The objective of this research is to analyse if there is a negative relationship between dividend policy and stock price volatility in the German stock market.  The data that was collected for this research consists of the 30 biggest companies listed on the German stock exchange Deutscher Aktienindex known as DAX 30 for the period 2000-2020. Fixed effect model estimated by panel data was applied to find the results of this research. The findings showed that the main variables of dividend policy (dividend yield and payout ratio) were negatively significant correlated with stock price volatility which provides evidence for our hypothesis. The results showed that the control variable earnings volatility had a positive significant relationship with stock price volatility. However, asset growth resulted in an insignificant relationship but the rest of the control variables such as leverage, market value and free float percentage showed a significant negative relationship with stock price volatility.

Background: Dividend and dividend policy is a research topic that is well known and frequently studied in financial economics. However there is no definite answer to if the dividend has an effect on the firm's value. The research surrounding dividend and its effect on firms value have shown a variety of different results. Aim: The purpose of this paper is to analyze the impact of the main independent variable dividend and control variables; total assets, revenue and Earnings Before Interest and Taxes (EBIT) on the dependent variable stock price. Method: The paper will be done in a quantitative approach based on a deductive research approach. The sample data of 60 companies for this study will be collected through Business Retriever, Orbit, Morningstar and annual reports of each company between the years 2011 to 2020. Results: The results in this study show that dividend has a statistically significant effect on the stock price of the companies observed. Furthermore the results indicate that Earnings Before Interest and Taxes, EBIT, has amedium-strong relationship with stock price, which shows a small significant association. The study could not prove that revenues and total assets have an effect on stock price in the regression analysis. However in the correlation analysis it was shown that there was a reasonable relationship between the dependent variable and the independent variables.

In this thesis the Black Scholes and the Heston stock prices are investigated and the models are compared. The Black Scholes model assumes that the volatility is constant, while the Heston model allows stochastic volatility which is more flexible and can perform better with empirical data. Both models are analysed and simulated, and the parameters are estimated based on empirical data of S&P 500. Results are based on simulations and characteristic functions which are presented with figures of probability density functions.

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abstract: Time series forecasting is the prediction of future data after analyzing the past data for temporal trends. This work investigates two fields of time series forecasting in the form of Stock Data Prediction and the Opioid Incident Prediction. In this thesis, the Stock Data Prediction Problem investigates methods which could predict the trends in the NYSE and NASDAQ stock markets for ten different companies, nine of which are part of the Dow Jones Industrial Average (DJIA). A novel deep learning model which uses a Generative Adversarial Network (GAN) is used to predict future data and the results are compared with the existing regression techniques like Linear, Huber, and Ridge regression and neural network models such as Long-Short Term Memory (LSTMs) models. In this thesis, the Opioid Incident Prediction Problem investigates methods which could predict the location of future opioid overdose incidences using the past opioid overdose incidences data. A similar deep learning model is used to predict the location of the future overdose incidences given the two datasets of the past incidences (Connecticut and Cincinnati Opioid incidence datasets) and compared with the existing neural network models such as Convolution LSTMs, Attention-based Convolution LSTMs, and Encoder-Decoder frameworks. Experimental results on the above-mentioned datasets for both the problems show the superiority of the proposed architectures over the standard statistical models. / Dissertation/Thesis / Masters Thesis Computer Science 2019

This dissertation examines the reaction of common stock prices to changes in bond ratings by Moody's Bond Service. The question is whether an announcement of a re-rating by Moody's is new information. There are only two studies of stock price reaction to bond changes and the results are conflicting. Pinches and Singleton (1978) [PS] concluded that any reaction comes well before the re-rating. Griffin and Sanvicente (1982) [GS] found that their portfolio test indicated that rating changes do convey new information. This was particularly true for downgradings. Both studies used monthly data and neither performed a statistical testing of residual reversals. PS provided a graph of the residuals which indicated the presence of a reversal trend. GS provided no information on this topic. This study, using daily data and the cumulative prediction error technique, finds that bond re-ratings offer new information. The results indicate that the market only partially anticipates the bond change. For the downgrades, the excess return on the announcement day is .6% which is statistically significant. The residuals reverse after the announcement day, but are not statistically significant. The upgrades do not have a significant reaction on the announcement day, but have a statistically significant negative reaction from day 1 to 10. The cumulative residual for days 1 to 10 is -2.8% with a test statistic of -3.85. This study finds as PS that there is some anticipation for both upgrades and downgrades. It extends their work by statistically testings the reversals after the announcement date and by testing the announcement day effect. There is significant abnormal return for the downgrades on the announcement day and the upgrades have a significant reversal in their residuals from day 1 to 10. This provides both support and extension of Griffin and Sanvicente's results and suggests that Moody's is offering the market new information.

