Background: Predicting stock prices in today’s complex financial landscape is asignificant challenge. An innovative approach to address this challenge is integrating sentiment analysis techniques with the well-established Autoregressive IntegratedMoving Average (ARIMA) model. Modern financial markets are influenced by various factors, including real-time news and social media trends, which demand accuratepredictions. This research recognizes the growing importance of market sentiment derived from news and aims to improve stock price prediction by combining ARIMA’sanalytical capabilities with sentiment analysis. This endeavor seeks to provide aclearer understanding of the intricate dynamics of stock price movements in an eramarked by abundant information and rapidly changing market conditions. The integration of these methods has the potential to enhance the accuracy of stock priceforecasts, offering benefits to investors and financial analysts alike. Objectives: The project involves three key components. It begins by gatheringhistorical stock data for a specific stock ticker and conducting essential data preprocessing. Next, it focuses on extracting news headlines from a prominent financial website and conducting a thorough sentiment analysis of these headlines. Thissentiment analysis provides valuable insights into public sentiment surrounding thechosen stocks, with visualizations representing positive, negative, and neutral trends.Finally, the project aims to combine the findings from both components using an Ensemble Method, resulting in a comprehensive suggestion to user whether to buy,holdor sell the stock. These components collectively aim to improve stock price predictions and assess the adaptability of the ARIMA model to changing market conditionsalong the time and significant events. Methods: This project explores an innovative approach to improve stock pricepredictions, combining the ARIMA model with sentiment analysis methods usingfinancial news data. The study involved collecting historical stock data from YahooFinance, employing moving averages like 5-day, 30-day and 90-day windows, andusing advanced models such as ARIMA for predictions. Our analysis also includestime series plots at various intervals, providing valuable perspectives. Through theEnsemble Method, which integrates quantitative predictions and sentiment analysis,we generated practical recommendations for a five-day forecast. Our work addressedgaps in integrating sentiment analysis into stock prediction models and adapting tochanging market conditions, contributing to the advancement of stock forecastingmethodologies. Results: The ensembled predictive model for stock prices demonstrates favorableoutcomes. The Mean Absolute Error (MAE) is 0.8659, indicating accuracy, and theRoot Mean Squared Error (RMSE) is 0.1732, showing the overall prediction error.The Mean Absolute Percentage Error (MAPE) is 1.8541, suggesting precision in comparison to actual stock prices. The R-squared value is 0.9804, indicating the model’sability to explain variation in stock price data. These findings highlight the model’seffectiveness in providing reliable insights for investors in the dynamic stock market. Conclusions: The analysis with the ARIMA model to enhance stock price predictions. It revealed that sentiment analysis complements traditional methods, providing valuable insights for decision-making. Evaluating ARIMA’s long-term performance suggests adaptable forecasting techniques. This work contributes to advancingfinancial analysis and improving stock price predictions.

The rapid development of wind energy in Sweden created a volatile environment for the electricity market. Variance in the daily prices and the reductions of the average prices over the years due to the merit order effect of intermittent wind energy resulted in increased unpredictability in financial returns, which led to many wind projects being cancelled. In this thesis, in order to shed more light on the impact of wind energy development on spot prices, an artificial neural network (ANN) electricity price forecasting model is designed in order to predict Sweden’s four electricity regions Nord Pool Elspot day-ahead electricity spot market prices. The model's final result displayed a mean absolute error of 3.3398 €/MWh. In order for the model to be able to generalize better, a ridge regression regularizer is added to the ANN. Alternative wind scenarios for Sweden are introduced and their spot prices are predicted by the ANN model. The results show that each 10% increase in wind energy production leads to a 0.9% spot price reduction in the Nord Pool Swedish energy market prices.

