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Journal articles on the topic 'FTSE 100 Index Revisions'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Daya, Wael, Khelifa Mazouz, and Mark Freeman. "Information efficiency changes following FTSE 100 index revisions." Journal of International Financial Markets, Institutions and Money 22, no. 4 (October 2012): 1054–69. http://dx.doi.org/10.1016/j.intfin.2012.01.002.

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2

Fernandes, Marcelo, and João Mergulhão. "Anticipatory effects in the FTSE 100 index revisions." Journal of Empirical Finance 37 (June 2016): 79–90. http://dx.doi.org/10.1016/j.jempfin.2016.02.009.

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3

Mazouz, Khelifa, and Bharim Saadouni. "New evidence on the price and liquidity effects of the FTSE 100 index revisions." International Review of Financial Analysis 16, no. 3 (January 2007): 223–41. http://dx.doi.org/10.1016/j.irfa.2006.11.001.

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4

Mazouz, Khelifa, and Brahim Saadouni. "The price effects of FTSE 100 index revision: what drives the long-term abnormal return reversal?" Applied Financial Economics 17, no. 6 (March 2007): 501–10. http://dx.doi.org/10.1080/09603100600690085.

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5

Danbolt, Jo, Ian Hirst, and Edward Jones. "Gaming the FTSE 100 index." British Accounting Review 50, no. 4 (June 2018): 364–78. http://dx.doi.org/10.1016/j.bar.2017.09.005.

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6

Coakley, Jerry, Periklis Kougoulis, and John C. Nankervis. "Comovement and FTSE 100 index changes." International Journal of Behavioural Accounting and Finance 4, no. 2 (2014): 93. http://dx.doi.org/10.1504/ijbaf.2014.061440.

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7

Mase, Bryan. "Comovement in the FTSE 100 Index." Applied Financial Economics Letters 4, no. 1 (January 2008): 9–12. http://dx.doi.org/10.1080/17446540701222425.

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8

Lin, Yueh-Neng, Norman Strong, and Xinzhong Xu. "Pricing FTSE 100 index options under stochastic volatility." Journal of Futures Markets 21, no. 3 (2001): 197–211. http://dx.doi.org/10.1002/1096-9934(200103)21:3<197::aid-fut1>3.0.co;2-3.

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9

Borovička, Adam. "Comparison of Volatility Models of PX Index and FTSE 100 Index." Acta Oeconomica Pragensia 19, no. 2 (April 1, 2011): 66–88. http://dx.doi.org/10.18267/j.aop.331.

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10

Mase, Bryan. "The Impact of Changes in the FTSE 100 Index." Financial Review 42, no. 3 (August 2007): 461–84. http://dx.doi.org/10.1111/j.1540-6288.2007.00179.x.

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11

Areal, Nelson, and Artur Rodrigues. "Discrete dividends and the FTSE-100 index options valuation." Quantitative Finance 14, no. 10 (October 5, 2011): 1765–84. http://dx.doi.org/10.1080/14697688.2011.618457.

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12

Liu, Xiaoquan. "Returns to trading portfolios of FTSE 100 index options." Applied Financial Economics 17, no. 15 (October 2007): 1211–25. http://dx.doi.org/10.1080/09603100600905079.

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13

Nuraeni, Risky, and Jihad Lukis Panjawa. "Analisis pengaruh indeks saham asing terhadap indeks harga saham gabungan dengan pendekatan Error Correction Model." Journal of Economics Research and Policy Studies 1, no. 1 (April 29, 2021): 25–39. http://dx.doi.org/10.53088/jerps.v1i1.37.

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The Composite Stock Price Index (IHSG) is a composite index of many shares listed on the stock exchange and their movements show conditions that occur in the capital market. JCI is confident of macroeconomic factors and foreign exchange indexes. The purpose of this study was to analyze the effect of the Dow Jones Index, the Straits Time Index, the Hang Seng Index, the Nikkei 225 Index, and the FTSE 100 Index on the composite price index. The research method used is the Error Correction Model (ECM). In the short term, the DJIA and FTSE 100 variables have a positive effect on the JCI, the STI and Hang Seng variables have no significant on the JCI, while the Nikkei 225 has a negative effect on the JCI. In the long term, the DJIA and STI variables have a positive effect on the IHSG, the JSI and FTSE 100 variables have no effect on the IHSG, while the Nikkei 225 variable has a negative effect on the JCI.

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14

Speight, Alan E. H., David G. McMillan, and Owain ap Gwilym. "Intra-day volatility components in FTSE-100 stock index futures." Journal of Futures Markets 20, no. 5 (May 2000): 425–44. http://dx.doi.org/10.1002/(sici)1096-9934(200005)20:5<425::aid-fut2>3.0.co;2-0.

