Analyzing Parts of Speech and Their Impact on Stock Price

Financial articles can move stock prices. The terms used in an article can be a predictor of both price direction and the magnitude of movement. By investigating the usage of terms in financial news articles and coupling them with a discrete machine-learning algorithm, we can build a model of short-term price movement. From our research, we investigated the terms creating the largest price movements amongst five part of speech textual representations; bag of words, noun phrases, named entities, proper nouns and verbs

From Data to Wisdom: The Progression of Computational Learning in Text Mining

The DIKW hierarchy has long been a standard framework with which researchers can differentiate between levels of what they see and know. However much of the research conducted explores the nuances and precise divisions between each hierarchy level and assumes that the user will know how to use them. We plan to restrict our study to textual Web documents and propose a framework extension to the DIKW hierarchy that encompasses acquisition, delivery and prediction elements. We feel that such an extension can help better define each level of the DIKW hierarchy into discrete units that can be applied to the content contained within the Internet

Data Mining the Harness Track and Predicting Outcomes

This paper presented the S&C Racing system that uses Support Vector Regression (SVR) to predict harness race finishes and analyzed it on fifteen months of data from Northfield Park. We found that our system outperforms the most common betting strategies of wagering on the favorites and the mathematical arbitrage Dr. Z system in five of the seven wager types tested. This work would suggest that an informational inequality exists within the harness racing market that is not apparent to domain experts

Machine Learning the Harness Track: A Temporal Investigation of Race History on Prediction

Machine learning techniques have shown their usefulness in accurately predicting greyhound races. Many of the studies within this domain focus on two things; win-only wagers and using a very particular combination of race history. Our study investigates altering these properties and studying the results. In particular we found a race history combination that optimizes our S&C Racing system’s predictions on seven different wager types. From this, S&C Racing posted an impressive 50.44% accuracy in selecting winning wagers with a payout of $609.34 and a betting return of$10.06 per dollar wagered

Table of Contents JITIM vol 26 issue 1, 2017

Table of Contents JITIM vol 26 issue 1, 201

Analysis of Stock Price Movement Following Financial News Article Release

What effect does a financial news article have on stock price? To answer this question we investigate stock price movements within the minutes following financial news releases, broken down by media outlet, time of release and article sentiment. Our data shown a Sharpe ratio (a measure for calculating risk-adjusted return) of 1.18 versus a random dataset of ‑0.06, indicating significant price movement immediately following article release. Second, we found that articles released through WSJ, Reuters – UK Focus, NYT and FT all experienced significant positive returns, whereas articles in Barrons, MarketWatch, Forbes and Bloomberg experienced significant negative returns. Third, we found that articles released at certain times had abnormally high price movements associated with them, more so than random chance. Lastly we discovered a minority of positive news articles trending upwards and suddenly reversing direction following a financial news article release. In one particular case there was a period of several days where the release of IBM articles triggered large price declines with steady prices otherwise. We believe these findings could be used by companies as a form of stock price management

Opioid Adjunct Drug Therapy: Evaluating Effectiveness Using Text Analytics of Real World Data

