Thesis advisor: Peter Ireland / It is not unreasonable to surmise that public sentiment views stock market behavior as an indicator of economic health. Historically, movements in the the stock market indeed correspond to business cycles, but this is not always the case, and the COVID-19 pandemic serves as a distinct case to highlight such an irregularity. The contrast between the behavior of the stock market and that of the economy during the pandemic compels an analysis of the pandemic's actual impact on the stock market, and this paper finds a negative and significant relationship between the interpolated daily closing prices of the S&P 500 and the daily number of COVID-19 cases. / Thesis (BA) — Boston College, 2023. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Departmental Honors. / Discipline: Economics.

Tracking ability of leveraged and inverse exchange traded funds can be very important to investors looking for a dependable return. If the investor wants to put their money on a certain index they feel strongly about, they expect their investment vehicle to track that return appropriately. Over the years, we have seen tremendous growth in the exchange traded fund industry. In 2006, leveraged and inverse funds were introduced to the market, allowing investors to take leveraged and directional trades on indices. These investment vehicles can be traded as easily as any stock, and therefore need some attention. Since any novice investor can access and trade these funds, they need to be aware of the risks they are taking. In this study, I test whether the ProShares S&P tracking leveraged and inverse exchange traded funds track their appropriate index multiple as promised. I did this by running regressions on each fund against the appropriate multiple of their underlying indices. I did this for funds of different market capitalization, for different holding periods, and with different amounts of leverage, to compare how these funds track in different conditions. I found that the large cap funds tend to track the best, with the small cap funds tracking the worst. I also find that tracking error tends to increase with longer holding periods. I find that the distribution of excess returns becomes less normal over longer holding periods, and begins to flatten out and widen. There does not seem to be a concrete conclusion as to whether or not the amount of leverage affects the tracking ability of the funds. I end up with mixed results when comparing amounts of leverage by model fit and by tracking error. Direction also does not seem to play any role in the tracking ability of these funds.

本研究嘗試整合類神經網路與法則基礎（rule-based）系統技術，以建立S&P 500指數期貨的交易策略。本研究不同於先前研究之處有下列二方面：一、本研究採用法則基礎系統的方式提供神經網路的訓練範例；二、本研究以理解神經網路（Reasoning Neural Networks）取代後向傳導網路（Back propagation networks）以解決局部最小值與隱藏結點數未知的困境，而實證結果也顯示理解神經網路之表現優於後向傳導網路。首先，由期貨的日價格資料計算出十種技術分析指標值，用這些指標值來表示期貨市場內的各種可能狀況（case）。接著，我們提出FFM（Futures Forecast Model）與EFFM（Extended Futures Forecast Model）來處理市場的各種狀況，預測出隔日的期貨價格改變方向。以法則基礎方法所建立的FFM是用來處理明顯的狀況（obvious cases），並且提供類神經網路好的訓練範例。而EFFM包括四個理解神經網路系統與一個決策機置（voting mechanism），它被用來處理那些不明顯的狀況（non-obvious cases）。從實證模擬的結果顯示，在預測市場時FFM與EFFM有良好的合作 關係。因此，我們以FFM與EFFM為基礎建立一個整合的期貨交易系統（Integrated Futures Trading System，IFTS），並將它用於S&P 500 指數期貨市場作模擬交易，結果我們發現在1988到1993年的測試期間，IFTS 的投資報酬率高於買入持有投資策略。 / This research adopts a hybrid approach to implementing the trading strategies in the S&P 500 index futures market. The hybrid approach integrates both the rule-based systems technique and the neural networks technique. Our methodology is different from previous studies in two aspects. First, we employ Reasoning Neural Networks (RN) instead of back propagation networks to resolve the undesired predicaments of local minimum and the unknown of the number of hidden nodes. Second, the rule-based systems approach is applied to provide neural networks with good training examples. We, first, categorize the daily conditions of the futures market into a variety of cases through processing futures historical data. Then, the dual-forecast models, FFM (futures forecast model) and EFFM (extended futures forecast model), are proposed to predict the direction of daily price changes. The rule-based model, FFM, is designed to deal with the obvious cases and to provide the neural network-based model, EFFM, with good training examples. Meanwhile, EFFM, which consists of four RNs and a voting mechanism, is designed to handle the non-obvious cases. The simulation results show that the cooperation of FFM and EFFM does a good job in predicting the direction of daily price change of S&P 500 index futures. Based on FFM and EFFM, the integrated futures trading system (IFTS) is developed and employed to trade the S&P 500 index futures contracts. The results show that IFTS outperforms the passive buy-and-hold investment strategy over the six-year testing period from 1988 to 1993.

