This report assesses different machine learning models’accuracies to predict whether a stock will go up or down invalue in a short term. The models that is used is linear regression,LSTM and Elman RNN. These models was trained on historicalprice data from the Nasdaq Stock Exchange. The idea that thereexist a relationship of the price movement of a stock and its futurevalue is called ’techncial analysis’. The result shows that neitherLSTM nor Elman RNN provides any statistical significance ofits accuracy for any of the implementations. Linear regression,provides a significant accuracy for longer time series predictionof the price when trained on 100 days of data and prediction ofits movement after five more days. / I denna report undersöks olika maskininlärningsmodeller noggrannhet för att förutspå om en aktie kommer att gå upp eller ner i värde på kort sikt. De evaluerade maskininlärningsmodellernamodellerna är följande: linjär regression, LSTM och Elman RNN. Dessa modeller tränades med hjälp av historisk prisdata från Nasdaq Stock Exchange. Ide´en om att det finns ett samband mellan prisrörelsen av en aktie och dess kortsiktiga framtida värde är benämnt som ’teknisk analys’. Resultaten visar att varken LSTM eller Elman RNN förmedlar en noggrannhet med statistisk signifikans för någon av de anänvda implementationerna. Linjär regression förmedlar en statistisk signikant noggrannhet för längre tidserie förutsägelser med träningsdata om 100 dagar och förutsägelse av aktiens rörelse efter fem fler dagar. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

While Machine Learning is one of the most popular research areas in Computer Science, there are still only a few deployed applications intended for use by the general public. We have developed an exemplary application that can be directly applied to eBay trading. Our system predicts how much an item would sell for on eBay based on that item's attributes. We ran our experiments on the eBay laptop category, with prior trades used as training data. The system implements a feature-weighted k-Nearest Neighbor algorithm, using genetic algorithms to determine feature weights. Our results demonstrate an average prediction error of 16%; we have also shown that this application greatly reduces the time a reseller would need to spend on trading activities, since the bulk of market research is now done automatically with the help of the learned model.

As the cryptocurrency markets continuously grow, so does the need for reliable analytical tools for price prediction. This study conducted a comparative analysis of machine learning (ML) algorithms for cryptocurrency price prediction. Through a literature review, three common and reliable ML algorithms for cryptocurrency price prediction were identified: Long Short-Term Memory (LSTM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Utilizing the Bitcoin All Time History dataset from TradingView, the study assessed both the individual performance of each algorithm and the potential of ensemble methods to enhance predictive accuracy. The results reveal that the LSTM algorithm outperformed RF and XGBoost in terms of predictive accuracy according to the metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Additionally, two ensemble approaches were tested: Ensemble 1, which enhanced the LSTM model with the combined predictions from RF and XGBoost, and Ensemble 2, which integrated predictions from all three models. Ensemble 2 demonstrated the highest predictive performance among all models, highlighting the advantages of using ensemble approaches for more robust predictions.

股價的漲跌變化是由於證券市場中眾多不同投資人及其投資決策後所產生的結果。然而，影響股價變動的因素眾多且複雜，新聞也屬於其中一種，新聞事件不但是投資人用來得知該股票上市公司的相關營運資訊的主要媒介，同時也是影響投資人決定或變更其股票投資策略的主要因素之一。本研究提出以新聞文件做為股價漲跌預測系統的基礎架構，透過文字探勘技術及分類技術來建置出能預測當日個股收盤股價漲跌趨勢之系統。 本研究共提出三種分類模型，分別是簡易貝氏模型、k最近鄰居模型以及混合模型，並設計了三組實驗，分別是分類器效能的比較、新聞樣本資料深度的比較、以及新聞樣本資料廣度的比較來檢驗系統的預測效能。實驗結果顯示，本研究所提出的分類模型可以有效改善相關研究中整體正確率高但各個類別的預測效能卻差異甚大的情況。而對於影響投資人獲利與否的關鍵類別"漲"及類別"跌"的平均預測效能上，本研究所提出的這三種分類模型亦同時具有良好的成效，可以做為投資人進行投資決策時的有效參考依據。 / Stocks' closing price levels can provide hints about investors' aggregate demands and aggregate supplies in the stock trading markets. If the level of a stock's closing price is higher than its previous closing price, it indicates that the aggregate demand is stronger than the aggregate supply in this trading day. Otherwise, the aggregate demand is weaker than the aggregate supply. It would be profitable if we can predict the individual stock's closing price level. For example, in case that one stock's current price is lower than its previous closing price. We can do the proper strategies(buy or sell) to gain profit if we can predict the stock's closing price level correctly in advance. In this thesis, we propose and evaluate three models for predicting individual stock's closing price in the Taiwan stock market. These models include a naïve Bayes model, a k-nearest neighbors model, and a hybrid model. Experimental results show the proposed methods perform better than the NewsCATS system for the "UP" and "DOWN" categories.

