Forecasting Foreign Exchange Rates Using Recurrent Neural Networks : The Role of Political Uncertainty

In June 2016, the majority of UK citizens voted to leave the EU (Brexit). The referendum outcome took both citizens and policymakers by surprise. No other member state has ever left the EU. As a result, the global stock and currency markets collapsed. The impact of uncertainty on financial markets has been studied for decades (Garfinkel, 1999). Studies show that political instability has a significant impact on economic performance. In addition to the market fluctuation, it has been found to increase the unemployment rate and decrease consumers’ and companies’ willingness to invest. Thus, prolonged political instability may lead to a scenario in which the capital moves less, the quality of public services decreases, and economic growth slows down. (Carmignani, 2003; Canes-Wrone et al., 2014). Exchange rate forecasting is an important area of financial research that has recently received more popularity due to its dynamic nonlinear features. In the past, exchange rates have been analyzed using traditional financial models. However, recently academics have started to use artificial learning approaches alongside the traditional ones. In particular, neural networks have been used in time series modeling, and thus exchange rates have been modeled with neural networks. Machine learning aims to improve efficiency and make financial forecasting more automated. The empirical part of this analysis is carried out using a recurrent neural network architecture known as the Long Short Term Memory (LSTM). The LSTM model enables the analysis of non-linear data as well as the detection of diverse cause-and-effect relations. Therefore, it is reasonable to believe that accurate results can be obtained using this approach. The results are analyzed by comparing two different error values - the Mean Squared Error and the Absolute Mean Error. The results prove that the LSTM model is capable of modeling exchange rate values even in times of high volatility. As the Brexit-related uncertainty is higher, the predictability of the Pound to Euro and Dollar decreases. This finding is consistent with previous studies that have shown that political instability reduces the predictability of exchange rates. On the contrary, as the uncertainty surrounding Brexit increased, the predictability of the Pound to Yen improved. This result can partly be explained by the Safe Haven effect, according to which the value of the Yen rises as the values of other developed countries’ currencies fall. Finally, it can be stated that exchange rates are complex financial instruments whose volatility is influenced by a variety of factors and this study is able to produce new perspectives for further research.Kesäkuussa 2016 enemmistö Iso-Britannian kansasta äänesti EU:sta eroamisen puolesta (Brexit). Kansanäänestyksen tulos yllätti niin kansalaiset kuin vallanpitäjätkin. Mikään muu jäsenvaltio ei ole aikaisemmin eronnut EU:sta. Tämän seurauksena valuutta- sekä osake-markkinat romahtivat globaalisti. Epävarmuuden vaikutusta rahoitusmarkkinoihin on tutkittu jo vuosikausien ajan (Garfinkel, 1999). Tutkimukset todistavat, että poliittisella epävakaudella on merkittävä vaikutus taloudelliseen suorituskykyyn. Rahoitusmarkkinoiden heilunnan lisäksi sen on todettu lisäävän työttömyyttä sekä vähentävän kuluttajien ja yritysten investointihalukkuutta. Täten pitkittynyt poliittinen epävakaus voi johtaa tilanteeseen, jossa pääoma liikkuu hitaammin, julkisten palvelujen laatu heikentyy sekä talouskasvu hidastuu. (Carmignani, 2003; Canes-Wrone ym., 2014). Valuuttakurssien ennustaminen on tärkeä rahoituksen tutkimusala, joka on kasvattanut suosiotaan sen haastavien ja epälineaaristen piirteiden vuoksi. Aikaisemmin valuuttakursseja on tutkittu perinteisillä rahoituksen menetelmillä, mutta lähivuosina tutkijat ovat alkaneet hyödyntämään yhä enemmän koneoppimista perinteisten mallien rinnalla. Erityisesti neuroverkkoja on hyödynnetty aikasarjojen mallintamisessa ja täten myös valuuttakursseja on mallinnettu neuroverkoilla. Koneoppimisen malleilla pyritään tekemään rahoitusmarkkinoiden ennustamisesta tehokkaampaa ja itseohjautuvampaa. Tämä tutkimus hyödyntää empiirisessä osuudessa takaisinkytketyn neuroverkon arkkitehtuuria nimeltä pitkäkestoinen lyhytkestomuisti (Long Short Term Memory, LSTM). LSTM-arkkitehtuuri mahdollistaa epälineaarisen datan analysoinnin sekä monipuolisten syy-seurausketjujen hahmottamisen. Näin ollen on perusteellista uskoa, että tällä metodilla on mahdollista saavuttaa tarkkoja tuloksia valuuttakursseja analysoitaessa. Tulosten analysointi toteutetaan vertailemalla eri valuutoilla saatavia virhearvoja (keskihajonta sekä absoluuttinen keskivirhe). Tulokset todistavat, että LSTM-malli on kykenevä mallintamaan valuuttakurssien arvoja myös epävakaina aikoina. Euron ja dollarin ennustettavuus heikentyy tutkituilla ajanjaksoilla, kun Brexitiin liittyvä epävarmuus lisääntyy. Tämä tutkimustulos on johdonmukainen aikaisemman tutkimuksen kanssa, jonka perusteella on todettu, että valuuttakurssien ennustettavuus heikentyy poliittisen epävarmuuden seurauksena. Jenin ennustettavuus taas päinvastoin paranee ajanjaksolla, kun Brexitiin liittyvä epävarmuus lisääntyy. Tämä tulos voidaan osittain perustella turvasatamailmiöllä, jonka mukaan jenin arvo nousee, kun muiden kurssien arvot laskevat. Lopuksi todetaan, että valuuttakurssit ovat monimutkaisia rahoitusinstrumentteja, joiden heilahteluun vaikuttaa useita eri tekijöitä. Tästä huolimatta, tämä työ onnistuu tarjoamaan uusia näkökulmia tulevaisuuden tutkimukselle

