Market states: A new understanding
MMarket states: A new understanding
Hirdesh K. Pharasi , Eduard Seligman , and Thomas H. Seligman Instituto de Ciencias F´ısicas, Universidad Nacional Aut ´onoma de M ´exico, Cuernavaca-62210, M ´exico Swiss Air Trainer S.A. Payerne, Vaud, Switzerland Centro Internacional de Ciencias, Cuernavaca-62210, M ´exico * [email protected] ABSTRACT
We present the clustering analysis of the financial markets of S&P 500 (USA) and Nikkei 225 (JPN) markets over a period of2006-2019 as an example of a complex system. We investigate the statistical properties of correlation matrices constructedfrom the sliding epochs. The correlation matrices can be classified into different clusters, named as “market states” basedon the similarity of correlation structures. We cluster the S&P 500 market into four and Nikkei 225 into six market states byoptimizing the value of intracluster distances. The market shows transitions between these market states and the statisticalproperties of the transitions to critical market states can indicate likely precursors to the catastrophic events. We also analyzethe same clustering technique on surrogate data constructed from average correlations of market states and the fluctuationsarise due to the white noise of short time series. We use the correlated Wishart orthogonal ensemble for the construction ofsurrogate data whose average correlation equals the average of the real data.
Introduction
Some time ago a proposal to consider correlation matrices to define market states was put forward and found some resonance .A noise suppression technique was applied directly to the correlation matrices and thereafter a clustering tree was establishedand eight market states were established with a certain degree of arbitrariness. Since then ideas from this paper have resonatedinside and outside our group. Alternative techniques to handle non-stationary time series through spectral properties have beendeveloped in . Other approaches to behave at critical or catastrophic moments have been put forward by various authors .More recently two of us participated in a proposal to improve the choice of cluster by simultaneously optimizing the clusteringprocess and the noise suppression parameter. We expanded the analysis from the original study of S&P 500 market by includingthe Nikkei 225 market. The results were essentially a dynamics consistent with the master equation and transitions to thehighest correlation cluster mainly from the previous one, which thus allowed to determine possible precursors. The studyhad the disadvantage of counting to a large extent old data of last century. We here propose to refine the methods further andto improve the criteria to reduce the number of precursors in a given time period and also to get a better understanding ofthe transition dynamics between states. We also address a basic weakness of the previous studies that implied roughly thatthe average correlation or equivalently the largest eigenvalues of the correlation matrix, largely determine to which cluster agiven correlation matrix belongs. In the Japanese market we found an interesting essential non-linear evolution, as well as asmaller basis of precursor states, thus reducing the risk base. Also we develop a technique of surrogate data based on correlatedorthogonal Wishart ensembles (CWOE) draping white noise around the average correlation of each state. This gives a strongindication as to the interrelation of correlation and noise that we see.In the present work we shall make actual connection to this matrix by noise to the correlation matrices pertaining to themarket states by forming a correlated Wishart ensemble from the average correlation matrix representing each market state.We shall find that the clusters obtained for each market state are reasonably related to the original clusters if we chose the sametime frame for the added noise. We extend the analysis to the Japanese market to confirm our findings as far as the validityof the clustering is concerned and we finally present the result, that a highly turbulent market-phase started near the LehmanBrother crash and terminated around 2016. The latter result is more clear for the US market than for the Japanese market. Thislikely results from two facts: The data basis (number of stocks) is smaller for the Japanese market and the Japanese market hassix states. Both properties make more noise. On the other hand the fact that two intermediate states of the Japanese marketdisplay practically the same average correlation eliminates the disturbing fact mentions in that the average correlation actuallydominates the definition of market states to large extent. We thus give a solid base to the concept of market states, as we show,how the clusters, i.e., the market states are made up from average correlations and noise.1 a r X i v : . [ q -f i n . C P ] N ov ethodology and results We begin with the identification of market states as a clusters of similar correlation matrices. We have used the adjusted dailyclosure prices of the stocks making up the two indices S&P 500 of US market (USA) and Nikkei 225 of Japanese market (JPN).We have considered N =
350 stocks of the S&P 500 index and N =
156 stocks of the Nikkei 225 index traded in the 14-yearperiod from January 2006 to December 2019 which correspond to T = T = N =
368 stocks of the S&P 500 index and N =
173 stocks of the Nikkei 225 index for 13-year period from January2006 to December 2018 which correspond to T = T = r i ( τ ) of stock i : C i j ( τ ) = ( (cid:104) r i r j (cid:105) − (cid:104) r i (cid:105)(cid:104) r j (cid:105) ) / σ i σ j , where the epoch average (cid:104) . . . (cid:105) and the standard deviations σ are computed over that epoch of size 20 days with i , j = , ..., N and τ is the end date of the epoch using daily returns. Notethat we do not compensate for weekends or holidays! However, the short time series the correlation matrices become highlysingular . We use the power map method , for noise-suppression. In this method, a nonlinear distortion is givento each cross-correlation coefficient within an epoch by: C i j = sign ( C i j ) | C i j | + ε , where ε ∈ ( , ) is the noise-suppressionparameter. We then study the evolution of the cross-correlation structures C ( τ ) of returns for epochs of 20 days with 10-dayoverlap. (a)(b) Figure 1.
Temporal evolution of ( a ) S&P 500 and ( b ) Nikkei 225 markets over a period of 2006-2019. The returns of the twomarket indices R ( τ ) as well as the corresponding average correlations of the stock returns µ ( τ ) are shown in the plots. a) (b)(c) (d) Figure 2.
Classification of market states based on optimal intra cluster distances for S&P 500 and Nikkei 225. The measure ofthe intra cluster distance d intra calculated for different number of clusters is shown in (a) and (b) for S&P 500 and Nikkei 225,respectively. The three dimensional (3D) k -means clustering is performed on 351 noise-suppressed correlation frames of (c)S&P 500 and 344 noise-suppressed correlation frames of (d) Nikkei 225. Here, we use best 2D projection of 3D plots. Theerrorbar in the plot shows the deviations of the intra cluster distances calculated for 1000 different initial conditions. The plotsshow the minima of standard deviations at k = k = d intra measured for differnet noise-suppression parameters ε and shows minima at ε = . ε = . R ( τ ) and mean market correlation µ ( τ ) for S&P 500 and Nikkei 225,respectively. The rather different behavior of the two markets, which we will elaborate on later, already show in these simplemeasures.We now define similarity measure for two correlation matrices C ( τ ) and C ( τ ) evaluated at different time τ and τ by thedistance: ζ ( C , C (cid:48) ) ≡ (cid:104)| C i j ( τ ) − C i j ( τ ) |(cid:105) i j , where (cid:104) ... (cid:105) i j denotes the average over all components. The similarity matrices forS&P 500 and Nikkei 225 are shown in supplementary Figs. S2 and S3 for periods 2006 − − N ( N − ) / to reproduce the distances as well as possible with a given tolerance. In our case wecan do this by projection into a three dimensional (3D) space, which will allow visualization of projections or animated 3D a)(b) Figure 3.
Plot shows dynamics of the S&P 500 and Nikkei 225 markets. (a) Evolution of S&P 500 market through thetransitions between four different characterized states S , S , S S − S , S , S S S S − k -means procedure where we slightly improve the techniques of Ref. toshow the clustering of the recent data set. We use the noise suppressed correlation matrix as a starting point, and explicitlydiscard cluster numbers smaller than 4! As before we choose a noise-suppression, and a cluster number, which minimizes thevariance of the intracluster distances generated by the random selections involved. The results are seen in Figs. 2 (a, c) for theUS and Figs. 2 (b, d) for the Japanese market using ε = . .
