A-23. Note: GJR refers to the Glosten et.al. (1993) method of threshold GARCH. See table 11 for additional explanations.
|
|
- Ralf Smith
- 6 years ago
- Views:
Transcription
1 Table 11: GARCH(1,1) Volatility Regressions Estimates Returns Market Bullishness Vol.Eq. Vol.Eq. Intraday Trades Messages Agreement Arch Garch Intercept Opening Bt 1 Intercept Opening Factor ln(n t 1) ln(1+m t 1) A t 1 Term Term Minimum Median Maximum Mean St.dev Mean (DIA) Mean (XLK) T-Statistics Returns Market Bullishness Vol.Eq. Vol.Eq. Intraday Trades Messages Agreement Arch Garch Intercept Opening Bt 1 Intercept Opening Factor ln(n t 1) ln(1+m t 1) A t 1 Term Term Minimum Median Maximum Mean St.dev Mean (DIA) Mean (XLK) Note: The volatility regressions are based on 15-minute intervals with the log difference in returns as the dependent variable. The number of mesages M t 1, the bullishness index B t 1 and the agreement index A t 1 are based on the 1-hour time period prior to a 15-minute interval. A-22
2 Table 12: GJR(1,1) Volatility Regressions Estimates Minimum Median Maximum Mean St.dev Mean (DIA) Mean (XLK) T-Statistics Minimum Median Maximum Mean St.dev Mean (DIA) Mean (XLK) Note: GJR refers to the Glosten et.al. (1993) method of threshold GARCH. See table 11 for additional explanations. A-23
3 Table 13: EGARCH(1,1) Volatility Regressions Estimates Minimum Median Maximum Mean St.dev Mean (DIA) Mean (XLK) T-Statistics Minimum Median Maximum Mean St.dev Mean (DIA) Mean (XLK) Note: See table 11 for explanations. A-24
4 Table 14: Time Sequencing Tests Yahoo! Finance (15 minutes) X Y X Y Y X X 1 X 2 X 3 X 4 SPY χ 2 Y 1 Y 2 Y 3 Y 4 SPY χ 2 messages return c a c 39.2 c c b c c 85.0 c messages volatility c b c c c 434. c c b c 269. c messages small a c a a c 153. c c a c c c messages medium c c c c a c c c c c c messages large c c a c 516. c c c c c c messages volume c c a c 624. c c a c c c messages spread c c a c 10.4 words return a c a a b c 39.1 c words volatility c b a c 225. c c c 155. c words small b c a a c 136. c c c words medium c c a c a 556. c c c c c c words large c c c 254. c c c 762. c words volume c c a c 404. c c a a a c words spread b a c c 5.76 bullishness return c c b c a 54.2 c bullishness volatility c c a c 122. c c b b c 415. c bullishness small c c c c c 743. c c c c c c c bullishness medium c c c c b 281. c c b a a b c bullishness large c c c c c 300. c c c c c c 661. c bullishness volume c c c c c 727. c c c c c c c bullishness spread a b c a 6.85 agreement return c b a 18.0 a agreement volatility b c b a c 77.6 c c c c c 323. c agreement small c c c c c 645. c c c c c c c agreement medium c c c c b 157. c c c c c b 654. c agreement large c c c c c 216. c c c c c c agreement volume c c c c c 587. c c c c c c c agreement spread a b a a 14.5 a Note: The results in this table are based on messages obtained from the Yahoo! Finance message boards. The unit of observation is a 15- minute interval during the 09:30-16:00 trading day for a given company. We estimate each equation as a panel with company fixed effects. The regressors X i and Y i are subscripted by their lags. SPY is a variable with the log of the price of the Standard & Poors Depositary Receipt S&P 500 Tracking Fund, except in the Return regressions where this variable is the time-differenced log of the price. NWK is an indicator variable for a day being the first trading day after a weekend or holiday. A coefficient that is signficant at the 99% level is indicated with a, while b and c denote significance at a 99.9% level and a 99.99% level, respectively. The four X variables were obtained from the message boards. The messages and words variables were transformed into logarithms, while bullishness and agreement are the B t and A t measures defined in the text. The seven financial Y variables are: the log differencess in daily closing price ( return ); the percentage ratio of daily price volatility relative to the day s average price; the log number of small (<$10k), medium ($10k $100k) and large (>$100k) trades; the log number of traded shares ( volume ); and the daily average of the bid-ask spread. A-25
