Financial conditions and the risks to economic growth in the United States since 1875

We explore the historical relationship between financial conditions and real economic growth for quarterly U.S. data from 1875 to 2017 with a flexible empirical copula modelling methodology. We compare specifications with both linear and non-linear dependence, and with both Gaussian and non-Gaussian marginal distributions. Our results indicate strong statistical support for models that are both non-Gaussian and nonlinear for our historical data, with considerable heterogeneity across sub-samples. We demonstrate that ignoring the contribution of financial conditions typically understates the conditional downside risks to economic growth in crises. For example, accounting for financial conditions more than doubles the probability of negative growth in the year following the 1929 stock market crash

High quality topic extraction from business news explains abnormal financial market volatility

Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc.) affect trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affect stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their "thematic" features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized fact in financial economies, namely that at certain times trading volumes appear to be "abnormally large," can be partially explained by the flow of news. In this sense, our results prove that there is no "excess trading," when restricting to times when news are genuinely novel and provide relevant financial information.Comment: The previous version of this article included an error. This is a revised versio

A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

Knowledge-based decision support systems for financial management are an important part of investment plans. Investors are avoiding investing in traditional investment areas such as banks due to low return on investment. The stock exchange is one of the major areas for investment presently. Various non-linear and complex factors affect the stock exchange. A robust stock exchange forecasting system remains an important need. From this line of research, we evaluate the performance of a regression-based model to check the robustness over large datasets. We also evaluate the effect of top stock exchange markets on each other. We evaluate our proposed model on the top 4 stock exchanges—New York, London, NASDAQ and Karachi stock exchange. We also evaluate our model on the top 3 companies—Apple, Microsoft, and Google. A huge (Big Data) historical data is gathered from Yahoo finance consisting of 20 years. Such huge data creates a Big Data problem. The performance of our system is evaluated on a 1-step, 6-step, and 12-step forecast. The experiments show that the proposed system produces excellent results. The results are presented in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE)

Rough sets for predicting the Kuala Lumpur Stock Exchange Composite Index returns

This study aims to prove the usability of Rough Set approach in capturing the relationship between the technical indicators and the level of Kuala Lumpur Stock Exchange Composite Index (KLCI) over time.Stock markets are affected by many interrelated economic, political, and even psychological factors.Therefore, it is generally very difficult to predict its movements. There are extensive literatures available describing attempts to use artificial intelligence techniques; in particular neural networks and genetic algorithm for analyzing stock market variations.However, drawbacks are found where neural networks have great complexity in interpreting the results; genetic algorithms create large data redundancies.A relatively new approach, the rough sets are suggested for its simple knowledge representation, ability to deal with uncertainties and lowering data redundancies.In this study, a few different discretization algorithms were used at data preprocessing. From the simulations and result produced, the rough sets approach can be a promising alternative to the existing methods for stock market prediction

Banking Efficiency and Stock Market Performance: An Analysis of Listed Indonesian Banks

This paper examines the monthly efficiency and productivity of listed Indonesian banks and their market performance through the prism of two modelling techniques, efficiency and super-efficiency, over the period January 2006 to July 2007. Within this research strategy we employ Tone’s (2001) non-parametric, Slacks-Based Model (SBM) and Tone’s (2002) super-efficiency SBM combining them with recent bootstrapping techniques, namely the non-parametric truncated regression analysis suggested by Simar and Wilson (2007). In the case of the SBM efficiency scores, the Simar and Wilson methodology was adapted to two truncations, whereas in the super-efficiency framework the original technique was utilised. As suggested by neo-classical theory, we find that the stock market values banks in accordance with their performance. Moreover, it is found that the JCI index of the Indonesian Stock Exchange is positively related to bank efficiency. Another interesting finding is that the coefficient for the share of foreign ownership is negative and statistically significant in the super-efficiency modelling. This suggests that Indonesian banks with foreign ownership tend to be less efficient than their domestic counterparts. Finally, Malmquist productivity results suggest that, over the study’s horizon, the sample banks displayed volatile productivity patterns in their profit-generating operations.Indonesian Banking, Emerging Markets, Productivity, Efficiency.