3, respectively. The differences mentioned above concerningindices and average correlations now become much more striking. Compared to earlier data, the Japanese market shows onemore cluster and while the clusters 3 and 4 are not entirely clear in the older data set. Now we have six clusters for a shortertime period and we encounter the phenomenon that the clusters by no means are in a quasi-linear arrangement which lookssomewhat trivial, though it actually is not. Indeed we find that the alignment is along the average correlation, as already pointedout in the very first paper .Figs. 3 (a) and (b) show the evolution of S&P 500 through the transitions among these four clusters named as market states S , S , S , and S S , S , S S S S − µ ( S , S , S , S ) = ( . , . , . , . ) are lined up according to the cluster index S , S , S , S
4, while we get for the Japanese market µ ( S , S , S , S , S , S ) =( . , . , , , . , . , . ) , where states S S S S S → S = S → S =
1) are not visible in the bar plot Fig. 4 (b). What properties of the two markets can reflect this nature should be openfor discussion.If the differences in variances of the intracluster measure d intra are small, we suggest adding additional criteria relates toFig. 4, where bar-plots for absolute transition numbers (see, Fig. 4 (a) and (b)) and probability graphs for the dynamics (see,Fig. 4 (c) and (d)) of both markets are shown. On one hand select a clustering that minimizes transitions between clusters,which is particularly apparent for the split pathways to high correlations seen above for the Japanese market. On the otherhand a clustering that looks consistent with surrogate data, obtained as CWOE from the average correlation obtained for eachcluster, as we shall discuss now. In the supplementary material we again show that the probabilities are consistent with a masterequation as seen already in Ref. .We can consider that the average correlation matrix obtained for each cluster to large extent characterizes the states. This a) (b)(c) (d) Figure 4.
Bar plots show the transition counts (frequencies) of paired market states (MS) for (a) S&P 500 and (b) Nikkei 225markets, respectively. The market show back and forth transitions between these states. Sometime the market remain in aparticular state for a long time and sometime it jumps shortly to another state and jumps back or evolve further. Transitions tothe nearby state are high probable. The networks plot of transition probabilities (see, Tables S1 and Tables S2) betweendifferent states of S&P 500 and Nikkei 225 are shown in (c) and (d) respectively. The probability of market state transition of S S S S (a) (b) (c) (d)(e) (f) (g) (h) Figure 5.
Plots of average correlation matrices of each market state of S&P 500 market and clustering analysis on surrogatedata (CWOE). (a-d) The mean correlation matrices Si evaluated over all the correlation frames correspond to each market state S , S , S , & S ε = S S ε = .
5. Red circles (k-means (CWOE)) in plot show the points of the first cluster of k -means clustering performed on CWOE (dots). Blue squares ( k -means (USA)) in the plot show the k -means clustering on theemperical data of S&P 500. k -means clustering on the CWOE and S&P 500 data shows a qualitative similar behavior. (f), (g),and (h) show the same for mean correlation matrices S , S
3, and S a) (b) (c)(d) (e) (f)(g) (h) (i)(j) (k) (l) Figure 6.
Plots of average correlation matrics of each market state of Nikkei 225 market and clustering analysis on surrogatedata (CWOE). (a-f) The mean correlation Si evaluated over all the frames correspond to each market state S , S , S , & S ε = S S ε = .
3. Red circles (k-means (CWOE)) in plot show the points of the first cluster of k -means clusteringperformed on CWOE (dots). Blue squares ( k -means (USA)) in the plot show the k -means clustering on the emperical data ofthe Nikkei market. k -means clustering on the CWOE and S&P 500 data shows a qualitative similar behavior. (g), (h), (i), (j),(k), and (l) show the same for mean correlation matrices S , S , S , S
5, and S
6, respectively.assumption implies that the cluster arises from noise around this average as given by a CWOE constructed from the averagecorrelation matrix. Average correlation matrices of each market state of the S&P 500 market are shown in Fig. 5 (a-d). Fig. 5(e), (f) (g), and (h) show the comparison of clustering analysis of surrogate data CWOE versus each state S , S , S
3, and S k -means clustering (red circles) on the surrogate data (black dots) shows quantitativelysimilar behavior with the market states S , S , S , S S , S , S S S nd S Discussions and Summary
We present improved criteria for the definition of clusters and thus market states and apply these to data sets starting in 2006and ending in 2019 for both the US and the Japanese stock market as represented by the stocks in the S&P 500 and the Nikkei225 indices. In general terms, we find that the turbulence that started in 2008 are awaiting to some extent. On the technical sidewe have improved the selection of precursors of market shocks and found a more detailed structure in the Japanese market thatwill allow a better understanding of market dynamics beyond the dominating influence of average correlation. This bringsthe idea of market states and their dynamics one step nearer to real market conditions and practical use. As shares are not theentire market at some point both derivative and bond prices as well as possible connections to intra-day trading will have to beincluded in such studies.
Acknowledgment
The authors are grateful to Anirban Chakraborti and Francois Leyvraz for their critical inputs and suggestions. H.K.P. is gratefulfor financial support provided by UNAM-DGAPA and CONACYT Proyecto Fronteras 952. T.H.S. and H.K.P. acknowledgesthe support grant by CONACyT through Project Fronteras 201 and UNAM-DGAPA-PAPIIT AG100819 and IN113620.
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UPPLEMENTARY INFORMATIONData considered for the analysis over a period of 2006-2019
Transition probabilities of the S&P 500 market between four states ( S , S , S , and S
4) and Nikkei 225 between six states( S , S , S S S
5, and S
6) over the period of 2006 − Table S1.
Transition probability of market states forUSA: Four market states (MS)S1 S2 S3 S4S1 0.703 0.243 0.036 0.018S2 0.232 0.455 0.313 0S3 0.077 0.33 0.484 0.11S4 0 0.111 0.222 0.667
Table S2.
Transition probability of market states for JPN: Six market states (MS)S1 S2 S3 S4 S5 S6S1 0.531 0.327 0.082 0.041 0.0 0.02S2 0.193 0.42 0.159 0.114 0.102 0.011S3 0.026 0.299 0.494 0.013 0.156 0.013S4 0.02 0.204 0.061 0.612 0.082 0.02S5 0.038 0.057 0.264 0.075 0.396 0.17S6 0.037 0.0 0.111 0.074 0.259 0.519 a) (b)(c) (d)
Figure S1.
Similarity measure ζ ( C , C (cid:48) ) among 351 correlation matrices of S&P 500 (top row) and 344 correlation matricesof Nikkei 225 (bottom row). (a) and (c) are without noise-suppression ε = ε = . ε = .
3, respectively. The evolution of the stock market over 14 years (2006–2019) can be understood from the similaritymatrices. Critical events of the markets in red-yellow strips are shown in the similarity matrices. S&P 500 market is lessvolatile than Nikkei 225 and shows less fluctuations after 2012.
Data considered for the analysis over a period of 2006-2018
We show here similar results with data running up to 2018. The results for Japanese market differ substantially indicating achange in the general market situation. The data considered for the analysis are given in Table S5 and Table S6 for the period2006-2018. a) (b)(c) (d)
Figure S2.
Plots of similarity measure ζ ( C , C (cid:48) ) of S&P 500 (top row) and Nikkei 225 (bottom row). Similarity measureamong 325 correlation matrices of S&P 500 (top row) and 320 of Nikkei 225 (bottom row): (a) and (c) without noise-reduction ε =
0, and (b) and (d) with noise-reduction ε = . ε = . a) (b)(c) (d) Figure S3.
Classification of market states using optimal intra cluster distances d intra for the S&P 500 and Nikkei 225. Themeasure of the d intra calculated for different number of clusters is shown in (a) and (b) for the S&P 500 and Nikkei 225 over aperiod of 2006-2018, respectively. The plots show the minima of standard deviations of intracluster distance σ ( d intra ) at k = k = k -means clusteringis used to cluster the S&P 500 and Nikkei 225 in four and five number of cluster, respectively. The alignment of the clusters isdifferent in two markets: clusters in (c) S&P 500 market are aligned along the average correlation and trivial but for (d) Nikkei225 market one more cluster forms between second and fourth cluster with nearly same average correlation. a)(b) Figure S4.
Market state dynamics of stock markets: (a) Evolution of S&P 500 market through the transitions among fourdifferent characterized states ( S , S , S
3, and S
4) for the period of 2006 − S , S , S S
4, and S
5) for the period of 2006 − a) (b)(c)(d) Figure S5.
Bar plots of transition counts (frequencies) of paired market states (MS) for S&P 500 and Nikkei 225 are shown in(a) and (b), respectively. The networks plots of transition probabilities between different states of (c) S&P 500 and (d) Nikkei225, respectively. The transition probability of market state transition of S S S S a) (b)(c) (d)(e) (f)(g) (h) Figure S6.
Average correlation of each market state of S&P 500 market and clustering of correlated Wishart orthogonalensembles (CWOE). (a-d) show mean correlation matrices Si evaluated over all the frames correspond to each market state S , S , S , & S ε = S S ε = .
7. Red circles ( k -means (CWOE)) in plot show the points of the S k -means clustering performed on CWOE (dots). Blue squares (k-means (USA)) in the plot show the k -means clustering on theemperical data of S&P 500. k -means clustering on the CWOE and S&P 500 data shows qualitatively similar behavior. (f), (g),and (h) show the same using mean correlation matrices S , S
3, and S a) (b) (c) (d) (e)(f) (g)(h) (i)(j) Figure S7.