5 Table 15: Time Sequencing Tests Raging Bull (15 minutes) X Y X Y Y X X 1 X 2 X 3 X 4 SPY χ 2 Y 1 Y 2 Y 3 Y 4 SPY χ 2 messages return c c 63.8 c c c c messages volatility c c c 282. c c c c messages small c c 50.6 c c a c c c c messages medium c c c b 683. c c a c c c messages large c c c 615. c c c c messages volume c c c 392. c c b c c c c messages spread a b a words return b c 21.6 b b c c words volatility c b c 175. c c b c words small c c c 71.8 c c c c c c words medium c c a b 466. c c a b c c words large c c c 368. c c c c words volume c c c 305. c c c c c words spread a b a bullishness return c c c 39.5 c c a a 32.1 c bullishness volatility c a c 56.3 c c c c 227. c bullishness small c c b c c 199. c c c c c c bullishness medium c c b a b 249. c c c a b c bullishness large c c c b c 235. c c c c c c bullishness volume c c c c c 267. c c c c c c bullishness spread a 13.2 agreement return c c 26.6 c a b a 25.9 c agreement volatility c a a c 56.4 c c c c c 249. c agreement small c c c c c 288. c c c c c c agreement medium c c c c b 198. c c c c c a c agreement large c c c c c 202. c c c c c c agreement volume c c c c c 293. c c c c c c c agreement spread a Note: A coefficient that is signficant at the 99% level is indicated with a, while b and c denote significance at a 99.9% level and a 99.99% level, respectively. See table 14 for further explanations. A-26
6 Table 16: Time Sequencing Tests Yahoo Finance (1 day) X Y Y = f(x 1, X 2,...) X = f(y 1, Y 2,...) X 1 X 2 WSJ 2 WSJ 1 WSJ 0 WSJ +1 NWK Market χ 2 Y 1 Y 2 WSJ 2 WSJ 1 WSJ 0 WSJ +1 NWK Market χ 2 messages return a a c 11.0 a c c a c messages volatility c a a c 19.5 c c c a c messages small c c c a b c c 179. c a c c c a c c messages medium c c c b c c c c a c messages large c c c a c c c c c a c messages volume c c c a c c c 258. c c c c b c b messages spread c c a c words return a a c a c c b 3.66 words volatility b c a c c a 2.64 words small c c b b c 56.5 c c a c c a 33.0 c words medium b b c b c b a c a c b 1.38 words large c b a c b c c c a c c words volume c a c b c c c 109. c b a c c c words spread a c c b 0.22 bullishness return a c a c a c 1.01 bullishness volatility b a b c 16.0 b a c a c 8.67 bullishness small c c c a b c 69.8 c c a c a c 127. c bullishness medium c c c b c c c a c a c 37.3 c bullishness large c a c a c c c a c a b bullishness volume c a c b b c c 70.6 c c a c a c 65.5 c bullishness spread a a a c a c 3.30 agreement return a c c c 0.41 agreement volatility b c c c 6.47 agreement small b c b b c 14.7 b c c c 92.5 c agreement medium a b c b c b b c c 47.5 c agreement large c a c b c c c a c c agreement volume c a c c b c c 18.5 c c c c 39.1 c agreement spread b a a c c 12.2 a Note: The results in this table are based on messages obtained from the Yahoo! Finance message boards. The unit of observation is the trading day for a given company. We estimate each equation as a panel with company fixed effects. The regressors X i and Y i are subscripted by their lags. SPY is a variable with the log of the price of the Standard & Poors Depositary Receipt S&P 500 Tracking Fund, except in the Return regressions where this variable is the time-differenced log of the price. NWK is an indicator variable for a day being the first trading day after a weekend or holiday. Variables WSJ 2, WSJ 1, WSJ 0, and WSJ +1 indicate how many stories were released in the Wall Street Journal on a given day: the day before yesterday, yesterday, today, or tomorrow. NWK is an indicator variable for a day being the first trading day after a weekend or holiday. A coefficient that is signficant at the 99% level is indicated with a, while b and c denote significance at a 99.9% level and a 99.99% level, respectively. The four X variables were obtained from the message boards. The messages and words variables were transformed into logarithms, while bullishness and agreement are the B t and A t measures defined in the text. The seven financial Y variables are: the log differencess in daily closing price ( return ); the percentage ratio of daily price volatility relative to the day s average price; the log number of small (<$10k), medium ($10k $100k) and large (>$100k) trades; the log number of traded shares ( volume ); and the daily average of the bid-ask spread. A-27
7 Table 17: Time Sequencing Tests Raging Bull (1 day) X Y Y = f(x 1, X 2,...) X = f(y 1, Y 2,...) X 1 X 2 WSJ 2 WSJ 1 WSJ 0 WSJ +1 NWK Market χ 2 Y 1 Y 2 WSJ 2 WSJ 1 WSJ 0 WSJ +1 NWK Market χ 2 messages return a c c c a c messages volatility b a c 12.9 a a c c a c a messages small c c a b c 87.4 c c b c c a c a 70.1 c messages medium c c c b c c c c a c c messages large c c c a c c c c c c a c c messages volume c a a c b b c c 144. c c c c a c a 44.4 c messages spread a a c c a c words return a c c a c words volatility b c c a c a 13.2 a words small c c b b c 26.6 c c b c c b 110. c words medium b a c b c b c a c c words large c a c b c c b c c a c c words volume c a c c b c c 54.2 c c a c c c 91.0 c words spread b a c a c bullishness return a c b c 0.08 bullishness volatility b c b c 5.92 bullishness small c c b b c 22.2 c c a c 77.1 c bullishness medium b c b c b b b b 47.2 c bullishness large b a c b c c a c b c bullishness volume c a c c b c c 19.0 c c a c 47.3 c bullishness spread a c 2.99 agreement return a c b 0.12 agreement volatility b c b 0.00 agreement small c b b c c 6.88 agreement medium c b c b 8.54 agreement large a c b c c agreement volume a c c b c c c 3.84 agreement spread b 0.04 Note: A coefficient that is signficant at the 99% level is indicated with a, while b and c denote significance at a 99.9% level and a 99.99% level, respectively. See table 16 for further explanations. A-28
