mining financial news to forecast intraday stock price movements
In this work, we develop a system that analyzes unstructured financial news using text classification in order to forecast stock price trends. We review similar systems to build on successful ideas and combine them with novel approaches. We discuss the different types of news that are potentially relevant to the stock prices and choose news sources for the system accordingly. To eliminate irrelevant news, we present suitable filtering approaches such as the implementation of a rule-based thesaurus. We develop an automatic labeling approach and compare it to a manual labeling approach. We evaluate the influence of different automatic labeling approaches on the prediction performance. In a data training phase, we introduce a set of features novel with respect to the price forecasting task. We compare different text mining techniques such as the feature vector dimensionality reduction and different classifiers. Finally, we investigate the influence of trading costs on potential profits and run a market simulation that is able to support or reject the practical profitability of the system.
Stock market dynamics have drawn the attention of analysts from varied academic disciplines and commercial circles. The advent of online trading and real time facilities in the stock markets has fired a new field of interest in developing automatic trading agents that conduct trades in a relatively autonomous fashion under fixed strategies. This book examines a trading strategy based on analysis of external input in the form of online news. A machine-learning model is built using the reaction of stock markets to news items spread over a period of time. The news-based agent uses this model in real time to predict the price movement of stocks, and place orders accordingly. The performance the agent is evaluated by conducting controlled experiments with proven opponent strategies based on statistical models.
This study examines the relationship between opacity in financial reporting and the distribution of stock returns. The performance of stocks in the Swiss stock market is analyzed in a sample period from 1985 till 2010 with regard to the level of firms' opaqueness in financial statements. Using earnings management as a measure for opacity, it is shown that opacity is associated with stock price crash risk. Opaque firms are more susceptible to stock price crashes, but not more prone to explore positive jump events than firms with high transparency in financial reporting.
This thesis compares the performance of various models that measure the relationship between news and stock price correlations. These models are analysed when they are applied on the different datasets. One dataset contains social media news while the other dataset contains news flows provided by Thomson Reuters. These datasets have consequently different characteristics. Social media news are much more dynamic, since it allows a wide range of audience an easy and fast access to gain and share information. On the other hand the news flow doesn’t spread as widely and quickly as the social media news flow. As a result of the different characteristics of these datasets, there is not one overall best performing model but one best performing model for each dataset.
A cutting-edge guide to quantum trading Original and thought-provoking, Quantum Trading presents a compelling new way to look at technical analysis and will help you use the proven principles of modern physics to forecast financial markets. In it, author Fabio Oreste shows how both the theory of relativity and quantum physics is required to makes sense of price behavior and forecast intermediate and long-term tops and bottoms. He relates his work to that of legendary trader W.D. Gann and reveals how Gann's somewhat esoteric theories are consistent with his applications of Einstein's theory of relativity and quantum theory to price behavior. Applies concepts from modern science to financial market forecasting Shows how to generate support/resistance areas and identify potential market turning points Addresses how non-linear approaches to trading can be used to both understand and forecast market prices While no trading approach is perfect, the techniques found within these pages have enabled the author to achieve a very attractive annual return since 2002. See what his insights can do for you.
The book threw light on the growth and development of the stock market and observed that the development of the stock market highly depends on volatility and forecasting is an important area of research in financial market. The book measured the extent of stock price volatility in select companies of Automobile, Infrastructure, Manufacturing, Pharmaceutical and Services and identified suitable model for forecasting the volatility of the share prices in India. It evaluated the comparative ability of different statistical and econometric forecasting models in the context of Indian Stocks. Three different competing models were considered for the book and for forecasting performance of different models two forecasting error statistics viz., Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) were used and the best model was suggested for each sector. The EGARCH model provides the most accurate forecast compared to other competing models in the book. The book also made a few observations which may help the investors to understand better about the stock market.
Larry Pasavento A Traders Guide to Financial Astrology. Forecasting Market Cycles Using Planetary and Lunar Movements
Look to the stars for a whole new approach to market cycle forecasting A Trader's Guide to Financial Astrology is the definitive guide to trading market cycles based on astrological data. Written by a highly-respected technical analyst, this book makes the connection between the movements of planets and the volatility of the market. Readers can draw upon one hundred years of historical data as they learn how to spot correlations from the past, and refer to planetary and lunar data for the next five years as they shape their own strategy. The book covers the principles of astrological forecasting as applied to the financial markets, explaining what to watch for and how to interpret planetary and lunar activity, plus expert insight on everyday practical application. A study by the Federal Reserve Bank of Atlanta determined that the U.S. stock markets tend to be negatively affected by geomagnetic storms, and the Royal Bank of Scotland demonstrated that a trading system based on the phases of the moon would have outperformed the market. A Trader's Guide to Financial Astrology shows traders how to tap into the planetary forces that influence market activity. Readers will: Learn how planetary and lunar movements relate to the financial markets Draw upon 100 years of historic correlations and five years of forecast data Forecast long-term and short-term activity based on planetary relationships and lunar movement Enter the markets at key turning points, using price patterns and other tools When integrated with technical trading patterns, astrology can be an effective way of shifting perspective and approaching the market differently. For traders who have always wanted to know what to do when Mercury is in retrograde or the moon is new, A Trader's Guide to Financial Astrology provides information and insight from a leading market educator.