As the world is gravitating toward an information economy, it has become more and more critical for an investor to understand the impact of data and information. One of the sources of data that can be converted into information are texts from microblogging platforms, such as Twitter. The user of such a microblogging account can filtrate opinion and information to millions of people. Depending on the account holder, the opinion or information originated from the designated account may lead to different societal impact. The microblogging scope of this investigation are politicians holding a Twitter account. This investigation will look into the relationship between political tweets' sentiment and market movement and the subsequent longevity of such an effect. The classified sentiments are positive or negative. The presence of artificial intelligence is vital for a data-driven investigation; in the context of this investigation, artificial intelligence will be used to classify the sentiment of the political tweet. The methods chose to assess the impact of a political tweet and market movement is event-study. The impact is expressed in either a positive or a negative cumulative abnormal return subsequent to the political tweet. The findings of the investigation indicate that on average, there is no statistical evidence that a political tweets' sentiment leads to an abnormal return. However, in specific cases, political tweet leads to abnormal return. Moreover, it has been determined that the longevity of the effect is rather short. This is an interdisciplinary approach that can be applied by individual and institutional investors and financial institutions.

Being able to predict stock prices might be the unspoken wish of stock investors. Although stock prices are complicated to predict, there are many theories about what affects their movements, including interest rates, news and social media. With the help of Machine Learning, complex patterns in data can be identified beyond the human intellect. In this thesis, a Machine Learning model for time series forecasting is created and tested to predict stock prices. The model is based on a neural network with several layers of Long Short-Term Memory (LSTM) and fully connected layers. It is trained with historical stock values, technical indicators and Twitter attribute information retrieved, extracted and calculated from posts on the social media platform Twitter. These attributes are sentiment score, favourites, followers, retweets and if an account is verified. To collect data from Twitter, Twitter’s API is used. Sentiment analysis is conducted with Valence Aware Dictionary and sEntiment Reasoner (VADER). The results show that by adding more Twitter attributes, the Mean Squared Error (MSE) between the predicted prices and the actual prices improved by 3%. With technical analysis taken into account, MSE decreases from 0.1617 to 0.1437, which is an improvement of around 11%. The restrictions of this study include that the selected stock has to be publicly listed on the stock market and popular on Twitter and among individual investors. Besides, the stock markets’ opening hours differ from Twitter, which constantly available. It may therefore introduce noises in the model. / Att kunna förutspå aktiekurser kan sägas vara aktiespararnas outtalade önskan. Även om aktievärden är komplicerade att förutspå finns det många teorier om vad som påverkar dess rörelser, bland annat räntor, nyheter och sociala medier. Med hjälp av maskininlärning kan mönster i data identifieras bortom människans intellekt. I detta examensarbete skapas och testas en modell inom maskininlärning i syfte att beräkna framtida aktiepriser. Modellen baseras på ett neuralt nätverk med flera lager av LSTM och fullt kopplade lager. Den tränas med historiska aktievärden, tekniska indikatorer och Twitter-attributinformation. De är hämtad, extraherad och beräknad från inlägg på den sociala plattformen Twitter. Dessa attribut är sentiment-värde, antal favorit-markeringar, följare, retweets och om kontot är verifierat. För att samla in data från Twitter används Twitters API och sentimentanalys genomförs genom VADER. Resultatet visar att genom att lägga till fler Twitter attribut förbättrade MSE mellan de förutspådda värdena och de faktiska värdena med 3%. Genom att ta teknisk analys i beaktande minskar MSE från 0,1617 till 0,1437, vilket är en förbättring på 11%. Begränsningar i denna studie innefattar bland annat att den utvalda aktien ska vara publikt listad på börsen och populär på Twitter och bland småspararna. Dessutom skiljer sig aktiemarknadens öppettider från Twitter då den är ständigt tillgänglig. Detta kan då introducera brus i modellen.

