Introducing new financial legislation to financial markets require caution to achieve the intended outcome. This thesis aims to investigate whether or not the newly installed revised Markets in Financial Instruments Directive- the MIFID II regulation - temporally influenced the trading stock volume levels of Nasdaq Stockholm during its introduction to the Swedish stock market. A first approach of a generalized Negative Binomial model is carried out on aggregated data, followed by an individual Fixed Effects model in an attempt to eliminate omitted variable bias caused by missing unobserved variables for the individual stocks. The aggregated data is attained by taking the equally weighted average of the trading volume and adjusting for seasonality through Seasonal and Trend decomposition using Loess in combination with a regression model with ARIMA errors to mitigate calendar effects. Due to robustness of the aggregated data, the Negative Binomial model manage to capture significant effects of the regulation on the Small Cap. segment, even though clusters of the data show signs of divergent reactions to MIFID II. Since the Fixed Effects model operate on non-aggregated TSCS data and because of the varying effects on each stock the Fixed Effect model fails in its attempt to do the same. / Implementation av nya finansiella regelverk på finansmarknaden kräver aktsamhet för att uppnå de tilltänka målen. Det här arbetet undersöker huruvida MIFID II regleringen orsakade en temporär medelvärdesskiftning av de handlade aktievolymerna på Nasdaq Stockholm under regelverkets introduktion på den svenska marknaden. Först testas en generaliserad Negative Binomial regression applicerat på aggregerad data, därefter en individuell Fixed Effects modell för att försöka eliminera fel på grund av saknade, okända variabler. Det aggrigerade datasettet erhålls genom att ta genomsnittet av handelsvolymerna och justera dessa för sässongsmässiga mönster med metoden STL i kombination med regression med ARIMA residualer för att även ta hänsyn till kalender relaterade effekter. Eftersom den aggrigerade datan är robust lyckas the Negative Binomial regressionen fånga signifikanta effekter av regleringen för Small Cap. segmentet trots att datat uppvisar tecken på att subgrupper inom segmentet reagerat väldigt olika på den nya regleringen. Eftersom Fixed Effects modellen är applicerad på icke-aggrigerad TSCS data och pågrund av den varierande effekten på de individuella aktierna lyckas inte denna modell med detta.

In this thesis we investigate whether the inclusion of an exogenous variable in the form of patentapproval can improve the ARIMA model's predictions for the pharmaceutical companyAstrazeneca. A point of departure for the study is the questioning of the efficient markethypothesis. When comparing data on patent date approval with stock exchange data for threepharmaceutical companies, it could be observed that share prices increased on the date ofapproval in 65 percent of the cases. This observed correlation combined with the fact that severalpapers have established that the stock market may not be efficient make it interesting to studywhether the value of a patent has been included in the stock price prior to approval date.To investigate this, an ARIMA and an ARIMAX model was estimated. The exogenous variable,which controls for patent approvals, was created by retrieving data from the EPO's databasePATSTAT. The retrieved data was then formatted into a dummy variable. The purpose ofincluding an exogenous variable is to investigate whether the market reacts to patent information.If the addition of the exogenous variable proves significant, the result is in conflict with theefficient market hypothesis.During the model selection, it was found that an ARIMA (4,1,2) was the superior model. Themodel was then compared with the corresponding ARIMAX model. When comparing themodels, it was found that the predictions of the ARIMAX model follow the observed datasomewhat better, but a t-test concluded that the improvement was not statistically significant.This implies that the value of the patent has already been included in stock prices prior to patentapproval and indicates that the price increase is random. This results thus lends support for theefficient market hypothesis. To investigate this further, the stock market data was compared witha random walk and by conducting a t-test it could be concluded that it was not possible to rejectthe hypothesis that share prices follow a random walk, thus the result further supports theefficient market hypothesis.

