Dividend policy, its determinants, and its impact on firm value are of significant academic interest, and many theories and explanations have been posited on the subject over the years, but there has not been a universal agreement. This thesis examines the links between dividend policy, various aspects of stock liquidity and price informativeness. We study a sample of UK firms over the period from 1996-2013. We show that, on average, stocks of dividend payers have significantly lower bid–ask spread and a lower illiquidity ratio than their counterparts of non-dividend payers. We also find that stocks of high-dividend payers are more liquid than those of firms that pay low or no dividends. These findings are consistent with the predictions of asymmetric information that posit that paying dividends reveals inside information to the market and hence decreases the level of asymmetric information, leading to higher stock liquidity. In the subsequent analysis, we suggest and examine a new channel through which dividend policy can impact firm value. Specifically, we show that dividend payers are less exposed to shocks in the aggregate market liquidity than non-dividend payers. Similarly, we find that the systematic liquidity risk is negatively associated with amount of dividends. Finally, in the context of signalling and agency costs models, we show that dividends are negatively related to stock price informativeness and that this relationship is stronger for firms with lower stock liquidity. The findings imply that dividend policy can both affect and be affected by stock markets. / University of Bradford

Background: Predicting stock prices in today’s complex financial landscape is asignificant challenge. An innovative approach to address this challenge is integrating sentiment analysis techniques with the well-established Autoregressive IntegratedMoving Average (ARIMA) model. Modern financial markets are influenced by various factors, including real-time news and social media trends, which demand accuratepredictions. This research recognizes the growing importance of market sentiment derived from news and aims to improve stock price prediction by combining ARIMA’sanalytical capabilities with sentiment analysis. This endeavor seeks to provide aclearer understanding of the intricate dynamics of stock price movements in an eramarked by abundant information and rapidly changing market conditions. The integration of these methods has the potential to enhance the accuracy of stock priceforecasts, offering benefits to investors and financial analysts alike. Objectives: The project involves three key components. It begins by gatheringhistorical stock data for a specific stock ticker and conducting essential data preprocessing. Next, it focuses on extracting news headlines from a prominent financial website and conducting a thorough sentiment analysis of these headlines. Thissentiment analysis provides valuable insights into public sentiment surrounding thechosen stocks, with visualizations representing positive, negative, and neutral trends.Finally, the project aims to combine the findings from both components using an Ensemble Method, resulting in a comprehensive suggestion to user whether to buy,holdor sell the stock. These components collectively aim to improve stock price predictions and assess the adaptability of the ARIMA model to changing market conditionsalong the time and significant events. Methods: This project explores an innovative approach to improve stock pricepredictions, combining the ARIMA model with sentiment analysis methods usingfinancial news data. The study involved collecting historical stock data from YahooFinance, employing moving averages like 5-day, 30-day and 90-day windows, andusing advanced models such as ARIMA for predictions. Our analysis also includestime series plots at various intervals, providing valuable perspectives. Through theEnsemble Method, which integrates quantitative predictions and sentiment analysis,we generated practical recommendations for a five-day forecast. Our work addressedgaps in integrating sentiment analysis into stock prediction models and adapting tochanging market conditions, contributing to the advancement of stock forecastingmethodologies. Results: The ensembled predictive model for stock prices demonstrates favorableoutcomes. The Mean Absolute Error (MAE) is 0.8659, indicating accuracy, and theRoot Mean Squared Error (RMSE) is 0.1732, showing the overall prediction error.The Mean Absolute Percentage Error (MAPE) is 1.8541, suggesting precision in comparison to actual stock prices. The R-squared value is 0.9804, indicating the model’sability to explain variation in stock price data. These findings highlight the model’seffectiveness in providing reliable insights for investors in the dynamic stock market. Conclusions: The analysis with the ARIMA model to enhance stock price predictions. It revealed that sentiment analysis complements traditional methods, providing valuable insights for decision-making. Evaluating ARIMA’s long-term performance suggests adaptable forecasting techniques. This work contributes to advancingfinancial analysis and improving stock price predictions.