面對市場未來趨勢的不確定性，投資者可以運用「投資組合保險」的概念，既能保障原本所投資的資產價值，又可以參與市場上漲時的獲利。本研究以類神經網路來研究證券市場的現象，一方面是已經有許多類神經網路在財務分析上的研究成果，另一方面是其具有學習以及預測的能力。 本研究首先探討投資組合保險策略，接著再比較投資組合保險策略在不同市況下的績效表現，隨後提出兩個階段的研究架構，經過設計與建置，以類神經網路模型進行對大盤未來漲跌型態的模擬預測，並利用預測的結果，輔助投資組合保險策略的決策，最後並將研究結果與大盤績效做綜合分析比較。 本研究的資料採取自台灣證券集中交易市場，期間為1991年1月3日至2002年12月31日，共3306個交易日，取大盤每日交易之歷史資料，經過處理後建立資料庫。類神經網路模型具有預測未來大盤漲跌區間的能力，在本研究所提出的漲跌區間劃分方式上，其預測正確率達到55%，預測的結果與實際漲跌完全相反的比例僅10%，其餘的35%為相鄰區間的預測誤差，其預測能力有助於投資組合保險策略的進行。 經過類神經網路模型輔助而進行的停損策略（SL），其年報酬率以及Sharpe Ratio，在大盤下跌的期間，兩個績效指標衡量結果皆為正值（21.125%＞0以及980.493＞0），充分發揮保險功能；而在大盤上漲的期間，兩個績效指標衡量結果皆優於大盤（46.544%＞17.137%以及393.808＞110.069）。 在年報酬率與Sharpe Ratio之間，本研究主張在探討投資組合保險時應著重風險的衡量，因此經過類神經網路模型輔助而進行的固定比例投資組合策略（CPPI），搭配槓桿乘數M值的調整，在大盤下跌的期間，其Sharpe Ratio依然可以維持正值，達到保險的效果，保護投資人的資產免於損失；而在大盤上漲的期間，其Sharpe Ratio更是高於大盤，可以享受資產價值提昇的獲利。 / Facing the uncertainty of the market trend, an investor can use the concept of “ Portfolio Insurance ” to protect the value of his portfolio in bear market and earn the benefit from bull market. There have been many researches about applying Neural Network in the financial analysis and Neural Network has the abilities to learn and forecast. This research evaluates the performances of the portfolio insurance strategies in different market trends. Then two-stage research structure has been designed and built. The first stage is forecasting the up-and-down trends of the equity market index by Neural network model. The second stage is using the forecasted results assisting the portfolio insurance decisions. Finally, the results of this research have been analyzed and compared with the benchmark. The Neural Network is able to forecast the future up-and-down trends. The accurate rate is 55%. During the bear market（2002）, the annual rate of return and Sharpe Ratio of the stop loss（SL） strategy which is assisted by NN are both positive（21.125%＞0 and 980.493＞0）. During the bull market（2001）, they both outperform the benchmark（46.544%＞17.137% and 393.808＞110.069）. The annual rate of return is more important than Sharpe Ratio because the risk measurement is an important factor in portfolio insurance strategy. Sharpe Ratios of the CPPI strategy which is assisted by NN outperform the benchmark in both above mentioned bear and bull market. In short, the SL and CPPI strategy assisted by NN not only protect the value of the portfolio from losing in bear market but also gain profit in bull market, so they are the ideal portfolio insurance strategies.

The demand for electricity is increasing daily due to technological advancement, and ?luxurious lifestyles. Increasing utilization of electricity means the depletion of fossil fuel ?reserves. Thus, governments around the world are seeking alternative and sustainable ?sources of energy such as the solar powered system. The main purpose of this research is ?to develop a knowledge base on residential electric generation from the grid and solar ?energy. This paper examined the economic feasibility of using grid-tied residential ?photovoltaic (GRPV) system in Saudi Arabia with the HOMER software. Models ?forecasting the price of oil barrels through artificial neural networks (ANN) were also ?employed in the analysis. The study shows that an oil-rich country like Saudi Arabia has ?potential to utilize the GRPV system as an alternative source of energy. / This paper examined the economic feasibility of using grid-tied residential photovoltaic ??(GRPV) system in Saudi Arabia with the HOMER software. Models forecasting the ?price of oil barrels through artificial neural networks (ANN) were also employed in the ?analysis. The study shows that an oil-rich country like Saudi Arabia has potential to ?utilize the GRPV system as an alternative source of energy. This study provides a ?discussion of the potential for applying solar-powered and an assessment of the ?performance of existing systems based on collecting output data.?