MPhil computer package lesson: getting started with Eviews
MPhil computer package lesson: getting started with Eviews Ryoko Ito (ri239@cam.ac.uk, itoryoko@gmail.com, www.itoryoko.com ) 1. Creating an Eviews workfile 1.1. Download Wage data.xlsx from my homepage:
More informationComparison of Linear Regression with K-Nearest Neighbors
Comparison of Linear Regression with K-Nearest Neighbors Rebecca C. Steorts, Duke University STA 325, Chapter 3.5 ISL Agenda Intro to KNN Comparison of KNN and Linear Regression K-Nearest Neighbors vs
More informationMetaStock RT Antenna
MetaStock RT Antenna Real time Program installation Guide Start Install the MS RT Antenna program...2 User Guide:...5 Other options: Symbols setting...9 Error data maintain:...10 Install Upgrade version...15
More information1. Solve the following system of equations below. What does the solution represent? 5x + 2y = 10 3x + 5y = 2
1. Solve the following system of equations below. What does the solution represent? 5x + 2y = 10 3x + 5y = 2 2. Given the function: f(x) = a. Find f (6) b. State the domain of this function in interval
More information/4 Directions: Graph the functions, then answer the following question.
1.) Graph y = x. Label the graph. Standard: F-BF.3 Identify the effect on the graph of replacing f(x) by f(x) +k, k f(x), f(kx), and f(x+k), for specific values of k; find the value of k given the graphs.
More informationReports. Chapter V. In This Chapter
Chapter V. Reports In This Chapter 1. Reports Overview 260 Explanation of the Reports window 260 Features of reports 262 2. Using Reports functions 264 Generating reports 264 Viewing reports 269 Printing
More informationTime-Varying Volatility and ARCH Models
Time-Varying Volatility and ARCH Models ARCH MODEL AND TIME-VARYING VOLATILITY In this lesson we'll use Gretl to estimate several models in which the variance of the dependent variable changes over time.
More informationPackage iclick. R topics documented: February 24, Type Package
Type Package Package iclick February 24, 2018 Title A Button-Based GUI for Financial and Economic Data Analysis Version 1.4 Date 2018-02-24 Author Ho Tsung-wu Maintainer A GUI designed to support the analysis
More informationSection 2.1: Intro to Simple Linear Regression & Least Squares
Section 2.1: Intro to Simple Linear Regression & Least Squares Jared S. Murray The University of Texas at Austin McCombs School of Business Suggested reading: OpenIntro Statistics, Chapter 7.1, 7.2 1 Regression:
More informationMath 121. Graphing Rational Functions Fall 2016
Math 121. Graphing Rational Functions Fall 2016 1. Let x2 85 x 2 70. (a) State the domain of f, and simplify f if possible. (b) Find equations for the vertical asymptotes for the graph of f. (c) For each
More informationUnit 1 Algebraic Functions and Graphs
Algebra 2 Unit 1 Algebraic Functions and Graphs Name: Unit 1 Day 1: Function Notation Today we are: Using Function Notation We are successful when: We can Use function notation to evaluate a function This
More informationStatistical Package for the Social Sciences INTRODUCTION TO SPSS SPSS for Windows Version 16.0: Its first version in 1968 In 1975.
Statistical Package for the Social Sciences INTRODUCTION TO SPSS SPSS for Windows Version 16.0: Its first version in 1968 In 1975. SPSS Statistics were designed INTRODUCTION TO SPSS Objective About the
More informationA Very Brief EViews Tutorial
A Very Brief EViews Tutorial Contents Importing data... 2 Transformations and generating new series... 4 Drawing graphs... 6 Regressions... 7 Forecasting... 9 Testing... 10 1 Importing data The easiest
More informationINFOREX SA. Financial Information Services. FX Quick Reference Guide
INFOREX SA Financial Information Services FX2000 - Quick Reference Guide I N F O R E X S A R E A L - T I M E F I N A N C I A L I N F O R M A T I O N S E R V I C E S FX2000 - Quick Reference Guide Inforex
More informationMDM 4UI: Unit 8 Day 2: Regression and Correlation
MDM 4UI: Unit 8 Day 2: Regression and Correlation Regression: The process of fitting a line or a curve to a set of data. Coefficient of Correlation(r): This is a value between and allows statisticians
More informationQUADRATIC FUNCTIONS. PROTOTYPE: f(x) = ax 2 + bx + c. (1) The leading coefficient a 0 is called the shape parameter.