The main thrust of time-series forecasting models in recent years has gone in the direction of pattern-based learning, in which the input variable for the models is a vector of past observations of the variable itself to predict. The most used models based on this traditional pattern-based approach are the autoregressive integrated moving average model (ARIMA) and long short-term memory neural networks (LSTM). The main drawback of the mentioned approaches is their inability to react when the underlying relationships in the data change resulting in a degrading predictive performance of the models. In order to solve this problem, various studies seek to incorporate external factors into the models treating the system as a black box using a machine learning approach which generates complex models that require a large amount of data for their training and have little interpretability. In this thesis, three different algorithms have been proposed to incorporate additional external factors into these pattern-based models, obtaining a good balance between forecast accuracy and model interpretability. After applying these algorithms in a study case of Ethereum price time-series forecasting, it is shown that the prediction error can be efficiently reduced by taking into account these influential external factors compared to traditional approaches while maintaining full interpretability of the model. / Huvudinstrumentet för prognosmodeller för tidsserier de senaste åren har gått i riktning mot mönsterbaserat lärande, där ingångsvariablerna för modellerna är en vektor av tidigare observationer för variabeln som ska förutsägas. De mest använda modellerna baserade på detta traditionella mönsterbaserade tillvägagångssätt är auto-regressiv integrerad rörlig genomsnittsmodell (ARIMA) och långa kortvariga neurala nätverk (LSTM). Den huvudsakliga nackdelen med de nämnda tillvägagångssätten är att de inte kan reagera när de underliggande förhållandena i data förändras vilket resulterar i en försämrad prediktiv prestanda för modellerna. För att lösa detta problem försöker olika studier integrera externa faktorer i modellerna som behandlar systemet som en svart låda med en maskininlärningsmetod som genererar komplexa modeller som kräver en stor mängd data för deras inlärning och har liten förklarande kapacitet. I denna uppsatsen har tre olika algoritmer föreslagits för att införliva ytterligare externa faktorer i dessa mönsterbaserade modeller, vilket ger en bra balans mellan prognosnoggrannhet och modelltolkbarhet. Efter att ha använt dessa algoritmer i ett studiefall av prognoser för Ethereums pristidsserier, visas det att förutsägelsefelet effektivt kan minskas genom att ta hänsyn till dessa inflytelserika externa faktorer jämfört med traditionella tillvägagångssätt med bibehållen full tolkbarhet av modellen.

Since 2019, COVID-19, as a new acute respiratory disease, has struck the whole world, causing millions of death and threatening the economy, politics, and civilization. Therefore, an accurate prediction of the future spread of COVID-19 becomes crucial in such a situation. In this comparative study, four different time-series analysis models, namely the ARIMA model, the Prophet model, the Long Short-Term Memory (LSTM) model, and the Transformer model, are investigated to determine which has the best performance when predicting the future case trends of COVID-19 in six countries. After obtaining the publicly available COVID-19 case data from Johns Hopkins University Center for Systems Science and Engineering database, we conduct repetitive experiments which exploit the data to predict future trends for all models. The performance is then evaluated by mean squared error (MSE) and mean absolute error (MAE) metrics. The results show that overall the LSTM model has the best performance for all countries that it can achieve extremely low MSE and MAE. The Transformer model has the second-best performance with highly satisfactory results in some countries, and the other models have poorer performance. This project highlights the high accuracy of the LSTM model, which can be used to predict the spread of COVID-19 so that countries can be better prepared and aware when controlling the spread. / Sedan 2019 har COVID-19, som en ny akut andningssjukdom, drabbat hela världen, orsakat miljontals dödsfall och hotat ekonomin, politiken och civilisationen. Därför blir en korrekt förutsägelse av den framtida spridningen av COVID-19 avgörande i en sådan situation. I denna jämförande studie undersöks fyra olika tidsseriemodeller, nämligen ARIMA-modellen, profetmodellen, Long Short-Term Memory (LSTM) -modellen och transformatormodellen, för att avgöra vilken som har bäst prestanda när man förutsäger framtida falltrender av COVID-19 i sex länder. Efter att ha fått offentligt tillgängliga COVID-19-falldata från Johns Hopkins University Center for Systems Science and Engineering-databasen utför vi repetitiva experiment som utnyttjar data för att förutsäga framtida trender för alla modeller. Prestandan utvärderas sedan med medelvärde för kvadratfel (MSE) och medelvärde för absolut fel (MAE). Resultaten visar att LSTM -modellen överlag har den bästa prestandan för alla länder att den kan uppnå extremt låg MSE och MAE. Transformatormodellen har den näst bästa prestandan med mycket tillfredsställande resultat i vissa länder, och de andra modellerna har sämre prestanda. Detta projekt belyser den höga noggrannheten hos LSTM-modellen, som kan användas för att förutsäga spridningen av COVID-19 så att länder kan vara bättre förberedda och medvetna när de kontrollerar spridningen.

