Spelling suggestions: "subject:"wow zones industrial average"" "subject:"wow jones industrial average""
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Do individual investors react to non-informative events?Lin, Ching-Ting. January 2009 (has links)
Thesis (Ph.D.)--University of Delaware, 2008. / Principal faculty advisor: Jay F. Coughenour, Dept. of Finance. Includes bibliographical references.
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PÅVERKAR DONALD J. TRUMPS TWEETS ANGÅENDE HANDELSKRIGET MELLAN USA OCH KINA DJIA? : En kvantitativ studie om Donald J. Trumps tweets angående handelskriget mellan USA och Kina påverkar Dow Jones Industrial Average.Tandan, Isabelle January 2019 (has links)
Etableringen av sociala medier har skapat ett nytt medielandskap där beslutsfattare och politiker kan kommunicera direkt med allmänheten. Donald J. Trump är en politiker som valt att kom- municera med sin publik via twitter. Han misstror traditionell medias förmåga att objektivt återge hans utsagor och underminerar journalistiken genom att använda begrepp som ’fake news’. Att en amerikansk presidenten twittrar är inget nytt fenomen men frekvensen och reto- riken i Donald J. Trumps tweets är något nytt. Dessa nya medievanor kan få konsekvenser inom flera sektorer inte minst på finansmarknaden. Vilken påverkan hans twittrande har på aktiemarknaden är frågan som behandlas i denna uppsats. Uppsatsens syfte är således att studera om Donald J. Trumps tweets påverkar Dow Jones In- dustrial Average (DJIA). Studien avgränsas till handelskriget mellan USA och Kina. Vidare avgränsas studien till en tidsperiod om ett år, från februari 2018 till februari 2019. Uppsatsen studerar enbart USA:s president Donald J. Trumps tweets. Studien genomförs genom event study som jämför normal returns innan händelsen (tweet) med den abnormal returns efter. Tweetsen är utvalda baserat på ett antal nyckelord som testas inom varaktighetsfönster på 5, 10, 15 och 20 minuter, vilket ger uttryck för hur länge effekten varar. Resultatet visar att 8 av 12 tweets har en statistisk signifikant påverkan på DJIA på antingen 1%, 5% eller 10% signifikansnivå. Studien tyder således att presidentens twittrande påverkar DJIA. Slutsatsen av studien är att Donald J. Trumps tweets utgör information som påverkar värde- ringen på aktiemarknaden (DJIA) och dess avkastning. Därför kan konstateras att sociala me- dier, såsom Twitter, är informationskällor som är högst väsentliga att följa för aktörer på finansmarknaden. Vidare får resultatet implikationer för rådande lagstiftning och regleringar, något som redan diskuteras i USA. Studier på området har varit svåra att finna varför vidare forsk- ning på området vore önskvärt.
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Can Online Sentiment Help Predict Dow Jones Industrial Average Returns?Krumwiede, Aria K. 01 January 2012 (has links)
In this paper, we explore the relationship between a Global Mood Time Series, provided by Wall Street Birds, and the Dow Jones Industrial Average (DJIA) from April 2011 to December 2011. My econometric results show that there is no long run equilibrium relationship between the level of global mood and the level of the DJIA. These results apply to the whole period, as well as in the six-month subperiods. Furthermore, daily changes in global mood do not Granger cause DJIA returns. However, changes in global mood do appear to be useful in forecasting the volatility of the DJIA, and my results suggest that GARCH models of volatility of large-cap indexes, and potentially the market as a whole, could be strengthened by including online sentiment measures of Big Data. Measuring global mood, and quantifying its impacts, can potentially lead to superior portfolio construction as forecasting volatility is an important input in portfolio optimization. The results, as a whole, suggest that Big Data can have important implications for investment decision-making.
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Economic and financial indexesWhite, Alan G. 11 1900 (has links)
This thesis examines the theoretical underpinnings and practical construction of select economic
and financial indexes. Such indexes are used for a variety of purposes, including the
measurement of inflation, portfolio return performance, and firm productivity.
Chapter 1 motivates interest in economic and financial indexes and introduces the principal
ideas in the thesis.