Average correlation of each market state of Nikkei 225 market and clustering of correlated Wishart orthogonalensembles (CWOE). (a-e) show mean correlation matrices Si evaluated over all the frames correspond to each market state S , S , S , S , & S ε = S S ε = .
7. Red circles ( k -means (CWOE)) in plot show the points of the S k -means clustering performed on CWOE (dots). Blue squares (k-means (USA)) in the plot show the k -means clustering on theemperical data of S&P 500. k -means clustering on the CWOE and S&P 500 data shows qualitatively similar behavior. (g), (h),(i), and (j) show the same using mean correlation matrices S , S S
4, and S able S3. List of 350 stocks of USA S&P 500 considered for the analysis present over the period of 2006-2019.
S.No. Code Company Name Sector Abbrv .No. Code Company Name Sector Abbrv
51 WYNN Wynn Resorts Ltd Consumer Discretionary CD52 YUM Yum! Brands Inc Consumer Discretionary CD53 ADM Archer-Daniels-Midland Co Consumer Staples CS54 CAG Conagra Brands Consumer Staples CS55 CHD Church & Dwight Consumer Staples CS56 CL Colgate-Palmolive Consumer Staples CS57 CLX The Clorox Company Consumer Staples CS58 COST Costco Wholesale Corp. Consumer Staples CS59 CPB Campbell Soup Consumer Staples CS60 CVS CVS Health Consumer Staples CS61 EL Estee Lauder Cos. Consumer Staples CS62 GIS General Mills Consumer Staples CS63 HRL Hormel Foods Corp. Consumer Staples CS64 HSY The Hershey Company Consumer Staples CS65 K Kellogg Co. Consumer Staples CS66 KMB Kimberly-Clark Consumer Staples CS67 KO Coca-Cola Company (The) Consumer Staples CS68 KR Kroger Co. Consumer Staples CS69 MDLZ Mondelez International Consumer Staples CS70 MKC McCormick & Co. Consumer Staples CS71 MNST Monster Beverage Consumer Staples CS72 MO Altria Group Inc Consumer Staples CS73 PEP PepsiCo Inc. Consumer Staples CS74 PG Procter & Gamble Consumer Staples CS75 SJM JM Smucker Consumer Staples CS76 STZ Constellation Brands Consumer Staples CS77 SYY Sysco Corp. Consumer Staples CS78 TAP Molson Coors Brewing Company Consumer Staples CS79 TSN Tyson Foods Consumer Staples CS80 WBA Walgreens Boots Alliance Consumer Staples CS81 WMT Wal-Mart Stores Consumer Staples CS82 APA Apache Corporation Energy EG83 COG Cabot Oil & Gas Energy EG84 COP ConocoPhillips Energy EG85 CVX Chevron Corp. Energy EG86 DVN Devon Energy Corp. Energy EG87 EOG EOG Resources Energy EG88 FTI TechnipFMC Energy EG89 HAL Halliburton Co. Energy EG90 HES Hess Corporation Energy EG91 HP Helmerich & Payne Energy EG92 MRO Marathon Oil Corp. Energy EG93 NBL Noble Energy Inc Energy EG94 NOV National Oilwell Varco Inc. Energy EG95 OKE ONEOK Energy EG96 PXD Pioneer Natural Resources Energy EG97 SLB Schlumberger Ltd. Energy EG98 VLO Valero Energy Energy EG99 WMB Williams Cos. Energy EG100 XEC Cimarex Energy Energy EG101 XOM Exxon Mobil Corp. Energy EG102 AFL AFLAC Inc Financials FN .No. Code Company Name Sector Abbrv
103 AIG American International Group, Inc. Financials FN104 AIZ Assurant Inc. Financials FN105 AJG Arthur J. Gallagher & Co. Financials FN106 AMG Affiliated Managers Group Inc Financials FN107 AMP Ameriprise Financial Financials FN108 AON Aon plc Financials FN109 AXP American Express Co Financials FN110 BAC Bank of America Corp Financials FN111 BEN Franklin Resources Financials FN112 BK The Bank of New York Mellon Corp. Financials FN113 BLK BlackRock Financials FN114 C Citigroup Inc. Financials FN115 CINF Cincinnati Financial Financials FN116 CMA Comerica Inc. Financials FN117 CME CME Group Inc. Financials FN118 ETFC E*Trade Financials FN119 FITB Fifth Third Bancorp Financials FN120 GS Goldman Sachs Group Financials FN121 HBAN Huntington Bancshares Financials FN122 HIG Hartford Financial Svc.Gp. Financials FN123 HRB Block H&R Financials FN124 ICE Intercontinental Exchange Financials FN125 IVZ Invesco Ltd. Financials FN126 JPM JPMorgan Chase & Co. Financials FN127 KEY KeyCorp Financials FN128 L Loews Corp. Financials FN129 LNC Lincoln National Financials FN130 MCO Moody’s Corp Financials FN131 MET MetLife Inc. Financials FN132 MMC Marsh & McLennan Financials FN133 MS Morgan Stanley Financials FN134 MTB M&T Bank Corp. Financials FN135 NDAQ Nasdaq, Inc. Financials FN136 NTRS Northern Trust Corp. Financials FN137 PBCT People’s United Financial Financials FN138 PFG Principal Financial Group Financials FN139 PGR Progressive Corp. Financials FN140 PNC PNC Financial Services Financials FN141 PRU Prudential Financial Financials FN142 RE Everest Re Group Ltd. Financials FN143 RF Regions Financial Corp. Financials FN144 RJF Raymond James Financial Inc. Financials FN145 SCHW Charles Schwab Corporation Financials FN146 SIVB SVB Financial Financials FN147 SPGI S&P Global, Inc. Financials FN148 STT State Street Corp. Financials FN149 TROW T. Rowe Price Group Financials FN150 TRV The Travelers Companies Inc. Financials FN151 UNM Unum Group Financials FN152 USB U.S. Bancorp Financials FN153 WFC Wells Fargo Financials FN154 WLTW Willis Towers Watson Financials FN155 ZION Zions Bancorp Financials FN .No. Code Company Name Sector Abbrv
156 A Agilent Technologies Inc Health Care HC157 ABC AmerisourceBergen Corp Health Care HC158 ABT Abbott Laboratories Health Care HC159 AGN Allergan, Plc Health Care HC160 ALGN Align Technology Health Care HC161 ALXN Alexion Pharmaceuticals Health Care HC162 AMGN Amgen Inc. Health Care HC163 ANTM Anthem Inc. Health Care HC164 BAX Baxter International Inc. Health Care HC165 BDX Becton Dickinson Health Care HC166 BIIB Biogen Inc. Health Care HC167 BMY Bristol-Myers Squibb Health Care HC168 BSX Boston Scientific Health Care HC169 CERN Cerner Health Care HC170 CI CIGNA Corp. Health Care HC171 CNC Centene Corporation Health Care HC172 COO The Cooper Companies Health Care HC173 DGX Quest Diagnostics Health Care HC174 DVA DaVita Inc. Health Care HC175 EW Edwards Lifesciences Health Care HC176 GILD Gilead Sciences Health Care HC177 HOLX Hologic Health Care HC178 HSIC Henry Schein Health Care HC179 HUM Humana Inc. Health Care HC180 IDXX IDEXX Laboratories Health Care HC181 ILMN Illumina Inc Health Care HC182 INCY Incyte Health Care HC183 ISRG Intuitive Surgical Inc. Health Care HC184 JNJ Johnson & Johnson Health Care HC185 LH Laboratory Corp. of America Holding Health Care HC186 LLY Lilly (Eli) & Co. Health Care HC187 MDT Medtronic plc Health Care HC188 MRK Merck & Co. Health Care HC189 MTD Mettler Toledo Health Care HC190 MYL Mylan N.V. Health Care HC191 PFE Pfizer Inc. Health Care HC192 PKI PerkinElmer Health Care HC193 PRGO Perrigo Health Care HC194 REGN Regeneron Health Care HC195 RMD ResMed Health Care HC196 SYK Stryker Corp. Health Care HC197 TMO Thermo Fisher Scientific Health Care HC198 UHS Universal Health Services, Inc. Health Care HC199 UNH United Health Group Inc. Health Care HC200 VRTX Vertex Pharmaceuticals Inc Health Care HC201 WAT Waters Corporation Health Care HC202 XRAY Dentsply Sirona Health Care HC203 ZBH Zimmer Biomet Holdings Health Care HC204 AAL American Airlines Group Industrials ID205 ALK Alaska Air Group Inc Industrials ID206 AME AMETEK Inc. Industrials ID .No. Code Company Name Sector Abbrv
