Showing papers in "Financial Innovation in 2019"

A systematic review of blockchain

Abstract: Blockchain is considered by many to be a disruptive core technology. Although many researchers have realized the importance of blockchain, the research of blockchain is still in its infancy. Consequently, this study reviews the current academic research on blockchain, especially in the subject area of business and economics. Based on a systematic review of the literature retrieved from the Web of Science service, we explore the top-cited articles, most productive countries, and most common keywords. Additionally, we conduct a clustering analysis and identify the following five research themes: “economic benefit,” “blockchain technology,” “initial coin offerings,” “fintech revolution,” and “sharing economy.” Recommendations on future research directions and practical applications are also provided in this paper.

Institutions and FDI: evidence from developed and developing countries

Abstract: This study investigates the impact of institutional quality on Foreign Direct Investment (FDI) inflows using panel data for low, lower-middle, upper-middle and high-income countries for the sample period of 1996–2016 using the system Generalized Method of Moments (GMM). The empirical results confirm that institutional quality has a positive impact on FDI in all group of countries. The magnitude of the coefficients of control of corruption, government effectiveness, political stability, regulatory quality, rule of law, and voice and accountability for FDI inflows are greater in developed countries than in developing countries. We conclude that institutional quality is a more important determinant of FDI in developed countries than in developed countries. However, GDP per capita, agriculture value-added as a percentage of GDP, and inflation influence FDI inflows negatively in developed countries, while GDP per capita, trade openness, agriculture value-added as a percentage of GDP, and infrastructure have positive and statistically significant impacts on FDI inflows in developing countries. Trade openness as a percentage of GDP and infrastructure positively affect FDI in developed countries. From our analysis, we infer that institutional quality is a more important determinant of FDI in developed countries than in developing countries.

Indian stock market prediction using artificial neural networks on tick data

Abstract: Nowadays, the most significant challenges in the stock market is to predict the stock prices. The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature. Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are widely used for prediction of stock prices and its movements. Every algorithm has its way of learning patterns and then predicting. Artificial Neural Network (ANN) is a popular method which also incorporate technical analysis for making predictions in financial markets. Most common techniques used in the forecasting of financial time series are Support Vector Machine (SVM), Support Vector Regression (SVR) and Back Propagation Neural Network (BPNN). In this article, we use neural networks based on three different learning algorithms, i.e., Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared. All three algorithms provide an accuracy of 99.9% using tick data. The accuracy over 15-min dataset drops to 96.2%, 97.0% and 98.9% for LM, SCG and Bayesian Regularization respectively which is significantly poor in comparison with that of results obtained using tick data.

Predicting the daily return direction of the stock market using hybrid machine learning algorithms

Abstract: Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine learning techniques, such as deep neural networks (DNNs), to perform the analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and model parameters, with their performance depending on the format of the data representation. This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF (ticker symbol: SPY) based on 60 financial and economic features. DNNs and traditional artificial neural networks (ANNs) are then deployed over the entire preprocessed but untransformed dataset, along with two datasets transformed via principal component analysis (PCA), to predict the daily direction of future stock market index returns. While controlling for overfitting, a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000. Moreover, a set of hypothesis testing procedures are implemented on the classification, and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset, as well as several other hybrid machine learning algorithms. In addition, the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested, including in a comparison against two standard benchmarks.

Forecasting cryptocurrency returns and volume using search engines

Abstract: In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting, this brief study analyzes the predictability of Bitcoin volume and returns using Google search values. We employed a rich set of established empirical approaches, including a VAR framework, a copulas approach, and non-parametric drawings, to capture a dependence structure. Using a weekly dataset from 2013 to 2017, our key results suggest that the frequency of Google searches leads to positive returns and a surge in Bitcoin trading volume. Shocks to search values have a positive effect, which persisted for at least a week. Our findings contribute to the debate on cryptocurrencies/Bitcoins and have profound implications in terms of understanding their dynamics, which are of special interest to investors and economic policymakers.

Remittances, financial development and economic growth in sub-Saharan African countries: evidence from a PMG-ARDL approach

Abstract: The study investigated the relationship among remittances, financial development and economic growth in a panel of 20 sub-Saharan African countries over the period of 2000 and 2015. The study used both Pooled Mean Group and Mean Group/ARDL estimations with panel unit root and cointegration tests. After establishing cointegration, remittances and financial development were found to have positive effects on economic growth both in the short and the long run. The interactive term showed that financial development acted as a substitute in the remittances-growth relationship. Finally, unidirectional causal relationships were found to exist from GDP to remittances and from financial development to GDP. However, no causality existed between remittances and financial development in the SSA countries.