This study proposes a performance comparison between machine learning regression algorithms and Artificial Neural Network (ANN). The regression algorithms used in this study are Multiple linear, Least Absolute Selection Operator (Lasso), Ridge, Random Forest. Moreover, this study attempts to analyse the correlation between variables to determine the most important factors that affect house prices in Malmö, Sweden. There are two datasets used in this study which called public and local. They contain house prices from Ames, Iowa, United States and Malmö, Sweden, respectively.The accuracy of the prediction is evaluated by checking the root square and root mean square error scores of the training model. The test is performed after applying the required pre-processing methods and splitting the data into two parts. However, one part will be used in the training and the other in the test phase. We have also presented a binning strategy that improved the accuracy of the models.This thesis attempts to show that Lasso gives the best score among other algorithms when using the public dataset in training. The correlation graphs show the variables' level of dependency. In addition, the empirical results show that crime, deposit, lending, and repo rates influence the house prices negatively. Where inflation, year, and unemployment rate impact the house prices positively.

Today the process of sorting second-hand clothes and textiles is mostly manual. In this master’s thesis, methods for automating this process as well as improving the manual sorting process have been investigated. The methods explored include the automatic prediction of price and intended usage for second-hand clothes, as well as different types of image retrieval to aid manual sorting. Two models were examined: CLIP, a multi-modal model, and MAE, a self-supervised model. Quantitatively, the results favored CLIP, which outperformed MAE in both image retrieval and prediction. However, MAE may still be useful for some applications in terms of image retrieval as it returns items that look similar, even if they do not necessarily have the same attributes. In contrast, CLIP is better at accurately retrieving garments with as many matching attributes as possible. For price prediction, the best model was CLIP. When fine-tuned on the dataset used, CLIP achieved an F1-Score of 38.08 using three different price categories in the dataset. For predicting the intended usage (either reusing the garment or exporting it to another country) the best model managed to achieve an F1-Score of 59.04.

Machine learning is an area of computer science that only grows as time goes on, and there are applications in areas such as finance, biology, and computer vision. Some common applications are stock price prediction, data analysis of DNA expressions, and optical character recognition. This thesis uses machine learning techniques to predict prices for different stocks and indices on the Swedish stock market. These techniques are then compared to see which performs best and why. To accomplish this, we used some of the most popular models with sets of historical stock and index data. Our best-performing models are linear regression and neural networks, this is because they are the best at handling the big spikes in price action that occur in certain cases. However, all models are affected by overfitting, indicating that feature selection and hyperparameter optimization could be improved.

Parametric models are used to understand dynamical systems and predict its future behavior. It is difficult to estimate the model’s parametric values since there are usually many parameters and they are highly correlated. The aim of this project is to apply the method of indirect inference and the two stage approach to estimate the drift and volatility parameters of a Geometric Brownian Motion. This was first done by estimating the parameters of a known Geometric Brownian process. Then, the Coca-Cola Company’s stock was used for a five-year forecast to study the estimators’ predictive power. The two stage approach struggles when the data does not truly follow a Geometric Brownian Motion, but when it does it produces highly efficient and accurate estimates. The method of indirect inference produces better estimates, than the two stage approach,for data that deviates from a Geometric Brownian Motion.Therefore, it is preferable to use indirect inference over two stage approach for stock price forecasting. / Parametriska modeller används för attförstå dynamiska system och förutspå dess framtida beteende.Det är utmanande att skatta modellens parametriska värdeneftersom det vanligtvis finns många parametrar och de är oftastarkt korrelerade. Målet med detta projekt är att tillämpametoderna indirect inference och two stage approach för attskatta drivnings- och volatilitetsparametrarna av en geometriskBrownsk rörelse. Först skattades parametrarna av en kändGeometrisk Brownsk rörelse. Sedan användes The Coca-ColaCompanys aktie i syfte att studera estimatorernas förmåga attförutspå en femårig period. Two stage approach fungerar dåligtför data som inte helt följer en geometrisk Brownsk rörelse, mennär datan gör det är skattningarna noggranna och effektiva.Indirect inference ger bättre skattningar än two stage approachnär datan inte helt följer en geometrisk Brownsk rörelse. Därförär indirect inference att föredra för aktieprognoser. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm