15,271 research outputs found

    The use of predictive analytics in finance

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    Combining quantitative narrative analysis and predictive modeling - an eye tracking study

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    As a part of a larger interdisciplinary project on Shakespeare sonnets’ reception (Jacobs et al., 2017; Xue et al., 2017), the present study analyzed the eye movement behavior of participants reading three of the 154 sonnets as a function of seven lexical features extracted via Quantitative Narrative Analysis (QNA). Using a machine learning- based predictive modeling approach five ‘surface’ features (word length, orthographic neighborhood density, word frequency, orthographic dissimilarity and sonority score) were detected as important predictors of total reading time and fixation probability in poetry reading. The fact that one phonological feature, i.e., sonority score, also played a role is in line with current theorizing on poetry reading. Our approach opens new ways for future eye movement research on reading poetic texts and other complex literary materials (cf. Jacobs, 2015c)

    Joint Report of Peer Review Panel for Numeric Nutrient Criteria for the Great Bay Estuary New Hampshire Department of Environmental Services June, 2009

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    This peer review was authorized through a collaborative agreement sponsored by the New Hampshire Department of Environmental Services (DES) and the Cities of Dover, Rochester and Portsmouth, New Hampshire. The purpose was to conduct an independent scientific peer review of the document entitled, “Numeric Nutrient Criteria for the Great Bay Estuary,” dated June, 2009 (DES 2009 Report)

    Essays on economic forecasting using machine learning

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    This thesis studies the additional value introduced by different machine learning methods to economic forecasting. Flexible machine learning methods can discover various complex relationships in data and are well-suited for analysing so called big data and potential problems therein. Several new extensions to existing machine learning methods are proposed from the viewpoint of economic forecasting. In Chapter 2, the main objective is to predict U.S. economic recession periods with a high-dimensional dataset. A cost-sensitive extension to the gradient boosting machine learning algorithm is proposed, which takes into account the scarcity of recession periods. The results show how the cost-sensitive extension outperforms the traditional gradient boosting model and leads to more accurate recession forecasts. Chapter 3 considers a variety of different machine learning methods when predicting daily returns of the S&P 500 stock market index. A new multinomial approach is suggested, which allows us to focus on predicting the large absolute returns instead of the noisy variation around zero return. In terms of both the statistical and economic evaluation criteria gradient boosting turns out to be the best-performing machine learning method. In Chapter 4, the asset allocation decisions between risky and risk-free assets are determined using a flexible utility maximization based approach. Instead of the merely considered two-step approach where portfolio weights are based on the excess return predictions obtained with statistical predictive regressions, here the optimal weights are found directly by incorporating a custom objective function to the gradient boosting algorithm. The empirical results using monthly U.S. market returns show that the utility-based approach leads to substantial and quantitatively meaningful economic value over the past approaches.Tässä väitöskirjassa tarkastellaan millaista lisäarvoa koneoppimismenetelmät voivat tuoda taloudellisiin ennustesovelluksiin. Joustavat koneoppimismenetelmät kykenevät mallintamaan monimutkaisia funktiomuotoja ja soveltuvat hyvin big datan eli suurten aineistojen analysointiin. Väitöskirjassa laajennetaan koneoppimismenetelmiä erityisesti taloudellisten ennustesovellusten lähtökohdista katsoen. Luvussa 2 ennustetaan Yhdysvaltojen talouden taantumajaksoja käyttäen hyvin suurta selittäjäjoukkoa. Gradient boosting -koneoppimismenetelmää laajennetaan huomioimaan aineiston merkittävä tunnuspiirre eli se, että taantumajaksoja esiintyy melko harvoin talouden ollessa suurimman osan ajasta noususuhdanteessa. Tulokset osoittavat, että laajennettu gradient boosting -menetelmä kykenee ennustamaan tulevia taantumakuukausia huomattavasti perinteisiä menetelmiä tarkemmin. Luvussa 3 hyödynnetään useampaa erilaista koneoppimismenetelmää S&P 500 -osakemarkkinaindeksin päivätuottojen ennustamisessa. Aiemmista lähestymistavoista poiketen tässä tutkimuksessa kategorisoidaan tuotot kolmeen eri luokkaan pyrkimyksenä keskittyä informatiivisempien suurten positiivisten ja negatiivisten tuottojen ennustamiseen. Tulosten perusteella gradient boosting osoittautuu parhaaksi menetelmäksi niin tilastollisten kuin taloudellistenkin ennustekriteerien mukaan. Luvussa 4 tarkastellaan, kuinka perinteisen tuottoennusteisiin nojautuvan kaksivaiheisen lähestymistavan sijaan allokaatiopäätös riskisen ja riskittömän sijoituskohteen välillä voidaan muodostaa suoraan sijoittajan kokeman hyödyn pohjalta. Hyödyn maksimoinnissa käytetään gradient boosting -menetelmää ja sen mahdollistamaa itsemäärättyä tavoitefunktiota. Yhdysvaltojen aineistoon perustuvat empiiriset tulokset osoittavat kuinka sijoittajan hyötyyn pohjautuva salkkuallokaatio johtaa perinteistä kaksivaiheista lähestymistapaa tuottavampiin allokaatiopäätöksiin

    Transfer Pricing: An Analysis Of The Impact Of Player Brand Value On Transfer Fees In European Football

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    In the past decade, the English Premier League has dramatically grown its commercial appeal and global audience. Yet, there has been little research on how the league’s growth could change the dynamics of the labour market for players. This study provides a unique framework for investigating player brand value through media coverage and exposure in order to determine its impact on player worth. Measuring player worth through the transfer fees that clubs pay to acquire players, this study provides insights on which factors are most predictive of transfer fee amounts. Throughout this study’s analysis, machine learning techniques are implemented and compared in order to assess the impacts of player brand value and determine a best fit model for predicting transfer fees. This study finds evidence that player brand value affects transfer fees and that the affect changes over time, but it concludes that more research is still needed
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