Showing papers on "Stock (geology) published in 2019"

Quality Minus Junk

Abstract: We define quality as characteristics that investors should be willing to pay a higher price for. Theoretically, we provide a tractable valuation model that shows how stock prices should increase in their quality characteristics: profitability, growth, and safety. Empirically, we find that high-quality stocks do have higher prices on average but not by a large margin. Perhaps because of this puzzlingly modest impact of quality on price, high-quality stocks have high risk-adjusted returns. Indeed, a quality-minus-junk (QMJ) factor that goes long high-quality stocks and shorts low-quality stocks earns significant risk-adjusted returns in the United States and across 24 countries. The price of quality varies over time, reaching a low during the internet bubble, and a low price of quality predicts a high future return of QMJ. Analysts’ price targets and earnings forecasts imply systematic quality-related errors in return and earnings expectations.

Stock Market Analysis: A Review and Taxonomy of Prediction Techniques

Abstract: Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that stock markets are essentially a random walk and it is a fool’s game to try and predict them. Predicting stock prices is a challenging problem in itself because of the number of variables which are involved. In the short term, the market behaves like a voting machine but in the longer term, it acts like a weighing machine and hence there is scope for predicting the market movements for a longer timeframe. Application of machine learning techniques and other algorithms for stock price analysis and forecasting is an area that shows great promise. In this paper, we first provide a concise review of stock markets and taxonomy of stock market prediction methods. We then focus on some of the research achievements in stock analysis and prediction. We discuss technical, fundamental, short- and long-term approaches used for stock analysis. Finally, we present some challenges and research opportunities in this field.

Temporal Relational Ranking for Stock Prediction

Abstract: Stock prediction aims to predict the future trends of a stock in order to help investors make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice for stock prediction. However, most existing deep learning solutions are not optimized toward the target of investment, i.e., selecting the best stock with the highest expected revenue. Specifically, they typically formulate stock prediction as a classification (to predict stock trends) or a regression problem (to predict stock prices). More importantly, they largely treat the stocks as independent of each other. The valuable signal in the rich relations between stocks (or companies), such as two stocks are in the same sector and two companies have a supplier-customer relation, is not considered. In this work, we contribute a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. Our RSR method advances existing solutions in two major aspects: (1) tailoring the deep learning models for stock ranking, and (2) capturing the stock relations in a time-sensitive manner. The key novelty of our work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution, which jointly models the temporal evolution and relation network of stocks. To validate our method, we perform back-testing on the historical data of two stock markets, NYSE and NASDAQ. Extensive experiments demonstrate the superiority of our RSR method. It outperforms state-of-the-art stock prediction solutions achieving an average return ratio of 98% and 71% on NYSE and NASDAQ, respectively.

A Prediction Approach for Stock Market Volatility Based on Time Series Data

Abstract: Time series analysis and forecasting is of vital significance, owing to its widespread use in various practical domains. Time series data refers to an ordered sequence or a set of data points that a variable takes at equal time intervals. The stock market is considered to be one of the most highly complex financial systems which consist of various components or stocks, the price of which fluctuates greatly with respect to time. Stock market forecasting involves uncovering the market trends with respect to time. All the stock market investors aim to maximize the returns over their investments and minimize the risks associated. Stock markets being highly sensitive and susceptible to quick changes, the main aim of stock-trend prediction is to develop new innovative approaches to foresee the stocks that result in high profits. This research tries to analyze the time series data of the Indian stock market and build a statistical model that could efficiently predict the future stocks.

Taking Stock of Built Environment Stock Studies: Progress and Prospects.

Abstract: Built environment stocks (buildings and infrastructures) play multiple roles in our socio-economic metabolism: they serve as the backbone of modern societies and human well-being, drive the material cycles throughout the economy, entail temporal and spatial lock-ins on energy use and emissions, and represent an extensive reservoir of secondary materials. This review aims at providing a comprehensive and critical review of the state of the art, progress, and prospects of built environment stocks research which has boomed in the past decades. We included 249 publications published from 1985 to 2018, conducted a bibliometric analysis, and assessed the studies by key characteristics including typology of stocks (status of stock and end-use category), type of measurement (object and unit), spatial boundary and level of resolution, and temporal scope. We also highlighted the strengths and weaknesses of different estimation approaches. A comparability analysis of existing studies shows a clearly higher level of ...