QUADRATIC FUNCTIONS PROTOTYPE: f(x) = ax 2 + bx + c. (1) The leading coefficient a 0 is called the shape parameter. SHAPE-VERTEX FORMULA One can write any quadratic function (1) as f(x) = a(x h) 2 + k,
More informationThe Exchange Rate Sensitivities of Stock Returns in Japan: The Japanese Machinery Industry Cases
The Exchange Rate Sensitivities of Stock Returns in Japan: The Japanese Machinery Industry Cases Chikashi TSUJI Abstract This paper investigates whether the exchange rates are priced in recent periods
More informationFebruary 8 th February 12 th. Unit 6: Polynomials & Introduction to Quadratics
Algebra I February 8 th February 12 th Unit 6: Polynomials & Introduction to Quadratics Jump Start 1) Use the elimination method to solve the system of equations below. x + y = 2 3x + y = 8 2) Solve: 13
More informationGeneral Autoregressive Conditional Heteroscedastic (GARCH) Modeling Using the SCAB34S-GARCH and SCA WorkBench
Modified: August 25, 2005 General Autoregressive Conditional Heteroscedastic (GARCH) Modeling Using the SCAB34S-GARCH and SCA WorkBench Houston H. Stokes Department of Economics University of Illinois
More informationNotes 9 4 Finding Exponential Equations
Notes 9 4 Finding Exponential Equations Dec 22 10:39 AM 1 y = ab x We need the initial condition (a) and the growth factor (b) Solving method #1 Need two points one of which is the y intercept. y intercept
More informationHigh frequency exchange rate behaviour of the NZD/USD exchange rate in response to macroeconomic announcements
High frequency exchange rate behaviour of the NZD/USD exchange rate in response to macroeconomic announcements Mahesh Chhagan 1 PricewaterhouseCoopers Auckland Alastair Marsden 2 The University of Auckland
More informationToday is the last day to register for CU Succeed account AND claim your account. Tuesday is the last day to register for my class
Today is the last day to register for CU Succeed account AND claim your account. Tuesday is the last day to register for my class Back board says your name if you are on my roster. I need parent financial
More informationMOBILE and SMART TELEPHONES Policy on the Reimbursement of Private Calls
NIPEC/14/15 (replacing NIPEC/13/13) NORTHERN IRELAND PRACTICE AND EDUCATION COUNCIL FOR NURSING AND MIDWIFERY Policy on the Reimbursement of Private Calls August 2014 Review Date: April 2016 Centre House
More informationTurquoise Equities Guide to Reference Data Services
TQ501 TECHNICAL SPECIFICATION Turquoise Equities Guide to Reference Data Services ISSUE 1.9 02 July 2013 Contents 1 INTRODUCTION... 3 1.1 Purpose 3 1.2 Readership 3 1.3 Document Series 3 1.4 Document History
More informationZeroWeb Manual. Securities offered to you by TradeZero America, Inc. Page 1 of 11
ZeroWeb Manual Securities offered to you by TradeZero America, Inc Page 1 of 11 Contents WATCH LIST...3 CHARTS...4 LEVEL 2, TIME and SALES, ORDER ENTRY...6 SHORT LIST and LOCATES...7 NEW WINDOWS and LAYOUT...8
More informationHUPX Information Packages
HUPX Information Packages Introduction Effective as of 1 th September, 2017 HUPX is licensed by the Hungarian Energy Office to operate the organized electricity market in Hungary. The company was established
More informationUsing. Research Wizard. Version 4.0. Copyright 2001, Zacks Investment Research, Inc.,
Using Research Wizard Version 4.0 Copyright 2001, Zacks Investment Research, Inc., Contents Introduction 1 Research Wizard 4.0 Overview...1 Using Research Wizard...1 Guided Tour 2 Getting Started in Research
More informationRelations and Functions 2.1
Relations and Functions 2.1 4 A 2 B D -5 5 E -2 C F -4 Relation a set of ordered pairs (Domain, Range). Mapping shows how each number of the domain is paired with each member of the range. Example 1 (2,
More informationRelease Notes FutureSource Release 3.6. Date: June 28, 2013
Release Notes FutureSource Release 3.6 Date: June 28, 2013 Interactive Data Desktop Solutions FutureSource Release Notes Page 2 of 25 1 Preface This document outlines the key new features, functionality
More informationPackage lgarch. September 15, 2015
Type Package Package lgarch September 15, 2015 Title Simulation and Estimation of Log-GARCH Models Version 0.6-2 Depends R (>= 2.15.0), zoo Date 2015-09-14 Author Genaro Sucarrat Maintainer Genaro Sucarrat
More informationVoluntary State Curriculum Algebra II
Algebra II Goal 1: Integration into Broader Knowledge The student will develop, analyze, communicate, and apply models to real-world situations using the language of mathematics and appropriate technology.
