I Wayan Budi Artha , Bambang Mulyana (2018). The Effect of Internal and External Factors of Companies on Profitability and its Implications on Stock Price Index of State-Owned Banks. The Economics and Finance Letters, 5(2): 58-71. DOI: 10.18488/journal.29.2018.52.58.71
This study aims to determine the effect of internal factors of the company (CAR, NPL, NIM, BOPO and CASA) and external factors of the company (inflation, economic growth and BI reference interest rates), both partially and jointly on the performance of State-Owned Banks measured with a Return on Assets ratio (ROA) and its implications on the Stock Price Index. The object of research is State-Owned Banks in the period of 2012 - 2017. The sampling technique is saturated sampling, that is, all members of the population are used as samples. The analysis technique used is Panel Data Regression. The results of this study indicate that CAR, NPL, NIM, BOPO, CASA, Inflation, Economic Growth and BI reference interest rate together have a significant effect on ROA. NIM, CASA and BI Reference Interest Rate partially had a positive and significant effect on ROA. BOPO, Inflation and Economic Growth partially have negative and significant effect on ROA. While CAR has a negative effect and NPL has a positive effect, but not significant on ROA. ROA has a negative and significant effect on Stock Price Index of State-Owned Banks.
This study contributes to the existing literature, useful for science in banking about the relationship between the company's internal and external factors on bank profitability and its implications for the Stock Price Index. In addition, adding literature in the financial sector is used as a guideline for subsequent research that will examine banking.
Electronic Banking Innovations and Selected Banks Performance in Nigeria
Anthony Orji , Jonathan E. Ogbuabor , Asidok N. Okon , Onyinye I. Anthony-Orji (2018). Electronic Banking Innovations and Selected Banks Performance in Nigeria. The Economics and Finance Letters, 5(2): 46-57. DOI: 10.18488/journal.29.2018.52.46.57
In the past few years, Nigerian banks have embraced the global trend of digitalization in banking operations. Thus, after the consolidation and recapitalization exercises, many banks have strengthened and streamlined their facilities, tailored their services as well as automated their operations. In the heat of competition, banks are now adding to the stock of e-banking in order to maintain a competitive edge over their competitors. However, despite the rapid development in electronic banking innovations, it is not clear whether e-banking innovations have impacted positively and significantly on banks’ performance in Nigeria. The main objective of this paper therefore is to estimate the impact of e-banking innovations (ATM transactions, mobile banking transactions, and point of sales transactions) on the performance of six selected banks in Nigeria. The study adopts a SURE model in the quantitative analysis of six selected old and new generation banks. The results indicate that automated teller machine transactions, point of sale transactions, mobile banking transactions are major e-banking innovations that contribute to old and new banks’ performance in Nigeria. The study therefore concludes that the selected banks and other banks should intensify efforts to increase their asset base and continue to invest in e-banking innovations in order keep preforming well and also remain profitable. The study also calls for efficient management and utilization of funds to train and educate bank workers and general public regularly on how to deploy and use e-banking channels and other related technological innovations respectively.
This study is among the first in Nigeria to estimate the impact of e-banking innovations (ATM transactions, mobile banking transactions, and point of sales transactions) on the performance of six selected banks in Nigeria since after the consolidation and recapitalization exercise. Unlike other similar studies, the study adopts a SURE model in the quantitative analysis of six selected old and new generation banks.
Financial Market Predictions with Factorization Machines: Trading the Opening Hour Based on Overnight Social Media Data
Johannes Stubinger , Dominik Walter , Julian Knoll (2018). Financial Market Predictions with Factorization Machines: Trading the Opening Hour Based on Overnight Social Media Data. The Economics and Finance Letters, 5(2): 28-45. DOI: 10.18488/journal.29.2018.52.28.45
This paper develops a statistical arbitrage strategy based on overnight social media data and applies it to high-frequency data of the S&P 500 constituents from January 2014 to December 2015. The established trading framework predicts future financial markets using Factorization Machines, which represent a state-of-the-art algorithm coping with high-dimensional data in very sparse settings. Essentially, we implement and analyze the effectiveness of support vector machines (SVM), second-order Factorization Machines (SFM), third-order Factorization Machines (TFM), and adaptive-order Factorization Machines (AFM). In the back-testing study, we prove the efficiency of Factorization Machines in general and show that increasing complexity of Factorization Machines provokes higher profitability – annualized returns after transaction costs vary between 5.96 percent for SVM and 13.52 percent for AFM, compared to 5.63 percent for a naive buy-and-hold strategy of the S&P 500 index. The corresponding Sharpe ratios range between 1.00 for SVM and 2.15 for AFM. Varying profitability during the opening minutes can be explained by the effects of market efficiency and trading turmoils. Additionally, the AFM approach achieves the highest accuracy rate and generates statistically and economically remarkable returns after transaction costs without loading on any systematic risk exposure.
This study contributes in the existing literature by predicting financial markets based on overnight social media data. For this purpose, we observe tweets about the S&P 500 companies during the time span in which stock markets are closed and forecast the future price changes based on the collected information.