Stock Returns over the FOMC Cycle

Abstract: We document that since 1994, the equity premium is earned entirely in weeks 0, 2, 4, and 6 in Federal Open Market Committee (FOMC) cycle time, that is, even weeks starting from the last FOMC meeting. We causally tie this fact to the Fed by studying intermeeting target changes, Fed funds futures, and internal Board of Governors meetings. The Fed has affected the stock market via unexpectedly accommodating policy, leading to large reductions in the equity premium. Evidence suggests systematic informal communication of Fed officials with the media and financial sector as a channel through which news about monetary policy has reached the market.

Stock Price Prediction Using News Sentiment Analysis

Abstract: Predicting stock market prices has been a topic of interest among both analysts and researchers for a long time. Stock prices are hard to predict because of their high volatile nature which depends on diverse political and economic factors, change of leadership, investor sentiment, and many other factors. Predicting stock prices based on either historical data or textual information alone has proven to be insufficient. Existing studies in sentiment analysis have found that there is a strong correlation between the movement of stock prices and the publication of news articles. Several sentiment analysis studies have been attempted at various levels using algorithms such as support vector machines, naive Bayes regression, and deep learning. The accuracy of deep learning algorithms depends upon the amount of training data provided. However, the amount of textual data collected and analyzed during the past studies has been insufficient and thus has resulted in predictions with low accuracy. In our paper, we improve the accuracy of stock price predictions by gathering a large amount of time series data and analyzing it in relation to related news articles, using deep learning models. The dataset we have gathered includes daily stock prices for S&P500 companies for five years, along with more than 265,000 financial news articles related to these companies. Given the large size of the dataset, we use cloud computing as an invaluable resource for training prediction models and performing inference for a given stock in real time.

Oil price increases and the predictability of equity premium

Abstract: We show that increases in oil prices, rather than changes in oil prices, can predict stock returns. The revealed stock return predictability is both statistically and economically significant. The forecasting performance of oil price increases is not affected by changes in the choice of subsample, a considerable advantage over other popular predictors. We obtain greater forecasting gains by adding oil price increases as an additional predictor to univariate macro models. This forecasting improvement is also present when using multivariate information methods. The success of oil-macro models in forecasting stock returns is robust to a large battery of robustness tests. Oil price increases predict stock returns by affecting future industrial production and discount rates.

Noisy Stock Prices and Corporate Investment

Abstract: Firms significantly reduce their investment in response to nonfundamental drops in the stock price of their product-market peers. We argue that this results stems from managers’ limited ability to filter out the noise in the stock prices when using them as signals about their investment opportunities. Ensuing losses of capital investment and shareholders’ wealth are economically large and even affect firms not facing severe financing constraints or agency problems. Our findings offer a novel perspective on how stock market inefficiencies can affect the real economy, even in the absence of financing or agency frictions.Received December 14, 2016; editorial decision July 30, 2018 by Editor Itay Goldstein. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Shareholder Wealth Consequences of Insider Pledging of Company Stock as Collateral for Personal Loans

Abstract: We study a widespread yet under-explored corporate governance phenomenon: the pledging of company stock by insiders as collateral for personal bank loans. Utilizing a regulatory change that exogenously decreases pledging, we document a negative causal impact of pledging on shareholder wealth. We study two channels that could explain this effect. First, we find that margin calls triggered by severe price falls exacerbate the crash risk of pledging firms. Second, since margin calls may cause insiders to suffer personal liquidity shocks or to forgo private benefits of control, we hypothesize and find that pledging is associated with reduced firm risk-taking.Received March 2, 2017; editorial decision January 24, 2019 by Editor David Denis. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Stability advances in robust portfolio optimization under parallelepiped uncertainty