More informationDAVENPORT ONLINE FREQUENTLY ASKED QUESTIONS
1. How do I add a Stock to my Watch List? 2. How often is the Watch List Updated? 3. How do I access the Quote Feature for a specific Stock? 4. How do I customize the look of the Summary Page? 5. How do
More informationTable of Contents. 1.0 Terms and Conditions of Use Accessing Your HilltopSecurities Account Information... 3
Table of Contents 1.0 Terms and Conditions of Use... 2 2.0 Accessing Your HilltopSecurities Account Information... 3 3.0 Accessing Your HilltopSecurities Account from the Quicken Download Page... 3 4.0
More informationAP Calculus AB Summer Review Packet
AP Calculus AB Summer Review Packet Mr. Burrows Mrs. Deatherage 1. This packet is to be handed in to your Calculus teacher on the first day of the school year. 2. All work must be shown on separate paper
More informationNasdaq ISE Trade Combo Feed Specification VERSION AUGUST 23, 2017
Nasdaq ISE Trade Combo Feed Specification VERSION 1.0.1 AUGUST 23, 2017 Nasdaq ISE Trade Combo Feed Version 1.01 Nasdaq ISE Trade Combo Feed Table of Contents 1. Overview 3 2. Architecture 4 3. Data Types
More informationKBC Securities Trader
KBC Securities Trader Welcome! This guide introduces you to the main functionality and possibilities of KBC Securities Trader. For more detailed information on each window, press F1 for Help or right-click
More informationActivity Overview A basic introduction to the many features of the calculator function of the TI-Nspire
TI-Nspire Activity: An Introduction to the TI-Nspire Calculator Function By: Leigh T Baker Activity Overview A basic introduction to the many features of the calculator function of the TI-Nspire Concepts
More informationAlgebra 2 Notes Name: Section 8.4 Rational Functions. A function is a function whose rule can be written as a of. 1 x. =. Its graph is a, f x
Algebra Notes Name: Section 8. Rational Functions DAY ONE: A function is a function whose rule can be written as a of two polynomials. The parent rational function is f. Its graph is a, which has two separate
More informationSome issues with R It is command-driven, and learning to use it to its full extent takes some time and effort. The documentation is comprehensive,
R To R is Human R is a computing environment specially made for doing statistics/econometrics. It is becoming the standard for advanced dealing with empirical data, also in finance. Good parts It is freely
More informationTHE FRACTAL THE BREAKAWAY TRADE FRACTAL BREAKOUT
THE FRACTAL THE BREAKAWAY TRADE Many experienced traders say that making money in trading the markets is easy; what is difficult is keeping it. The pattern of all markets is that they spend most of their
More informationThe "R" Statistics library: Research Applications
Edith Cowan University Research Online ECU Research Week Conferences, Symposia and Campus Events 2012 The "R" Statistics library: Research Applications David Allen Edith Cowan University Abhay Singh Edith
More information3. parallel: (b) and (c); perpendicular (a) and (b), (a) and (c)
SECTION 1.1 1. Plot the points (0, 4), ( 2, 3), (1.5, 1), and ( 3, 0.5) in the Cartesian plane. 2. Simplify the expression 13 7 2. 3. Use the 3 lines whose equations are given. Which are parallel? Which
More information3.1. 3x 4y = 12 3(0) 4y = 12. 3x 4y = 12 3x 4(0) = y = x 0 = 12. 4y = 12 y = 3. 3x = 12 x = 4. The Rectangular Coordinate System
3. The Rectangular Coordinate System Interpret a line graph. Objectives Interpret a line graph. Plot ordered pairs. 3 Find ordered pairs that satisfy a given equation. 4 Graph lines. 5 Find x- and y-intercepts.
More informationData Mining. ❷Chapter 2 Basic Statistics. Asso.Prof.Dr. Xiao-dong Zhu. Business School, University of Shanghai for Science & Technology
❷Chapter 2 Basic Statistics Business School, University of Shanghai for Science & Technology 2016-2017 2nd Semester, Spring2017 Contents of chapter 1 1 recording data using computers 2 3 4 5 6 some famous
More informationA. Lesson Context. B. Lesson Objectives. C. Fast Five (Skills Review Focus)
A. Lesson Context BIG PICTURE of this UNIT: How & why do we build NEW knowledge in Mathematics? What NEW IDEAS & NEW CONCEPTS can we now explore with specific references to QUADRATIC FUNCTIONS? How can
More informationConditional Volatility Estimation by. Conditional Quantile Autoregression
International Journal of Mathematical Analysis Vol. 8, 2014, no. 41, 2033-2046 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ijma.2014.47210 Conditional Volatility Estimation by Conditional Quantile
More informationSection 2.3: Simple Linear Regression: Predictions and Inference
Section 2.3: Simple Linear Regression: Predictions and Inference Jared S. Murray The University of Texas at Austin McCombs School of Business Suggested reading: OpenIntro Statistics, Chapter 7.4 1 Simple
More informationFocus On: The Deal Pipeline
By Penny Crossland This article was published in December 2009 and was accurate as of that date. Used under license agreement with Free Pint Limited; all other rights reserved. For further information
More informationZagTrader Mobile User Guide Version 1.01
ZagTrader Mobile User Guide Version 1.01 Ghassan Al Masri ZagTrader 6/9/2014 Table of Content - Installation... 3 - Watchlist... 5 - Quote... 6 *Overview... 7 *Chart... 8 *News... 9 *Gainers/Losers...