207 AOS A.O. Smith Corp Industrials ID208 ARNC Arconic Inc. Industrials ID209 BA Boeing Company Industrials ID210 CAT Caterpillar Inc. Industrials ID211 CHRW C. H. Robinson Worldwide Industrials ID212 CMI Cummins Inc. Industrials ID213 CSX CSX Corp. Industrials ID214 CTAS Cintas Corporation Industrials ID215 DE Deere & Co. Industrials ID216 DOV Dover Corp. Industrials ID217 EFX Equifax Inc. Industrials ID218 EMR Emerson Electric Company Industrials ID219 ETN Eaton Corporation Industrials ID220 EXPD Expeditors International Industrials ID221 FAST Fastenal Co Industrials ID222 FDX FedEx Corporation Industrials ID223 FLS Flowserve Corporation Industrials ID224 GD General Dynamics Industrials ID225 GE General Electric Industrials ID226 GWW Grainger (W.W.) Inc. Industrials ID227 IR Ingersoll-Rand PLC Industrials ID228 ITW Illinois Tool Works Industrials ID229 JBHT J. B. Hunt Transport Services Industrials ID230 JCI Johnson Controls International Industrials ID231 KSU Kansas City Southern Industrials ID232 LMT Lockheed Martin Corp. Industrials ID233 LUV Southwest Airlines Industrials ID234 MAS Masco Corp. Industrials ID235 NOC Northrop Grumman Corp. Industrials ID236 NSC Norfolk Southern Corp. Industrials ID237 PCAR PACCAR Inc. Industrials ID238 PH Parker-Hannifin Industrials ID239 PNR Pentair Ltd. Industrials ID240 PWR Quanta Services Inc. Industrials ID241 RHI Robert Half International Industrials ID242 ROK Rockwell Automation Inc. Industrials ID243 ROP Roper Technologies Industrials ID244 RSG Republic Services Inc Industrials ID245 RTN Raytheon Co. Industrials ID246 TXT Textron Inc. Industrials ID247 UNP Union Pacific Industrials ID248 UPS United Parcel Service Industrials ID249 URI United Rentals, Inc. Industrials ID250 UTX United Technologies Industrials ID251 WM Waste Management Inc. Industrials ID252 AAPL Apple Inc. Information Technology IT253 ACN Accenture plc Information Technology IT254 ADBE Adobe Systems Inc Information Technology IT255 ADI Analog Devices, Inc. Information Technology IT256 ADP Automatic Data Processing Information Technology IT257 ADS Alliance Data Systems Information Technology IT258 ADSK Autodesk Inc. Information Technology IT .No. Code Company Name Sector Abbrv
259 AKAM Akamai Technologies Inc Information Technology IT260 AMAT Applied Materials Inc. Information Technology IT261 AMD Advanced Micro Devices Inc Information Technology IT262 ANSS ANSYS Information Technology IT263 APH Amphenol Corp Information Technology IT264 ATVI Activision Blizzard Information Technology IT265 CDNS Cadence Design Systems Information Technology IT266 CRM Salesforce.com Information Technology IT267 CSCO Cisco Systems Information Technology IT268 CTSH Cognizant Technology Solutions Information Technology IT269 CTXS Citrix Systems Information Technology IT270 DXC DXC Technology Information Technology IT271 EA Electronic Arts Information Technology IT272 EBAY eBay Inc. Information Technology IT273 FFIV F5 Networks Information Technology IT274 FIS Fidelity National Information Services Information Technology IT275 FISV Fiserv Inc Information Technology IT276 FLIR FLIR Systems Information Technology IT277 GLW Corning Inc. Information Technology IT278 GOOG Alphabet Inc Class C Information Technology IT279 GOOGL Alphabet Inc Class A Information Technology IT280 GPN Global Payments Inc. Information Technology IT281 HPQ HP Inc. Information Technology IT282 IBM International Business Machines Information Technology IT283 INTC Intel Corp. Information Technology IT284 INTU Intuit Inc. Information Technology IT285 IT Gartner Inc Information Technology IT286 JNPR Juniper Networks Information Technology IT287 KLAC KLA-Tencor Corp. Information Technology IT288 LRCX Lam Research Information Technology IT289 MCHP Microchip Technology Information Technology IT290 MSFT Microsoft Corp. Information Technology IT291 MSI Motorola Solutions Inc. Information Technology IT292 MU Micron Technology Information Technology IT293 NFLX Netflix Inc. Information Technology IT294 NTAP NetApp Information Technology IT295 NVDA Nvidia Corporation Information Technology IT296 ORCL Oracle Corp. Information Technology IT297 PAYX Paychex Inc. Information Technology IT298 QCOM QUALCOMM Inc. Information Technology IT299 SNPS Synopsys Inc. Information Technology IT300 STX Seagate Technology Information Technology IT301 SWKS Skyworks Solutions Information Technology IT302 TTWO Take-Two Interactive Information Technology IT303 TXN Texas Instruments Information Technology IT304 VRSN Verisign Inc. Information Technology IT305 WDC Western Digital Information Technology IT306 XLNX Xilinx Inc Information Technology IT307 APD Air Products & Chemicals Inc Materials MT308 AVY Avery Dennison Corp Materials MT309 BLL Ball Corp Materials MT .No. Code Company Name Sector Abbrv
310 CF CF Industries Holdings Inc Materials MT311 ECL Ecolab Inc. Materials MT312 FCX Freeport-McMoRan Inc. Materials MT313 FMC FMC Corporation Materials MT314 IFF Intl Flavors & Fragrances Materials MT315 IP International Paper Materials MT316 MOS The Mosaic Company Materials MT317 NEM Newmont Mining Corporation Materials MT318 NUE Nucor Corp. Materials MT319 PKG Packaging Corporation of America Materials MT320 PPG PPG Industries Materials MT321 SEE Sealed Air Materials MT322 SHW Sherwin-Williams Materials MT323 VMC Vulcan Materials Materials MT324 CTL CenturyLink Inc Telecommunication Services TC325 T AT&T Inc. Telecommunication Services TC326 VZ Verizon Communications Telecommunication Services TC327 AEE Ameren Corp Utilities UT328 AEP American Electric Power Utilities UT329 AES AES Corp Utilities UT330 CMS CMS Energy Utilities UT331 CNP CenterPoint Energy Utilities UT332 D Dominion Energy Utilities UT333 DTE DTE Energy Co. Utilities UT334 DUK Duke Energy Utilities UT335 ED Consolidated Edison Utilities UT336 EIX Edison Int’l Utilities UT337 ES Eversource Energy Utilities UT338 ETR Entergy Corp. Utilities UT339 EXC Exelon Corp. Utilities UT340 FE FirstEnergy Corp Utilities UT341 LNT Alliant Energy Corp Utilities UT342 NEE NextEra Energy Utilities UT343 NI NiSource Inc. Utilities UT344 NRG NRG Energy Utilities UT345 PEG Public Serv. Enterprise Inc. Utilities UT346 PNW Pinnacle West Capital Utilities UT347 SO Southern Co. Utilities UT348 SRE Sempra Energy Utilities UT349 WEC Wec Energy Group Inc Utilities UT350 XEL Xcel Energy Inc Utilities UT able S4.
List of 156 stocks of JPN Nikkei 225 considered for the analysis present over the period of 2006-2019.