Opioid analgesics continue to be the mainstay of pharmacologic treatment of moderate to severe pain. An adjunct is a drug that in its pharmacological characteristic is not identified primarily as an analgesic, but that has been found in clinical practice to have either an independent analgesic effect or additive analgesic properties when used with opioids. By using an adjunct to maximize the level of analgesia, the required opioid dosage may be reduced, together with concomitant adverse effects. BACKGROUND Real World Data (RWD) refers to data that describe observations in normal clinical practice obtained by any non- interventional methodology, such as Randomized Controlled Trials (RCTs). The U.S. Food and Drug Administration (FDA) maintains one of the largest government databases in the country, the FDA Adverse Event Reporting System (FAERS). It is comprised of adverse event reports submitted to the FDA through the “MedWatch” reporting program and contains a plethora of Real World Data: thousands of case reports on opioids and adjunct drugs, comprised of unstructured textual data. The objective of this study is to identify the therapeutic effectiveness of adjunct drugs with opioids by examination of narrative text in MedWatch cases. METHODS This project follows the traditional approach of knowledge discovery in databases, comprised of five steps: 1) Data selection, 2) Pre-processing, 3) Transformation, 4) Data mining and 5) Interpretation. The strategy employed will transform the narrative text data into an organized and concise summary of key endpoints. An appropriate sample (500 to 1,000 relevant patient cases) that describe opioids and adjunct drugs will be included in the case report data set. Key task 1: Data selection and pre-processing (Steps 1,2). MedWatch narratives of patient cases that describe the types of opioid and adjunct drug combinations used in real-life clinical settings will be obtained from the FAERS database. Key task 2: Data transformation and mining (Steps 3,4). Cases will be organized in a Structured Query Language (SQL) database. A lexicon of words and terms clinically or theoretically related to opioid and adjunct drug therapy will be developed, which will serve as a reference for analysis of the text. Using Natural Language Processing (NLP) techniques, textual data will be transformed into n-grams using a MySQL n-gram parser. N-gram extraction will identify notes containing n-grams matching terms from the theory-and expert-derived lexicon. Categories will be formed from the most frequently identified n-grams and their total frequency. RESULTS (PROJECTED) Key task 3: Evaluate and interpret results (Step 5) and compile the information into a useful format for healthcare providers. The most commonly extracted n-grams will be identified by category, then frequency, and displayed in tabular format. N-gram analysis of the corpus of case reports reveals the frequency with which and adjunct drug was used with an opioid, and indicate impact on analgesic effect. Completion of key tasks provides evidence on the associated outcomes of treatment; whether the adjunct drug therapy indicates treatment success or failure. CONCLUSION Findings of this project will add to the existing body of knowledge on opioid adjunct therapy for analgesia and may corroborate or refute other existing evidence for adjunct drug therapeutic effectiveness derived from case reports or clinical trials

Cosmological Perturbations of Quantum-Mechanical Origin and Anisotropy of the Microwave Background

Cosmological perturbations generated quantum-mechanically (as a particular case, during inflation) possess statistical properties of squeezed quantum states. The power spectra of the perturbations are modulated and the angular distribution of the produced temperature fluctuations of the CMBR is quite specific. An exact formula is derived for the angular correlation function of the temperature fluctuations caused by squeezed gravitational waves. The predicted angular pattern can, in principle, be revealed by the COBE-type observations.Comment: 9 pages, WUGRAV-92-17 Accepted for Publication in Phys. Rev. Letters (1993

Experiences of Formal Caregivers Providing Dementia Care to American Indians

Alzheimer’s disease (AD) is a significant public health concern for all elders in the United States. It is a particular concern for the American Indian (AI) population, which is one of the fastest aging populations in the United States and the smallest, most underrecognized, and most culturally diverse group in the country. A formal caregiver understanding of AD in the AI population is scarce. This phenomenological study was designed to discern what is known about AD in the AI population by exploring the cultural beliefs and experiences of formal caregivers who provide care for AI dementia patients. Specifically, this study sought to document formal caregiver and AI dementia beliefs about AD. Data came from four in-depth interviews that included three Western and one AI formal caregiver. These interviews explored the variability of cultural beliefs regarding AD and dementia among a sample of formal caregivers who minister to AI patients; in the interviews, these participants also provided examples of challenges they faced, providing a better cultural understanding of AI dementia. The findings included using a bicultural approach to AD, illuminating interactions between patient and provider, and fostering awareness of cultural competency. Research on this topic is critical in advancing cultural, public health, and evidence-based health practices regarding AI dementia patients. The potential implications for social change include enhancing cross cultural provider–patient interactions and advancing public health policy and practice for this underserved population. Many of the issues and challenges explored may have implications for other ethnocultural minority groups

Evaluating sentiment in financial news articles: Working paper series--11-10

We investigate the pairing of a financial news article prediction system, AZFinText, with sentiment analysis techniques. From our comparisons we found that news articles of a subjective nature were easier to predict in both price direction (59.0% vs 50.4% without sentiment) and through a simple trading engine (3.30% return vs 2.41% without sentiment). Looking into sentiment further, we found that news articles of a negative sentiment were easiest to predict in both price direction (50.9% vs 50.4% without sentiment) and our simple trading engine (3.04% return vs 2.41% without sentiment). Investigating the negative sentiment further, we found that AZFinText was best able to predict price decreases in articles of a positive sentiment (53.5%) and price increases in articles of a negative or neutral sentiment (52.4% and 49.5% respectively)