Chapter 2 focuses on one potential source of bias in the Canadian consumer price index
(CPI) that arises from the emergence of large discount/warehouse stores—the so-called outlet
substitution bias. Such outlets have gained market share in Canada in recent years, but
current CPI procedures fail to capture the declines in average prices that consumers enjoy
when they switch to such outlets. Unrepresentative sampling, and the fact that discount
stores often deliver lower rates of price increase can further bias the CPI. Bias estimates
for some elementary indexes are computed using data from Statistics Canada's CPI production
files for the province of Ontario. It is shown that the effect on the Canadian CPI of
inappropriately accounting for such discount outlets can be substantial.
Another area in which indexes are frequently used is the stock market. Several stock
market indexes exist, including those produced by Dow Jones and Company, Standard and
Poor's Corporation, Frank Russell and Company, among others. These indexes differ in two
fundamental respects: their composition and their method of computation—with important
implications for their usage and interpretation. Chapter 3 introduces the concept of a stock
index by asking what, in fact a stock market index is—this is tantamount to considering the
purpose for which the index is intended, since stock indexes should be constructed according
to their usage. Because stock indexes are most commonly used as measures of returns on
portfolios, the main considerations in constructing such return indexes are examined.
Chapter 4 uses the Dow Jones Industrial Average (DJIA) as a case study to examine
its properties as a return index. It is shown that the DJIA is not the return on a market
portfolio consisting of its thirty component stocks: in fact the DJIA measures the return
performance on a very particular (and unusual) investment strategy, a fact that is not well
understood by institutional investors. An examination of some other popular stock indexes
shows that they all differ in their computational formula and that each is consistent with a
particular investment strategy. Numerical calculations reveal that the return performance of
the DJIA can vary considerably with the choice of basic index number formula, particularly
over shorter time horizons.
Given the numerous ways of constructing stock market return indexes, the user is left to
determine which is 'best' in some sense. The choice of an appropriate (or 'best') formula for
a stock market index is formally addressed in chapter 5. The test or axiomatic approach to
standard bilateral index number theory as in Eichhorn & Voeller (1983), Diewert (1993a),
and Balk (1995) is adapted here. A number of a priori desirable properties (or axioms) are
proposed for a stock index whose purpose is to measure the gross return on a portfolio of
stocks. It is shown that satisfaction of a certain subset of axioms implies a definite functional
form for a stock market return index.
Chapter 6 evaluates the various stock indexes is use today in terms of their usefulness
as measures of gross returns on portfolios. To this end the axioms developed in chapter 5
are used to provide a common evaluative framework, in the sense that some of the indexes
satisfy certain axioms while others do not. It is shown that the shortcomings of the DJIA as
a measure of return arise from its failure to satisfy a number of the basic axioms proposed.
Notwithstanding this, each index corresponds to a different investment strategy. Thus, when
choosing an index for benchmarking purposes an investor should select one which closely
matches his/her investment strategy—a choice that cannot be made by appealing to axioms
alone.
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The Dow theory a historical test as interpreted by Richard Russell / c Stefan P. Sideris.Sideris, Stefan P. January 2008 (has links) (PDF)
Thesis (M.B.A.)--University of North Carolina Wilmington, 2008. / Title from PDF title page (viewed May 27, 2009) Includes bibliographical references (p. 42-44)
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Economic and financial indexesWhite, Alan G. 11 1900 (has links)
This thesis examines the theoretical underpinnings and practical construction of select economic
and financial indexes. Such indexes are used for a variety of purposes, including the
measurement of inflation, portfolio return performance, and firm productivity.
Chapter 1 motivates interest in economic and financial indexes and introduces the principal
ideas in the thesis.
Chapter 2 focuses on one potential source of bias in the Canadian consumer price index
(CPI) that arises from the emergence of large discount/warehouse stores—the so-called outlet
substitution bias. Such outlets have gained market share in Canada in recent years, but
current CPI procedures fail to capture the declines in average prices that consumers enjoy
when they switch to such outlets. Unrepresentative sampling, and the fact that discount
stores often deliver lower rates of price increase can further bias the CPI. Bias estimates
for some elementary indexes are computed using data from Statistics Canada's CPI production
files for the province of Ontario. It is shown that the effect on the Canadian CPI of
inappropriately accounting for such discount outlets can be substantial.
