The efficient market hypothesis states that stock prices fully reflect availablei nformation and that stocks thereby always are priced correctly. Hence, it should be impossible to predict future prices in the stock market, and investors will gain no benefits from engaging themselves into historical analyzes. This is a quantitative study which aim to investigate if there is any difference in market efficiency in Nordic stock markets during and after the financial crisis of 2008. By applying various statistical methods, such as unitroot tests, autocorrelation tests and runs test on the returns from each country’s leading market index, the study tries to find evidence for or against the weak form of market efficiency. The study finds evidence both for and against weak form market efficiency but concludes that there is no distinct difference in market efficiency during and after the financial crisis.

The intent of this thesis is to investigate how US equity funds performance differ due to their standard deviation. In order to accomplish this study, we collected daily data for 99 US equity funds for the period 2011-2020 and divided the funds into three risk classification groups based on their standard deviation for the year 2011. The collected data was used to perform an CAPM regression and to calculate returns on a three-, five- and ten-year basis. The results for the regression and the returns for the funds was later presented as average values for the different risk classification groups. We then compared the average outcomes for the three risk classifications with each other and the index S&P 500. Our result showed that the index S&P 500 outperformed the three risk classification groups average returns for every time period. We also noticed that the difference between the average returns and the index got greater by time. We did not find any big differences between our risk classifications when it comes to their performance. Our regression analysis resulted in many negative alpha values indicating that S&P 500, as many previous studies claims, outperforms actively mutual funds. The conclusion is therefore that we could not show any evidence that the there is a major different in performance between our risk groups but also that it is difficult for fund managers to outperform index.

Syfte: Undersökningens syfte är att få en förståelse i vilken omfattning privata investerare beaktar utomstående faktorer vid sina investeringsbeslut på aktiemarknaden. Studien har för avsikt att undersöka om verkligheten stämmer överens med teorin, därmed om människor är rationella vid sina beslut, och vilka faktorer som styr individens beslut vid aktiehandel.  Metod: Denna undersökning är en kvantitativ tvärsnittsstudie med ett deskriptivt förhållningssätt. Undersökningen grundar sig i en genomgripande studie av litteratur följt av en enkätundersökning. Slutsats: Resultatet från undersökningen visar att psykologiska faktorer samt information har en påtaglig effekt på privatpersoners investeringsbeslut. Vidare indikerar undersökningen att många privata investerare som använder sig främst av nyckeltal vid aktieanalys samt vid investering. / Purpose: The purpose of this study is to gain an understanding of the extent to which private investors take external factors into account when making their investment decisions in the stock market. The study intends to investigate whether reality is consistent with the theory, thus whether people are rational in their decisions, and what factors govern the individual's decisions in stock trading. Method: This study is a quantitative cross-sectional study with a descriptive approach. The survey is based on a comprehensive study of literature followed by a survey.  Conclusions: The results from the survey shows that psychological factors and information have a significant effect on private investment decisions. Furthermore, the survey indicates that many private investors use key figures in stock analysis and their investments.

This paper investigates the impact of Basel III on the valuation of banks in the EEA through an event study of the stock market. It contributes to academic literature by enhancing the study by Bruno, Onali & Schaeck (2018) with another event date after the conclusion of their study. This paper investigates two hypotheses: 1) Did the announcement by the Basel Committee on Banking Supervision on December 7th, 2017 (the event), affect the market capitalization of banks in the EEA; 2) Did domestic liquidity regulation prior to Basel III positively affect how those banks' market capitalization changed in response to the event. Using t-tests and a multivariate regression analysis, this study finds no statistical significance at a 10% level for either of the hypotheses. However, three findings appear to be found: 1) There was a small negative reaction to the event; 2) The negative reaction was larger for banks without prior regulation; and 3) The variance was larger for banks without prior regulation. These three findings all point towards both: 1) A negative effect from the event on banks’ market capitalization; and 2) A positive effect from prior domestic liquidity regulation. No statistically significant conclusions can be drawn from this study, however. This study's largest limitation is that it does not account for expectations prior to the event, and an effect may thus already have been priced into the market capitalization prior to the event.

