FinTech

Nowadays, blockchain bloat is an endangering issue caused by inefficient transaction storage mechanisms. Based on the Distributed File System (DFS), the blockchain network can reduce the local storage to solve the blockchain bloat problem. However, storing all blocks on DFS is not durable or scalable. Hence, classifying blocks into hot and cold was adopted in previous works. The blockchain nodes can reduce the time consumption and storage consumption by storing hot blocks locally. However, the previous works are not able to periodically check block integrity and do not provide a reward mechanism to encourage DFS system nodes to store blocks. We extend previous works based on the InterPlanetary File System (IPFS) and design an innovative scheme to incentivize IPFS nodes. The IPFS nodes are regulated with smart contracts and behave under the pricing strategy controls to increase profit. By adopting proof of retrievability, we guarantee the integrity of the blocks. Further, the redundant scheme extends our pricing strategy to improve the durability of our proposed framework. A load-balancing pricing strategy and a general pricing strategy are provided in the framework to reward the DFS nodes. Extensive experiments are presented to demonstrate that the latency and throughputs of our model are competitive, while still maintaining data integrity in the system. The additional increased throughput takes only 0.167% of that produced by the original Bitcoin and the upload latency takes only 6.67% of the mining time of the Bitcoin Mainnet. Furthermore, our load-balancing pricing strategy achieves the effectiveness to ensure the redundancy of blocks and reduces the overall storage consumption up to 97% using the load-balancing pricing strategy, compared to the non-load-balancing pricing strategy. Full article

This paper discusses critical considerations in the design of central bank digital currency (CBDC) in West Africa through a comparative case study of Ghana’s (eCedi) and Nigeria’s (eNaira) design frameworks. This paper analyses CBDC design options framed through context (digital payment landscape and CBDC objectives), technical aspects (design principles, architecture, risks), use cases, and deployment plans. This study conducted a thematic analysis of official CBDC design documents to identify similarities, differences, and patterns. The results indicate more similarities between the eCedi and eNaira designs than differences. Differences were observed in the CBDC deployment context, risk profiles, and plans. Surprisingly, neither country has articulated the detailed legal and regulatory environments for CBDC. This paper highlights the use of CBDC designs to promote citizens’ welfare by using financially inclusive policy goals within central banking’s welfare functions, thereby extending their traditional role. Policymakers should focus on adaptive legal and policy design outlooks to address uncertainties associated with CBDC. This paper is important because it is one of the first to contribute to a detailed comparison of Ghana and Nigeria’s CBDC design frameworks. Full article

The Metaverse refers to a shared vision among technology entrepreneurs of a three-dimensional virtual world, an embodied internet with humans and the physical world in it. As such, the Metaverse is thought to expand the domain of human activity by overcoming spatial, temporal, and resource-related constraints imposed by nature. The technological infrastructure of the Metaverse, i.e., Web3, consists of blockchain technology, smart contracts, and Non-Fungible Tokens (NFTs), which reduce transaction and agency costs, and enable trustless social and economic interactions thanks to decentralized consensus mechanisms. The emerging Metaverse may give rise to new products and services, new job profiles, and new business models. In this brief note, I assess the promises and challenges of the Metaverse, offer a first empirical glimpse at the emerging Metaverse economy, and discuss some simple Metaverse economics that revolve around building and operating the Metaverse. Full article

Non-fungible tokens (NFTs) are transferrable rights to digital assets, such as art, in-game items, collectables, or music. The phenomenon and its markets have grown significantly since early 2021. We investigate the interrelationships between NFT sales, NFT users (unique active blockchain wallets), and the pricing of Bitcoin (BTC) and Ether (ETH). Using daily data between January 2018 and April 2021, we show that a Bitcoin price shock triggers an increase in NFT sales. Also, Ether price shocks reduce the number of active NFT wallets. The results suggest that (larger) cryptocurrency markets affect the growth and development of the (smaller) NFT market, but there is no reverse effect. Full article

