Background: Asset prices, investment choices, and market mood can all be greatly impacted by macroeconomic factors and risk perception. Therefore, for investors, portfolio managers, policymakers, and regulators looking to negotiate the complexity of financial markets, knowing how macroeconomic factors affect risk is crucial. Objective: This study delves into the intricate relationship between macroeconomic indicators and market risk propensity, offering a comprehensive analysis of both cross-sectional and panel data. Focusing on key factors such as inflation, interest rates, exchange rates, and the Index of Industrial Production (IPP), this study explore their multifaceted impacts on market risk dynamics. Methods: To reveal the complex linkages that determine risk-taking behaviors and affect business outcomes, the study uses sophisticated econometric methodologies. Results: According to our research, inflation has a significant impact on investor sentiment and corporate profitability. Reduced profit margins and increased market risk are the results of higher inflation. Similar to this, interest rates become an important variable that affects borrowing costs, investment options, and the level of competition on the stock market. Exchange rate fluctuations, which are a key component of the global financial landscape, have been shown to have an effect on investor returns, corporate operations, and dynamics of international trade, which in turn shapes market risk. Additionally, this research reveals the complex relationship between the IPP and stock market performance, wherein good growth in industrial output signifies an expansion of the economy and investor confidence, which in turn affects demand for and the price of stocks. In contrast, a drop in the IPP denotes an economic slowdown and increased market risk. The paper also discusses the unusual impact of the COVID-19 pandemic on international financial markets, emphasizing the interaction between pandemic-induced uncertainty, exchange rate changes, and monetary policy reactions to produce novel market risk dynamics. Conclusion: In conclusion, this study offers a thorough grasp of the interactions between macroeconomic data and market risk inclination. For investors, companies, and politicians looking to comprehend the complexity of the global economic landscape, make educated decisions, and successfully manage financial risks, these insights are essential. Keyword: Propensity Risk, Stock Market, Inflation, Exchange rate and IPP.

ABSTRACT MAJID MEMARI, for the Masters of Science degree in Computer Science, presented on November 3rd, 2017 at Southern Illinois University, Carbondale, IL. Title: PREDICTING THE STOCK MARKET USING NEWS SENTIMENT ANALYSIS Major Professor: Dr. Norman Carver Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. GDELT is the largest, most comprehensive, and highest resolution open database ever created. It is a platform that monitors the world's news media from nearly every corner of every country in print, broadcast, and web formats, in over 100 languages, every moment of every day that stretches all the way back to January 1st, 1979, and updates daily [1]. Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. The efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed information thus are inherently unpredictable [2]. On the other hand, other studies show that it is predictable. The stock market prediction has been a long-time attractive topic and is extensively studied by researchers in different fields with numerous studies of the correlation between stock market fluctuations and different data sources derived from the historical data of world major stock indices or external information from social media and news [6]. The main objective of this research is to investigate the accuracy of predicting the unseen prices of the Dow Jones Industrial Average using information derived from GDELT database. Dow Jones Industrial Average (DJIA) is a stock market index, and one of several indices created by Wall Street Journal editor and Dow Jones & Company co-founder Charles Dow. This research is based on data sets of events from GDELT database and daily prices of the DJI from Yahoo Finance, all from March 2015 to October 2017. First, multiple different classification machine learning models are applied to the generated datasets and then also applied to multiple different Ensemble methods. In statistics and machine learning, Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Afterwards, performances are evaluated for each model using the optimized parameters. Finally, experimental results show that using Ensemble methods has a significant (positive) impact on improving the prediction accuracy. Keywords: Big Data, GDELT, Stock Market, Prediction, Dow Jones Index, Machine Learning, Ensemble Methods

This thesis describes a method of modeling financial markets by utilizing concepts from mechanical vibration. The models developed represent multi-degree of freedom, mass-spring systems. The economic principles that drive the design are supply and demand, which act as springs, and shareholders, which act as masses. The primary assumption of this research is that events cannot be predicted but the responses to those events can be. In other words, economic stimuli create responses to a stock’s price that is predictable, repeatable and scientific. The approach to determining the behavior of various financial markets encompassed techniques such as Fast Fourier Transform and discretized wavelet analysis. The researched developed in three stages; first an appropriate model of causation in the stock market was established. Second, a model of steady state properties was determined. Third, experiments were conducted to determine the most effective model and to test its predictive capabilities on ten stocks. The experiments were evaluated based on the model’s hypothetical return on investment. The results showed a positive gain on capital for nine out of the ten stocks and supported the claim that stocks behave in accordance to the natural laws of vibration. As scientific approaches to modeling the stock market are beginning to develop, engineering principles are proving to be the most relevant and reliable means of financial market prediction.