Efficacy of Targeted 5-day Combined Parenteral and Intramammary Treatment of Clinical Mastitis Caused by Penicillin-Susceptible or Penicillin-Resistant Staphylococcus aureus

Combined parenteral and intramammary treatment of mastitis caused by Staphylococcus aureus was compared to parenteral treatment only. Cows with clinical mastitis (166 mastitic quarters) caused by S. aureus treated by veterinarians of the Ambulatory Clinic of the Faculty of Veterinary Medicine during routine farm calls were included. Treatment was based on in vitro susceptibility testing of the bacterial isolate. Procaine penicillin G (86 cases due to β-lactamase negative strains) or amoxycillin-clavulanic acid (24 cases due to β-lactamase positive strains) was administered parenterally and intramammarily for 5 days. Efficacy of treatments was assessed 2 and 4 weeks later by physical examination, bacteriological culture, determination of CMT, somatic cell count and NAGase activity in milk. Quarters with growth of S. aureus in at least one post-treatment sample were classified as non-cured. As controls we used 41 clinical mastitis cases caused by penicillin-susceptible S. aureus isolates treated with procaine penicillin G parenterally for 5 days and 15 cases due to penicillin-resistant isolates treated with spiramycin parenterally for 5 days from the same practice area. Bacteriological cure rate after the combination treatment was 75.6% for quarters infected with penicillin-susceptible S. aureus isolates, and 29.2% for quarters infected with penicillin-resistant isolates. Cure rate for quarters treated only parenterally with procaine penicillin G was 56.1% and that for quarters treated with spiramycin 33.3%. The difference in cure rates between mastitis due to penicillin-susceptible and penicillin-resistant S. aureus was highly significant. Combined treatment was superior over systemic treatment only in the β-lactamase negative group

Resonance between planar self-affine measures

We show that if $\lbrace \phi_i\rbrace_{i\in \Gamma}$ and $\lbrace \psi_j\rbrace_{j\in\Lambda}$ are self-affine iterated function systems on the plane that satisfy strong separation, domination and irreducibility, then for any associated self-affine measures $\mu$ and $\nu$, the inequality $$\dim_{\rm H}(\mu*\nu) < \min \lbrace 2, \dim_{\rm H} \mu + \dim_{\rm H} \nu \rbrace$$ implies that there is algebraic resonance between the eigenvalues of the linear parts of $\phi_i$ and $\psi_j$. This extends to planar non-conformal setting the existing analogous results for self-conformal measures on the line.Comment: 45 pages. v2: Included Corollary 1.2 regarding resonance between self-affine sets, and other minor improvements. v3: Some typos fixe

