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Hateful language is a growing problem in digital spaces. Human moderators are not enough to eliminate the problem. Automated hateful language detection systems are used to aid the human moderators. One of the issues with the systems is that their performance can differ depending on who is the target of a hateful text. This project evaluated the performance of the two systems (Perspective and Hatescan) with respect to who is the target of hateful texts. The analysis showed, that the systems performed the worst for texts directed at women and immigrants. The analysis involved tools such as a synthetic dataset based on the HateCheck test suite, as well as wild datasets created from forum data. Improvements to the test suite HateCheck have also been proposed. / Hatiskt språk är ett växande problem i digitala miljöer. Datamängderna är för stora för att enbart hanteras av mänskliga moderatorer. Automatiska system för hatdetektion används därför som stöd. Ett problem med dessa system är att deras prestanda kan variera beroende på vem som är målet för en hatfull text. Det här projektet evaluerade prestandan av de två systemen Perspective och Hatescan med hänsyn till olika mål för hatet. Analysen visade att systemen presterade sämst för texter där hatet riktades mot kvinnor och invandrare. Analysen involverade verktyg som ett syntetiskt dataset baserat på testsviten HateCheck och vilda dataset med texter inhämtade från diskussionsforum på internet. Dessutom har projektet utvecklat förslag på förbättringar till testsviten HateCheck.

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The aim of this paper is first of all to determine whether deep learning methods can recover trading strategies based on historical price and volume data, with scarcity of real data in mind. The second aim is to evaluate the methods to generate a deep learning blueprint for strategy extraction. Trading strategies can be built on many different types of data, often combined from different areas. In this paper, we focus on trading strategies based solely on historical price and volume data to limit the scope of the problem. Combinations of different deep learning architectures and methods such as transfer- and ensemble methods were evaluated. The results clearly show that deep learning models can recover relatively complex trading strategies to some extent. Models leveraging transfer learning outperform other models when data is scarce and ensemble methods elevate performance in certain regards. / Målet med denna rapport är i första hand att ta reda på om djupinlärningsmetoder kan återskapa handlingsstragetier baserat på historiska priser och volymdata, med vetskapen att datan är begränsad. Det andra målet är att utvärdera metoder för att skapa en djupinlärningsmall för att utvinna handelsstrategier. Handelsstrategier kan vara byggda på många olika datatyper, ofta i kombination från olika områden. I denna rapport fokuserar vi på strategier som enbart är baserade på historiska priser och volymdata för att begränsa problemet. Kombinationer av olika djupinlärningsarkitekturer tillsammans med metoder som till exempel överföringsinlärning och ensembleinlärning utvärderades. Resultaten visar tydligt att djupinlärningsmodeller kan återskapa relativt komplexa handlingsstrategier. Modeller som utnyttjade överföringsinlärning presterade bättre än andra modeller när datan var begränsad och ensembleinlärning ökade prestandan ytterligare i vissa sammanhang. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

Bakgrund: Bakgrunden presenterar digitaliseringens utveckling och jämför det med media som tidigare varit analogt. Därefter redogörs för utvecklingen av automatiserade finansiella rådgivare och de utvecklingsstadier det genomgått. I samband med utvecklingen påträffas olika aspekter hos unga småsparare som påverkar förtroendet som övergången från att använda mänskliga finansiella rådgivare till robotrådgivare. Syftet: Syftet med uppsatsen är att undersöka hur företag inom fondförsäljning och aktiemäklarbanker kan nyttja robotrådgivningstjänster bland unga småsparare. Undersökningen fokuserar främst på aspekterna informationsspridning och riskupplevelse.  Teoretisk referensram: Denna studie utgår från tre olika teorier, vilka omfattar Unified theory of acceptance and use of technology, Theory of perceived risk och Innovation Diffusion Theory. Metod: Studien utför en kvantitativ ansats genom en tvärsnittsdesign. Urvalsramen inkluderar unga vuxna som är 18 till 30 år. Datainsamlingen genomfördes via sociala medier och skolplattformar, och totalt deltog 151 unga högskolestudenter i enkätundersökningen.  Slutsats: Utifrån resultaten förekommer det en positiv korrelation mellan användningen och riskupplevelsen, vilket gör att robotrådgivare har en möjlighet att attrahera unga vuxna till segmentet.