Background: Sustainability in business has become an essential part for firms to consider, as stakeholder’s concerns are increasing, and the use of ESG scores has increased in the last years. Prior research mostly examines the European and US market and the relationship between ESG score and financial performance. Hence, a gap regarding ESG scores effect on stock performance, and possibilities for sustainable strategic management, was found on the Indian market as an emerging economy.  Purpose: The purpose of this thesis is to examine the relationship between ESG, stock performance, and sustainability reporting of Indian companies. Thus, enable a deeper understanding of the impact of sustainable development on firms.  Method: This research has followed positivism and social constructionism, with a deductive approach, and both a quantitative and qualitative method. Purposive sampling is used, by collecting the stock prices, volatility and ESG scores for firms from the index Nifty 50. Two portfolios were conducted from the samplings, representing the firms with the highest and the lowest ESG scores, along with an analysation of the individual companies. The data were analysed in STATA by an OLS regression analysis and Pearson correlation test, to test the hypotheses. Observations of the firm’s sustainability reports were analysed to gather their sustainable actions.  Conclusion: The results indicated that ESG have a positive effect on stock prices, on the Indian market. Thus, firms would benefit from achieving a higher ESG-score. However, due to varying results regarding volatility, results could not be determined whether firms achieve any improved stability from a higher ESG score. The findings also show that there are differences between high and low ESG scored firms regarding SDGs, policy frameworks, anti-corruption and the ESG approach. Which indicate opportunities for Indian firms to develop their sustainable actions, to increase value creation for stakeholders.

This dissertation is a study of fundamental questions in macro-finance using modern tools from textual analysis. These questions include how financial constraints affect firm investment and financing decisions when they are not presently binding, and whether stock returns are predictable based on concerns revealed in conversations between firms and investors. The first chapter examines whether financial covenants are an important consideration for firm decisions when they are not presently in violation. A key empirical challenge is measuring the risk of future covenant violations, which is not directly observed. I propose a novel measure of concerns about future violations by distinguishing between discussions of covenants in earnings calls that relate to the future as opposed to the past or present. As validation, I show that the measure predicts future violations and covaries intuitively with earnings, leverage, and default risk. Importantly, I find that concerns about covenants are significantly associated with reductions in investment as well as debt and equity financing activities. These responses persist even after controlling for standard measures of investment opportunities and are economically large relative to the effects of actual violations. The second chapter empirically analyzes two explanations for how covenants concerns relate to a firm's investment decisions. One explanation is that covenant concerns coincide with a deterioration in expected profitability, which dampens firms' incentives to invest. A second explanation is that firms become concerned when they expect violations to be more costly, which indicates future difficulties with funding investments. To shed light on the relevance of these two explanations, I examine empirical patterns in analyst expectations of future earnings, loan amendments in SEC filings, and the stock returns of firms that mention covenant concerns. The evidence suggest that both explanations are relevant mechanisms driving the correlation between covenant concerns and firm activity. However, I find that the second channel is more economically significant, suggesting that covenant concerns are informative about the degree to which firms are constrained by financial covenants. In the third chapter, I investigate how covenant concerns relate to firm policies in a standard model of investments with financial frictions. In the model, the theoretical object that most naturally links to covenant concerns is the expected shadow cost of the borrowing constraint. As in the data, the shadow cost of the borrowing constraint covaries negatively with earnings as well as firm investment and financing activity. Through an analysis of impulse response functions, I show how the empirical correlations between covenant concerns and firm policy arise in the model. One channel is through negative productivity shocks, which raises covenant concerns and leads to a fall in investment, debt, and equity issuance. The second channel is through higher leverage, holding fixed productivity. In the model, firm with higher debt levels are more concerned about covenants when hit by a negative productivity shock, and also choose less investment, debt issuance, and equity issuance. In this chapter, I also discuss several shortcomings of the model and suggest avenues for modifications. The final chapter investigates a new question: are stock returns predictable based on the extent to which firms are concerned about the macroeconomy? We document that firms that pay more attention to the macroeconomy earn lower average returns relative to firms that pay less attention to the macroeconomy. Differences in returns are economically significant and are not explained by traditional asset pricing factors, such as market beta, size, value, and idiosyncratic volatility. To explain the negative macroeconomic attention premium, we propose a model of attention allocation that links analyst attention to fundamental shocks affecting firm cash flows. In the model, attention to the macroeconomy is increasing in the share of earning news explained by the macroeconomic component. Firms with a greater share of cash flow news explained by the macroeconomic component face lower cash flow risk, hence earn lower expected returns.