S.No. Code Company Name Sector Abbrv .No. Code Company Name Sector Abbrv
52 8601.T DAIWA SECURITIES GROUP INC. Financials FN53 8604.T NOMURA HOLDINGS, INC. Financials FN54 2768.T SOJITZ CORP. Materials MT55 3101.T TOYOBO CO., LTD. Materials MT56 3103.T UNITIKA, LTD. Materials MT57 3401.T TEIJIN LTD. Materials MT58 3402.T TORAY INDUSTRIES, INC. Materials MT59 3405.T KURARAY CO., LTD. Materials MT60 3407.T ASAHI KASEI CORP. Materials MT61 3861.T OJI HOLDINGS CORP. Materials MT62 4004.T SHOWA DENKO K.K. Materials MT63 4005.T SUMITOMO CHEMICAL CO., LTD. Materials MT64 4021.T NISSAN CHEMICAL CORP. Materials MT65 4042.T TOSOH CORP. Materials MT66 4061.T DENKA CO., LTD. Materials MT67 4063.T SHIN-ETSU CHEMICAL CO., LTD. Materials MT68 4183.T MITSUI CHEMICALS, INC. Materials MT69 4208.T UBE INDUSTRIES, LTD. Materials MT70 4272.T NIPPON KAYAKU CO., LTD. Materials MT71 4452.T KAO CORP. Materials MT72 4631.T DIC CORP. Materials MT73 4901.T FUJIFILM HOLDINGS CORP. Materials MT74 4911.T SHISEIDO CO., LTD. Materials MT75 5101.T THE YOKOHAMA RUBBER CO., LTD. Materials MT76 5108.T BRIDGESTONE CORP. Materials MT77 5202.T NIPPON SHEET GLASS CO., LTD. Materials MT78 5232.T SUMITOMO OSAKA CEMENT CO., LTD. Materials MT79 5233.T TAIHEIYO CEMENT CORP. Materials MT80 5301.T TOKAI CARBON CO., LTD. Materials MT81 5333.T NGK INSULATORS, LTD. Materials MT82 5401.T NIPPON STEEL CORP. Materials MT83 5406.T KOBE STEEL, LTD. Materials MT84 5541.T PACIFIC METALS CO., LTD. Materials MT85 5703.T NIPPON LIGHT METAL HOLDINGS CO., LTD. Materials MT86 5706.T MITSUI MINING & SMELTING CO. Materials MT87 5707.T TOHO ZINC CO., LTD. Materials MT88 5711.T MITSUBISHI MATERIALS CORP. Materials MT89 5713.T SUMITOMO METAL MINING CO., LTD. Materials MT90 5714.T DOWA HOLDINGS CO., LTD. Materials MT91 5801.T FURUKAWA ELECTRIC CO., LTD. Materials MT92 5802.T SUMITOMO ELECTRIC IND., LTD. Materials MT93 5803.T FUJIKURA LTD. Materials MT94 5901.T TOYO SEIKAN GROUP HOLDINGS, LTD. Materials MT95 6988.T NITTO DENKO CORP. Materials MT96 8001.T ITOCHU CORP. Materials MT97 8002.T MARUBENI CORP. Materials MT98 8015.T TOYOTA TSUSHO CORP. Materials MT99 8031.T MITSUI & CO., LTD. Materials MT100 8053.T SUMITOMO CORP. Materials MT101 8058.T MITSUBISHI CORP. Materials MT102 3105.T NISSHINBO HOLDINGS INC. Technology TC103 4151.T KYOWA KIRIN CO., LTD. Technology TC104 4502.T TAKEDA PHARMACEUTICAL CO., LTD. Technology TC .No. Code Company Name Sector Abbrv
105 4503.T ASTELLAS PHARMA INC. Technology TC106 4506.T SUMITOMO DAINIPPON PHARMA CO., LTD. Technology TC107 4507.T SHIONOGI & CO., LTD. Technology TC108 4519.T CHUGAI PHARMACEUTICAL CO., LTD. Technology TC109 4543.T TERUMO CORP. Technology TC110 4902.T KONICA MINOLTA, INC. Technology TC111 6479.T MINEBEA MITSUMI INC. Technology TC112 6503.T MITSUBISHI ELECTRIC CORP. Technology TC113 6504.T FUJI ELECTRIC CO., LTD. Technology TC114 6506.T YASKAWA ELECTRIC CORP. Technology TC115 6701.T NEC CORP. Technology TC116 6702.T FUJITSU LTD. Technology TC117 6703.T OKI ELECTRIC IND. CO., LTD. Technology TC118 6724.T SEIKO EPSON CORP. Technology TC119 6758.T SONY CORP. Technology TC120 6762.T TDK CORP. Technology TC121 6770.T ALPS ALPINE CO., LTD. Technology TC122 6841.T YOKOGAWA ELECTRIC CORP. Technology TC123 6902.T DENSO CORP. Technology TC124 6952.T CASIO COMPUTER CO., LTD. Technology TC125 6954.T FANUC CORP. Technology TC126 6976.T TAIYO YUDEN CO., LTD. Technology TC127 7201.T NISSAN MOTOR CO., LTD. Technology TC128 7202.T ISUZU MOTORS LTD. Technology TC129 7203.T TOYOTA MOTOR CORP. Technology TC130 7205.T HINO MOTORS, LTD. Technology TC131 7211.T MITSUBISHI MOTORS CORP. Technology TC132 7261.T MAZDA MOTOR CORP. Technology TC133 7269.T SUZUKI MOTOR CORP. Technology TC134 7270.T SUBARU CORP. Technology TC135 7272.T YAMAHA MOTOR CO., LTD. Technology TC136 7731.T NIKON CORP. Technology TC137 7733.T OLYMPUS CORP. Technology TC138 7735.T SCREEN HOLDINGS CO., LTD. Technology TC139 7752.T RICOH CO., LTD. Technology TC140 8035.T TOKYO ELECTRON LTD. Technology TC141 9001.T TOBU RAILWAY CO., LTD. Transportation and Utilities UT142 9005.T TOKYU CORP. Transportation and Utilities UT143 9007.T ODAKYU ELECTRIC RAILWAY CO., LTD. Transportation and Utilities UT144 9008.T KEIO CORP. Transportation and Utilities UT145 9009.T KEISEI ELECTRIC RAILWAY CO., LTD. Transportation and Utilities UT146 9062.T NIPPON EXPRESS CO., LTD. Transportation and Utilities UT147 9064.T YAMATO HOLDINGS CO., LTD. Transportation and Utilities UT148 9101.T NIPPON YUSEN K.K. Transportation and Utilities UT149 9104.T MITSUI O.S.K.LINES, LTD. Transportation and Utilities UT150 9107.T KAWASAKI KISEN KAISHA, LTD. Transportation and Utilities UT151 9301.T MITSUBISHI LOGISTICS CORP. Transportation and Utilities UT152 9501.T TOKYO ELECTRIC POWER COMPANY HOLDINGS,I Transportation and Utilities UT153 9502.T CHUBU ELECTRIC POWER CO., INC. Transportation and Utilities UT154 9503.T THE KANSAI ELECTRIC POWER CO., INC. Transportation and Utilities UT155 9531.T TOKYO GAS CO., LTD. Transportation and Utilities UT156 9532.T OSAKA GAS CO., LTD. Transportation and Utilities UT able S5.
List of 368 stocks of USA S&P 500 considered for the analysis present over the period of 2006-2018.