This thesis tackles the problem of accurately pricing apartments in large Swedish cities using geospatial data. The aim is to determine if geospatial data and population statistics can be used in conjunction with direct apartment data to accurately price apartments in large cities. There has previously been little research in this domain due to a lack of available data in many countries. In Sweden, apartment transaction data is public which enabled this thesis to be performed. We apply and compare a multiple linear regression, a multi-layer perceptron and a random forest to appraise apartments in six of the largest cities in Sweden. To perform the appraisals, geospatial data and population statistics were gathered in the areas surrounding the apartments. Five of the six cities were used to train and test the models, whereas one city was only used for testing. The two best performing models, the multi-layer perceptron and random forest achieved a mean absolute percentage error of 8.68% and 8.76% respectively within cities they were previously trained within and a mean absolute percentage error of 22.62% and 20.6% respectively on apartment in the test city dataset. In conclusion this thesis suggests that with the use of this data, multi-layer perceptrons and random forests are useful for appraising apartments in different cities, however that more data is probably needed to appraise apartments in cities previously unseen by the models. / Detta masterarbete tar upp problemet med att korrekt prissätta lägenheter i stora svenska städer med hjälp av geospatiala data. Syftet är att avgöra om geospatiala data och befolkningsstatistik kan användas tillsammans med direkt lägenhetsdata för att korrekt prissätta lägenheter i storstäder. Det har tidigare utförts lite forskning inom detta område på grund av brist på tillgängliga data i många länder. I Sverige är uppgifter om lägenhetstransaktioner offentliga vilket gjorde att denna avhandling kunde utföras. Vi tillämpar och jämför en multipel linjär regression, en flerskiktsperceptron och en slumpmässig skog för att värdera lägenheter i sex av de största städerna i Sverige. För att göra värderingarna samlades geospatiala data och befolkningsstatistik i de områden som omger lägenheterna. Fem av de sex städerna användes för att träna och testa modellerna, medan en stad endast användes för testning. De två bäst presterande modellerna, flerskiktsperceptronen och slumpmässig skog uppnådde ett genomsnittligt absolut procentfel på 8,68% respektive 8,76% inom städer som de tidigare var tränade inom och ett genomsnittligt absolut procentfel på 22,62% respektive 20,6% på lägenheter i teststadens dataset. Sammanfattningsvis tyder detta verk på att med hjälp av dessa data är flerskiktsperceptroner och slumpmässiga skogar användbara för att värdera lägenheter i olika städer, men att mer data förmodligen behövs för att värdera lägenheter i städer som modellerna tidigare inte har tränats på.