Another area in which indexes are frequently used is the stock market. Several stock
market indexes exist, including those produced by Dow Jones and Company, Standard and
Poor's Corporation, Frank Russell and Company, among others. These indexes differ in two
fundamental respects: their composition and their method of computation—with important
implications for their usage and interpretation. Chapter 3 introduces the concept of a stock
index by asking what, in fact a stock market index is—this is tantamount to considering the
purpose for which the index is intended, since stock indexes should be constructed according
to their usage. Because stock indexes are most commonly used as measures of returns on
portfolios, the main considerations in constructing such return indexes are examined.
Chapter 4 uses the Dow Jones Industrial Average (DJIA) as a case study to examine
its properties as a return index. It is shown that the DJIA is not the return on a market
portfolio consisting of its thirty component stocks: in fact the DJIA measures the return
performance on a very particular (and unusual) investment strategy, a fact that is not well
understood by institutional investors. An examination of some other popular stock indexes
shows that they all differ in their computational formula and that each is consistent with a
particular investment strategy. Numerical calculations reveal that the return performance of
the DJIA can vary considerably with the choice of basic index number formula, particularly
over shorter time horizons.
Given the numerous ways of constructing stock market return indexes, the user is left to
determine which is 'best' in some sense. The choice of an appropriate (or 'best') formula for
a stock market index is formally addressed in chapter 5. The test or axiomatic approach to
standard bilateral index number theory as in Eichhorn & Voeller (1983), Diewert (1993a),
and Balk (1995) is adapted here. A number of a priori desirable properties (or axioms) are
proposed for a stock index whose purpose is to measure the gross return on a portfolio of
stocks. It is shown that satisfaction of a certain subset of axioms implies a definite functional
form for a stock market return index.
Chapter 6 evaluates the various stock indexes is use today in terms of their usefulness
as measures of gross returns on portfolios. To this end the axioms developed in chapter 5
are used to provide a common evaluative framework, in the sense that some of the indexes
satisfy certain axioms while others do not. It is shown that the shortcomings of the DJIA as
a measure of return arise from its failure to satisfy a number of the basic axioms proposed.
Notwithstanding this, each index corresponds to a different investment strategy. Thus, when
choosing an index for benchmarking purposes an investor should select one which closely
matches his/her investment strategy—a choice that cannot be made by appealing to axioms
alone. / Arts, Faculty of / Vancouver School of Economics / Graduate
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Evaluation of a Portfolio in Dow Jones Industrial Average Optimized by Mean-Variance Analysis / Utvärdering av en portfölj i Dow Jones Industrial Average optimerad genom mean-variance analysisStrid, Alexander, Liu, Daniel January 2020 (has links)
This thesis evaluates the mean-variance analysis framework by comparing the performance of an optimized portfolio consisting of stocks from the Dow Jones Industrial Average to the performance of the Dow Jones Industrial Average index itself. The results show that the optimized portfolio performs better than the corresponding index when evaluated on the period between 2015 and 2019. However, the variance of the returns are high and therefore it is difficult to determine if mean-variance analysis performs better than its corresponding index in the general case. Furthermore, it is shown that individual stocks can still influence the movement of an optimized portfolio significantly, even though the model is supposed to diversify firm-specific risk. Thus, the authors recommend modifying the model by restricting the amount that is allowed to be invested in a single stock, if one wishes to apply mean-variance analysis in reality. To be able to draw further conclusions, more practical research within the subject needs to be done. / Denna uppsats utvärderar ramverket ”mean-variance analysis” genom att jämföra prestandan av en optimerad portfölj bestående av aktier från Dow Jones Industrial Average med prestandan av indexet Dow Jones Industrial Average självt. Resultaten visar att att den optimerade portföljen presterar bättre än motsvarande index när de utvärderas på perioden 2015 till 2019. Dock är variansen av avkastningen hög och det är därför svårt att bedöma om mean-variance analysis generellt sett presterar bättre än sitt motsvarande index. Vidare visas det att individuella aktier fortfarande kan påverka den optimerade portföljens rörelser, fastän modellen antas diversifiera företagsspecifik risk. På grund av detta rekommenderar författarna att modifiera modellen genom att begränsa mängden som kan investeras i en individuell aktie, om man önskar att tillämpa mean-variance analysis i verkligheten. För att kunna dra vidare slutsatser så krävs mer praktisk forskning inom området.
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