Sammanfattning Titel: Imorgon blir det börsfall! En studie om hur olika börser påverkar varandra i fördröjning. Seminariedatum: 28 maj, 2012 Ämne/kurs: FEK 61-90 Kandidatuppsats i Corporate Finance, 15 poäng  Författare: Stefan Koch, Malin Molin Handledare: Hans Mörner Examinator: Kent Sahlgren Nyckelord: Anomali, anomalier, veckodagseffekt, måndagseffekt, index, korrelation, S&P 500, OMXS 30, effektiva marknadshypotesen Syfte: Att undersöka ifall en eventuell anomali på det svenska OMXS 30 indexet eller det amerikanska S&P 500 ger en effekt på nästföljande dag på det motsatta indexet. Om en veckodagseffekt kan påvisas och den fördröjda korrelationen mellan indexen är tillräckligt stark kan metoden användas för att generera överavkastning.   Metod: Vi använder oss av en kvantitativ ansats för att med hjälp av statistiska metoder svara på vår problemformulering. De metoder vi har använt är hypotestestning av medelvärden med ett z test, beräknat korrelationskoefficienten mellan de två indexen och utfört en multipel regressionsanalys med dummyvariabler. Slutsats: Genom vår analys kom vi fram till att en veckodagseffekt inte kan påvisas på någon av de två undersökta indexen. En korrelation kunde finnas mellan de två indexen, däremot går det att ifrågasätta om korrelationens styrka är tillräckligt stark att handla utifrån. För att generera överavkastning krävs dessutom att den extra avkastning som genereras med hjälp utav vår metod med korta aktieaffärer överstiger den eventuella transaktionskostnaden som uppstår vid aktiehandel, något vi starkt betvivlar att den gör. / Abstract Title: Tomorrow the market falls! A study about how different stock markets affect each other in delay. Date of seminar: May, 28th, 2012 Course: FEK 61-90 Bachelor Thesis in Corporate Finance, 15 credits Authors: Stefan Koch, Malin Molin Advisor: Hans Mörner Examiner: Kent Sahlgren Key words: Anomaly, anomalies, day-of-the-week effect, weekend effect, index, correlation, S&P 500, OMXS 30, effective market hypothesis Objective: To examine whether a potential anomaly on the Swedish OMXS 30 index or the American S&P 500 has an effect on the next day on the opposite index. If a day-of-the-week effect can be proved and the delayed correlation between the indices is strong enough, our method could be used to generate excess returns. Methodology: We use a quantitative approach and statistic methods to answer our problem formulation. The methods we have been using are hypothesis testing of mean values with a z-test, calculations of correlation coefficients between the indices, and a multiple regression analysis with dummy variables. Conclusions: Through our analysis we found out that there was no day-of-the-week effect on any of the two examined indices. We could find a correlation between the two indices; however, we question whether the correlation is strong enough to trade on. To get excess returns it is required that the extra return that would be generated through our method with short trades exceed eventual transaction costs that occur through stock trading, something we strongly doubt that it would.

The object of the present study is to introduce three analytical time series models for the purpose of developing more effective economic and environmental forecasting models, among others. Given a stochastic realization, stationary or nonstationary in nature, one can utilize exciting methodology to develop an autoregressive, moving average or a combination of both for short and long term forecasting. In the present study we analytically modify the stochastic realization utilizing (a) a k-th moving average, (b) a k-th weighted moving average and (c) a k-th exponential weighted moving average processes. Thus, we proceed in developing the appropriate forecasting models with the new (modified) time series using the more recent methodologies in the subject matter. Once the proposed statistical forecasting models have been developed, we proceed to modify the analytical process back into the original stochastic realization. The proposed methods have been successfully applied to real stock data from a Fortune 500 company. A similar forecasting model was developed and evaluated for the daily closing price of S&P Price Index of the New York Stock Exchange. The proposed forecasting model was developed along with the statistical model using classical and most recent methods. The effectiveness of the two models was compared using various statistical criteria. The proposed models gave better results. Atmospheric temperature and carbon dioxide, CO2, are the two variables most attributable to GLOBAL WARMING. Using the proposed methods we have developed forecasting statistical models for the continental United States, for both the atmospheric temperature and carbon dioxide. We have developed forecasting models that performed much better than the models using the classical Box-Jenkins type of methodology. Finally, we developed an effective statistical model that relates CO2 and temperature; that is, knowing the atmospheric temperature we can at the specific location estimate the carbon dioxide and vice versa.