Machine learning algorithms have been used for price prediction within several application areas. Examples include real estate, the stock market, tourist accommodation, electricity, art, cryptocurrencies, and fine wine. Common approaches in studies are to evaluate the accuracy of predictions and compare different algorithms, such as Linear Regression or Neural Networks. There is a thriving global second-hand market for vinyl records, but the research of price prediction within the area is very limited. The purpose of this project was to expand on existing knowledge within price prediction in general to evaluate some aspects of price prediction of vinyl records. That included investigating the possible level of accuracy and comparing the efficiency of algorithms. A dataset of 37000 samples of vinyl records was created with data from the Discogs website, and multiple machine learning algorithms were utilized in a controlled experiment. Among the conclusions drawn from the results was that the Random Forest algorithm generally generated the strongest results, that results can vary substantially between different artists or genres, and that a large part of the predictions had a good accuracy level, but that a relatively small amount of large errors had a considerable effect on the general results.

Nobel Prize-winning modern portfolio theory (MPT) has been considered to be one of the most important and influential economic theories within finance and investment management. MPT assumes investors to be riskaverse and uses the variance of asset returns as a proxy of risk to maximise the performance of a portfolio. Successful portfolio management reply, thus on accurate risk estimate and asset return prediction. Risk estimates are commonly obtained through traditional asset pricing factor models, which allow the systematic risk to vary over time domain but not in the frequency space. This approach can impose limitations in, for instance, risk estimation. To tackle this shortcoming, interest in applications of spectral analysis to financial time series has increased lately. Among others, the novel spectral portfolio theory and the spectral factor model which demonstrate enhancement in portfolio performance through spectral risk estimation [1][11]. Moreover, stock price prediction has always been a challenging task due to its non-linearity and non-stationarity. Meanwhile, Machine learning has been successfully implemented in a wide range of applications where it is infeasible to accomplish the needed tasks traditionally. Recent research has demonstrated significant results in single stock price prediction by artificial LSTM neural network [6][34]. This study aims to evaluate the combined effect of these two advancements in a portfolio optimisation problem and optimise a spectral portfolio with stock prices predicted by LSTM neural networks. To do so, we began with mathematical derivation and theoretical presentation and then evaluated the portfolio performance generated by the spectral risk estimates and the LSTM stock price predictions, as well as the combination of the two. The result demonstrates that the LSTM predictions alone performed better than the combination, which in term performed better than the spectral risk alone. / Den nobelprisvinnande moderna portföjlteorin (MPT) är utan tvekan en av de mest framgångsrika investeringsmodellerna inom finansvärlden och investeringsstrategier. MPT antar att investerarna är mindre benägna till risktagande och approximerar riskexponering med variansen av tillgångarnasränteavkastningar. Nyckeln till en lyckad portföljförvaltning är därmed goda riskestimat och goda förutsägelser av tillgångspris. Riskestimering görs vanligtvis genom traditionella prissättningsmodellerna som tillåter risken att variera i tiden, dock inte i frekvensrummet. Denna begränsning utgör bland annat ett större fel i riskestimering. För att tackla med detta har intresset för tillämpningar av spektraanalys på finansiella tidsserier ökat de senast åren. Bland annat är ett nytt tillvägagångssätt för att behandla detta den nyintroducerade spektralportföljteorin och spektralfak- tormodellen som påvisade ökad portföljenprestanda genom spektralriskskattning [1][11]. Samtidigt har prediktering av aktierpriser länge varit en stor utmaning på grund av dess icke-linjära och icke-stationära egenskaper medan maskininlärning har kunnat använts för att lösa annars omöjliga uppgifter. Färska studier har påvisat signifikant resultat i aktieprisprediktering med hjälp av artificiella LSTM neurala nätverk [6][34]. Detta arbete undersöker kombinerade effekten av dessa två framsteg i ett portföljoptimeringsproblem genom att optimera en spektral portfölj med framtida avkastningar predikterade av ett LSTM neuralt nätverk. Arbetet börjar med matematisk härledningar och teoretisk introduktion och sedan studera portföljprestation som genereras av spektra risk, LSTM aktieprispredikteringen samt en kombination av dessa två. Resultaten visar på att LSTM-predikteringen ensam presterade bättre än kombinationen, vilket i sin tur presterade bättre än enbart spektralriskskattningen.