As new digitalization strategies storm the banking industry, banks which are behind the technological curve may struggle to keep pace. This is a well-known challenge in the Islamic banking sector in particular; however, this research shows that little is being done in order to achieve unified digitalization in operations. The 2020 Global Islamic Bankers Survey (GIBS) from CIBAFI sought opinions and data from 101 Islamic banks, which outlined both their institutions’ adoption of financial technology and their awareness of existing technologies. In addition, several technology trends—such as AI, machine learning, DLTs, and P2P lending—were analyzed separately in order to understand how they may be implemented within Islamic banking. This paper performed different statistical procedures to answer these research questions via correlation analysis and one-way ANOVA. The data were compiled and analyzed using SPSS software. In doing so, this study clarified the perspective of Islamic banks on digital transformation and answered whether Islamic banks are taking the right direction in terms of their digitalization strategies. Interestingly, most newly developing technologies have a low implementation level in Islamic banking operations globally, with the exception of mobile banking, which already has a vast global infrastructure. The results may serve as a warning to Islamic banks to invest more capital and energy in the developing fields of financial technologies in order to keep abreast of their conventional banking counterparts. Full article

Firms are eager to adopt new technologies, such as Artificial Intelligence (A.I.), Cloud Computing, Big Data, etc., as they witness successful business applications. As one of the disruptive technologies, Blockchain technology (BCT) has been drawing attention stemming from cryptocurrency proliferation (e.g., Bitcoin and Ethereum), for which Blockchain serves as the backbone. However, the public is haunted by the bewilderment between cryptocurrencies and BCT. Furthermore, the burgeoning of Metaverse and non-fungible tokens (NFT) has raised BCT to another notch. This study conducts a holistic literature review on BCT features, implementations, and business implications. In particular, by reviewing and analyzing 2265 up-to-date articles that reveal BCT’s applications across various fields, this Blockchain-centered study reveals the research status and delineates future research directions. It is shown that, among various characteristics of BCT, traceability is the main characteristic fueling BCT’s application in supply chain management (SCM). We further find that the BCT-related research has been extremely growing in SCM, healthcare, and government, while declining in the areas of banking and cyber security. Geographically, the top countries with BCT-related publications are China, U.S., and India. Finally, it is emphasized that BCT-related research in environmental sciences and agriculture have potential to be explored. Full article

This research aims to study the pricing risks of options by using improved LSTM artificial neural network models and make direct comparisons with the Black–Scholes option pricing model based upon the option prices of 50 ETFs of the Shanghai Securities Exchange from 1 January 2018 to 31 December 2019. We study an LSTM model, a mathematical option pricing model (BS model), and an improved artificial neural network model—the regulated LSTM model. The method we adopted is first to price the options using the mathematical model—i.e., the BS model—and then to construct the LSTM neural network for training and predicting the option prices. We further form the regulated LSTM network with optimally selected key technical indicators using Python programming aiming at improving the network’s predicting ability. Risks of option pricing are measured by MSE, RMSE, MAE and MAPE, respectively, for all the models used. The results of this paper show that both the ordinary LSTM and the traditional BS option pricing model have lower predictive ability than the regulated LSTM model. The prediction ability of the regulated LSTM model with the optimal technical indicators is superior, and the approach adopted is effective. Full article

In the paper we investigate consensus formation, from an economic perspective, in a Proof-of-Stake (PoS) based platform inspired by the Algorand blockchain. In particular, we consider PoS in relation to governance, focusing on two main issues. First we discuss alternative sampling schemes, which can be adopted to select voting committees and to define the number of votes of committee members. The selection probability is proportional to one’s stake and increases with it. Participation in governance allows users to affect the platform’s decisions as well as to obtain a reward. Then, based on such preliminary analysis, we introduce a microeconomic model to investigate the optimal stake size for a generic user. In the model we conceptualize an optimal stake, for a user, as striking the balance between having Algos immediately available for transactions and setting aside currency units to increase the probability of becoming a committee member. Our main findings suggest that the optimal stake can be quite sensitive to the user’s preferences and to the rules for selecting committees. We believe the findings may support policy decisions in PoS based platforms. Full article