A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks

Abstract: Giving loans and issuing credit cards are two of the main concerns of banks in that they include the risks of non-payment. According to the Basel 2 guidelines, banks need to develop their own credit risk assessment systems. Some banks have such systems; nevertheless they have lost a large amount of money simply because the models they used failed to accurately predict customers’ defaults. Traditionally, banks have used static models with demographic or static factors to model credit risk patterns. However, economic factors are not independent of political fluctuations, and as the political environment changes, the economic environment evolves with it. This has been especially evident in Iran after the 2008–2016 USA sanctions, as many previously reliable customers became unable to repay their debt (i.e., became bad customers). Nevertheless, a dynamic model that can accommodate fluctuating politico-economic factors has never been developed. In this paper, we propose a model that can accommodate factors associated with politico-economic crises. Human judgement is removed from the customer evaluation process. We used a fuzzy inference system to create a rule base using a set of uncertainty predictors. First, we train an adaptive network-based fuzzy inference system (ANFIS) using monthly data from a customer profile dataset. Then, using the newly defined factors and their underlying rules, a second round of assessment begins in a fuzzy inference system. Thus, we present a model that is both more flexible to politico-economic factors and can yield results that are max compatible with real-life situations. Comparison between the prediction made by proposed model and a real non-performing loan indicates little difference between them. Credit risk specialists also approve the results. The major innovation of this research is producing a table of bad customers on a monthly basis and creating a dynamic model based on the table. The latest created model is used for assessing customers henceforth, so the whole process of customer assessment need not be repeated. We assert that this model is a good substitute for the static models currently in use as it can outperform traditional models, especially in the face of economic crisis.

Do migrant remittances matter for financial development in Kenya

Abstract: The paper analyzes the relationship between remittances and financial development using Kenyan quarterly data from 2006 to 2016. Five different indicators of financial development are used: credit to the private sector as a share of GDP, the number of mobile transactions, the value of these mobile transactions, the number of mobile agents, and the number of bank accounts. The results from using an autoregressive distributed lag demonstrate a strong, positive relationship between remittances and financial development in long-run equations. This suggests that higher levels of remittances provide opportunities for recipients to open bank accounts, enhance their savings, and access financial systems, in addition to exposing the previously unbanked to both new and existing financial products. The results also confirm the potential advantage of embracing modern and advanced technology to facilitate international mobile transfers. Using international remittance transfers through mobile technology reduces costs by eliminating the need for physical branches and personnel to attend to walk-in customers. Aside from offering convenience and safety for remittance actors, this method also dominates traditional remittance business models. Therefore, a policy window exists for the government to leverage on remittances as a tool of financial inclusion and depth, and particularly through the continued expansion of regulatory space to accommodate the wider use of international mobile remittance transfer channels. Moreover, given the strong, positive relationship between remittances and credit to the private sector as indicated by its share of GDP and number of bank accounts, commercial banks and other players in the remittance market may also find it useful to develop customized products for migrants to access their remittances. For example, financial intermediaries can consider providing better deposit interest rates for diaspora deposits compared to deposits made in the local currency. Further, these institutions can allow regular remittance flows to act as collateral for the allocation of credit, among other incentives to tap into the significant potential of money remitted by migrants to Kenya. The study also recommends that the government consider expanding exploitation of diaspora bonds and diaspora savings and credit cooperative societies while drawing lessons from other countries’ previous attempts.

Determinants of corporate cash holdings in tranquil and turbulent period: evidence from an emerging economy

Abstract: Using a sample of 280 firms listed on the Pakistan Stock Exchange, we empirically investigate factors that determine corporate cash holdings in different periods from 2005 to 2014. We divide the sample into three sub-periods—pre-crisis, crisis, and post-crisis—and apply a panel data model to estimate the results. The results suggest that financial crises affect firms’ cash holdings policies. Further, findings show that financial crisis has influenced the relationship of size and leverage with cash holdings. In particular, cash flow, liquidity, and tangibility are major determinants of cash holdings in the sub-periods. We present important implications for corporate managers, academicians, and policymakers.

Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures

Abstract: In this study, we analyze three portfolio selection strategies for loss-averse investors: semi-variance, conditional value-at-risk, and a combination of both risk measures. Moreover, we propose a novel version of the non-dominated sorting genetic algorithm II and of the strength Pareto evolutionary algorithm 2 to tackle this optimization problem. The effectiveness of these algorithms is compared with two alternatives from the literature from five publicly available datasets. The computational results indicate that the proposed algorithms in this study outperform the others for all the examined performance metrics. Moreover, they are able to approximate the Pareto front even in cases in which all the other approaches fail.

Evaluation of forecasting methods from selected stock market returns

Abstract: Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification. There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making. Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices. However, there have been very few studies of groups of stock markets or indices. The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets. In this context, this study aimed to examine the predictive performance of linear, nonlinear, artificial intelligence, frequency domain, and hybrid models to find an appropriate model to forecast the stock returns of developed, emerging, and frontier markets. We considered the daily stock market returns of selected indices from developed, emerging, and frontier markets for the period 2000–2018 to evaluate the predictive performance of the above models. The results showed that no single model out of the five models could be applied uniformly to all markets. However, traditional linear and nonlinear models outperformed artificial intelligence and frequency domain models in providing accurate forecasts.

Alternative measure of financial development and investment-cash flow sensitivity: evidence from an emerging economy

Abstract: This study examines the impact of financial development on corporate investment in terms of their influence on financing constraints. This study also tries to find the effect of financial development on the investment-cash flow sensitivity across the size, degree of financial constraints and group affiliation of the firm. This study employs dynamic panel data model or more specifically system generalized method of moments (GMM) estimation technique. The estimation results reveal that cash flow affects the investment decision of the company positively, which implies that Indian firms are financially constrained. Also, we observe that financial development reduces the investment-cash flow sensitivity and the effect of financial development is more prominent for small size and standalone firms. The results are robust across the period and, for both financially constrained and unconstrained firms. This study contributes to the existing literature by analyzing the impact of financial development on the role of cash flow in determining investments undertaken by the Indian firms, which is an unexplored issue from an emerging market perspective.

Co-movement in crypto-currency markets: evidences from wavelet analysis

Abstract: We study the time varying co-movement patterns of the crypto-currency prices with the help of wavelet-based methods; employing daily bilateral exchange rate of four major crypto-currencies namely Bitcoin, Ethereum, Lite and Dashcoin. First, we identify Bitcoin as potential market leader using Wavelet multiple correlation and Cross correlation. Further, Wavelet Local Multiple Correlation for the given crypto-currency prices are estimated across different time-scales. From the results, it is found that that the correlation follows an aperiodic cyclical nature, and the crypto-currency prices are driven by Bitcoin price movements. Based on the results obtained, we suggest that constructing a portfolio based on crypto-currencies may be risky at this point of time as the other crypto-currency prices are mainly driven by Bitcoin prices, and any shocks in the latter is immediately transformed to the former.

Effect of family control on corporate dividend policy of firms in Pakistan

Abstract: This study examines the impact of family control on the dividend policy of firms in Pakistan, covering the period from 2009 to 2016. It also investigates whether family control moderates the impact of firm-specific factors on the dividend policy. The GMM model for panel data estimation is used. The mean difference univariate analysis shows that family firms differ from nonfamily firms based on financial characteristics. The multivariate analysis shows that family firms pay lower dividends than nonfamily firms. Besides, firm size inversely affects the dividend policy, whereas tangibility positively affects it. Moreover, family control does not moderate the impact of all firm-specific factors on the dividend policy. Overall, family control, size, and tangibility are found to be the main determinants of the dividend policy in Pakistan.

Modeling and forecasting time series of precious metals: a new approach to multifractal data

Abstract: We introduce a novel approach to multifractal data in order to achieve transcended modeling and forecasting performances by extracting time series out of local Hurst exponent calculations at a specified scale. First, the long range and co-movement dependencies of the time series are scrutinized on time-frequency space using multiple wavelet coherence analysis. Then, the multifractal behaviors of the series are verified by multifractal de-trended fluctuation analysis and its local Hurst exponents are calculated. Additionally, root mean squares of residuals at the specified scale are procured from an intermediate step during local Hurst exponent calculations. These internally calculated series have been used to estimate the process with vector autoregressive fractionally integrated moving average (VARFIMA) model and forecasted accordingly. In our study, the daily prices of gold, silver and platinum are used for assessment. The results have shown that all metals do behave in phase movement on long term periods and possess multifractal features. Furthermore, the intermediate time series obtained during local Hurst exponent calculations still appertain the co-movement as well as multifractal characteristics of the raw data and may be successfully re-scaled, modeled and forecasted by using VARFIMA model. Conclusively, VARFIMA model have notably surpassed its univariate counterpart (ARFIMA) in all efficacious trials while re-emphasizing the importance of co-movement procurement in modeling. Our study’s novelty lies in using a multifractal de-trended fluctuation analysis, along with multiple wavelet coherence analysis, for forecasting purposes to an extent not seen before. The results will be of particular significance to finance researchers and practitioners.