S.No. Code Company Name Sector Abbrv .No. Code Company Name Sector Abbrv
52 TJX TJX Companies Inc. Consumer Discretionary CD53 TPR Tapestry, Inc. Consumer Discretionary CD54 UAA Under Armour Class A Consumer Discretionary CD55 VFC V.F. Corp. Consumer Discretionary CD56 VIAB Viacom Inc. Consumer Discretionary CD57 WHR Whirlpool Corp. Consumer Discretionary CD58 WYNN Wynn Resorts Ltd Consumer Discretionary CD59 YUM Yum! Brands Inc Consumer Discretionary CD60 ADM Archer-Daniels-Midland Co Consumer Staples CS61 CAG Conagra Brands Consumer Staples CS62 CHD Church & Dwight Consumer Staples CS63 CL Colgate-Palmolive Consumer Staples CS64 CLX The Clorox Company Consumer Staples CS65 CPB Campbell Soup Consumer Staples CS66 CVS CVS Health Consumer Staples CS67 EL Estee Lauder Cos. Consumer Staples CS68 GIS General Mills Consumer Staples CS69 HRL Hormel Foods Corp. Consumer Staples CS70 HSY The Hershey Company Consumer Staples CS71 K Kellogg Co. Consumer Staples CS72 KMB Kimberly-Clark Consumer Staples CS73 KO Coca-Cola Company (The) Consumer Staples CS74 KR Kroger Co. Consumer Staples CS75 MDLZ Mondelez International Consumer Staples CS76 MKC McCormick & Co. Consumer Staples CS77 MNST Monster Beverage Consumer Staples CS78 MO Altria Group Inc Consumer Staples CS79 PEP PepsiCo Inc. Consumer Staples CS80 PG Procter & Gamble Consumer Staples CS81 SJM JM Smucker Consumer Staples CS82 STZ Constellation Brands Consumer Staples CS83 SYY Sysco Corp. Consumer Staples CS84 TAP Molson Coors Brewing Company Consumer Staples CS85 TSN Tyson Foods Consumer Staples CS86 WBA Walgreens Boots Alliance Consumer Staples CS87 WMT Wal-Mart Stores Consumer Staples CS88 APA Apache Corporation Energy EG89 APC Anadarko Petroleum Corp Energy EG90 BHGE Baker Hughes, a GE Company Energy EG91 COG Cabot Oil & Gas Energy EG92 COP ConocoPhillips Energy EG93 CVX Chevron Corp. Energy EG94 DVN Devon Energy Corp. Energy EG95 EOG EOG Resources Energy EG96 EQT EQT Corporation Energy EG97 FTI TechnipFMC Energy EG98 HAL Halliburton Co. Energy EG99 HES Hess Corporation Energy EG100 HP Helmerich & Payne Energy EG101 MRO Marathon Oil Corp. Energy EG102 NBL Noble Energy Inc Energy EG103 NOV National Oilwell Varco Inc. Energy EG104 PXD Pioneer Natural Resources Energy EG .No. Code Company Name Sector Abbrv
105 RRC Range Resources Corp. Energy EG106 SLB Schlumberger Ltd. Energy EG107 VLO Valero Energy Energy EG108 WMB Williams Cos. Energy EG109 XEC Cimarex Energy Energy EG110 XOM Exxon Mobil Corp. Energy EG111 AFL AFLAC Inc Financials FN112 AIG American International Group, Inc. Financials FN113 AIZ Assurant Inc. Financials FN114 AJG Arthur J. Gallagher & Co. Financials FN115 AMG Affiliated Managers Group Inc Financials FN116 AMP Ameriprise Financial Financials FN117 AON Aon plc Financials FN118 AXP American Express Co Financials FN119 BAC Bank of America Corp Financials FN120 BBT BB&T Corporation Financials FN121 BEN Franklin Resources Financials FN122 BK The Bank of New York Mellon Corp. Financials FN123 BLK BlackRock Financials FN124 C Citigroup Inc. Financials FN125 CINF Cincinnati Financial Financials FN126 CMA Comerica Inc. Financials FN127 CME CME Group Inc. Financials FN128 ETFC E*Trade Financials FN129 FITB Fifth Third Bancorp Financials FN130 GS Goldman Sachs Group Financials FN131 HBAN Huntington Bancshares Financials FN132 HRB Block H&R Financials FN133 ICE Intercontinental Exchange Financials FN134 IVZ Invesco Ltd. Financials FN135 JPM JPMorgan Chase & Co. Financials FN136 KEY KeyCorp Financials FN137 L Loews Corp. Financials FN138 LNC Lincoln National Financials FN139 MCO Moody’s Corp Financials FN140 MET MetLife Inc. Financials FN141 MMC Marsh & McLennan Financials FN142 MS Morgan Stanley Financials FN143 MTB M&T Bank Corp. Financials FN144 NDAQ Nasdaq, Inc. Financials FN145 NTRS Northern Trust Corp. Financials FN146 PBCT People’s United Financial Financials FN147 PFG Principal Financial Group Financials FN148 PGR Progressive Corp. Financials FN149 PNC PNC Financial Services Financials FN150 PRU Prudential Financial Financials FN151 RE Everest Re Group Ltd. Financials FN152 RF Regions Financial Corp. Financials FN153 RJF Raymond James Financial Inc. Financials FN154 SCHW Charles Schwab Corporation Financials FN155 SIVB SVB Financial Financials FN156 SPGI S&P Global, Inc. Financials FN .No. Code Company Name Sector Abbrv
157 STI SunTrust Banks Financials FN158 STT State Street Corp. Financials FN159 TMK Torchmark Corp. Financials FN160 TROW T. Rowe Price Group Financials FN161 TRV The Travelers Companies Inc. Financials FN162 UNM Unum Group Financials FN163 USB U.S. Bancorp Financials FN164 WFC Wells Fargo Financials FN165 WLTW Willis Towers Watson Financials FN166 ZION Zions Bancorp Financials FN167 A Agilent Technologies Inc Health Care HC168 ABC AmerisourceBergen Corp Health Care HC169 ABT Abbott Laboratories Health Care HC170 AGN Allergan, Plc Health Care HC171 ALGN Align Technology Health Care HC172 ALXN Alexion Pharmaceuticals Health Care HC173 AMGN Amgen Inc. Health Care HC174 ANTM Anthem Inc. Health Care HC175 BAX Baxter International Inc. Health Care HC176 BDX Becton Dickinson Health Care HC177 BIIB Biogen Inc. Health Care HC178 BMY Bristol-Myers Squibb Health Care HC179 BSX Boston Scientific Health Care HC180 CELG Celgene Corp. Health Care HC181 CERN Cerner Health Care HC182 CI CIGNA Corp. Health Care HC183 CNC Centene Corporation Health Care HC184 COO The Cooper Companies Health Care HC185 DVA DaVita Inc. Health Care HC186 EW Edwards Lifesciences Health Care HC187 GILD Gilead Sciences Health Care HC188 HOLX Hologic Health Care HC189 HSIC Henry Schein Health Care HC190 HUM Humana Inc. Health Care HC191 IDXX IDEXX Laboratories Health Care HC192 ILMN Illumina Inc Health Care HC193 INCY Incyte Health Care HC194 ISRG Intuitive Surgical Inc. Health Care HC195 LH Laboratory Corp. of America Holding Health Care HC196 LLY Lilly (Eli) & Co. Health Care HC197 MDT Medtronic plc Health Care HC198 MRK Merck & Co. Health Care HC199 MTD Mettler Toledo Health Care HC200 MYL Mylan N.V. Health Care HC201 PFE Pfizer Inc. Health Care HC202 PKI PerkinElmer Health Care HC203 PRGO Perrigo Health Care HC204 REGN Regeneron Health Care HC205 RMD ResMed Health Care HC206 SYK Stryker Corp. Health Care HC207 TMO Thermo Fisher Scientific Health Care HC208 UHS Universal Health Services, Inc. Health Care HC .No. Code Company Name Sector Abbrv