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Modern society has created more opportunities and risks, an example of which is the stock market. The stock market entails financial risk-taking when it comes to investments and it is a domain that has always been dominated by men. Despite the high presence of women in the labor market, there is an absence in the stock market. The reasons for women's absence can be several, where one reason may be that men are considered to thrive better in risky contexts than women.The purpose of this study is to gain an understanding of how gender roles affect risk-taking, specifically with regard to investments in the stock market. This will be done through our questions at issue that include how men and women experience their financial actions and their risk-taking in investments and also if the experiences differ depending on gender roles. Previous research on the subject has shown results where gender roles have a decisive influence on how men and women act and relate to financial risk-taking. The study's theoretical framework is based on Anthony Gidden's risk theory, Ulrich Beck's conception of the risk society and Simone de Beauvoir's depiction of gender roles. The study was conducted through semi-structured qualitative interviews, to get more enriching information with room left for details. The conclusion of the study is that gender roles were irrelevant when it came to the respondents' actions in the stock market, except in terms of knowledge. The knowledge and risk assessment was clearly characterized by gender roles, as we found patterns where women had a higher level of knowledge than men, and where men have a more positive attitude to risk-taking than women. / Det moderna samhället har skapat fler möjligheter och risker, exempel på detta är börsmarknaden. Börsmarknaden medför ett finansiellt risktagande när det gäller investeringar och det är en domän som genom tiderna varit dominerad av män. Trots kvinnors höga närvaro på arbetsmarknaden, råder det en frånvaro på börsmarknaden. Anledningarna till kvinnors frånvaro kan vara flera, där en anledning kan vara att män anses trivas bättre i riskfyllda sammanhang än kvinnor. Syftet med denna studie är att få en förståelse för om risktagandet skiljer sig mellan studiens män och kvinnor beträffande investeringar inom börsmarknaden. Detta sker genom frågeställningar som innefattar hur män och kvinnor upplever sitt finansiella agerande och risktagandet inom investeringar samt om upplevelserna skiljer sig beroende på könsroller. Tidigare forskning om ämnet har visat resultat där könsroller har en avgörande påverkan om hur män och kvinnor agerar och förhåller sig till finansiellt risktagande. Studiens teoretiska ramverket grundas på Anthony Giddens riskteori, Ulrich Becks föreställning av risksamhället och Simone de Beauvoir skildring av könsroller. Studien är utförd genom semistrukturerade kvalitativa intervjuer, för att få en mer berikande information med utrymme för detaljer. Slutsatsen av studien är att könsroller var irrelevant när det kom till respondenternas agerande på börsmarknaden, förutom när det gällde kunskapen. Kunskapen och riskbedömningen var tydligt präglad av könsroller, då vi fann mönster där kvinnorna hade en högre kunskap än männen, och där männen har en mer positiv attityd till risktagande än vad kvinnorna har.

Knowing when one does not know is crucial in decision making. By estimating uncertainties humans can recognize novelty both by intuition and reason, but most AI systems lack this self-reflective ability. In anomaly detection, a common approach is to train a model to learn the distinction between some notion of normal and some notion of anomalies. In contrast, we let the models build their own notion of normal by learning directly from the data in a self-supervised manner, and by introducing estimations of model uncertainty the models can recognize themselves when novel situations are encountered. In our work, the aim is to investigate the relationship between model uncertainty and anomalies in time series data. We develop a method based on a recurrent autoencoder approach, and we design an anomaly score function that aggregates model error with model uncertainty to indicate anomalies. Use the Monte Carlo Dropout as Bayesian approximation to derive model uncertainty. Asa proof of concept we evaluate our method qualitatively on real-world complex time series using stock market data. Results show that our method can identify extreme events in the stock market. We conclude that the relation between model uncertainty and anomalies can be utilized for anomaly detection in time series data.

The global economy has been severely impacted during the Covid-19 period. The U.S. stock market has also experienced greater volatility. Based on data from January 2020 to June 2021, this paper studies the volatility of daily returns on the stock market in the United States. The Standard and Poor's 500 (SPX) index and eight companies traded on major exchanges such as the New York Stock Exchange and the Nasdaq are used to calculate volatility. Combining the statistical analysis methods GARCH, GARCH-M, and TARCH, the time series of each security is modeled. It is demonstrated that the conditional heteroskedasticity of stock returns depends not only on the observed historical volatility (ARCH term) but also on the conditional heteroskedasticity of prior periods (GARCH term). As expected for financial markets, the COVID-19 outbreak increased the volatility of U.S. stock market returns. After the COVID-19 outbreak, the volatility of the U.S. stock market rose dramatically. It reached an extremely high level for the first quarter of 2020 and continued to move downwards in the following quarters. The significant heteroskedasticity in the return volatility indicates that external variables significantly affect the stock. Furthermore, this study combines the Capital Asset Pricing Model (CAPM) and the research of Engle et al. (1987), which provides a way to quantify the liquidity premium. However, with the results of the GARCH-M model, this study does not find a significant liquidity premium over time. Additionally, The TARCH model reveals a significant asymmetry in stock market returns during this epidemic, suggesting that negative news has a more substantial impact on U.S. financial markets. For investors and financial institutions, this research helps identify potential volatility in the face of similar risk events. It is helpful for investors to comprehensively consider various factors when investing in special periods or consider other investment portfolios to reduce investment risks in specific periods based on research results.

The reaction of asset prices to monetary policy is essential for investors andpolicymakers. However, previous research on the area in Sweden is limited, and there isno evidence of any impact on stock market prices from repo rate changes. This study estimates how stock market indices respond to repo rate changes, including different sector indices. The repo rate is the primary interest rate tool for the Swedish central bank. The utilised methodology is based on previous studies and follows a regression methodology. The paper's findings are that some sectoral stock market indices are affected by changes in repo rate. Bank and Financial sector indexes are positivelyaffected, while Health, Technology, Construction & Materials, Mid Cap, Small Cap,and Financial Services indices are negative. The result is estimated using two different variables for expectations of repo rate changes. The results are justified using a larger sample, including all monetary policy meetings. The results do only have a slight change in coefficients. This paper can be used to further investigate the impact of monetary policy on asset prices in Sweden.