With Industry 4.0, companies are faced with the challenge of managing an ever-increasing amount of data and re-evaluating and innovating their production planning methods. An important aspect of demand forecasting is the accuracy of forecasts compared to outcomes. Research has shown that more complex models perform better in demand forecasting, however, this research has focused on demand forecasting in the IT, finance and e-commerce sectors.   This thesis investigates the application of predictive modelling in demand forecasting in the context of production planning for a medium-sized manufacturing company. The study mainly compares the performance of two predictive models: Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, with the aim of assessing its usefulness in improving the accuracy of demand forecasts. Based on historical sales data, this quantitative case study investigates how these models can improve operational efficiency that can be applied to production planning processes such as optimal inventory and production schedules.    The study found that the LSTM model, through Automated Machine Learning (AutoML), was significantly better than the ARIMA model in terms of forecast accuracy. This was evidenced by lower Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values, indicating that LSTM's ability to capture long-term dependencies and adapt to non-linear patterns provides a more robust tool for demand forecasting in production planning.   This study contributes to the field of industrial engineering by demonstrating the practical benefits of integrating advanced predictive models into manufacturing companies' production planning processes. It highlights the potential of machine learning techniques to transform traditional production planning systems and thus provides insights into the strategic implementation of AI in industrial operations. Future research could explore and compare more models to get a broader picture of how different models perform against each other in terms of prediction errors. / Med Industri 4.0 står företagen inför utmaningen att hantera en ständigt ökande mängd data och att omvärdera och förnya sina metoder för produktionsplanering. En viktig aspekt av efterfrågeprognoser är prognosernas träffsäkerhet jämfört med utfallet. Forskning har visat att mer komplexa modeller presterar bättre vid efterfrågeprognoser, men denna forskning har fokuserat på efterfrågeprognoser inom IT-, finans- och e-handelssektorerna.   Denna studie undersöker tillämpningen av prediktiv modellering vid efterfrågeprognoser i samband med produktionsplanering för ett medelstort tillverkningsföretag. Studien jämför främst prestandan hos två prediktiva modeller: Autoregressive Integrated Moving Average (ARIMA) och Long Short-Term Memory (LSTM) nätverk, i syfte att bedöma hur användbara de är för att förbättra precisionen i efterfrågeprognoser. Baserat på historiska försäljningsdata undersöker denna kvantitativa fallstudie hur dessa modeller kan förbättra den operativa effektiviteten som kan tillämpas på produktionsplaneringsprocesser, såsom lagerhållning och produktionsscheman.    Studien visade att LSTM-modellen, genom automatiserad maskininlärning (AutoML), var betydligt bättre än ARIMA-modellen när det gäller prognosprecision. Detta framgick av lägre RMSE-värden (Root Mean Squared Error) och MAE-värden (Mean Absolute Error), vilket tyder på att LSTM:s förmåga att fånga upp långsiktiga beroenden och anpassa sig till icke-linjära mönster ger ett mer robust verktyg för efterfrågeprognoser inom produktionsplanering.   Denna studie bidrar till området industriell ekonomi genom att visa på de praktiska fördelarna med att integrera avancerade prediktiva modeller i tillverkningsföretagens produktionsplaneringsprocesser. Den belyser maskininlärningsteknikernas potential att omvandla traditionella produktionsplaneringssystem och ger därmed insikter i den strategiska implementeringen av AI i industriell verksamhet. Framtida forskning skulle kunna utforska och jämföra fler modeller för att få en bredare bild av hur olika modeller presterar mot varandra när det gäller prediktionsfel.