The strive towards sustainability and its singular importance for all creatures on Earth has become the rallying cry of a generation. It has permeated into legislation, social practices, and the world of business. As companies start to implement increasingly more sustainable practices to meet these expectations placed on them by various stakeholders, a potential ‘conflict’ arises. One proxy for sustainability is the Environmental, Social, and Governance (ESG) measurement. This can give stakeholders an insight into how sustainable a given company is. It has also become quite prevalent in modern research. However, one field of business is seemingly under-researched; namely the mining industry. Taking an inter-supply chain perspective, dichotomized into upstream and downstream companies, it can be inferred that mining companies are seen to be upstream.  On the one hand, these upstream companies are far from the public consciousness and thus potentially outside the sphere of influence of the strive towards sustainability. On the other hand, if these companies are publicly traded, reports placing a given company in a negative light could potentially set a downward pressure on that company’s stock price; conceptualized as volatility. This causes a series of questions: Is this the case for upstream companies? Is it the case for downstream companies as well? Is there a difference between up- and downstream companies? From this, one arrives at the following research question: Is there a difference in the association between ESG scores and price volatility among North American mining companies and companies listed on the S&P 500?  To answer this research question, an empirical positivist study is undertaken. OLS regressions are made on the data and then the difference in coefficients is tested for significance. The results suggest a positive association between ESG score and price volatility for North American mining companies which is statistically different from the association in a similar regression for companies listed on the S&P 500. This result is further placed within the theoretical framework of stakeholder and agency theory. This study contributes by applying established methodologies in an under-researched field and illuminating the effect of heterogeneous expectations on different levels of global supply chains.

Accurate prediction of stock prices plays an increasingly prominent role in the stock market where returns and risks fluctuate wildly, and both financial institutions and regulatory authorities have paid sufficient attention to it. As a method of asset allocation, stocks have always been favored by investors because of their high returns. The research on stock price prediction has never stopped. In the early days, many economists tried to predict stock prices. Later, with the in-depth research of mathematical theory and the vigorous development of computer technology, people have found that the establishment of mathematical models can be very good, such as time series model, because its model is relatively simple and the forecasting effect is better. Time series model is applied in a period of time The scope gradually expanded. However, due to the non-linearity of stock data, some machine learning methods, such as support vector machines. Later, with the development of deep learning, some such as RNN, LSTM neural Networks, they can not only process non-linear data, but also retain memory for the sequence and retain useful information, which is positive. It is required for stock data forecasting. This article introduces the theoretical knowledge of time series model and LSTM neural network, and select real stocks in the stockmarket, perform modeling analysis and predict stock prices, and then use the root mean square error to compare the prediction results of several models. Since the time series model cannot make good use of the non-linear part of the stock data, can’t perform long-term memory, and LSTM neural network makes better use of non-linear data and has better use of sequence data. Useful information in the long-term memory, which makes the root mean square error of the prediction result, the LSTM neural network needs smaller than the time series model, indicating that LSTM neural network is a better stock price forecasting method. The time series for stock prices belong to non-stationary and non-linear data, making the prediction of future price trends extremely challenging. In order to learnthe long-term dependence of stock prices, deep learning methods such as the LSTM method are used to obtain longer data dependence and overall change patterns of stocks. This thesis uses 5000 observations from S&P500 index for empirical research, and introduce benchmark models, such as ARIMA, GARCH and other research methods for comparison, to verify the effectiveness and advantages of deep learning methods.