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15

Butterworth, Darren, and Phil Holmes. "Mispricing in stock index futures contracts: evidence for the FTSE 100 and FTSE mid 250 contracts." Applied Economics Letters 7, no. 12 (December 2000): 795–801. http://dx.doi.org/10.1080/135048500444822.

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16

Gomaa A. Mohamed, Ghada. "The Correlation between the Value of Mortgage-Backed Securities & the Value of FTSE 100 Shares Price Index: September 2013 Prices." Applied Economics and Finance 8, no. 2 (February 8, 2021): 17. http://dx.doi.org/10.11114/aef.v8i2.5156.

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Using the latest information and prices for mortgage-backed securities in September 2013 this analytical piece tests the correlation between the value of these instruments and the value of the FTSE 100 share price index.The correlation between the value of mortgage-backed securities and the value of FTSE 100 shares price index.

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17

Liao, Wen Ju, and Hao-Chang Sung. "Implied risk aversion and pricing kernel in the FTSE 100 index." North American Journal of Economics and Finance 54 (November 2020): 100826. http://dx.doi.org/10.1016/j.najef.2018.08.009.

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18

McMillan, David G., and Alan E. H. Speight. "Asymmetric volatility dynamics in high frequency FTSE-100 stock index futures." Applied Financial Economics 13, no. 8 (January 2003): 599–607. http://dx.doi.org/10.1080/0960310022000040715.

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19

Abhyankar, A., L. S. Copeland, and W. Wong. "LIFFE cycles: intraday evidence from the FTSE-100 Stock Index futures market." European Journal of Finance 5, no. 2 (June 1999): 123–39. http://dx.doi.org/10.1080/135184799337136.

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20

Yao, Hongxing, and Bilal Ahmed Memon. "Network topology of FTSE 100 Index companies: From the perspective of Brexit." Physica A: Statistical Mechanics and its Applications 523 (June 2019): 1248–62. http://dx.doi.org/10.1016/j.physa.2019.04.106.

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21

Mase, Bryan. "Investor awareness and the long-term impact of FTSE 100 index redefinitions." Applied Financial Economics 16, no. 15 (October 15, 2006): 1113–18. http://dx.doi.org/10.1080/09603100500447479.

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22

Tabner, Isaac T. "Benchmark Concentration: Capitalization Weights Versus Equal Weights in the FTSE 100 Index." Multinational Finance Journal 13, no. 3/4 (December 1, 2009): 209–28. http://dx.doi.org/10.17578/13-3/4-3.

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23

Riedlinger, Flavio Ivo, and João Nicolau. "The Profitability in the FTSE 100 Index: A New Markov Chain Approach." Asia-Pacific Financial Markets 27, no. 1 (September 10, 2019): 61–81. http://dx.doi.org/10.1007/s10690-019-09282-4.

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24

Gregoriou, Andros, and Ngoc Dung Nguyen. "Stock liquidity and investment opportunities: New evidence from FTSE 100 index deletions." Journal of International Financial Markets, Institutions and Money 20, no. 3 (July 2010): 267–74. http://dx.doi.org/10.1016/j.intfin.2010.03.005.

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25

Yuen, Man-Chung, Sin-Chun Ng, and Man-Fai Leung. "Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100." Journal of Physics: Conference Series 1828, no. 1 (February 1, 2021): 012111. http://dx.doi.org/10.1088/1742-6596/1828/1/012111.

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26

Mateus, Cesario, Irina Mateus, and Alex Stojanovic. "Diversity on British boards and personal traits that impact career progression from AIM towards FTSE 100." Corporate Ownership and Control 17, no. 4 (2020): 183–99. http://dx.doi.org/10.22495/cocv17i4art15.

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This study proposes a new approach to examining executive remuneration and manager characteristics disaggregated by market index peer clusters and analyses personal attributes that differentiate managers across companies of different market caps (proxied my market indices such as FTSE 100, FTSE 250, FTSE SmallCap, and AIM). Our sample is composed of biographical data on 790 executive directors from 125 UK financial firms covering a 2004-2016 time period. The results show that network and education are the most important factors for career progression. On average, FTSE 100 executive directors are three times better connected and two times better educated than FTSE SmallCap and AIM board members. The larger the firm, the more diverse the board with more international (non-British) and female directors (even though male executives mostly dominate). The higher position is associated with greater age, while new executives tend to be younger and better connected. We highlight a change in the new managers’ skill-set after the financial crisis which may presumably be explained by risk aversion. New directors appointed after 2008 are, on average, older and better educated. Even though after the crisis we document that all the boardrooms, except FTSE SmallCap, appear to have become more gender diverse, the female presence in the boards is scarce and the highest number of women was mainly employed during the financial crisis. After 2008, British boards have become less nationality diverse. Thus, for the purpose of maintaining companies’ competitive advantage in increasingly diverse markets, it requires further attention from policy regulators.