In chapter one, I examine the predictive content of subjective return expectations derived from price targets issued by equity analysts. Equity price targets are an ubiquitous feature of the financial information landscape, but it is not clear how informative they actually are. In this chapter, I show that the cross-section of price-target implied subjective return expectations contains rich informational content for forecasting returns. In-sample, I find that expected returns correlate strongly with average cross-sectional returns to a large panel of portfolios formed on the basis of observable firm characteristics. In out-of-sample exercises, forecasting models using subjective expectations are shown to offer more accurate predictions for portfolio returns than several other commonly employed, cross-sectional predictors, including the book-to-market and dividend-price ratios, momentum, and forward-looking cash-flow measures. Furthermore, these differences are shown to be economically relevant, with conditional portfolios formed on the basis of subjective expectations offering substantially improved risk-adjusted returns compared to many of the other predictors considered. The relative informational content, as well as the production by analysts, of subjective return expectations is found, however, to peak during recessions, with negligible predictive advantage discernible in expansions. In chapter two, my coauthors (Adam Rej, with CFM; David Thesmar, with MIT, CEPR, and NBER) and I empirically analyze a large panel of firm sales growth expectations. We find that the relationship between forecast errors and lagged revision is non-linear. Forecasters underreact to typical (positive or negative) news about future sales, but overreact to very significant news. To account for this non-linearity, we propose a simple framework, where (1) sales growth dynamics have a fat-tailed high frequency component and (2) forecasters use a simple linear rule. This framework qualitatively fits several additional features of data on sales growth dynamics, forecast errors, and stock returns. In chapter three, my coauthor (Ken Teoh, with Columbia) and I construct a novel text-based measure of firm-level attention to macroeconomic conditions and document that stocks associated with higher macroeconomic attention earn lower returns. Moving from the bottom decile to top decile of macroeconomic attention decreases a stock’s average return by 11.6\% per year. We propose a risk-based explanation in which stocks with higher macroeconomic attention contribute less idiosyncratic cash flow risk to the investor’s portfolio, hence earn lower expected returns. Decomposing the unexpected returns of macroeconomic attention-sorted portfolios into cash flow and discount rate news, we find that portfolios with higher macroeconomic attention stocks have lower cash flow risk.

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This study has theoretical, substantive, and methodological objectives following Brinberg and McGrath (1985). First, the resource-based view of the firm provides a context to support relationships determined from theory in Sociology, Finance and Entrepreneurshp. Using these interdisciplinary theories, the expected contributions of National Venture Capital Association (NVCA) member venture capital firm networks' human-based factors to the performance of initial public offerings are examined. Second, the substantive domain-venture capital-lacks articulation and quantification regarding the impact of venture capital firms on the start-up firms they support, which in this study is identified as IPO performance. Third, methodologically, the operationalization of organizational-related capital is proposed. The independent variables (human-based factors) include reputational capital, cumulative experience, social capital, and organizational-related capital. Organizational-related capital is a construct representing a firm's strategy that incorporates preferences specific to the venture capital industry, namely financing stage preference, industry relatedness, and geographic proximity. Venture capital firm networks are assessed at the syndicate and constellation levels (within and between industries) and bounded by membership in the National Venture Capital Association. Abnormal IPO stock price performance (the dependent variable) is assessed as the new issue's stock price benchmarked to the NASDAQ index and compounded over 21-day periods for up to 126 consecutive days after offering. Control variables were gleaned from economic-based theories found in the finance literature. Positive relationships were hypothesized between the independent variables and the dependent variable. Data constraints limited the number of observations examined, and the selection of IPOs investigated displayed little variance. Thus, explaining additional abnormal performance variance in IPOs backed by NVCA-member venture capital firms above and beyond that controlled for by economic-based theory was not fruitful. Although this study's findings were not statistically significant, many insights were generated that may positively influence future research in this area. The quest to better understand venture capital firms' contributions to entrepreneurial firms and the impact they have on publicly traded stocks remains meaningful. / Ph. D.

Stock price prediction is of strong interest but a challenging task to both researchers and investors. Recently, sentiment analysis and machine learning have been adopted in stock price movement prediction. In particular, retail investors’ sentiment from online forums has shown their power to influence the stock market. In this paper, a novel system was built to predict stock price movement for the following trading day. The system includes a web scraper, an enhanced sentiment analyzer, a machine learning engine, an evaluation module, and a recommendation module. The system can automatically select the best prediction model from four state-of-the-art machine learning models (Long Short-Term Memory, Support Vector Machine, Random Forest, and Extreme Boost Gradient Tree) based on the acquired data and the models’ performance. Moreover, stock market lexicons were created using large-scale text mining on the Yahoo Finance Conversation boards and natural language processing. Experiments using the top 30 stocks on the Yahoo users’ watchlists and a randomly selected stock from NASDAQ were performed to examine the system performance and proposed methods. The experimental results show that incorporating sentiment analysis can improve the prediction for stocks with a large daily discussion volume. Long Short-Term Memory model outperformed other machine learning models when using both price and sentiment analysis as inputs. In addition, the Extreme Boost Gradient Tree (XGBoost) model achieved the highest accuracy using the price-only feature on low-volume stocks. Last but not least, the models using the enhanced sentiment analyzer outperformed the VADER sentiment analyzer by 1.96%.

Yes / This study examines the effect of carbon price uncertainty on stock price crash risk. Utilizing the dynamic panel model on the data of Chinese listed firms from 2011 to 2018, we find that high carbon price uncertainty increases stock price crash risk. The impact of carbon price uncertainty is more prominent in the heavily polluting industries and during the post-period of the Paris agreement. The two underlying channels through which carbon price uncertainty induces stock price crashes are managers' hoarding of bad news and investors' heterogeneity. Furthermore, reducing information asymmetry inside and outside the firms can mitigate the influence of carbon price uncertainty on stock price crash risk. Our findings demonstrate that carbon price uncertainty as a newly underexplored factor induced by the prevailing curb of catastrophe risks has unintended but important implications on stock prices. / This study was supported by the Project of Social Science Achievement Evaluation Committee of Hunan Province (Grant No. XSP22YBZ160), Hunan Provincial Natural Science Foundation of China (Grant No. 2022JJ40644 and No. 2022JJ40647). / The full-text of this article will be released for public view at the end of the publisher embargo on 24th Oct 2024.