209 UNH United Health Group Inc. Health Care HC210 VRTX Vertex Pharmaceuticals Inc Health Care HC211 WAT Waters Corporation Health Care HC212 XRAY Dentsply Sirona Health Care HC213 ZBH Zimmer Biomet Holdings Health Care HC214 AAL American Airlines Group Industrials ID215 ALK Alaska Air Group Inc Industrials ID216 AME AMETEK Inc. Industrials ID217 AOS A.O. Smith Corp Industrials ID218 ARNC Arconic Inc. Industrials ID219 AYI Acuity Brands Inc Industrials ID220 BA Boeing Company Industrials ID221 CAT Caterpillar Inc. Industrials ID222 CHRW C. H. Robinson Worldwide Industrials ID223 CMI Cummins Inc. Industrials ID224 CSX CSX Corp. Industrials ID225 CTAS Cintas Corporation Industrials ID226 DE Deere & Co. Industrials ID227 DOV Dover Corp. Industrials ID228 EFX Equifax Inc. Industrials ID229 EMR Emerson Electric Company Industrials ID230 ETN Eaton Corporation Industrials ID231 EXPD Expeditors International Industrials ID232 FAST Fastenal Co Industrials ID233 FDX FedEx Corporation Industrials ID234 FLR Fluor Corp. Industrials ID235 FLS Flowserve Corporation Industrials ID236 GD General Dynamics Industrials ID237 GE General Electric Industrials ID238 GWW Grainger (W.W.) Inc. Industrials ID239 IR Ingersoll-Rand PLC Industrials ID240 ITW Illinois Tool Works Industrials ID241 JBHT J. B. Hunt Transport Services Industrials ID242 JCI Johnson Controls International Industrials ID243 JEC Jacobs Engineering Group Industrials ID244 KSU Kansas City Southern Industrials ID245 LLL L-3 Communications Holdings Industrials ID246 LMT Lockheed Martin Corp. Industrials ID247 LUV Southwest Airlines Industrials ID248 MAS Masco Corp. Industrials ID249 NOC Northrop Grumman Corp. Industrials ID250 NSC Norfolk Southern Corp. Industrials ID251 PCAR PACCAR Inc. Industrials ID252 PH Parker-Hannifin Industrials ID253 PNR Pentair Ltd. Industrials ID254 PWR Quanta Services Inc. Industrials ID255 RHI Robert Half International Industrials ID256 ROK Rockwell Automation Inc. Industrials ID257 ROP Roper Technologies Industrials ID258 RSG Republic Services Inc Industrials ID259 RTN Raytheon Co. Industrials ID260 SRCL Stericycle Inc Industrials ID261 TXT Textron Inc. Industrials ID .No. Code Company Name Sector Abbrv
262 UNP Union Pacific Industrials ID263 UPS United Parcel Service Industrials ID264 URI United Rentals, Inc. Industrials ID265 UTX United Technologies Industrials ID266 WM Waste Management Inc. Industrials ID267 AAPL Apple Inc. Information Technology IT268 ACN Accenture plc Information Technology IT269 ADBE Adobe Systems Inc Information Technology IT270 ADI Analog Devices, Inc. Information Technology IT271 ADP Automatic Data Processing Information Technology IT272 ADS Alliance Data Systems Information Technology IT273 ADSK Autodesk Inc. Information Technology IT274 AKAM Akamai Technologies Inc Information Technology IT275 AMAT Applied Materials Inc. Information Technology IT276 AMD Advanced Micro Devices Inc Information Technology IT277 ANSS ANSYS Information Technology IT278 APH Amphenol Corp Information Technology IT279 ATVI Activision Blizzard Information Technology IT280 CDNS Cadence Design Systems Information Technology IT281 CRM Salesforce.com Information Technology IT282 CSCO Cisco Systems Information Technology IT283 CTSH Cognizant Technology Solutions Information Technology IT284 CTXS Citrix Systems Information Technology IT285 DXC DXC Technology Information Technology IT286 EA Electronic Arts Information Technology IT287 EBAY eBay Inc. Information Technology IT288 FFIV F5 Networks Information Technology IT289 FIS Fidelity National Information Services Information Technology IT290 FISV Fiserv Inc Information Technology IT291 FLIR FLIR Systems Information Technology IT292 GLW Corning Inc. Information Technology IT293 GOOG Alphabet Inc Class C Information Technology IT294 GOOGL Alphabet Inc Class A Information Technology IT295 GPN Global Payments Inc. Information Technology IT296 HPQ HP Inc. Information Technology IT297 HRS Harris Corporation Information Technology IT298 IBM International Business Machines Information Technology IT299 INTC Intel Corp. Information Technology IT300 INTU Intuit Inc. Information Technology IT301 IT Gartner Inc Information Technology IT302 JNPR Juniper Networks Information Technology IT303 KLAC KLA-Tencor Corp. Information Technology IT304 LRCX Lam Research Information Technology IT305 MCHP Microchip Technology Information Technology IT306 MSFT Microsoft Corp. Information Technology IT307 MSI Motorola Solutions Inc. Information Technology IT308 MU Micron Technology Information Technology IT309 NFLX Netflix Inc. Information Technology IT310 NTAP NetApp Information Technology IT311 NVDA Nvidia Corporation Information Technology IT312 ORCL Oracle Corp. Information Technology IT313 PAYX Paychex Inc. Information Technology IT314 QCOM QUALCOMM Inc. Information Technology IT .No. Code Company Name Sector Abbrv
315 RHT Red Hat Inc. Information Technology IT316 SNPS Synopsys Inc. Information Technology IT317 STX Seagate Technology Information Technology IT318 SWKS Skyworks Solutions Information Technology IT319 SYMC Symantec Corp. Information Technology IT320 TSS Total System Services Information Technology IT321 TTWO Take-Two Interactive Information Technology IT322 TXN Texas Instruments Information Technology IT323 VRSN Verisign Inc. Information Technology IT324 WDC Western Digital Information Technology IT325 XLNX Xilinx Inc Information Technology IT326 APD Air Products & Chemicals Inc Materials MT327 AVY Avery Dennison Corp Materials MT328 BLL Ball Corp Materials MT329 CF CF Industries Holdings Inc Materials MT330 DWDP DowDuPont Materials MT331 ECL Ecolab Inc. Materials MT332 FCX Freeport-McMoRan Inc. Materials MT333 FMC FMC Corporation Materials MT334 IFF Intl Flavors & Fragrances Materials MT335 IP International Paper Materials MT336 MOS The Mosaic Company Materials MT337 NEM Newmont Mining Corporation Materials MT338 NUE Nucor Corp. Materials MT339 PKG Packaging Corporation of America Materials MT340 SEE Sealed Air Materials MT341 SHW Sherwin-Williams Materials MT342 CTL CenturyLink Inc Telecommunication Services TC343 T AT&T Inc. Telecommunication Services TC344 VZ Verizon Communications Telecommunication Services TC345 AEE Ameren Corp Utilities UT346 AEP American Electric Power Utilities UT347 AES AES Corp Utilities UT348 CMS CMS Energy Utilities UT349 CNP CenterPoint Energy Utilities UT350 D Dominion Energy Utilities UT351 DTE DTE Energy Co. Utilities UT352 DUK Duke Energy Utilities UT353 ED Consolidated Edison Utilities UT354 EIX Edison Int’l Utilities UT355 ES Eversource Energy Utilities UT356 ETR Entergy Corp. Utilities UT357 EXC Exelon Corp. Utilities UT358 FE FirstEnergy Corp Utilities UT359 LNT Alliant Energy Corp Utilities UT360 NEE NextEra Energy Utilities UT361 NI NiSource Inc. Utilities UT362 NRG NRG Energy Utilities UT363 PCG PG&E Corp. Utilities UT364 PEG Public Serv. Enterprise Inc. Utilities UT .No. Code Company Name Sector Abbrv
365 SO Southern Co. Utilities UT366 SRE Sempra Energy Utilities UT367 WEC Wec Energy Group Inc Utilities UT368 XEL Xcel Energy Inc Utilities UT
Table S6.
List of 173 stocks of JPN Nikkei 225 considered for the analysis present over the period of 2006-2018.
S.No. Code Company Name Sector Abbrv .No. Code Company Name Sector Abbrv