More informationDealing with Data in Excel 2013/2016
Dealing with Data in Excel 2013/2016 Excel provides the ability to do computations and graphing of data. Here we provide the basics and some advanced capabilities available in Excel that are useful for
More informationSpecialized Quote Interface (SQF) VERSION 6.4N October 31, 2017
Specialized Quote Interface (SQF) VERSION 6.4N October 31, 2017 Nasdaq Options Market Nasdaq PHLX Nasdaq BX Options Specialized Quote Interface Version 6.4n Version 6.4n Page 1 Table of Contents 1 Overview...
More informationVertical and Horizontal Translations
SECTION 4.3 Vertical and Horizontal Translations Copyright Cengage Learning. All rights reserved. Learning Objectives 1 2 3 4 Find the vertical translation of a sine or cosine function. Find the horizontal
More informationQuadratic Equations Group Acitivity 3 Business Project Week #5
MLC at Boise State 013 Quadratic Equations Group Acitivity 3 Business Project Week #5 In this activity we are going to further explore quadratic equations. We are going to analyze different parts of the
More informationUTP Snap-Shot 1.0 Version 1.0 Published October 2018
UTP Snap-Shot 1.0 Version 1.0 Published October 2018 Table of Contents 1 Overview... 3 2 Architecture... 3 3 Data Types... 5 4 Message Formats... 6 4.1 Control Message... 7 4.2 Issue Symbol Directory Message
More informationProperties of a Function s Graph
Section 3.2 Properties of a Function s Graph Objective 1: Determining the Intercepts of a Function An intercept of a function is a point on the graph of a function where the graph either crosses or touches
More informationTexas Tech University Health Sciences Center Finance & Administration. Document Search
Texas Tech University Health Sciences Center Finance & Administration Document Search This chapter will focus on finding documents within TechBuy using the Document Search feature. The Document Search
More informationQuadratic Functions. Full Set of Notes. No Solutions
Quadratic Functions Full Set of Notes No Solutions Graphing Quadratic Functions The graph of a quadratic function is called a parabola. Applications of Parabolas: http://www.doe.virginia.gov/div/winchester/jhhs/math/lessons/calc2004/appparab.html
More informationGSE Algebra 1 Name Date Block. Unit 3b Remediation Ticket
Unit 3b Remediation Ticket Question: Which function increases faster, f(x) or g(x)? f(x) = 5x + 8; two points from g(x): (-2, 4) and (3, 10) Answer: In order to compare the rate of change (roc), you must
More informationTechnical Report of ISO/IEC Test Program of the M-DISC Archival DVD Media June, 2013
Technical Report of ISO/IEC 10995 Test Program of the M-DISC Archival DVD Media June, 2013 With the introduction of the M-DISC family of inorganic optical media, Traxdata set the standard for permanent
More informationEstimating survival from Gray s flexible model. Outline. I. Introduction. I. Introduction. I. Introduction
Estimating survival from s flexible model Zdenek Valenta Department of Medical Informatics Institute of Computer Science Academy of Sciences of the Czech Republic I. Introduction Outline II. Semi parametric
More informationAlgebra 2 Chapter Relations and Functions
Algebra 2 Chapter 2 2.1 Relations and Functions 2.1 Relations and Functions / 2.2 Direct Variation A: Relations What is a relation? A of items from two sets: A set of values and a set of values. What does
More informationMEASURES OF CENTRAL TENDENCY
11.1 Find Measures of Central Tendency and Dispersion STATISTICS Numerical values used to summarize and compare sets of data MEASURE OF CENTRAL TENDENCY A number used to represent the center or middle
More informationProperties of Quadratic functions
Name Today s Learning Goals: #1 How do we determine the axis of symmetry and vertex of a quadratic function? Properties of Quadratic functions Date 5-1 Properties of a Quadratic Function A quadratic equation
More informationStockFinder 5 User Guide
StockFinder 5 User Guide Updated April 2010 STOCKFINDER 5 USER GUIDE Worden Brothers, Inc. www.worden.com Five Oaks Office Park 4905 Pine Cone Drive Durham, NC 27707 STOCKFINDER 5 USER GUIDE 2010 Worden
More informationCompatible with TradeStation 9.5! for TradeStation
Compatible with TradeStation 9.5! for TradeStation BAR ANALYZER Version 4.0 Market Indicator The BAR ANALYZER allows traders to visualize the forces inside a price bar. Using advanced concepts that include
More information2-3 Graphing Rational Functions
2-3 Graphing Rational Functions Factor What are the end behaviors of the Graph? Sketch a graph How to identify the intercepts, asymptotes and end behavior of a rational function. How to sketch the graph