Reporting regarding issues that are related to Corporate Social Responsibility have come into more and more focus lately. Most countries currently have a limited or no mandatory regulations regarding what should be included in either an annual report or in a stand-alone report in terms of CSR. However Denmark is one of the pioneers regarding mandatory CSR regulations and as such has certain rules and regulations that their companies have to follow. Even if today’s regulations are heavily focused on financial information that companies have to disclose there also exists regulations regarding non-financial information. As with the financial crisis that occurred in the early 21st century that led to stricter disclosures requirements for financial information we see a need for regulating non-financial information and especially CSR information. We have been able to see that some companies have been able to manipulate their CSR report so as to put themselves in a good light. Therefore the question arises if mandatory CSR disclosure will have any influence on the stock market.The purpose of this study was to examine if Swedish companies and the Swedish stock market could benefit from having mandatory CSR regulations, similar to those that exist in Denmark. We sought to examine if fulfilling certain amount of CSR criteria would reduce the volatility of a company’s stock price.In order for us to achieve the purpose of our research we had to conduct an experiment on the Swedish companies. In order for us to conduct the experiment we firstly had to select what type of research we would conduct and what type of research was most suitable for our research. In order for us to achieve an answer to our research question and to be able to fulfill the purpose of our research we decided to conduct a quantitative research. We have chosen to utilize the quantitative research approach as this would allow gathering sufficient data from existing databases and reports. The database that we chose to utilize in order for us to find our sample population was NASDAQ OMX Nordic where the companies had be listed as of 2015-03-31 as well as having financial data for the entire year of 2014, meaning between 2014-01-01 and 2014-12-31. NASDAQ OMX Nordic was also used in order for us to find market indexes. In order for us to able to answer our research question we developed three different hypotheses based on our theoretical framework that would later be tested.From the testing of our hypotheses we could determine that there is a relationship between the amount of CSR that a company reports, in terms of how many of our CSR criteria they fulfill, and the historical volatility of the company’s stock price. We were also able to determine that there exists a relationship between the amount of CSR that a company reports and the level of Beta that a company has. This implied that the Swedish stock market could benefit from mandatory CSR regulation as it would reduce the volatility which would also be beneficial for the company’s different stakeholders.

by Ng Wai-Leung Anthony. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 83-87). / Abstracts in English and Chinese. / Chapter I. --- INTRODUCTION --- p.1 / Chapter II. --- LITERATURE REVIEW --- p.5 / Chapter 2.1 --- The Importance of Stock Index and Index Futures --- p.6 / Chapter 2.2 --- Importance of Index Forecasting --- p.6 / Chapter 2.3 --- Reasons for the Lead-Lag Relationship between Stock and Futures Markets --- p.9 / Chapter 2.4 --- Importance of the lead-lag relationship --- p.10 / Chapter 2.5 --- Some Empirical Findings of the Lead-Lag Relationship --- p.10 / Chapter 2.6 --- New Approach to Financial Forecasting - Artificial Neural Network --- p.12 / Chapter 2.7 --- Artificial Neural Network Architecture --- p.14 / Chapter 2.8 --- Evidence on the Employment of ANN in Financial Analysis --- p.20 / Chapter 2.9 --- Hong Kong Securities and Futures Markets --- p.25 / Chapter III. --- GENERAL GUIDELINE IN DESIGNING AN ARTIFICIAL NEURAL NETWORK FORECASTING MODEL --- p.28 / Chapter 3.1 --- Procedure for using Artificial Neural Network --- p.29 / Chapter IV. --- METHODOLOGY --- p.37 / Chapter 4.1 --- ADF Test for Unit Root --- p.38 / Chapter 4.2 --- "Error Correction Model, Error Correction Model with Short- term Dynamics, and ANN Models for Comparisons" --- p.38 / Chapter 4.3 --- Comparison Criteria of Different Models --- p.39 / Chapter 4.4 --- Data Analysis --- p.39 / Chapter 4.5 --- Data Manipulations --- p.41 / Chapter V. --- RESULTS --- p.42 / Chapter 5.1 --- The Resulting Models --- p.42 / Chapter 5.2 --- The Prediction Power among the Models --- p.45 / Chapter 5.3 --- ANN Model of Input Variable Selection Using Contribution Factor --- p.46 / Chapter VI. --- CAUSALITY ANALYSIS --- p.54 / Chapter 6.1 --- Granger Casuality Analysis --- p.55 / Chapter 6.2 --- Results Interpretation --- p.56 / Chapter VII --- CONSISTENCE VALIDATION --- p.61 / Chapter VIII --- ARTIFICIAL NEURAL NETWORK TRADING SYSTEM --- p.67 / Chapter 7.1 --- Trading System Architecture --- p.68 / Chapter 7.2 --- Simulation Runs using the Trading System --- p.77 / Chapter XI. --- CONCLUSIONS AND FUTURE WORKS --- p.79