advisory (consulting) roles.2 The boards’ corporate governance role has been examined extensively.3 However, directors are typically individuals with considerable management experience and expertise and serve as a natural resource for top management in making crucial strategic and operational decisions.4,5 In fact, surveys have indicated that most directors view advising as their primary role (Mace 1972; Demb and Neubauer 1992; Adams 2009). Nevertheless, the BoD’s advisory role has received relatively little attention in the literature. Adams and Ferreira (2007), for example, examine the BoD’s monitoring and advisory role and show that a less independent BoD is sometimes optimal because it is less likely to monitor management, which, in turn, induces management to share information with the BoD, and receive better advice leading to greater investment efficiency.6 If this advisory role is indeed value-enhancing for shareholders, it cannot be ignored when examining the short-term and long-term incentive effects of BoD compensation. We use a simple agency model in which the BoD performs three roles: contracting, monitoring and consulting. The BoD contracts with the manager to supply some productive input that results in firm output. A performance evaluation system that produces an informative signal about firm output, and consequently about managerial effort, is used to contract with the manager. By monitoring, the BoD improves the precision of this information signal. By serving as a consultant, the BoD makes the manager more productive, that in turn means higher expected firm output. The BoD and the manager’s inputs are unobservable and personally costly.7 We assume that board members are themselves rational and self-serving, and must be motivated to provide consulting and monitoring inputs. Consequently, there are two agency problems in our model. The first is between the BoD and the manager, and the second is between the BoD and the shareholders. Both the agency problems arise because the BoD and manager’s respective inputs are unobservable and personally costly. In this respect, our paper adds to the growing literature that models shareholder-manager conflict as arising from a two-tier agency relationship. In our context, a single-tier model that examines shareholder-manager agency conflict stemming from the separation of ownership and control does not permit a role for the BoD. Therefore, by examining a multi-tier agency relationship our paper helps us better understand organizations (Bolton and Scharfstein 1998). In related work, Kumar and Sivaramakrishnan (2008) examine the BoD’s corporate governance role using a double agency model. They focus on the impact of the lack of BoD independence from management on corporate governance, and characterize optimal equity awards to the BoD to create the right BoD incentives. Harris and Raviv (2008) present a model where control of the board can be given to either insiders (the non-independent board) or outsiders (the independent board) — both insiders and outsiders have private payoff-related information. They show that it is sometimes beneficial to give board control to insiders in order to better exploit their information. We begin our analysis by examining a benchmark setting in which the BoD’s inputs are commonly observable. In this benchmark case, it suffices to compensate the BoD for the personal cost of providing consulting and monitoring inputs. When the BoD’s inputs are not observable, explicit BoD incentives become necessary. We show that long-term incentives (i.e., incentives tied to firm output) make the BoD explicitly care about the firm’s output and thereby motivate the BoD to play an active consulting role. Thus, compensating the BoD with restricted stock awards (equity) motivates the BoD to supply consulting input. However, we identify conditions under which long-term incentives alone do not suffice in motivating the BoD’s monitoring input. The is that the BoD’s monitoring input improves the of the performance evaluation system used to managerial and has on firm output. The BoD, does not have incentive to supply monitoring input. the need for short-term incentives. 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Listed real estate companies, just like all other listed companies around the world, have to publish quarterly as well as annual reports, informing the public and especially the share and stake holders about the current development of the company. These reports are always awaited with great anticipation. Will there be bad news or good news in the report? And if there is bad news, is it as bad as suspected or better and vise versa are good financial news as good as everybody expected. Depending on this news, changes in the stock price are expected, as well as observed changes in stock price are often explained with an interpretation of the content of the reports. It is commonly expected and believed that bad news have a negative effect on the performance and vise versa with god news. As obvious as it seems, so difficult it is to proof, as god and bad is not an absolute definition but rather a relative one in the eye of the beholder. An interesting question arising from this is, if the publication of financial reports have an general impact, meaning ìalwaysî significantly positive or negative, basically regardless of the content. In order to verify whether the publication of these reports has an effect, an event study shall observe abnormal returns around the time of the publication. Further options are to test if relatively late / early publication has an effect ñ a problem here will be the definition of the ìnormalî time of publication ñ and if the timing within the week or the timing with regard to the period of the year have an influence. // ï The aim of this study is to find out whether a general effect (always positive or always negative) on stock performance can be observed caused by the publication of financial reports, although it is commonly expected to depend upon the content; ï Does it matter whether the reports are published relatively early or relatively late; ï Does timing matter with regard to day of the week or period of the year // On a scientific level, the contribution of this study lies in analyzing whether behavioral effects influence the stock performance of listed real estate companies. From a professional point of view, implications for an optimized investment strategy can be obtained as well as implications for the optimal information strategy of listed real estate companies with regard to their stock performance.
Value at risk (VaR) is a central concept in risk management. As stressed by Artzner et al. (1999, Coherent measures of risk, Math. Finance 9(3) 203–228), VaR may not possess the subadditivity property required to be a coherent measure of risk. The key idea of this paper is that, when tail thickness is responsible for violation of subadditivity, eliciting proper conditioning information may restore VaR rationale for decentralized risk management. The argument is threefold. First, since individual traders are hired because they possess a richer information on their specific market segment than senior management, they just have to follow consistently the prudential targets set by senior management to ensure that decentralized VaR control will work in a coherent way. The intuition is that if one could build a fictitious conditioning information set merging all individual pieces of information, it would be rich enough to restore VaR subadditivity. Second, in this decentralization context, we show that if senior management has access ex post to the portfolio shares of the individual traders, it amounts to recovering some of their private information. These shares can be used to improve backtesting to check that the prudential targets have been enforced by the traders. Finally, we stress that tail thickness required to violate subadditivity, even for small probabilities, remains an extreme situation because it corresponds to such poor conditioning information that expected loss appears to be infinite. We then conclude that lack of coherence of decentralized VaR management, that is VaR nonsubadditivity at the richest level of information, should be an exception rather than a rule.
Abstract— Cryptocurrency markets have experienced rapid growth, attracting attention from investors, traders, and researchers. Accurate price prediction is critical for effective risk management and investment strategies. This paper proposes a hybrid deep learning model that combines Transformer Encoder and Gated Recurrent Unit (GRU) architectures with technical indicators to predict cryptocurrency prices. The model uses daily OHLC data and selected technical indicators, achieving strong predictive performance on BTC-USD, ETH-USD, and BNB-USD. The results demonstrate that the model accurately captures price trends with low prediction errors, achieving RMSE and MAPE values of 2607.32 and 3.70% for BTC-USD, 205.86 and 6.02% for ETH-USD, and 21.45 and 4.09% for BNB-USD, respectively. These findings highlight the model’s potential for developing adaptive trading strategies and advancing decision-making in cryptocurrency markets.
An important function of the financial system is to serve as a key source of information that helps coordinate decentralized decision-making in various sectors of the economy. Households and investors use interest rates, futures prices and security prices in making their consumption-saving decisions and portfolio allocation decisions. Interest rates and prices provide important signals to managers of firms in their selection of investment projects and financings. This paper illustrates the role played by financial markets in providing information about the future volatility-that is, the degree of uncertainty-of economic variables such as interest rates, exchange rates, commodity prices, and stock, bond and other security prices. It has two basic goals: (1) to show the importance of volatility for all sorts of policy decisions in the private and public sectors of the economy; and (2) to show how ex ante estimates of future volatility can be extracted from the prices of securities.