Financial Innovation

This paper studies the effect of option trading on corporate investment and financing policies. Based on prior literature, I hypothesize that option market induces informed trading and thus reduces information asymmetry and the cost of capital. As a result, firms with high option trading have more investment and financing. Specifically, based on the United States public data, this paper finds that option trading volume increases corporate investment and financing, but reduces cash holdings and corporate payouts. These results are robust to the inclusion of industry or firm fixed effect, a control for endogenous options trading, and the use of alternative measures of option trading and corporate policies. The effect of option trading is stronger for firms with higher information asymmetry problems. Finally, this paper finds the results are inconsistent with the “quiet Life” hypothesis and the catering hypothesis.

This study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange. To achieve the objectives, the study uses descriptive statistics; tests including variance ratio, Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski Phillips Schmidt and Shin; and Autoregressive Integrated Moving Average (ARIMA). The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series, using the ARIMA model. The results reveal that the mean returns of both indices are positive but near zero. This is indicative of a regressive tendency in the long-term. The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values, with few deviations. Hence, the ARIMA model is capable of predicting medium- or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

Blockchain has become a new frontier of venture capitals that has attracted the attention of banks, governments, and other business corporations. The recent blockchain related attempts included legal blockchains by Fadada.com and Microsoft and pork tracking blockchains by Walmart and IBM. Blockchain is poised to become the most exciting invention after the Internet; while the latter connects the world to enable new business models based on online business processes, the former will help resolve the trust issue more efficiently via network computing. In this paper, we give an overview on blockchain research and development as well as introduce the papers in this special issue. We show that while blockchain has enabled Bitcoin, the most successful digital currency, its widespread adoption in finance and other business sectors will lead to many business innovations as well as many research opportunities.

This study aims to identify which determinants are responsible for impacting the user experience of three peer-to-peer (P2P) payment services in the Spanish market. A sample of all online reviews (n = 16,048) published in Google Play of three paytech apps—Bizum, Twyp, and Verse—was analyzed using text mining and sentiment analysis. A holistic interpretation of the seed terms included in each aspect allowed to label them based on the preferences expressed by paytech app users in their reviews. Six latent aspects were identified: ease of use, usefulness, perceived value, performance expectancy, perceived quality, and user experience. In addition, the results of the analysis suggest a positivity bias in the online reviews of fintech P2P app users. Our results also show that online reviews of apps associated with banks or financial institutions, such as Bizum (to a greater extent) or Twyp, show more negative emotions, whereas independent apps (Verse) show more positive emotions. Moreover, the most critical users are those of unidentified gender, while women remain in a more neutral position, and men tend to express their opinions more positively regarding P2P payment apps. Paytech providers should analyze the problems faced by users immediately after an encounter. By applying text mining analysis, service providers can gain efficiency in understanding user sentiments and emotions without tedious and time-consuming reviews. This is a pioneering study on peer-to-peer (P2P) mobile payment systems from the user’s perspective because it investigates the emotions and sentiments that users convey through bank reviews.

Nghiên cứu này điều tra mối liên kết giữa Bitcoin và các loại tiền tệ Fiat tại hai nhóm quốc gia: các nước phát triển G7 và các nước mới nổi BRICS. Phương pháp nghiên cứu áp dụng copula R-vine thông thường (R) và so sánh với hai mô hình chuẩn: copula t đa biến và mô hình GARCH điều kiện động (DCC). Hơn nữa, nghiên cứu này xem xét liệu sự sụp đổ của Bitcoin vào năm 2013, đợt bán tháo năm 2018, đại dịch COVID-19, sự sụp đổ năm 2021, và cuộc xung đột Nga-Ukraine có ảnh hưởng đến mối liên kết với các đồng tiền thông thường hay không. Kết quả cho thấy rằng đối với cả hai giỏ tiền tệ, R-vine vượt trội hơn so với các mô hình chuẩn. Do đó, sự phụ thuộc được mô hình hóa tốt hơn bằng cách cung cấp đủ thông tin về con đường truyền tải cú sốc. Hơn nữa, mối liên kết giữa các thị trường có xu hướng tăng nhẹ trong các đợt sụp đổ của Bitcoin, và đạt mức độ đáng kể trong các cuộc khủng hoảng năm 2021 và 2022, điều này có thể chỉ ra sự kết thúc của tình trạng cách ly trên thị trường của đồng tiền ảo.

Decentralized Finance (DeFi) is a system of financial products and services built and delivered through smart contracts on various blockchains. In recent years, DeFi has gained popularity and market capitalization. However, it has also been connected to crime, particularly various types of securities violations. The lack of Know Your Customer requirements in DeFi poses challenges for governments trying to mitigate potential offenses. This study aims to determine whether this problem is suited to a machine learning approach, namely, whether we can identify DeFi projects potentially engaging in securities violations based on their tokens’ smart contract code. We adapted prior works on detecting specific types of securities violations across Ethereum by building classifiers based on features extracted from DeFi projects’ tokens’ smart contract code (specifically, opcode-based features). Our final model was a random forest model that achieved an 80% F-1 score against a baseline of 50%. Notably, we further explored the code-based features that are the most important to our model’s performance in more detail by analyzing tokens’ Solidity code and conducting cosine similarity analyses. We found that one element of the code that our opcode-based features can capture is the implementation of the SafeMath library, although this does not account for the entirety of our features. Another contribution of our study is a new dataset, comprising (a) a verified ground truth dataset for tokens involved in securities violations and (b) a set of legitimate tokens from a reputable DeFi aggregator. This paper further discusses the potential use of a model like ours by prosecutors in enforcement efforts and connects it to a wider legal context.

The recently developed Bitcoin futures and options contracts in cryptocurrency derivatives exchanges mark the beginning of a new era in Bitcoin price risk hedging. The need for these tools dates back to the market crash of 1987, when investors needed better ways to protect their portfolios through option insurance. These tools provide greater flexibility to trade and hedge volatile swings in Bitcoin prices effectively. The violation of constant volatility and the log-normality assumption of the Black–Scholes option pricing model led to the discovery of the volatility smile, smirk, or skew in options markets. These stylized facts; that is, the volatility smile and implied volatilities implied by the option prices, are well documented in the option literature for almost all financial markets. These are expected to be true for Bitcoin options as well. The data sets for the study are based on short-dated Bitcoin options (14-day maturity) of two time periods traded on Deribit Bitcoin Futures and Options Exchange, a Netherlands-based cryptocurrency derivative exchange. The estimated results are compared with benchmark Black–Scholes implied volatility values for accuracy and efficiency analysis. This study has two aims: (1) to provide insights into the volatility smile in Bitcoin options and (2) to estimate the implied volatility of Bitcoin options through numerical approximation techniques, specifically the Newton Raphson and Bisection methods. The experimental results show that Bitcoin options belong to the commodity class of assets based on the presence of a volatility forward skew in Bitcoin option data. Moreover, the Newton Raphson and Bisection methods are effective in estimating the implied volatility of Bitcoin options. However, the Newton Raphson forecasting technique converges faster than does the Bisection method.

This study proposes a non-homogeneous continuous-time Markov regenerative process with recurrence times, in particular, forward and backward recurrence processes. We obtain the transient solution of the process in the form of a generalized Markov renewal equation. A distinguishing feature is that Markov and semi-Markov processes result as special cases of the proposed model. To model the credit rating dynamics to demonstrate its applicability, we apply the proposed stochastic process to Standard and Poor’s rating agency’s data. Further, statistical tests confirm that the proposed model captures the rating dynamics better than the existing models, and the inclusion of recurrence times significantly impacts the transition probabilities.

After publication of this article [1], it is reported this article contained an error in the section ‘Risk-free return (rf)’