More informationSection 2.1: Intro to Simple Linear Regression & Least Squares
Section 2.1: Intro to Simple Linear Regression & Least Squares Jared S. Murray The University of Texas at Austin McCombs School of Business Suggested reading: OpenIntro Statistics, Chapter 7.1, 7.2 1 Regression:
More informationServer/Database Administration/Tape Backup and Hosting Services. Service Provider: Office of Information Technology Infrastructure Technology Services
OIT Service Level Agreement (SLA) Server/Database Administration/Tape Backup and Hosting Services Service Provider: Office of Information Technology Infrastructure Technology Services 1. Service Level
More information2-5 Rational Functions
Find the domain of each function and the equations of the vertical or horizontal asymptotes, if any. 3. f (x) = The function is undefined at the real zeros of the denominator b(x) = (x + 3)(x 4). The real
More informationPBOT Data Distribution System
FINANCIAL AUTOMATION PBOT Data Distribution System Vendor Interface Specification Document No.: OTS -04-668-SPEC Revision History Version Date Comments Approval Draft 5/25/05 Draft Note: This document
More informationAutomated Trading with MATLAB Stuart Kozola Computational Finance
Automated Trading with MATLAB Stuart Kozola Computational Finance 2012 The MathWorks, Inc. 1 Challenges when developing and implementing trading strategies and systems Increasing complexity More data More
More information2. Functions, sets, countability and uncountability. Let A, B be sets (often, in this module, subsets of R).
2. Functions, sets, countability and uncountability I. Functions Let A, B be sets (often, in this module, subsets of R). A function f : A B is some rule that assigns to each element of A a unique element
More informationMAT 124 Solutions Sample Questions for Exam 2
MAT 124 Solutions Sample Questions for Exam 2 Note: Most of these results can be checked graphically. 1. a) The slope of l " is computed as follows: m " = & '(& ) * ' (* ) = +(, -(. = /, = 2. So the equation
More informationApplied Multivariate Analysis
Department of Mathematics and Statistics, University of Vaasa, Finland Spring 2017 Choosing Statistical Method 1 Choice an appropriate method 2 Cross-tabulation More advance analysis of frequency tables
More informationImportant!!! First homework is due on Monday, September 26 at 8:00 am.
Important!!! First homework is due on Monday, September 26 at 8:00 am. You can solve and submit the homework on line using webwork: http://webwork.dartmouth.edu/webwork2/m3cod/. If you do not have a user
More informationUnit Title Key Concepts Vocabulary CCS
Unit Title Key Concepts Vocabulary CCS Unit 1 Writing and Evaluating s Unit 2 Writing and Solving Equations s and Equations Write numerical expressions Evaluate numerical expressions Write algebraic expressions
More informationPart I. Fill in the blank. 2 points each. No calculators. No partial credit
Math 108 (105) Final Exam Page 1 Spring 2015 Part I. Fill in the blank. 2 points each. No calculators. No partial credit 1) Fill in the blank a) 2 8 h) 5 0 21 4 b) 5 7 i) 8 3 c) 2 3 = j) 2 7 d) The additive
More informationXL2B: Excel2013: Model Trendline Multi 1/24/2018 V0M. Process Advice.
XL2B: Excel2013: Model Trendline Multi 1/24/2018 V0M 1 Model Using Trendline Multiple Models in Excel 2013 by Milo Schield Member: International Statistical Institute US Rep: International Statistical
More informationEstimating Asset Pricing Models by GMM using EViews
Estimating Asset Pricing Models by GMM using EViews Benedikt Heid Department of Statistics, Econometrics, and Empirical Economics Professor Joachim Grammig) University of Tübingen June 2005 Summary: This
More information14.2 The Regression Equation
14.2 The Regression Equation Tom Lewis Fall Term 2009 Tom Lewis () 14.2 The Regression Equation Fall Term 2009 1 / 12 Outline 1 Exact and inexact linear relationships 2 Fitting lines to data 3 Formulas
More informationCCSSM Curriculum Analysis Project Tool 1 Interpreting Functions in Grades 9-12
Tool 1: Standards for Mathematical ent: Interpreting Functions CCSSM Curriculum Analysis Project Tool 1 Interpreting Functions in Grades 9-12 Name of Reviewer School/District Date Name of Curriculum Materials:
More information2014 Forecast Results
214 Forecast Results Duration Forecast Result* Accuracy US GDP 15 $16.98 Trillion $16.345 Trillion 98.5% US Ind. Prod. 13 11.5 (12MMA) 14.2 97.3% EU Ind. Prod. 14 1.6 (12MMA) 11.6 99.% Canada Ind Prod
More informationChapter 9 Review. By Charlie and Amy
Chapter 9 Review By Charlie and Amy 9.1- Inverse and Joint Variation- Explanation There are 3 basic types of variation: direct, indirect, and joint. Direct: y = kx Inverse: y = (k/x) Joint: y=kxz k is