The interaction between stock prices and interest rates in Turkey: empirical evidence from ARDL bounds test cointegration

Abstract: This paper demonstrates a significant, long-running relationship between stock prices and domestic interest rates in Turkey’s financial markets for the period of 2001 M1 – 2017 M4. Cointegration analysis is investigated using the autoregressive-distributed lag bounds (ARDL Bounds) test and vector autoregressive cointegration. Additionally, cointegrating equations such as the fully modified ordinary least square, dynamic ordinary least squares, and canonical cointegrating regression are applied to check the long-run elasticities in the concerned relationship. The ARDL Bounds and Johansen Cointegration test results show that, dynamically, both prices are significantly related to each other. The cointegrating equation outcomes demonstrate elasticities whereby both coefficients have negative signs. Additionally, the same results are corroborated by the impulse response where all variables respond negatively to each other.

A group decision model for credit granting in the financial market

Abstract: Group decision models that contemplate the particularities of the decision-making process help organizations pursue their strategic objectives. In the financial market, the primary interest of organizations consists in ensuring financial returns, which guarantee stability for the organization. This study identifies major problems in the current process of credit granting in the financial market and argues the need for automatizing the organizational decision process while respecting the autonomy of decision-makers. To this end, this study proposes a group decision model based on the Strategic Choice Approach (SCA) for granting credit in a financial market organization. The results show that the adoption of the proposed model offers considerable gains in terms of organizational goals, transparency of the decision-making process, security for decision-makers, and reduction of organizational conflicts.

A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

Abstract: Accurate prediction of stock market behavior is a challenging issue for financial forecasting. Artificial neural networks, such as multilayer perceptron have been established as better approximation and classification models for this domain. This study proposes a chemical reaction optimization (CRO) based neuro-fuzzy network model for prediction of stock indices. The input vectors to the model are fuzzified by applying a Gaussian membership function, and each input is associated with a degree of membership to different classes. A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model. CRO was chosen because it requires fewer control parameters and has a faster convergence rate. Five statistical parameters are used to evaluate the performance of the model, and the model is validated by forecasting the daily closing indices for five major stock markets. The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior. We conducted the Deibold-Mariano test to check the statistical significance of the proposed model, and it was found to be significant. This model can be used as a promising tool for financial forecasting.

Impact of risk management strategies on the credit risk faced by commercial banks of Balochistan

Abstract: This study aims to identify risk management strategies undertaken by the commercial banks of Balochistan, Pakistan, to mitigate or eliminate credit risk. The findings of the study are significant as commercial banks will understand the effectiveness of various risk management strategies and may apply them for minimizing credit risk. This explanatory study analyses the opinions of the employees of selected commercial banks about which strategies are useful for mitigating credit risk. Quantitative data was collected from 250 employees of commercial banks to perform multiple regression analyses, which were used for the analysis. The results identified four areas of impact on credit risk management (CRM): corporate governance exerts the greatest impact, followed by diversification, which plays a significant role, hedging and, finally, the bank’s Capital Adequacy Ratio. This study highlights these four risk management strategies, which are critical for commercial banks to resolve their credit risk.

Stock market and macroeconomic variables: new evidence from India

Abstract: Understanding the relationship between macroeconomic variables and the stock market is important because macroeconomic variables have a systematic effect on stock market returns. This study uses monthly data from India for the period from April 1994 to July 2018 to examine the long-run relationship between the stock market and macroeconomic variables. The empirical findings suggest that standard cointegration tests fail to identify any relationship among these variables. However, a transformation that extracts the actual functional relationship between these variables using the alternating conditional expectations algorithm of (J Am Stat Assoc 80:580–598, 1985) identifies strong evidence of cointegration and indicates nonlinearity in the long-run relationship. Further, the continuous partial wavelet coherency model identifies strong coherency at a lower frequency for the transformed variables, establishing the fact that the long-run relationship between stock prices and macroeconomic variables in India is nonlinear and time-varying. This evidence has far-reaching implications for understanding the dynamic relationships between the stock market and macroeconomic variables.

