A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. With the merit of integrating contextual information and cross-documental knowledge, the DRNews model creates news vectors that describe both the semantic information and potential linkages among news events through an attributed news network. Two stock market prediction tasks, namely the short-term stock movement prediction and stock crises early warning, are implemented in the framework of the attention-based Long Short Term-Memory (LSTM) network. It is suggested that DRNews substantially enhances the results of both tasks comparing with five baselines of news embedding models. Further, the attention mechanism suggests that short-term stock trend and stock market crises both receive influences from daily news with the former demonstrates more critical responses on the information related to the stock market {\em per se}, whilst the latter draws more concerns on the banking sector and economic policies.Comment: 25 page

Detecting Fraud in Chinese Listed Company Balance Sheets

This study investigates the links between accounting values in Chinese listed companies’ balance sheets and the exposure of their fraudulent activities. Every balance sheet account is proposed to be a potential vehicle to manipulate financial statements. Other receivables, inventories, prepaid expenses, employee benefits payables and long-term payables are important indicators of fraudulent financial statements. These results confirm that asset account manipulation is frequently carried out and cast doubt on earlier conclusions by researchers that inflation of liabilities is the most common source of financial statement manipulation. Prior practices of solely scaling balance sheet values by assets are revealed to produce spurious relationships, while scaling by both assets and sales effectively detects fraudulent financial statements and provides a useful fraud prediction tool for Chinese auditors, regulators and investors

Sovereign Wealth Fund Investment Patterns and Performance

This study describes the newly created Monitor-FEEM Sovereign Wealth Fund Database and discusses the investment patterns and performance of 1,216 individual investments, worth over $357 billion, made by 35 sovereign wealth funds (SWFs) between January 1986 and September 2008. Approximately half of the investments we document occur after June 2005, reflecting a recent surge of SWF activity. We document large SWF investments in listed and unlisted equity, real estate, and private equity funds, with the bulk of investments being targeted in cross-border acquisitions of sizeable but non-controlling stakes in operating companies and commercial properties. The average (median) SWF investment is a$441 million ($55 million) acquisition of a 42.3$57billion on their holdings of listed stock investments alone through March 2009.Sovereign Wealth Funds, International Financial Markets, Government Policy and Regulation

Predicting Romanian Financial Distressed Companies

The study consisted in collecting financial information for a group of distressed and non-distressed Romanian listed companies during the period 2006–2008, in order to create early warning signals for financial distressed companies using the following methodologies: the Logistic and the Hazard model, the CHAID decision tree model and the Artificial Neural Network model (ANN). For each company a set of 14 financial ratios, that reflect the company’s profitability, solvency, asset utilization, growth ability and size, were calculated and then used in the study. A Principal Component Analysis was also used to reduce the dimensionality of the data space and to allow seeing that the 2 types of companies do form 2 distinct groups suggesting that the ratios used are useful enough to predict financial distress. The following 4 data sets were separately analyzed: first-year data to predict distress one year ahead, second-year data for a 2 year-ahead prediction, third-year data for a 3 year-ahead prediction, as well as cumulative three-year data to predict distress 1 year ahead by letting the ratios vary in time. For each data set, several prediction models were created using CHAID, the Logit and Hazard models as well as the ANN and the hybrid-ANN. The results are consistent with the theory and also to previous studies and the out-of-sample forecast accuracy of the estimated models of 73%-100% indicates that the proposed early warning models for the Romanian listed companies are quite efficient.early warning signals, CHAID, ANN

Seeking out non-public information : sell-side analysts and the freedom of information act

A number of sell-side healthcare analysts gain access to information outside the purview of management through Freedom of Information Act requests to the Food and Drug Administration for records on factory inspections, complaints, and drug and medical device applications. Using a difference-in-differences methodology, we find that buy (sell) recommendations and upgrades (downgrades) earn higher (lower) stock returns over the year following the receipt of FDA records. We also examine the type of information revealed in FDA factory inspection reports, and find that analysts are less likely to downgrade and are less pessimistic in their recommendations than the consensus recommendation when the information contained in the FDA report is not particularly severe. Our findings are consistent with a subset of analysts utilizing non-public information channels independent of management to gain value-relevant information about their covered firms

Impact Of Information Disclosure Violation On Firm Value In Chinese Listed Firms

We analyze the association between information disclosure violation (IDV) and firm value, based on a sample of Chinese listed firms that were subject to China Securities Regulatory Commission (CSRC) enforcement actions from 2000 to 2014. Using Tobin’s Q as a proxy for firm value at the end of the enforcement action year, we find that firm value in violating firms is significantly lower than firm value in non-violating firms. Further, we find IDV with the following characteristics can cause serious damage to firm value: IDV related to inflated profit or asset fabrication have a more damaging effect on firm value; the total number of violation types, fines and the total number of admonishment types are negatively associated with firm value; violation frequency and number of years between violations and CSRC enforcement action are negatively related to firm value