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Perhaps the most popular pricing model among Bitcoin enthusiasts is the stock-to-flow (S2F) model. The model gained significant traction after successfully predicting the meteoric rise of Bitcoin prices from late 2020 to early 2021. This paper dissects the S2F model for Bitcoin empirically to determine its viability and investigate whether investors can profit from an S2F-based trading strategy.

This paper, dissects the S2F model for Bitcoin by putting it through a battery of tests to examine its design, characteristics, robustness and appropriateness.

Overall, this paper finds the S2F model to be insensitive to differing assumptions in the early stages of the model, alleviating concerns about data mining. This paper produces a dynamic S2F model with no peek-ahead bias and shows evidence that prediction accuracy increases over time. Finally, this paper shows that a dynamic trading strategy that goes long (short) when Bitcoin is undervalued (overvalued) according to S2F is far less profitable than a classic buy-and-hold strategy.

To the best of the authors’ knowledge, this is the first paper to analyze the S2F model in an academic setting by providing a rigorous assessment of the model's construction. This paper demonstrates how the model can be implemented realistically without the peek-ahead bias, creating a tool that can be used contemporaneously by investors.

This paper aims to investigate the consequences of mergers and acquisitions (M&As) on information asymmetry in the banking sector. Specifically, the authors look at whether specific firm or deal characteristic influence information asymmetry levels between insiders and investors, as well as the impact of recent regulation such as the Dodd–Frank Act.

The authors decompose the M&A process into three periods (pre-announcement, negotiation and post-completion period) and document changes in the information asymmetry levels between insiders and investors through the M&A process. The authors capture changes in information asymmetry using six different spread-based information asymmetry measures.

The authors find evidence that information asymmetry increases following M&A announcement and decreases following deal completion. These findings are more pronounced for acquisitions involving a private target, all-cash deals and for mergers, as opposed to acquisition of assets. We find that overall, successful mergers improve the quality of the information environment, while failed deals degrade it. Additionally, the enactment of Dodd–Frank reduced the magnitude of the changes in information asymmetry during the M&A process. The results are important to regulators, policy makers and investors.

To authors’ knowledge, this is the first study that looks at the effect of bank M&As on information asymmetry as well as the effect of regulations on information asymmetry.

The purpose of this paper is to identify whether the rise in intangible asset investment is related to trade credit investment and whether this relationship is driven by financial constraint and other firm factors.

The study conducts fixed effect regressions testing the relationship between trade credit investment and intangible asset levels. The relationship is further examined for all firms based on product type, financial constraint and sales growth.

There is a negative relationship between investment in trade credit and the level of intangible assets as a proportion of total assets. This negative relationship is largely explained by firms in industries that traditionally utilize more trade credit, firms with financial constraints and firms with low sales growth.

The level of investment in intangible assets continues to rise, while investment in trade credit is declining. This paper is the first to identify whether these trends could be related and to provide some explanation why.

This study is the first to link investment in trade credit with investment in intangible assets. There is a negative relationship that is most pronounced for firms that typically offer more trade credit, that are experiencing financial constraint and that are experiencing low growth.

Professionals and academics alike hold polarized opinions about Bitcoin’s purpose and its fundamental value. This paper aims to describe Bitcoin’s unique features that make it such an intriguing asset and proposes a new way to consider Bitcoin and its underlying value.

In this paper the author discusses Bitcoin’s defining features that make it a unique asset. The author argues that Bitcoin should not be considered as a single purpose asset only, but rather as a new digital financial asset serving several functions, at least partially. The author discusses the role of Bitcoin in the traditional financial system, contrasts Bitcoin to gold, considers the implications of the continuance of expansionary policies on Bitcoin and discusses the impact of the emergence of cryptocurrencies as a new asset class on public policies.

In addition to functioning as a means of payment (at least partially) and a diversification tool, part of Bitcoin’s value proposition stems from its worth as a short position on modern expansionary monetary policies. Indeed, Bitcoin’s value should rise if expansionary monetary policies are maintained, amounting to a tool to short these policies, which should be considered in future attempts to value Bitcoin.

The author adds a new layer to the ongoing thought process by arguing of a function played by Bitcoin unaccounted for thus far by the literature. Additionally, the author describes the features and mechanisms, allowing Bitcoin to play that role.

The purpose of this paper is to estimate the performance of 188 mutual funds relative to the risk/return frontier accounting for the transaction costs of producing a portfolio of investments.

The directional output distance function is used to estimate mutual fund performance. The method allows the data to define a frontier of return and risk accounting for the transaction costs associated with securities management and production of risky returns. Proxies for the transaction costs of producing a portfolio of securities include the turnover ratio, load, expense ratio, and net asset value. The estimates of mutual fund performance are bootstrapped to account for the unknown data generating process. By comparing each mutual fund’s performance relative to the capital market line the authors determine how the fund should adjust their portfolio in regard to risk and return in order to maximize the inefficiency adjusted Sharpe ratio.

The bootstrapped estimates indicate that the average mutual fund could simultaneously expand return and contract risk by 3.2 percent if it were to operate on the efficient frontier. After projecting each mutual fund’s return and risk to the efficient frontier the authors find that a majority of the mutual funds should reduce risk to be consistent with the capital market line.

Many researchers have used data envelopment analysis to estimate a piecewise linear frontier of risk and return to measure mutual fund performance. To the authors’ knowledge the research is the first to use a twice-differentiable quadratic directional distance function to measure the managerial performance and risk/return tradeoff of mutual funds.