The influence of news and investor sentiment on exchange rate determination: new evidence using panel data in the banking sector

Exchange rates behaviour in open economies strongly influences the country's macroeconomic policy as the extent and frequency of exchange rate changes are important indicators of the country's economic stability. Commercial banks are fairly exposed to exchange rate changes and may be directly and heavily affected. The primary goal of this study is to investigate whether exchange rates news plays a significant role in banks’ financial performance, and what other channels (factors) potentially affect the banks’ profitability. The study collected data on more than 800 US banks over the period of 21 years (1998 to 2020). Following a filtering process, 148 banks were retained, as a significant number of these institutions either declared bankruptcy or underwent mergers with larger organizations, whether in banking or investment sectors. The contribution of this study is twofold. Firstly, the investigation of the association between exchange rates news and banks' profitability, creating a net sentiment index based on the unexpected announcements of domestic currency, US dollar, and then using GMM techniques, and secondly, the examination of this net sentiment index on banks’ profitability in combination with other banking or macroeconomic factors. While the determinants of banks' profitability have been studied by many scholars, the relationship between exchange rate news and profitability has not been analyzed by anyone so far. The analysis relies on public news categorized as favourable and unfavourable exchange rate news based on exchange rate fluctuations for 3 exchange rates. This analysis generates an index that describes the net sentiment of this news based on the characteristics of those announcements. The data of this net sentiment index is obtained from 3 basic exchange rates fluctuations per year, defining the US dollar as the domestic currency. Based on the major changes in exchange rates over time, news is classified as either positive or negative. Using panel data for 148 US banks during the period 1998-2018 and applying the GMM method, the first goal is to find out if the unexpected exchange rate news has a negative or positive impact on the whole banking system, especially if this news affects Return on Assets (ROA), Return on Equity (ROE) or Net Interest Margin (NIM) which have been defined as measures for the profitability of banks. To do this, empirical econometric tests were performed, finding the best autoregressive model and then applying the Stepwise Forward method selected the most statistically significant variables in each model (p-value < 0.01). The panel unit root, OLS (Fixed Effect) method, and GMM method (GMM single and GMM system) two-step robust estimator, will then be applied for further analysis. This study showed that banks’ profitability is not affected by unexpected exchange rate announcements, which automatically implies that investors underreact immediately to new information. The evidence presented in this article does not justify banking profit or debt management activities if banks react to good or bad information about the appreciation or depreciation of the dollar. Banks appear to underreact to exchange rates news as well as to information conveyed by the event. So, there is no support for the overreaction hypothesis to unexpected exchange rate news in the banking system, suing any technique. Finally, the analysis does not address whether a different explanation of behavior is based on other phenomena. It may be necessary to reinterpret the evidence in this paper. This is left as an area for future research

Forecasting Hospital Readmissions with Machine Learning

Hospital readmissions are regarded as a compounding economic factor for healthcare systems. In fact, the readmission rate is used in many countries as an indicator of the quality of services provided by a health institution. The ability to forecast patients’ readmissions allows for timely intervention and better post-discharge strategies, preventing future life-threatening events, and reducing medical costs to either the patient or the healthcare system. In this paper, four machine learning models are used to forecast readmissions: support vector machines with a linear kernel, support vector machines with an RBF kernel, balanced random forests, and weighted random forests. The dataset consists of 11,172 actual records of hospitalizations obtained from the General Hospital of Komotini “Sismanogleio” with a total of 24 independent variables. Each record is composed of administrative, medical-clinical, and operational variables. The experimental results indicate that the balanced random forest model outperforms the competition, reaching a sensitivity of 0.70 and an AUC value of 0.78

Predicting Bitcoin Prices Using Machine Learning

In this paper we predict Bitcoin movements by utilizing a machine-learning framework. We compile a dataset of 24 potential explanatory variables that are often employed in the finance literature. Using daily data from 2nd of December 2014 to July 8th 2019, we build forecasting models that utilize past Bitcoin values, other cryptocurrencies, exchange rates and other macroeconomic variables. Our empirical results suggest that the traditional logistic regression model outperforms the linear support vector machine and the random forest algorithm, reaching an accuracy of 66%. Moreover, based on the results, we provide evidence that points to the rejection of weak form efficiency in the Bitcoin market

Paradigm of tunable clustering using binarization of consensus partition matrices (Bi-CoPaM) for gene discovery

Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.National Institute for Health Researc

Supervised Human-Guided Data Exploration

Peer reviewe

Towards elucidating carnosic acid biosynthesis in Lamiaceae: Functional characterization of the three first steps of the pathway in Salvia fruticosa and Rosmarinus officinalis

Carnosic acid (CA) is a phenolic diterpene with anti-tumour, anti-diabetic, antibacterial and neuroprotective properties that is produced by a number of species from several genera of the Lamiaceae family, including Salvia fruticosa (Cretan sage) and Rosmarinus officinalis (Rosemary). To elucidate CA biosynthesis, glandular trichome transcriptome data of S. fruticosa were mined for terpene synthase genes. Two putative diterpene synthase genes, namely SfCPSand SfKSL, showing similarities to copalyl diphosphate synthase and kaurene synthase-like genes, respectively, were isolated and functionally characterized. Recombinant expression in Escherichia coli followed by in vitro enzyme activity assays confirmed that SfCPS is a copalyl diphosphate synthase. Coupling of SfCPS with SfKSL, both in vitro and in yeast, resulted in the synthesis miltiradiene, as confirmed by 1D and 2D NMR analyses (1H, 13C, DEPT, COSY H-H, HMQC and HMBC). Coupled transient in vivo assays of SfCPS and SfKSL in Nicotiana benthamiana further confirmed production of miltiradiene in planta. To elucidate the subsequent biosynthetic step, RNA-Seq data of S. fruticosa and R. officinalis were searched for cytochrome P450 (CYP) encoding genes potentially involved in the synthesis of the first phenolic compound in the CA pathway, ferruginol. Three candidate genes were selected, SfFS, RoFS1 and RoFS2. Using yeast and N. benthamiana expression systems, all three where confirmed to be coding for ferruginol synthases, thus revealing the enzymatic activities responsible for the first three steps leading to CA in two Lamiaceae genera