Background: Investing in stocks seems to be more widespread than ever. The question is whether there are strategies that mean that investors systematically and over a longer period of time can generate excess returns. Proponents of the efficient market hypothesis believe that this is not possible as a higher return than the market is obtained only as a result of fortuity or a higher risk-taking. In contrast, there are those who believe that investment strategies, by exploiting market inefficiencies, can generate excess returns. This study examines whether this is possible by applying an investment strategy based on theoretically derived multiples. Purpose: The purpose of this study is to analyze whether an investment strategy based on theoretically derived multiples can generate excess returns by identifying mispricings in the Swedish stock market. Methodology: This study has been conducted with a quantitative research method and deductive approach to be able to achieve the purpose of the study. The study's measurement period extends from 2007-2022 and is limited to companies that have been listed on the OMX Stockholm Large Cap. During the measurement period, the investment strategy has been applied with three different investment horizons for the multiples P/E, P/BV, EV/EBITDA and EV/S. To examine the strategy's ability to generate excess returns, the return and risk of constructed portfolios have been analyzed in relation to OMXSPI. Result: The results of the study show that the strategy should be applied with a longer investment horizon. All portfolios with an investment horizon of one or three years have generated a higher return than the study's benchmark index. The strategy thus seems to be able to generate excess returns, even though the results indicate that it works better during periods of upturn in the market. As the proportion of shares developed in line with the forecast was over 50% for all multiples, the excess return also seems to be explained by the fact that the strategy works well for identifying mispricings.

Background: As the goal of most investors is to generate excess returns as compared tothe broad market, different investment strategies to perform such a feat have been studied thoroughly for decades. One strategy which has performed particularly well is the value investment strategy, where securities that appear cheap relative to some of their fundamental values are invested in. More recently, the growth investment strategy, where securities are instead bought if some of their fundamental values are expected to rise rapidly in the future, has caught more attention from investors. As the efficient market hypothesis suggests that no investment strategy should be able to consistently generate excess returns without any luck involved, it is of interest to examine whether the aforementioned strategies act in congruence with the hypothesis. Purpose: The purpose of this study is to analyze if the value and growth investment strategies generate superior returns as well as risk-adjusted excess returns when compared to the Swedish stock market. The study also aims to analyze how the performance of the strategies varies during periods of different market sentiments. Methodology: This study used a quantitative method in its data collection and was conducted using a deductive approach. Six synthetic portfolios were created to test the strategies’ performance. The stocks which constituted the synthetic portfolios were chosen based on their P/E and P/B values from the Refinitiv Eikon platform, and the portfolios were rebalanced annually over the entire analyzed time period. The Swedish All-Share index, OMXSGI, was used as a proxy for the market portfolio. Conclusion: Based on the results of the study, the growth portfolios, more so than the value portfolios, were found to generate greater statistically significant returns as compared to the broad market during the analyzed time period. Although not all portfolios generated excess returns, the study may still add to the evidence that disproves the efficient market hypothesis.