The rapid development of Fintechs has brought opportunities and challenges to the profitability of banks. In this paper, we theoretically expound how Fintechs impact on banks’ profitability, then we establish the Error Correction Model (ECM) and combine this with the Granger causal relation test based on the data of the Industrial and Commercial Bank of China (ICBC) in 2011–2020. The research results show the following findings: (1) banks’ profitability (ROE) has a cooperative relationship with the development of Fintechs (FTI), banks’ assets (TA), the profitability of interest-bearing assets (NIM), credit risks (NPL) and cost control (CTI). (2) Fintechs have a “U”-shaped impact on the banks’ profitability. In the initial stages, Fintechs impact the business of banks, which reduces the profitability of banks; the advantages of Fintechs gradually increase in the middle and later stages, and the profitability gradually increases. (3) The assets of banks (TA) and the profitability of interest-bearing assets (NIM) change in the same direction as banks’ profitability (ROE), while credit risks (NPL) and cost control (CTI) change in the opposite direction from ROE. (4) The level of bank profitability and the development of Fintechs are Granger causes of each other, the size of the bank’s assets is the Grange reason for the increase in profitability and the increase in profitability is the Granger cause for the improvement of NIM and the decline in NPL. Full article

Information theoretic measures were applied to the study of the randomness associations of different financial time series. We studied the level of similarities between information theoretic measures and the various tools of regression analysis, i.e., between Shannon entropy and the total sum of squares of the dependent variable, relative mutual information and coefficients of correlation, conditional entropy and residual sum of squares, etc. We observed that mutual information and its dynamical extensions provide an alternative approach with some advantages to study the association between several international stock indices. Furthermore, mutual information and conditional entropy are relatively efficient compared to the measures of statistical dependence. Full article

We study the sensitivity of stock returns to the tail risk of major equity market indices, including the G10 countries. We model the sensitivity relationship via extreme downside hedging and estimate the parameters via a Bayesian graph structural learning method. The empirical application examines whether downside risk connections among the major stock markets are merely anecdotal or provide a signal of contagion and the nature of sensitivity among major equity markets during the global financial crisis and the coronavirus pandemic. The result showed that the COVID-19 crisis recorded the historically highest spike in the downside risk interconnectedness among the major equity market indices, suggesting higher financial market vulnerability in the coronavirus pandemic than during the global financial crisis. Full article

Algorithmic trading has become the standard in the financial market. Traditionally, most algorithms have relied on rule-based expert systems which are a set of complex if/then rules that need to be updated manually to changing market conditions. Machine learning (ML) is the natural next step in algorithmic trading because it can directly learn market patterns and behaviors from historical trading data and factor this into trading decisions. In this paper, a complete end-to-end system is proposed for automated low-frequency quantitative trading in the foreign exchange (Forex) markets. The system utilizes several State of the Art (SOTA) machine learning strategies that are combined under an ensemble model to derive the market signal for trading. Genetic Algorithm (GA) is used to optimize the strategies for maximizing profits. The system also includes a money management strategy to mitigate risk and a back-testing framework to evaluate system performance. The models were trained on EUR–USD pair Forex data from Jan 2006 to Dec 2019, and subsequently evaluated on unseen samples from Jan 2020 to Dec 2020. The system performance is promising under ideal conditions. The ensemble model achieved about 10% nett P&L with −0.7% drawdown level based on 2020 trading data. Further work is required to calibrate trading costs & execution slippage in real market conditions. It is concluded that with the increased market volatility due to the global pandemic, the momentum behind machine learning algorithms that can adapt to a changing market environment will become even stronger. Full article

Since FinTech was stimulated by the invention of blockchain, without the full realization of blockchain technologies in the following years, FinTech has not been fully realized. We discuss some myths and reasons for why blockchain technologies were not fully realized. The lack of distributed synchronization might be the most difficult challenge such that the trust provided by blockchain is not good enough for public use. We propose a mathematical solution with a new consensus mechanism based on general Proof-of-Work mining, called Proof-of-PowerTimestamp, to reach distributed synchronization and reduce power consumption to less than one billionth of Bitcoin. We also discuss related issues toward blockchain realization once the distributed synchronization and energy consumption problems are solved. Since the issues are mostly interdisciplinary or multidisciplinary, researchers are invited to cooperate to help blockchain realization as soon as possible. Full article

