No abstract

Domestic investment is a significant component of economic activities affecting Nigerian economy for decades. Sequel to this, this paper examines the effect of Foreign Direct Investment (FDI), exchange rate and energy infrastructure on domestic investment in Nigeria. Time series data obtained from Central Bank of Nigeria (CBN) Statistical Bulletin and World Development Indicator were employed using Autoregressive Distributive Lag (ARDL) Model. Empirical findings show that FDI has positive and significant effect on domestic investment while exchange rate and energy infrastructure have a positive effect on domestic investment but non significant. The policy implications of this finding is that government should adopt more stringent supervision on exchange rate, and policy to regulate execution of energy infrastructure project; and more funds needed to emancipate energy infrastructure in order to obtain desired level of domestic investment in Nigeria.

The creation of distributed ledger technology resulted in the use of secured peer-to-peer interactions that pave way for the invention of Bitcoin and other cryptocurrencies. Since its invention, the price of Bitcoin has exhibited excessive volatility and has attracted increasing attentions. This paper considers the isolated influence of network activities (confirmed payments and users’ adoptions), mining information (network difficulty, Hashrate and transaction fees) and market factors (such as, bitcoin supply and trade volume) as key drivers of Bitcoin price. Using the vector autoregressive model (VECM), the results identified the existence of both long-term equilibrium and short-term dynamic relationship amongst the endogenous system’s variables. The cointegration relation has reversed adjustment effects on the bitcoin return. Accordingly, any deviation from the equilibrium dynamics due to perturbations of network events, market forces and mining data would be minimised. This explains why the Bitcoin price, and by implication its return, continues to experience different massive run-up, spiky protrusions, resistance, reversals, strong supports and consolidations. Based on the finding, the study recommends increased regulatory efforts to curb the excessive fluctuations in Bitcoin price in order to prevent significant loss which could discourage digital investors in the cryptocurrency markets.

Earnings management among firms remains a central focus for academics, auditors and regulatory bodies. Benchmark-motivated earnings management occurs when managers engage in opportunistic activities, including flexible use of accounting standards to misrepresent the information in a firm’s financial reports. Academic research has focused on how firms manage earnings to beat benchmarks, but the evidence regarding firms in emerging African stock markets is scarce and none is available for Nigeria. We applied both accruals quality and discretionary accruals models to detect whether firms that beat earnings benchmarks report earnings differently from others. Using 161 firms listed on the Nigerian Stock Exchange from 2002 to 2019, the study verifies how benchmark beaters manage earnings under the framework of two earnings thresholds – earnings (level) and positive earnings changes. Earnings persistence tests were carried out to verify whether benchmark beaters are consistent manipulators relative to non-beaters. The findings indicate that positive earnings benchmarks differ among the dichotomized groups. The evidence is not sufficient to validate that the change in earnings benchmarks motivates earnings discretions. However, the evidence may improve for larger samples. The study offers insights for informed decisions on the expectation of investment returns for investors, creditors, and other market partakers that require earnings information.

Purpose: A major challenge traders, speculators and investors are grappling with is how to accurately forecast Bitcoin price in the cryptocurrency market. This study is aimed to uncover the best model for the forecasts of Bitcoin price as well as to verify the price series that offers the best predictions performance under different periodicity of datasets. Design/methodology/approach: The study adopts three different data periods to verify whether frequency matters in forecasting Bitcoin price. The Bitcoin price, from 01/01/15 to 11/01/2021, is trained and validated on selected forecast models, including the Naïve, Linear, Exponential Smoothing Model, ARIMA, Neural Network, STL and Holt-Winters filters. Five forecast accuracy measures (RSME, MAE, MPE, MAPE and MASE) are applied to confirm the best performing model. The Diebold‐Mariano test is used to compare the forecasts based on the daily price with those based on the weekly and monthly. Findings: Based on the accuracy measures, the results indicate that the Naïve model provides more accurate performance for the daily series, while the linear model outperforms others for the weekly and monthly series. Using the Diebold‐Mariano statistics, there is evidence that forecasting Bitcoin price is not sensitive to the data periodicity. Research limitations/implications: The study has a major limitation, which is the shared sentiment to apply actual Bitcoin price series, and not the returns or log transformation for the forecast models. Notably, actual data may sometimes be loud, hence increasing the possibility of over predictions. Originality/value: In forecasting, different approaches have been used, this paper compares outputs of both statistical and machine learning methods in order to arrive at the best option for the Bitcoin price forecasts. Hence, we investigate whether the machine learning tools offer better forecasts in terms of lower error and higher model’s accuracy relative to the traditional models.