Abstract: In financial markets with high uncertainties, the trade-off between maximizing expected return and minimizing the risk is one of the main challenges in modeling and decision making. Since investors mostly shape their invested amounts towards certain assets and their risk aversion level according to their returns, scientists and practitioners have done studies on that subject since the beginning of the stock markets’ establishment. In this study, we model a Robust Optimization problem based on data. We found a robust optimal solution to our portfolio optimization problem. This approach includes the use of Robust Conditional Value-at-Risk under Parallelepiped Uncertainty, an evaluation and a numerical finding of the robust optimal portfolio allocation. Then, we trace back our robust linear programming model to the Standard Form of a Linear Programming model; consequently, we solve it by a well-chosen algorithm and software package. Uncertainty in parameters, based on uncertainty in the prices, and a risk-return analysis are crucial parts of this study. A numerical experiment and a comparison (back testing) application are presented, containing real-world data from stock markets as well as a simulation study. Our approach increases the stability of portfolio allocation and reduces the portfolio risk.

Fama–French in China: Size and Value Factors in Chinese Stock Returns

Abstract: We investigate the size and value factors in the cross‐section of returns for the Chinese stock market. We find a significant size effect but no robust value effect. A zero‐cost small‐minus‐big (SMB) portfolio earns an average premium of 0.61% per month, which is statistically significant with a t‐value of 2.89 and economically important. In contrast, neither the market portfolio nor the zero‐cost high‐minus‐low (HML) portfolio has average premiums that are statistically different from zero. In both time‐series regressions and Fama–MacBeth cross‐sectional tests, SMB represents the strongest factor in explaining the cross‐section of Chinese stock returns. Our results contradict several existing studies which document a value effect. We show that this difference comes from the extreme values in a few months in the early years of the market with a small number of stocks and high volatility. Their impact becomes insignificant with a longer sample and proper volatility adjustment.

Can economic policy uncertainty, oil prices, and investor sentiment predict Islamic stock returns? A multi-scale perspective

Abstract: This study investigates the causal relationships between economic policy uncertainty, oil prices, investor sentiment, and stock returns of nine Dow Jones Islamic Market Indices. Specifically, this study analyzes the causal effect of these variables on Islamic stock returns at different time scales using the ensemble empirical mode decomposition model. First, we decompose the economic policy uncertainty proxy, oil prices, and investor sentiment into different independent components called intrinsic mode functions (IMFs): short-term IMFs designate the effects of irregular events; medium-term IMFs present the effects of extreme events; and long-term IMFs capture long-term effects. Second, we employ a nonlinear non-parametric causality model to test the causal relationship between different variables and Islamic stock returns at both the original and decomposed levels. We find causal relationships between the underlying variables and Islamic stock returns in several time frequencies rather than in the whole sample period. Our results suggest that the use of lagged economic policy uncertainty, oil prices, and investor sentiment may improve the predictability of Islamic stock returns. A test of forecast accuracy indicated the robustness of our results.

Corporate Governance, ESG, and Stock Returns around the World

Abstract: Nonfinancial performance measures, such as environmental, social, and governance (ESG) measures, are potentially leading indicators of companies’ financial performance. In the study reported here, ...

Financial Statement Comparability and the Informativeness of Stock Prices About Future Earnings

Abstract: We find that financial statement comparability enhances the ability of current period returns to reflect future earnings, as measured by the future earnings response coefficient (FERC). This suggests that comparability improves the informativeness of stock prices and allows investors to better anticipate future firm performance. In addition, using both the FERC and stock price synchronicity tests, we find that comparability increases the amount of firm‐specific information (rather than market/industry‐level information) reflected in stock prices. Analysts play an important role in improving stock price informativeness by producing more firm‐specific information when comparability is high. These findings suggest that comparability lowers the costs of gathering and processing firm‐specific information. This article is protected by copyright. All rights reserved.

What is the Expected Return on a Stock

Abstract: We derive a formula for the expected return on a stock in terms of the risk-neutral variance of the market and the stock's excess risk-neutral variance relative to that of the average stock. These quantities can be computed from index and stock option prices; the formula has no free parameters. The theory performs well empirically both in and out of sample. Our results suggest that there is considerably more variation in expected returns, over time and across stocks, than has previously been acknowledged.

Risk Compensation and Market Returns: The Role of Investor Sentiment in the Stock Market

Abstract: We investigate the effect of investor risk compensation (IRC) on stock market returns and the role of investor sentiment in influencing the link between IRC and stock returns. Results reveal that c...