Assessing wood properties in standing timber with laser scanning

Managed forests play crucial roles in ongoing climatic and environmental changes. Among other things, wood is capable of sinking and storing carbon in both standing timber and wood products. To promote these positive effects, more precise planning is required that will ensure sustainable forest management and maximal deposition of harvested wood for long-term applications. Information on wood properties plays a key role; i.e. the wood properties can impact the carbon stocks in forests and the suitability of wood for structural timber. With respect to the theoretical background of wood formation, stem, crown, and branching constitute potential inputs (i.e. wood quality indicators) to allometric wood property, tree biomass, and wood quality models. Due to the complex nature of wood formation, measurements of wood quality indicators that could predict wood properties along the relevant directions of variation have previously been elusive in forest inventories. However, developments in laser scanning from aerial and terrestrial platforms support more complex mapping and modeling regimes based on dense three-dimensional point clouds. The aim here was to determine how wood properties could be estimated in remote-sensing-aided forest inventories. For this purpose, methods for characterizing select wood quality indicators in standing timber, using airborne and terrestrial laser scanning (ALS and TLS, respectively) were developed and evaluated in managed boreal Scots pine (Pinus sylvestris L.) forests. Firstly, the accuracies of wood quality indicators resolved from TLS point clouds were assessed. Secondly, the results were compared with x-ray tomographic references from sawmills. Thirdly, the accuracies of tree-specific crown features delineated from the ALS data in predictive modeling of the wood quality indicators were evaluated. The results showed that the quality and density of point clouds significantly impacted the accuracies of the extracted wood quality indicators. In the assessment of wood properties, TLS should be considered as a tool for retrieving as dense stem and branching data as possible from carefully selected sample trees. Accurately retrieved morphological data could be applied to allometric wood property models. The models should use tree traits predictable with aerial remote sensing (e.g. tree height, crown dimensions) to enable extrapolations. As an outlook, terrestrial and aerial remote sensing can play an important role in filling in the knowledge gaps regarding the behavior of wood properties over different spatial and temporal extents. Further interdisciplinary cooperation will be needed to fully facilitate the use of remote sensing and spatially transferable wood property models that could become useful in tackling the challenges associated with changing climate, silviculture, and demand for wood.Hoidetuilla metsillä on useita tärkeitä rooleja muuttuvassa ilmastossa ja ympäristössä. Puu sitoo ja varastoi hiiltä niin kasvaessaan, kuin pitkäikäisiksi puutuotteiksi jalostettuna. Näiden vaikutusten huomioiminen metsänhoidossa vaatii tarkkaa suunnittelua, jolla varmistetaan metsänhoidon ja puunkäytön kestävyys. Tieto puuaineen ominaisuuksista on keskeisessä osassa, sillä ne vaikuttavat hiilivarastojen suuruuteen metsissä, sekä puun käytettävyyteen pitkäikäisenä rakennesahatavarana. Puunmuodostuksen teoreettisen taustan mukaisesti, runko, latvus ja oksarakenne ovat potentiaalisia selittäviä muuttujia (eli puun laatuindikaattoreita), kun mallinnetaan puuaineen ominaisuuksia, puubiomassaa ja puun laatua. Puunmuodostuksen monimutkaisuudesta ja moniulotteisesta vaihtelusta johtuen, tarvittavien laatuidikaattorien mittaaminen osana metsävarojen inventointia ja riittävällä yksityiskohtaisuudella on ollut aiemmin mahdotonta. Monialustaisen laserkeilauksen kehittyminen kuitenkin tukee aiempaa monipuolisempien kartoitus- ja mallinnusjärjestelmien rakentamista, jotka perustuvat tiheisiin kolmiulotteisiin pistepilviin. Tämän työn tavoitteena oli määritellä, kuinka puuaineen ominaisuuksia voidaan arvioida kaukokartoitusta hyödyntävässä metsävarojen inventoinnissa. Tätä tarkoitusta varten kehitettiin menetelmiä puun laatuindikaattorien mittaamiseksi hoidetuissa männiköissä (Pinus sylvestris L.) lento- ja maastolaserkeilauksen avulla, ja arvioitiin niiden toimivuutta. Ensin arvioitiin laatuindikaattorien mittatarkkuus pistepilvissä. Toiseksi verrattiin pistepilvimittauksia röntgentomografiamittauksiin teollisilla sahoilla. Kolmanneksi arvioitiin lentolaserkeilauksella tuotettujen latvuspiirteiden tarkkuutta laatuindikaattorien ennustamisessa. Tuloksien perusteella pistepilvien laatu ja pistetiheys vaikuttivat merkittävästi mitattujen laatuindikaattorien tarkkuuteen. Puuaineen ominaisuuksien arvioimisessa, maastolaserkeilausta tulisi käyttää työkaluna mahdollisimman yksityiskohtaisten runko- ja oksikkuustietojen keräämiseen tarkkaan valikoiduista näytepuista. Tarkasti mitatut laatuindikaattorit voivat selittää puuaineen ominaisuuksia mallinnuksessa. Käytettyjen mallien tulisi perustua laatuindikaattoreille, jotka voidaan ennustaa lentolaserkeilausaineistosta (esim. puun pituus ja latvuksen mittasuhteet), jotta ennusteet ovat yleistettävissä laajoille alueille. Tulevaisuudessa, maasta ja ilmasta tehtävällä kaukokartoituksella voi olla tärkeä rooli puuaineen ominaisuuksien aikaan ja paikkaan sidotun vaihtelun tutkimuksessa. Lisää poikkitieteellistä työtä tarvitaan, jotta kaukokartoitusta ja puuaineen ominaisuuksia ennustavia spatiaalisia malleja voidaan täysimittaisesti hyödyntää kiihtyvän ilmastonmuutoksen, muuttuvan metsänhoidon ja lisääntyvän puunkäytön tuomien haasteiden kohtaamisessa