This dissertation contains three independent essays relating energy economics. The first essay investigates price asymmetry of diesel in South Korea by using the error correction model. Analyzing weekly market prices in the pass-through of crude oil, this model shows asymmetric price response does not exist at the upstream market but at the downstream market. Since time-variant residuals are found by the specified models for both weekly and daily retail prices at the downstream level, these models are implemented by a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process. The estimated results reveal that retail prices increase fast in the rise of crude oil prices but decrease slowly in the fall of those. Surprisingly, retail prices rarely respond to changes of crude oil prices for the first five days. Based on collusive behaviors of retailers, this price asymmetry in Korea diesel market is explained. The second essay aims to evaluate the new incentive system for biodiesel in South Korea, which keeps the blend mandate but abolishes tax credits for government revenues. To estimate changed welfare from the new policy, a multivariate stochastic simulation method is applied into time-series data for the last five years. From the simulation results, the new biodiesel policy will lead government revenues to increases with the abolishment of tax credit. However, increased prices of blended diesel will cause to decrease demands of both biodiesel and blended diesel, so consumer and producer surplus in the transport fuel market will decrease. In the third essay, the Regression - Seasonal Autoregressive Integrated Moving Average (REGSARIMA) model is employed to predict the impact of air temperature on daily peak load demand in Houston. Compared with ARIMA and Seasonal Model, a REGARIMA model provides the more accurate prediction for daily peak load demand for the short term. The estimated results reveal air temperature in the Houston areas causes an increase in electricity consumption for cooling but to save that for heating. Since the daily peak electricity consumption is significantly affected by hot air temperature, this study makes a conclusion that it is necessary to establish policies to reduce urban heat island phenomena in Houston.

本研究使用含外生多變數為門檻值之TAR模型，分析並預測103年到105年的台股指數。建構多變量之門檻自迴歸模式較傳統以時變或自變數自動控制值更能反映出時間數列結構改變的過程與趨勢。這對於模式分析與預測有更優的解釋能力。且含外生多變數為門檻值之多變量門檻模式的可適用範圍很廣，尤其是當時間數列中的結構改變的現象，來自於外在多個變數衝擊，或非線性現象。此時加入多個外生變數作為考量，更能精準分析資料和做預測。我們以台股指數為例，實證結果顯示，我們所提出之模型，較傳統預測方法有更高之準確度。 / In this research, we use exogenous multi-variables as threshold values to construct a threshold autoregressive model in order to analysis and forecast TAIEX index between 103 years and 105 years. Constructing the threshold autoregressive model with multi-variables is better to reflect the process and trend of the change in time series structure than traditional model. This provides the better explanatory ability for model analysis and forecast. Also, the threshold autoregressive model with multi-variables containing exogenous multi-variables can apply more range, especially, as the structure change in time series due to the exogenous multi-variables shock. Through adding more exogenous variables, one can analyze data and forecast accurately. In this paper, the empirical results of TAIEX index shows that the threshold autoregressive model with multi-variables containing exogenous multi-variables is more precise than the traditional way.