More informationEXECUTIVE SUMMARY HOUSING COMMISSION EXECUTIVE SUMMARY SHEET
ITEM 100 EXECUTIVE SUMMARY HOUSING COMMISSION EXECUTIVE SUMMARY SHEET DATE: July 28, 2017 COUNCIL DISTRICT(S): Citywide ORIGINATING DEPARTMENT: Information Technology CONTACT/PHONE NUMBER: Beto Juarez,
More informationQuadratic Equations. Learning Objectives. Quadratic Function 2. where a, b, and c are real numbers and a 0
Quadratic Equations Learning Objectives 1. Graph a quadratic function using transformations. Identify the vertex and axis of symmetry of a quadratic function 3. Graph a quadratic function using its vertex,
More informationSection 2.1 Graphs. The Coordinate Plane
Section 2.1 Graphs The Coordinate Plane Just as points on a line can be identified with real numbers to form the coordinate line, points in a plane can be identified with ordered pairs of numbers to form
More informationAlgebra 1, 4th 4.5 weeks
The following practice standards will be used throughout 4.5 weeks:. Make sense of problems and persevere in solving them.. Reason abstractly and quantitatively. 3. Construct viable arguments and critique
More informationComparing Fitted Models with the fit.models Package
Comparing Fitted Models with the fit.models Package Kjell Konis Acting Assistant Professor Computational Finance and Risk Management Dept. Applied Mathematics, University of Washington History of fit.models
More informationLevel 3 Mathematical Studies Assumed knowledge MATHEMATICS
AQA Guidance Practice Papers - Set 1- Teacher Booklet Level 3 Mathematical Studies Assumed knowledge MATHEMATICS The assumed prior knowledge for AQA s Level 3 Mathematical Studies is all of the content
More informationAuthor: Information Services Date: 6 March 2018 Version: Version 1.0 Status: Final
Integrated Trading and Clearing (ITaC) Non-Live Market Data Subscriber User Acceptance Test Plan For the Equity Derivative and Currency Derivative Markets Author: Information Services Date: 6 March 2018
More informationGetting to Know Your Data
Chapter 2 Getting to Know Your Data 2.1 Exercises 1. Give three additional commonly used statistical measures (i.e., not illustrated in this chapter) for the characterization of data dispersion, and discuss
More information3.1 INTRODUCTION TO THE FAMILY OF QUADRATIC FUNCTIONS
3.1 INTRODUCTION TO THE FAMILY OF QUADRATIC FUNCTIONS Finding the Zeros of a Quadratic Function Examples 1 and and more Find the zeros of f(x) = x x 6. Solution by Factoring f(x) = x x 6 = (x 3)(x + )
More informationISE, GEMX & MRX Top Combo Quote Feed VERSION 1.0 AUGUST 23, 2017
ISE, GEMX & MRX Top Combo Quote Feed VERSION 1.0 AUGUST 23, 2017 Top Combo Quote Feed Version 1.0 Nasdaq ISE Top Combo Quote Feed Nasdaq ISE Glimpse for Top Combo Quote Feed Table of Contents 1. Overview
More informationExcel Primer CH141 Fall, 2017
Excel Primer CH141 Fall, 2017 To Start Excel : Click on the Excel icon found in the lower menu dock. Once Excel Workbook Gallery opens double click on Excel Workbook. A blank workbook page should appear
More informationAn Introduction to DLPAL
An Introduction to DLPAL Deep Learning Price Action Lab Manual Introduction Deep Learning Price Action Lab (DLPAL) identifies strategies in historical price data that fulfill user-defined performance statistics
More informationExample 1 of panel data : Data for 6 airlines (groups) over 15 years (time periods) Example 1
Panel data set Consists of n entities or subjects (e.g., firms and states), each of which includes T observations measured at 1 through t time period. total number of observations : nt Panel data have
More informationProquote Web User Guide
Proquote Web User Guide Version 1.0 07/03/2013 Table of Contents 1 Accessing Proquote Web... 3 2 Proquote Web Homepage... 3 2.1 Homepage Contents... 3 3 List Menu... 4 3.1 Stocks... 4 3.1.1 Stock Detail...
More informationSTA 570 Spring Lecture 5 Tuesday, Feb 1
STA 570 Spring 2011 Lecture 5 Tuesday, Feb 1 Descriptive Statistics Summarizing Univariate Data o Standard Deviation, Empirical Rule, IQR o Boxplots Summarizing Bivariate Data o Contingency Tables o Row
More informationA Modified Approach for Detection of Outliers
A Modified Approach for Detection of Outliers Iftikhar Hussain Adil Department of Economics School of Social Sciences and Humanities National University of Sciences and Technology Islamabad Iftikhar.adil@s3h.nust.edu.pk
More informationAccess through Banner Forms:
Viewing Documents Scanned by Finance and Administration There are two ways to view scanned documents in BDM. You can access the scanned documents from Banner Forms, or by directly accessing BDM. Before
More information