46 7004.T HITACHI ZOSEN CORP. Capital Goods/Others CP47 7011.T MITSUBISHI HEAVY IND., LTD. Capital Goods/Others CP48 7012.T KAWASAKI HEAVY IND., LTD. Capital Goods/Others CP49 7013.T IHI CORP. Capital Goods/Others CP50 7911.T TOPPAN PRINTING CO., LTD. Capital Goods/Others CP51 7912.T DAI NIPPON PRINTING CO., LTD. Capital Goods/Others CP52 7951.T YAMAHA CORP. Capital Goods/Others CP53 8801.T MITSUI FUDOSAN CO., LTD. Capital Goods/Others CP54 8802.T MITSUBISHI ESTATE CO., LTD. Capital Goods/Others CP55 8804.T TOKYO TATEMONO CO., LTD. Capital Goods/Others CP56 8830.T SUMITOMO REALTY & DEVELOPMENT CO.,LTD. Capital Goods/Others CP57 8253.T CREDIT SAISON CO., LTD. Financials FN58 8303.T SHINSEI BANK, LTD. Financials FN59 8331.T THE CHIBA BANK, LTD. Financials FN60 8355.T THE SHIZUOKA BANK, LTD. Financials FN61 8601.T DAIWA SECURITIES GROUP INC. Financials FN62 8604.T NOMURA HOLDINGS, INC. Financials FN63 8628.T MATSUI SECURITIES CO., LTD. Financials FN64 2768.T SOJITZ CORP. Materials MT65 3101.T TOYOBO CO., LTD. Materials MT66 3103.T UNITIKA, LTD. Materials MT67 3401.T TEIJIN LTD. Materials MT68 3402.T TORAY INDUSTRIES, INC. Materials MT69 3405.T KURARAY CO., LTD. Materials MT70 3407.T ASAHI KASEI CORP. Materials MT71 3436.T SUMCO CORP. Materials MT72 3861.T OJI HOLDINGS CORP. Materials MT73 4004.T SHOWA DENKO K.K. Materials MT74 4005.T SUMITOMO CHEMICAL CO., LTD. Materials MT75 4042.T TOSOH CORP. Materials MT76 4043.T TOKUYAMA CORP. Materials MT77 4061.T DENKA CO., LTD. Materials MT78 4063.T SHIN-ETSU CHEMICAL CO., LTD. Materials MT79 4183.T MITSUI CHEMICALS, INC. Materials MT80 4188.T MITSUBISHI CHEMICAL HOLDINGS CORP. Materials MT81 4208.T UBE INDUSTRIES, LTD. Materials MT82 4272.T NIPPON KAYAKU CO., LTD. Materials MT83 4452.T KAO CORP. Materials MT84 4631.T DIC CORP. Materials MT85 4901.T FUJIFILM HOLDINGS CORP. Materials MT86 4911.T SHISEIDO CO., LTD. Materials MT87 5020.T JXTG HOLDINGS, INC. Materials MT88 5101.T THE YOKOHAMA RUBBER CO., LTD. Materials MT89 5108.T BRIDGESTONE CORP. Materials MT90 5201.T AGC INC. Materials MT91 5202.T NIPPON SHEET GLASS CO., LTD. Materials MT92 5232.T SUMITOMO OSAKA CEMENT CO., LTD. Materials MT93 5233.T TAIHEIYO CEMENT CORP. Materials MT94 5301.T TOKAI CARBON CO., LTD. Materials MT95 5333.T NGK INSULATORS, LTD. Materials MT .No. Code Company Name Sector Abbrv
96 5401.T NIPPON STEEL CORP. Materials MT97 5406.T KOBE STEEL, LTD. Materials MT98 5411.T JFE HOLDINGS, INC. Materials MT99 5541.T PACIFIC METALS CO., LTD. Materials MT100 5703.T NIPPON LIGHT METAL HOLDINGS CO., LTD. Materials MT101 5706.T MITSUI MINING & SMELTING CO. Materials MT102 5707.T TOHO ZINC CO., LTD. Materials MT103 5711.T MITSUBISHI MATERIALS CORP. Materials MT104 5713.T SUMITOMO METAL MINING CO., LTD. Materials MT105 5714.T DOWA HOLDINGS CO., LTD. Materials MT106 5801.T FURUKAWA ELECTRIC CO., LTD. Materials MT107 5802.T SUMITOMO ELECTRIC IND., LTD. Materials MT108 5803.T FUJIKURA LTD. Materials MT109 5901.T TOYO SEIKAN GROUP HOLDINGS, LTD. Materials MT110 6988.T NITTO DENKO CORP. Materials MT111 8001.T ITOCHU CORP. Materials MT112 8002.T MARUBENI CORP. Materials MT113 8015.T TOYOTA TSUSHO CORP. Materials MT114 8031.T MITSUI & CO., LTD. Materials MT115 8053.T SUMITOMO CORP. Materials MT116 8058.T MITSUBISHI CORP. Materials MT117 3105.T NISSHINBO HOLDINGS INC. Technology TC118 4151.T KYOWA KIRIN CO., LTD. Technology TC119 4502.T TAKEDA PHARMACEUTICAL CO., LTD. Technology TC120 4503.T ASTELLAS PHARMA INC. Technology TC121 4506.T SUMITOMO DAINIPPON PHARMA CO., LTD. Technology TC122 4507.T SHIONOGI & CO., LTD. Technology TC123 4519.T CHUGAI PHARMACEUTICAL CO., LTD. Technology TC124 4523.T EISAI CO., LTD. Technology TC125 4543.T TERUMO CORP. Technology TC126 4902.T KONICA MINOLTA, INC. Technology TC127 6479.T MINEBEA MITSUMI INC. Technology TC128 6501.T HITACHI, LTD. Technology TC129 6503.T MITSUBISHI ELECTRIC CORP. Technology TC130 6504.T FUJI ELECTRIC CO., LTD. Technology TC131 6506.T YASKAWA ELECTRIC CORP. Technology TC132 6645.T OMRON CORP. Technology TC133 6674.T GS YUASA CORP. Technology TC134 6701.T NEC CORP. Technology TC135 6703.T OKI ELECTRIC IND. CO., LTD. Technology TC136 6724.T SEIKO EPSON CORP. Technology TC137 6758.T SONY CORP. Technology TC138 6762.T TDK CORP. Technology TC139 6770.T ALPS ALPINE CO., LTD. Technology TC140 6841.T YOKOGAWA ELECTRIC CORP. Technology TC141 6902.T DENSO CORP. Technology TC142 6952.T CASIO COMPUTER CO., LTD. Technology TC143 6954.T FANUC CORP. Technology TC144 7201.T NISSAN MOTOR CO., LTD. Technology TC145 7202.T ISUZU MOTORS LTD. Technology TC146 7203.T TOYOTA MOTOR CORP. Technology TC147 7205.T HINO MOTORS, LTD. Technology TC148 7211.T MITSUBISHI MOTORS CORP. Technology TC .No. Code Company Name Sector Abbrv
149 7261.T MAZDA MOTOR CORP. Technology TC150 7269.T SUZUKI MOTOR CORP. Technology TC151 7270.T SUBARU CORP. Technology TC152 7272.T YAMAHA MOTOR CO., LTD. Technology TC153 7731.T NIKON CORP. Technology TC154 7735.T SCREEN HOLDINGS CO., LTD. Technology TC155 7752.T RICOH CO., LTD. Technology TC156 8035.T TOKYO ELECTRON LTD. Technology TC157 9412.T SKY PERFECT JSAT HOLDINGS INC. Technology TC158 9984.T SOFTBANK GROUP CORP. Technology TC159 9001.T TOBU RAILWAY CO., LTD. Transportation and Utilities UT160 9005.T TOKYU CORP. Transportation and Utilities UT161 9007.T ODAKYU ELECTRIC RAILWAY CO., LTD. Transportation and Utilities UT162 9008.T KEIO CORP. Transportation and Utilities UT163 9009.T KEISEI ELECTRIC RAILWAY CO., LTD. Transportation and Utilities UT164 9062.T NIPPON EXPRESS CO., LTD. Transportation and Utilities UT165 9064.T YAMATO HOLDINGS CO., LTD. Transportation and Utilities UT166 9101.T NIPPON YUSEN K.K. Transportation and Utilities UT167 9104.T MITSUI O.S.K.LINES, LTD. Transportation and Utilities UT168 9107.T KAWASAKI KISEN KAISHA, LTD. Transportation and Utilities UT169 9301.T MITSUBISHI LOGISTICS CORP. Transportation and Utilities UT170 9501.T TOKYO ELECTRIC POWER COMPANY HOLD-INGS, I Transportation and Utilities UT171 9502.T CHUBU ELECTRIC POWER CO., INC. Transportation and Utilities UT172 9503.T THE KANSAI ELECTRIC POWER CO., INC. Transportation and Utilities UT173 9532.T OSAKA GAS CO., LTD. Transportation and Utilities UT149 7261.T MAZDA MOTOR CORP. Technology TC150 7269.T SUZUKI MOTOR CORP. Technology TC151 7270.T SUBARU CORP. Technology TC152 7272.T YAMAHA MOTOR CO., LTD. Technology TC153 7731.T NIKON CORP. Technology TC154 7735.T SCREEN HOLDINGS CO., LTD. Technology TC155 7752.T RICOH CO., LTD. Technology TC156 8035.T TOKYO ELECTRON LTD. Technology TC157 9412.T SKY PERFECT JSAT HOLDINGS INC. Technology TC158 9984.T SOFTBANK GROUP CORP. Technology TC159 9001.T TOBU RAILWAY CO., LTD. Transportation and Utilities UT160 9005.T TOKYU CORP. Transportation and Utilities UT161 9007.T ODAKYU ELECTRIC RAILWAY CO., LTD. Transportation and Utilities UT162 9008.T KEIO CORP. Transportation and Utilities UT163 9009.T KEISEI ELECTRIC RAILWAY CO., LTD. Transportation and Utilities UT164 9062.T NIPPON EXPRESS CO., LTD. Transportation and Utilities UT165 9064.T YAMATO HOLDINGS CO., LTD. Transportation and Utilities UT166 9101.T NIPPON YUSEN K.K. Transportation and Utilities UT167 9104.T MITSUI O.S.K.LINES, LTD. Transportation and Utilities UT168 9107.T KAWASAKI KISEN KAISHA, LTD. Transportation and Utilities UT169 9301.T MITSUBISHI LOGISTICS CORP. Transportation and Utilities UT170 9501.T TOKYO ELECTRIC POWER COMPANY HOLD-INGS, I Transportation and Utilities UT171 9502.T CHUBU ELECTRIC POWER CO., INC. Transportation and Utilities UT172 9503.T THE KANSAI ELECTRIC POWER CO., INC. Transportation and Utilities UT173 9532.T OSAKA GAS CO., LTD. Transportation and Utilities UT