本論文採用了由Box and Jenkins(1976)所提出的ARIMA模型，以及由BOX and Tiao(1975)所提出的Intervention Model，去配適台灣的失業率型態，以及比較其預測的結果。 結果顯示出台灣的失業率具有季節性的型態，亦即台灣的失業率並非僅僅受到月分之間的相關，年分之間也有所關連。是故，當本論文在預測失業率的水準時，也考慮到此一因素，加入季節性的ARIMA模型對台灣的失業率加以預測。另外，時間序列的資料常常受到外生因素的干擾。對於失業率來說，政策上的改變將會影響失業率本身的結構，因此利用介入模式預測失業率，可以得到一組較精確的預測值。介入模式的事件有以下五個，分別是解嚴、六年國建、台灣引進外勞、中共飛彈試射、新十大建設。前四個事件的確影響了失業率的結構，不過第五項，也就是新十大建設並沒有顯著影響失業率的結構。理由可能是新十大建設的內容並不能合宜的解決經濟上與社會上的問題，以及這些建設尚未完工，以致無法達到期預期的效果。 比較兩模型的預測結果時，採用了MPE、MSE、MAE、MAPE作為模型評估的準則，結果指出介入模式的預測結果比起季節性ARIMA的預測結果來的有效率。 / This article adopts the ARIMA model, which was first introduced by Box and Jenkins (1976), and the intervention model, which was developed by Box and Tiao (1975), to fit the time series data for the unemployment rate in Taiwan, and thus to compare the results of the forecasts. The results reveal that there is a seasonal effect in the data on the unemployment rate. This indicates that the unemployment rate figures are not only related from month to month but are also related from year to year. When forecasting the level of unemployment, we should examine not only the neighboring months but also the corresponding months in the previous year. Time series are frequently affected by certain external events. In the discussion on the unemployment rate, the policies implemented by the government as well as military threats indeed influence the structure of the series. By making a forecast using the intervention model, we can evaluate the effect of the external events which would give rise to more accurate forecasts. In this study, there were five interventions included in relation to the unemployment rate series, which were as follows. First, the lifting of Martial Law in February 1987. Second, the Six-year National Development Plan launched in June 1991. Third, the hiring of foreign labor in Taiwan, which took effect in October 1991. Fourth, the threats of missile tests from the PRC in Feb 1996. Fifth, the ten new construction programs launched in November 2003. The first four events were indeed found to give rise to a structural change in the unemployment rate series at the moment when they occurred. This result might also have implied that not all of the actual effect of expansionary policies could have exactly decreased the unemployment rate, and therefore have solved the economic and social problems simultaneously. When we refer to the comparison of the above two models, the ultimate choice of a model may depend on its goodness of fit, such as the residual mean square, AIC, or BIC. As the main purpose of this study is to forecast future values, the alternative criteria for model selection can be based on forecast errors. The comparison is based on statistics such as MPE, MSE, MAE and MAPE. The results indicate that the intervention model outperforms the seasonal ARIMA model.

In this work, neural networks are used to forecast daily Realized Volatility of the EUR/USD, GBP/USD and USD/CHF currency pairs time series. Their performan-ce is benchmarked against nowadays popular Hetero-genous Autoregressive model of Realized Volatility (HAR) and traditional ARIMA models. As a by-product of our research, we introduce a simple yet effective enhancement to HAR model, naming the new model HARD extension. Forecasting performance tests of HARD model are conducted as well, promoting it to become a reference benchmark for neural networks and ARIMA.

Sabe-se que, na literatura, existem muitos modelos para se fazer previsão para séries temporais financeiras. Sabe-se também que não há um modelo perfeito e que os mais utilizados atualmente são os modelos de redes neurais recorrentes e os da família GARCH. Referências internacionais apontam que existe uma técnica de medição de uma janela temporal para se identificar o tipo de comportamento existente em uma série temporal; tal técnica é conhecida como Expoente de Hurst. É uma medida que qualifica a série como persistente ou anti-persistente. Este trabalho analisou se o Expoente de Hurst, interfere na qualidade das previsões feitas com o modelo de redes neurais recorrentes com e sem o uso do filtro de ondaletas, utilizando os preços diários das principais commodities, ações negociadas no mercado e a taxa de câmbio. no período de janeiro de 1998 a dezembro de 2010. Com a pesquisa observa-se, na maioria dos casos, há uma possível melhora na qualidade das previsões para as séries antipersistentes. / It is known that there are a lot of models to forecast financial time series. It is known, also, that there is not a perfect model and the most used nowadays are the Recurrent Neural Network models and those from the GARCH family. International references point to a technique of measurement using windowing in order to identify the kind of behavior that is present in time series. This technique is known as Hurst Exponent. It is a measure that qualifies the time series as persistent or anti-persistent. This work analyzed if the Hurst Exponent interferes in the quality of the forecasts made with the Neural Network models with and without the wavelet filter, using the main commodities, stock prices, Ibovespa index and the Dollar/Real exchange rate in the period ranging from January 1998 to December 2010. The initial conclusions concerning the models worked out are positives.