This paper investigates whether an Islamic screened benchmark index shows a different risk adjusted performance in comparison to a non-screened benchmark index. In contrast to other papers this study analyzes daily observations in the years from 2007 to 2012, a period heavily affected by the financial crisis. The Capital Asset Pricing Model and the Jensen measure of abnormal returns are used to estimate and compare the indexes mean risk-adjusted returns. The results show that the Islamic index does not reveal any different level of daily mean risk-adjusted returns compared to the conventional non-screened index. Hence, Muslims who align their investments according to the teachings of Islam are not worse off than non-restricted investors following the screened Islamic index.

This Final thesis is focused on analysis of stock markets with more detailed view at S&P 500 index. The goal of market analysis is to reveal possible price bubble in relation to effect of quantitative easing, Federal Reserve Bank policy and behavioural factors. The content of practical part is to evaluate possible significant overvalue of S&P 500 index and possible price bubble of mentioned index.

This thesis investigates how the U.S President’s Twitter mentions of individual companies’ investment announcements influence the short-term price of their stock. By assuming that the President’s comments on a company’s plans should be incorporated by markets as new information, testing the Efficient Market Hypothesis assumption that the markets incorporate all new information, the thesis seeks to contribute to a new, unexplored and growing, research field. This thesis utilizes a qualitative analysis method, studying Twitter mentions on the topic of Trump’s Tax Reform. The data in this thesis is derived from the President’s personal Twitter-account, company announcements, stock price charts, and the Standard & Poor’s S&P500 Index. To conclude, this study finds that although the President’s Twitter comments may signal his public approval of a company and its plans, it appears that any market reaction is only short-term, and that as the market incorporates additional information it returns to an informed state in terms of stock valuations. This study suggests that there are few observable indicators that Trump’s positive mentions on Twitter causes any significant market reaction.

This thesis develops a new Bayesian approach to structural break modeling. The focuses of the approach are the modeling of in-sample structural breaks and forecasting time series allowing out-of-sample breaks. Our model has some desirable features. First, the number of regimes is not fixed and is treated as a random variable in our model. Second, our model adopts a hierarchical prior for regime coefficients, which allows for the regime coefficients of one regime to contain information about regime coefficients of other regimes. However, the regime coefficients can be analytically integrated out of the posterior distribution and therefore we only need to deal with one level of the hierarchy. Third, the implementation of our model is simple and the computational cost is low. Our model is applied to two different time series: S&P 500 monthly returns and U.S. real GDP quarterly growth rates. We linked breaks detected by our model to certain historical events.

Abstract Introduction: The effect of Environmental, Social and Governance issues on stock prices is highly debated in literature. Different authors state that ESG has an influence on stock price and company value while others state that it has no or limited effect. Purpose: The purpose of this research is to explain the effect of ESG changes on stock prices and add information to the debate between both sides if there is, or if there is not an effect from ESG on stock prices. Research questions: 1. What is the effect of changes in Environmental concerns in stock prices? 2. What is the effect of changes in Social concerns in stock prices? 3.What is the effect of changes in Governance concerns in stock prices? Methodology: Event study method with a sample size of 484 companies from the S&P 500 which will be analyzed for the period of 2015-2017, which gave 1.420 different events. These companies got ratings for Environmental Pillar, Social Pillar, Governance Pillar, ESG Controversies, ESG Score and ESG Combined Score. For each event the abnormal stock returns were compared with the rating changes. The data is taken from EikonThomsonReuters. Conclusion: The results showed no correlation between Environmental, Social and Governance rating changes and abnormal returns. Also, the combined ratings did not show any correlations. Therefore, our study will support and contribute to the side of researcher Friedman (1970), Jacobs et al. (2010), Walley and Whitehead (1994), Drobetz et al. (2004) and other researchers which state there is no correlation between ESG and stock prices. Limitations: The study is based on ratings provided by EIKON, we assumed they are a clear and correct reflection of the actual ESG within companies. The second limitation is the anticipation effect, the response of the stock market is based on unawareness from investors. If the bases where the rating changes on is already known than there is no effect from investors because they already anticipated the decreased rating. There are also a few companies excluded from the research because of missing ratings. Also, these results are based on the S&P500 and therefore do not have to be true for other financial markets. Keywords: ESG, Stock Price, Environmental, Social, Governance, ESG-ratings, S&P 500, Event Study, EIKON