Increase of entropy under convolution and self-similar sets with overlaps

Abstract. Sets that consist of finitely many smaller-scale copies of itself are known as self-similar. Due to the likely irregularity in their structure, the size of these sets is often measured in the form of dimension. The existence of tools that can be used to calculate this quantity depends greatly on whether the cylinders of which the set consists of are sufficiently separated from each other. If this is the case, the dimension of the set is known to equal its similarity dimension, a quantity that is relatively easy to calculate. There is a long-standing open conjecture stating that, for a general set on the real line, the only case in which the dimension of the set does not equal its similarity dimension, is when at some scale there is an exact overlap among the cylinders of the set. The main result in this thesis is a step towards showing that this is indeed the case; in the presence of an exact overlap, the distance between the cylinders of the set decreases exponentially. This result is due to M. Hochman and it appeared in his paper “On self-similar sets with overlaps and inverse theorems for entropy” (2012) and forms the basis of our discussion in Section 4. In Section 1, we analyse the growth of entropy of a probability measure under convolution. The main result of this section is a generalization of the Freiman theorem from additive combinatorics to the fractal regime, stating that if the entropy of a convolution measure is not too large, then one of the marginal measures has to be either locally uniform or locally atomic. This result is also due to Hochman and is one of the main tools used in proving the results of Section 4. In Sections 2 and 3, we introduce the concepts of dimension and the main tools required in understanding the structure of a self-similar set or measure with sufficient separation conditions in place. Most of the results here can be found in any text-book concerning fractal geometry, e.g. Falconer’s “Fractal Geometry” (1990)

Using Activity Theory to transform medical work and learning

This article introduces key concepts of activity theory and expansive learning. Expansive learning builds on the foundational ideas of the cultural-historical activity theory (CHAT). It is a research approach designed for studying the complexities and contradictions in authentic workplace environments. Change Laboratory is a formative intervention method developed for studying workplaces in transition and for stimulating collaborative efforts to design improved patterns of activity. We present concrete examples of formative interventions in healthcare, where good patient care was compromised by the fragmentation of care and disturbances in collaboration between the healthcare experts. This implies that physicians are challenged to develop collaborative and transformative expertise. We present three spearheads into a zone of proximal development, representing opportunities for change of medical expertise: (1) reconceptualizing expertise as object-oriented and contradiction-driven activity systems, (2) pursuing expertise as negotiated knotworking, and (3) building expertise as expansive learning. While medical expertise needs to expand, medical education must also look for ways to evolve and meet the challenges of the surrounding society. We call for adopting an interventionist approach for developing medical education and intensifying collaboration with the practitioners in healthcare units, their patients, and target communities.Peer reviewe

Scholarship is not just research : Nurturing scholarship in health professions education

Peer reviewe

Destabilising institutions to make healthcare more equitable: Clinicians, educators, and researchers co-producing change

Non peer reviewe

What factors of the teaching and learning environment support the learning of generic skills? First-year students’ perceptions in medicine, dentistry and psychology

Future health professions need generic skills in their working lives, such as knowledge analysis, collaboration, communication and problem-solving skills. The teaching and learning environment is crucial in the development of generic skills when studying at university. The aim of this research was to examine students’ perceptions of learning generic skills during their first study year and how the teaching and learning environment related to their learning perceptions. The data were collected from first-year students (medicine n = 215, dentistry n = 70 and psychology n = 89) who completed a questionnaire at the end of their first study year. Two cohorts of first-year students from 2020 and 2021 were combined. The teaching and learning environments in medicine, dentistry and psychology differed from each other. The results showed that learning of problem-solving, communication and collaboration skills were emphasized more among medical and dental students, whereas analytical skills more among psychology students. There were no statistically significant differences in perceptions of the teaching and learning environment. Perceptions of generic skills and the teaching and learning environment were positively related to each other. In medicine, the strongest predictors of generic skills were peer support and feedback and in dentistry, peer support, interest and relevance. In psychology, the strongest predictors were interest and relevance. The results emphasize the relevance of the teaching and learning environment in learning generic skills.Peer reviewe