In this research, we discuss the so-called “turbo scandal”, an event that agitated Austrian public in 2009 when information about the alleged manipulation of turbo-certificates on the Vienna Stock Exchange appeared. Investors claimed that some issuers of turbo-certificates were pushing down the prices of underlying assets on purpose in order to break the barrier level of turbo-certificates and in this way make them valueless. Turbo scandal belongs to trade based manipulation which is also the most difficult to be disguissed. Research is divided in three analyses two daily and one intraday event study. Examples of empirical research based on real prosecuted cases are rare what is mainly due to the complex research and financial costs that has to be undertaken in order to disguise manipulators and their techniques. Hence this kind of research is often left to financial market authorities rather than to a single researcher. Nevertheless, we do hope that our results will be of certain help to other researchers and competent authorities who tackle the problem of market manipulation.
The enourmous impact stock exchanges and financial markets have on contemporary economy and society deserve renewed, systematic attention by sociologists. This book provides students and researchers with the theoretical and methodological tools essential to explore new research paths in the sociology of finance. Together with an introduction to the efficient market theory, the random walk hypothesis, and the heuristics and biases discovered by cognitive psychology and behavioral finance, different schools of thought directed at forecasting stock price movements are presented and critically examined. The book also displays empirical case studies dedicated to some institutional and informal restraints to speculation, as well as to the impact of the media on stock prices during the latest terrorist attacks in Paris and in the recent Chinese financial crisis.
THE NEWS AND THE NOISE: AN INVESTOR'S GUIDE TO FINANCIAL MEDIA
Stock Markets world over are full of surprises. Its a skill to trade in the stocks. Markets do wonders with its irrational reactions to some news. What are the information that markets world over trade upon? What may be the magnitude of such reactions? This work attempt to address these questions. This book will be a good key for investors to form the foundation of their research. This study will provide a base for value investors. This study will also be attracting academicians for serious research around stock price behaviour.
This work studies the behaviour of the four most traded stocks on the Prague Stock Exchange from January 2007 to July 2010. Its main goal is to describe how the financial crisis influenced the Prague Stock Exchange. Employing standard statistical methods, ARMA, GARCH, and VAR models I examine on daily data the following phenomena: volatility, price jumps, the day of the week effect, validity of the efficient market hypothesis, and information flow between the stocks. The results imply that the financial crisis had stronger impact on the banking sector stocks than on other stocks. The crisis was mainly characterized by rapid growth in volatility and correlation between the stocks. It also influenced the information flow and the day of the week effect. However, the crisis did not trigger growth in the number of extreme price movements, and it did not cause the market to be less information efficient.
This study has tested the semi-strong form of efficient market hypothesis by examining the stock price responses to quarterly earnings announcements. The sample consists of 156 companies listed on Bombay Stock Exchange, India. The companies are divided into three portfolios, good news, bad news and overall portfolio on the basis of percentage changes in quarterly earnings and sales. We use raw and log returns, market model, event study methodology, t-test, runs test and sign test. This study presents results on stock price responses to quarterly earnings announcements and seasonal analysis. For all the three portfolios under market model with raw returns and market model with log returns stock price behaviour around quarterly earnings on an average produced abnormal returns in pre-and post-announcement periods. Further, the abnormal returns were found to persist up to 31 trading days after the quarterly earnings announcement. The results indicate that the stock price adjustment to the event is delayed and persists throughout the event window. Therefore, the results of this study show that Indian stock market is not efficient in semi-strong form.
In 2007, as the US subprime mortgage market began to fall down, which reached its peak with the catastrophic collapse of the Lehman Brothers, no one was aware of that this was going to be the worst financial crisis since the Great Depression. Evaluating the advantages and disadvantages connected with financial globalization demands a pure understanding of the influence of international market integration and financial volatility. This work therefore focuses on the analysis of the integration of stock markets and forecast performances of stock market and macroeconomic volatility for the period of last global, financial crisis. It has explores the effects of financial volatility during the last global crisis. Moreover, it underlies the importance of stock market volatility during financial crises and introduces another important tool to assess the volatility clustering behavior, namely macroeconomic volatility.
It has been widely noted that as the world becomes more connected the movements of the Developed and the main Emerging Stock Markets of the world are getting closer over time. This co-movement of the world’s developed and emerging markets reduces the benefit that could be obtained from diversification across more than one national market. The key question for international investors who are seeking higher returns and less volatility in their portfolios is whether diversifying into the smaller, less liquid Frontier Emerging Markets, simply known as Frontier Markets, would provide the needed variation in equity price movements. This book seeks to answer this question by investigating the degree of correlation in the movements of the equity prices of Frontier African Stock Markets with those of the Developed Stock Markets of the world.
The purpose of our paper is to examine the relationship and interactions between oil price movements and stock markets in main two oil exporting countries - Russia and Norway and test how and to what extent oil prices together with other variables influence stock markets. Some macroeconomic explanatory variables that are directly linked to stock market performance are included to our model, too. The notion of comparative analysis of oil price changes and stock market performance between a developing country- Russia and a developed country- Norway is also one of the major empirical aspects of our master thesis. First, we run simple OLS regression to understand the effect of oil prices on stock returns. In order to examine deeply the interaction and impact among different variables, we employ a VAR model. Results reveal a diverse pattern in all share and industrial level in two markets. Finally, for further analysis, we run asymmetric tests using dummy variables to show the difference between oil price increases and the normal case.