Machine learning models as tools for predicting time series have in recent years proven to perform exceptionally well. With financial time series in the form of stock indices being inherently complex and subject to noise and volatility, the prediction of stock market movements has proven to be especially difficult throughout extensive research. The objective of this study is to thoroughly analyze the LSTM architecture for neural networks and its performance when applied to the S&P 500 stock index. The main research question revolves around quantifying the impact of varying the number of time steps in the LSTM model on predictive performance when applied to the S&P 500 index. The data used in the model is of high reliability downloaded from the Bloomberg Terminal, where the closing price has been used as feature in the model. Other constituents of the model have been based in previous research, where satisfactory results have been reached. The results indicate that among the evaluated time steps, ten steps provided the superior performance. However, the impact of varying time steps is not all too significant for the overall performance of the model. Finally, the implications of the results for the field of research present themselves as good basis for future research, where parameters are varied and fine-tuned in pursuit of optimal performance. / Maskininlärningsmodeller som redskap för att förutspå tidsserier har de senaste åren visat sig prestera exceptionellt bra. Vad gäller finansiella tidsserier i formen av aktieindex, som har en inneboende komplexitet, och är föremål för störningar och volatilitet, har förutsägelse av aktiemarknadsrörelser visat sig vara särskilt svårt igenom omfattande forskning. Målet med denna studie är att grundligt undersöka LSTM-arkitekturen för neurala nätverk och dess prestanda när den appliceras på aktieindexet S&P 500. Huvudfrågan kretsar kring att kvantifiera inverkan som varierande av antal tidssteg i LTSM-modellen har på prediktivprestanda när den appliceras på aktieindexet S&P 500. Data som använts i modellen är av hög pålitlighet, nedladdad frånBloomberg-terminalen, där stängningskurs har använts som feature i modellen. Andra beståndsdelar av modellen har baserats i tidigare forskning, där tillfredsställande resultat har uppnåtts. Resultaten indikerar att bland de testade tidsstegen så producerartio tidssteg bäst resultat. Dock verkar inte påverkan av antalet tidssteg vara särskilt signifikant för modellens övergripandeprestanda. Slutligen så presenterar sig implikationerna av resultaten för forskningsområdet som god grund för framtida forskning, där parametrar kan varieras och finjusteras i strävan efter optimal prestanda.

Att finna den mest effektiva strategin för att maximera sin avkastning på aktiemarknaden har varit en fråga som har intresserat investerare i hundratals år. Denna studie avser att undersöka vilken av investeringsstrategierna, Gyllene korset eller Buy and hold som är mest lönsam under perioden 2004 - 2022 på Stockholmsbörsen för att dra slutsatser om marknadens effektivitet. Genom att mäta avkastningen av tio aktier från large-cap listan och tio aktier från mid-cap-listan visade studiens resultat att Buy and hold under perioden gav en högre genomsnittlig avkastning relativt Gyllene korset under parametrarna glidande medelvärde 50, 200. Detta ger stöd för den effektiva marknadshypotesen som säger att det inte går att få överavkastning genom teknisk analys. Studien finner dock stöd för att köpsignalen som gavs vid Gyllene korset skapade stora vinster, och att det snarare var en försenad säljsignal, dödskorset, som var huvudanledningen till att Buy and hold var överlägsen Gyllene korset-strategin. / Finding the most effective strategy to maximize returns in the stock market has been a question that has interested investors for hundreds of years. This study aims to see which of the investment strategies, Golden cross or Buy and hold that is most profitable during the period 2004 - 2022 on the Stockholm Stock Exchange to draw conclusions about the efficiency of the market. By measuring the return of ten stocks from the large-cap list and ten stocks from the mid-cap list, the study's results showed that during the period, Buy and hold gave a higher average return than the Golden cross under the parameters moving average 50, 200. This supports the efficient market hypothesis, which states that it is not possible to obtain excess returns through technical analysis. However, the study finds support that the buy signal given at the Golden cross created large returns, and that it was rather a delayed sell signal, the death cross, that was the reason why Buy and hold was superior to the Golden cross strategy.

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.

Screening models are tools for predicting which stock are the most likely to perform well on a stock market. They do so by examining the financial ratios of the companies behind the stock. The ratios examined by the model are chosen according to the personal preferences of the particular investor. Furthermore, an investor can apply different weights to the different parameters they choose to consider, according to the importance they apply to each included parameter. In this thesis, it is investigated whether a screening model can beat the market average in the long term. It is also explored whether parameter-weight-optimization in the context of equity trading can be used to improve an already existing screening model. More specifically, a starting point is set in a screening model currently in use at a successful asset management firm, through data analysis and an optimization algorithm, it is then examined whether a programmatic approach can identify ways to improve the original screening model by adjusting the parameters it looks at as well as the weights assigned to each parameter. The data set used in the model contains daily price data and annual data on financial ratios for all stocks on the Stockholm Stock Exchange as well as the NASDAQ-100 over the time period 2004-2018. The results indicate that it is possible to beat the market average in the long term. Results further show that a programmatic approach is suitable for optimizing screening models.