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The master thesis is focused on how a local volatility surfaces can be extracted by optimization with respectto smoothness and price error. The pricing is based on utility based pricing, and developed to be set in arisk neutral pricing setting. The pricing is done in a discrete multinomial recombining tree, where the timeand price increments optionally can be equidistant. An interpolation algorithm is used if the option that shallbe priced is not matched in the tree discretization. Power utility functions are utilized, where the log-utilitypreference is especially studied, which coincides with the (Kelly) portfolio that systematically outperforms anyother portfolio. A fine resolution of the discretization is generally a property that is sought after, thus a seriesof derivations for the implementation are done to restrict the computational encumbrance and thus allow finer discretization. The thesis is mainly focused on the derivation of the method rather than finding optimal parameters thatgenerate the local volatility surfaces. The method has shown that smooth surfaces can be extracted, whichconsider market prices. However, due to lacking available interest and dividend data, the pricing error increasessymmetrically for longer option maturities. However, the method shows exponential convergence and robustnessto different initial (flat) volatilities for the optimization initiation. Given an optimal smooth local volatility surface, an arbitrary payoff function can then be used to price thecorresponding option, which could be path-dependent, such as barrier options. However, only vanilla optionswill be considered in this thesis. Finally, we find that the developed

<p dir="ltr">Machine learning (ML) algorithms are increasingly being used in a variety of applications and are heavily relied upon to make decisions that impact people’s lives. ML models are often praised for their precision, yet they can discriminate against certain groups due to biased data. These biases, rooted in historical inequities, pose significant challenges in developing fair and unbiased models. Central to addressing this issue is the mitigation of biases inherent in the training data, as their presence can yield unfair and unjust outcomes when models are deployed in real-world scenarios. This study investigates the efficacy of data acquisition, i.e., one of the stages of data preparation, akin to the pre-processing bias mitigation technique. Through experimental evaluation, we showcase the effectiveness of data acquisition, where the data is acquired using data valuation techniques to enhance the fairness of machine learning models.</p>

There has always been competition among hedge funds, mutual funds, and other types of investors to perform better than index, meaning, creating alpha. How can you create alpha? Are there any patterns to follow? Any trends? There are many questions one may ask in order to find patterns that are creating. The purpose of this study is to see how CEO departures affect equity value in the short- medium- and long term and its comparison to indices. This study has collected data from a majority of publicly traded Nordiccompanies with a market capitalisation of over 10 billion Swedish crowns. The collected data has been collected within the last 20 years (2003-2023) with market-adjusted return, market capitalisation, volume, and CEO tenure being the prominent variables analysed.As CEOs have the operative responsibility of a company, they thereby are at the top of the company and effectively guide the company towards its goals. The changes in CEOs could thereby be of interest to investors as there is potential for larger structural changes when a new CEO is appointed. Applying this to its equity value, there is potential formispricing. Using mainly Fama’s and Malkiel’s research on the Efficient Market Hypothesis (EMH) and Random Walk as the theoretical framework there are different ways in which equity price could move. EMH states that all markets are efficient by the equity representing all available information. Random Walk instead states that equity price moves randomly and cannot be predicted in accordance with historical movements. The empirical results showed that there were no statistically significant findings in our employed regression analysis. However, on average, the descriptive statistics show thatthe market-adjusted return for a company with a CEO departure is negative compared to its comparable index. The intraday MAR highly deviate from 1 day until 1 quarter and thereafter the deviation becomes less. The conclusion could be drawn that EMH might be contradicted in the short term but holds long term. It is also difficult to deny the theory of random walks in equities.