Feature selection is a popular topic. The main approaches to deal with it fall into the three main categories of filters, wrappers and embedded methods. Advancement in algorithms, though proving fruitful, may be not enough. We propose to integrate an explainable AI approach, based on Shapley values, to provide more accurate information for feature selection. We test our proposal in a real setting, which concerns the prediction of the probability of default of Small and Medium Enterprises. Our results show that the integrated approach may indeed prove fruitful to some feature selection methods, in particular more parsimonious ones like LASSO. In general the combination of approaches seems to provide useful information which feature selection algorithm can improve their performance with. Full article

The aim of this paper is to propose a portfolio selection methodology capable to take into account asset tail co-movements as additional constraints in Markowitz model. We apply the methodology to the observed time series of the 10 largest crypto assets, in terms of market capitalization, over the period 20 September 2017–31 December 2020 (1200 daily observations). The results indicate that the portfolios selected considering tail risk are more diversified and, therefore, more resilient to financial shocks. Full article

Artificial neural networks (ANNs) are suitable procedures for predicting financial time series (FTS). Cryptocurrencies are good investment assets; therefore, the effective prediction of cryptocurrencies has become a trending area of research. Capturing inherent uncertainties associated with cryptocurrency FTS with conventional methods is difficult. Though ANNs are the better alternative, fixing the optimal parameters of ANNs is a tedious job. This article develops a hybrid ANN through Rao algorithm (RA + ANN) for the effective prediction of six popular cryptocurrencies such as Bitcoin, Litecoin, Ethereum, CMC 200, Tether, and Ripple. Six comparative models such as GA + ANN, PSO + ANN, MLP, SVM, LSE, and ARIMA are developed and trained in a similar way. All these models are evaluated through the mean absolute percentage of error (MAPE) and average relative variance (ARV) metrics. It is found that the proposed RA + ANN generated the lowest MAPE and ARV values, statistically different as compared with existing methods mentioned above, and hence can be recommended as a potential financial instrument for predicting cryptocurrencies. Full article

An accurate prediction of the Exchange Rate (ER) serves as the basis for effective financial management, monetary policies, and long-term strategic decision making worldwide. A stable and competitive ER enables economic diversification. Economists, researchers, and investors have conducted several studies to predict trends and facts that influence the ER’s rise or fall. This paper used the Long Short-Term Memory Networks (LSTM) framework to predict the weekly exchange rate of one Ghanaian Cedis (GH₵) to three different currencies (United States Dollar, British Pound, and Euro), using Google Trends and historical macroeconomic data. We fused past exchange rates, fundamental macroeconomic variables, commodity prices (cocoa, gold, and crude oil) and public search queries (Google Trends) as input parameters. An empirical analysis using publicly available ER data from the Bank of Ghana (BoG) from January 2004 to October 2019 showed satisfactory results. We observed that the proposed LSTM model outperformed the Support Vector Regressor (SVR) and Back-propagation Neural Network (BPNN) models in accuracy and closeness metrics. That is, our LSTM model obtained (MAE = 0.033, MSE = 0.0035, RMSE = 0.0551, R2 = 0.9983, RMSLE = 0.0129 and MAPE = 0.0121) compared with SVR (MAE = 0.05, MAE = 0.005, RMSE = 0.0683, R2 = 0.9973, RMSLE = 0.0191 and MAPE = 0.0241) and BPNN (MAE = 0.04, MAE = 0.0056, RMSE = 0.0688, R2 = 0.9974, RMSLE = 0.0172 and MAPE = 0.0168). Moreover, we observed a strong positive correction (0.98–0.99) between Google Trends on the currency of focus and its exchange rate to the Ghanaian cedis. The study results show the importance of incorporating public search queries from search engines to predict the ER accurately. Full article