Cointegration between macroeconomic factors and the exchange rate USD/CNY

Abstract: This research paper investigates the effect of macroeconomic variables on the exchange rate USD/CYN using yearly time series data for China economy from 1980 to 2017. ARDL bounds test approach for cointegration is applied to test the long-run relation between the dependent and the independent variables. The results of long-run ARDL indicate that gross domestic product growth and trade openness have a positive effect on the exchange rate USD/CNY while interest and inflation rates have a negative effect on the exchange rate. Based on the results of this study, it is recommended that the policymakers of the Chinese government should implement vital monetary and fiscal policies to determine the less volatile and productive exchange rate for China to manage sustainable economic growth for a long time with its trading partners.

Macroeconomic effects of Mobile money: evidence from Uganda

Abstract: This study examined the effects of mobile money—a recent innovation in Uganda’s financial-sector landscape—on aggregate economic activity and other macroeconomic variables. We first estimated the long-run mobile-money demand function using vector error correction (VEC) techniques, distinguishing between balances and transfers/transactions. We then estimated the short-run effects of mobile money on selected macroeconomic variables using structural vector autoregressive (SVAR) methods. The results showed that mobile money had moderate positive effects on monetary aggregates, consumer price index, private-sector credit, and aggregate economic activity. Mobile money balances responded to changes in monetary policy instruments, signaling possible ameliorating effects for the conduct of monetary policy. Finally, the results showed that transactional motives related to mobile money had stronger macroeconomic effects than savings motives.

The impact of bank lending on Palestine economic growth: an econometric analysis of time series data

Abstract: Banking is an essential sector of Palestine’s economy. More credits provided by banks are considered to have a positive impact on economic growth so that the overall objective of this study is to examine the impact of bank lending on economic growth in Palestine. The study employs the Augmented Dickey-Fuller to test for stationarity in the time series, The Johansen co-integration, Vector Autoregressive Model and Vector Error Correction Model are employed to identify the long-run and short-run dynamics among the variables, and Granger causality test in order to determine the direction of causality. The study finds that a long run relationship exists among the variables and insignificant short run relationship. Also, the study findings show that there is unidirectional causality and runs from GDP to bank lending. The insignificant contribution of bank lending to GDP is attributed to the fact that banks are not highly interested in lending to the production sector of the economy due to the high level of risk. However, the primary empirical evidence reveals that bank lending doesn’t cause economic growth, but economic growth causes bank lending.

A hybrid Bayesian-network proposition for forecasting the crude oil price

Abstract: This paper proposes a hybrid Bayesian Network (BN) method for short-term forecasting of crude oil prices. The method performed is a hybrid, based on both the aspects of classification of influencing factors as well as the regression of the out-of-sample values. For the sake of performance comparison, several other hybrid methods have also been devised using the methods of Markov Chain Monte Carlo (MCMC), Random Forest (RF), Support Vector Machine (SVM), neural networks (NNET) and generalized autoregressive conditional heteroskedasticity (GARCH). The hybrid methodology is primarily reliant upon constructing the crude oil price forecast from the summation of its Intrinsic Mode Functions (IMF) and its residue, extracted by an Empirical Mode Decomposition (EMD) of the original crude price signal. The Volatility Index (VIX) as well as the Implied Oil Volatility Index (OVX) has been considered among the influencing parameters of the crude price forecast. The final set of influencing parameters were selected as the whole set of significant contributors detected by the methods of Bayesian Network, Quantile Regression with Lasso penalty (QRL), Bayesian Lasso (BLasso) and the Bayesian Ridge Regression (BRR). The performance of the proposed hybrid-BN method is reported for the three crude price benchmarks: West Texas Intermediate, Brent Crude and the OPEC Reference Basket.

Savings, investment, and growth in Nepal: an empirical analysis

Abstract: This study analyzes the relationship between savings, investment, and economic growth in Nepal over 1975–2016. The structural breaks in the variables have been accounted for using the (Zivot and Andrews’s, J Bus Econ Stat 10: 251–270 1992) unit root test along with (Gregory and Hansen’s, Oxf Bull Econ Stat 58: 555–560, 1996) cointegration approach. The ARDL approach to cointegration in the presence of structural breaks has also been utilized to analyze the long-and short-run dynamics of savings, investment, and growth in Nepal. The results show structural breaks in the real GDP per capita during 2001 when the Royal Massacre and a state of emergency have taken place in Nepal. After allowing for this structural break, evidence of a cointegration relationship amongst savings, investment, and economic growth was identified. The estimates of the ARDL approach suggest that investment has a significant and positive impact on economic growth. However, gross domestic savings have a negative impact on growth in the long run. These results clearly show weaknesses of the economy in mobilizing savings into productive sectors.

Size, efficiency, market power, and economies of scale in the African banking sector

Abstract: There is a growing body of evidence that interest rate spreads in Africa are higher for big banks compared to small banks. One concern is that big banks might be using their market power to charge higher lending rates as they become larger, more efficient, and unchallenged. In contrast, several studies found that when bank size increases beyond certain thresholds, diseconomies of scale are introduced that lead to inefficiency. In that case, we also would expect to see widened interest margins. This study examines the connection between bank size and efficiency to understand whether that relationship is influenced by exploitation of market power or economies of scale. Using a panel of 162 African banks for 2001–2011, we analyzed the empirical data using instrumental variables and fixed effects regressions, with overlapping and non-overlapping thresholds for bank size. We found two key results. First, bank size increases bank interest rate margins with an inverted U-shaped nexus. Second, market power and economies of scale do not increase or decrease the interest rate margins significantly. The main policy implication is that interest rate margins cannot be elucidated by either market power or economies of scale. Other implications are discussed.

Does abnormal lending behavior increase bank riskiness? Evidence from Turkey

Abstract: This study empirically analyzes whether the rapid growth of loans and risk-taking behavior during the expansion of loans affected non-performing loans (NPLs) and the solvency of financial institutions in the Turkish banking system. Using the GMM Generalized Method of Moments, this study used data on Turkish banks from 2011 to 2017 to test two hypotheses on the effects of loan growth on NPLs and solvency. This study finds significant results for the effect of loan growth on NPLs and solvency. NPLs rose from the previous year’s loan growth, which tended to reduce solvency. Due to selected research methods, the results may lack generality. Therefore, future studies should test the propositions herein further. The results indicate that careful allocation behavior is required when lending. Additionally, these findings may be helpful to financial managers and decision makers. This study confirms the need to determine how to allocate loans during the loan boom periods.

Evaluation of the robusticity of mutual fund performance in Ghana using Enhanced Resilient Backpropagation Neural Network (ERBPNN) and Fast Adaptive Neural Network Classifier (FANNC)

Abstract: Mutual fund investment continues to play a very important role in the world financial markets especially in developing economies where the capital market is not very matured and tolerant of small scale investors. The total mutual fund asset globally as at the end of 2016 was in excess of $40.4 trillion. Despite its success there are uncertainties as to whether mutual funds in Ghana obtain optimal performance relative to their counterparts in United States, Luxembourg, Ireland, France, Australia, United Kingdom, Japan, China and Brazil. We contribute to the extant literature on mutual fund performance evaluation using a collection of more sophisticated econometric models. We selected six continuous historical years that is 2010–2011, 2012–2013 and 2014–2015 to construct a mutual fund performance evaluation model utilizing the fast adaptive neural network classifier (FANNC), and to compare our results with those from an enhanced resilient back propagation neural networks (ERBPNN) model. Our FANNC model outperformed the existing models in terms of processing time and error rate. This makes it ideal for financial application that involves large volume of data and routine updates.

Assessing the spillover effects of U.S. monetary policy normalization on Nigeria sovereign bond yield

Abstract: This study examines the spillover effects of U.S. monetary policy normalization on Nigeria 10-Year Treasury bond yield between 2011 and 2017, using the vector error correction model approach. Our results reveal that domestic factors, such as exchange rate and inflation, rather than the U.S. 10-Year sovereign bond yield, are the key drivers of Nigeria 10-Year bond yield. Additionally, the spillover effect from the U.S. monetary policy was amplified by oil price shocks and changes in Nigeria’s monetary policy rates. Our counterfactual analysis confirms the findings.

The relative importance of competition to contagion: evidence from the digital currency market

Abstract: How does the valuation change of an industry leader influence its competitors? Does it induce a competitive effect or a contagion effect? What are the driving forces of such influences? We attempted to answer these questions within digital currency markets. We found that both close and distant competitors against an industry leader experience high competitive effects, while moderate competitors experience high contagion effects. Next, we empirically demonstrated how this U-shaped pattern reduces to a linear relationship depending on the industry concentration. Lastly, we identified eight distinct information categories from a social media platform of the industry leader and compared the influence of the eight information categories on the industry leader’s competitors. Our analysis suggests that the relative importance of the competitive effect to the contagion effect in the industry depends on the category of the information.