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27

Mardini, Ghassan H., and Sameh Ammar. "Quality and quantity of FTSE-100 segmental information reporting." Accounting Research Journal 32, no. 3 (September 27, 2019): 326–43. http://dx.doi.org/10.1108/arj-05-2017-0093.

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Purpose This study aims to explore the impact of international financial reporting standard no. 8 (IFRS 8) on segmental information reporting (SIR) after the post-implementation review (PIR) issued by international accounting standards board (IASB). This impact is examined in relation to quality and quantity as SIR dimensions represent, respectively, the level of reported items and segments. As a complement to this, the chief operating decision maker (CODM) identity is considered to understand the patterns of SIR dimensions. Design/methodology/approach The SIR of the UK financial times stock exchange 100 (FTSE-100) listed companies over the period 2013-2016 is the research’s scope. Several criteria were developed to ensure a representative research sample. A disclosure index approach was used facilitating the use of content analysis for data collection, which pertained to the dimensions of SIR published by the FTSE-100 following IFRS 8 PIR. Findings The IFRS 8 PIR has had several implications shaping the growing trend that is underpinned by the SIR dimensions published by FTSE-100 companies. First, the SIR quantity dimension positively corresponds over 2013-2016, but it still does not meet IASB’s demands. This, secondly, also applies to the quality dimension of SIR to uncover inconsistency with the existing knowledge being held regarding the introduction of IFRS 8. More specifically, the response of the FTSE-100 to mandatory and voluntary items seems to be in transition of substitution. Third, CODM’s identity was an insightful dimension in rationalising the understanding through the aforementioned dimensions. It is undertaken by boards of directors or executive committees and the case of the latter is associated with more disclose in relation to the CODM’s identity. Practical implications These findings reveal implications to: academics undertaking further research about IFRS 8 PIR to challenge or endorse this conclusion, using similar or alternative approaches; the stakeholders’ decision-making process; and policymakers to re-think the structure of mandatory and voluntary items. Originality/value This paper provides empirical evidence on the quality and quantity of SIR published by FTSE-100 companies following IFRS 8 PIR.

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28

Butterworth, Darren, and Phil Holmes. "The hedging effectiveness of stock index futures: evidence for the FTSE-100 and FTSE-mid250 indexes traded in the UK." Applied Financial Economics 11, no. 1 (February 2001): 57–68. http://dx.doi.org/10.1080/09603100150210264.

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29

Butterworth, Darren, and Phil Holmes. "Ex Ante Hedging Effectiveness of UK Stock Index Futures Contracts: Evidence for the FTSE 100 and FTSE Mid 250 Contracts." European Financial Management 6, no. 4 (December 2000): 441–57. http://dx.doi.org/10.1111/1468-036x.00134.

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30

Peker, Sinem, Manuela Tvaronavičienė, and Bora Aktan. "Sustainable risk management: fuzzy approach to volatility and application on FTSE 100 index." Entrepreneurship and Sustainability Issues 2, no. 1 (September 30, 2014): 30–36. http://dx.doi.org/10.9770/jesi.2014.2.1(4).

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31

Garrett, Ian, and Nicholas Taylor. "Intraday and Interday Basis Dynamics: Evidence from the FTSE 100 Index Futures Market." Studies in Nonlinear Dynamics and Econometrics 5, no. 2 (July 1, 2001): 133–52. http://dx.doi.org/10.1162/108118201317283644.

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32

Aboud, Ahmed, and Malin Karlsen. "Changes in liquidity associated with removal of companies from the FTSE 100 index." International Journal of Managerial and Financial Accounting 11, no. 1 (2019): 38. http://dx.doi.org/10.1504/ijmfa.2019.097829.

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33

Karlsen, Malin, and Ahmed Aboud. "Changes in liquidity associated with removal of companies from the FTSE 100 index." International Journal of Managerial and Financial Accounting 11, no. 1 (2019): 38. http://dx.doi.org/10.1504/ijmfa.2019.10019095.

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34

Frijns, Bart, and Yiuman Tse. "The Informativeness of Trades and Quotes in the FTSE 100 Index Futures Market." Journal of Futures Markets 35, no. 2 (February 17, 2014): 105–26. http://dx.doi.org/10.1002/fut.21652.

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35

Triana, Neni. "HUBUNGAN KAUSALITAS ANTARA INDEKS HARGA SAHAM SYARIAH DI NEGARA MALAYSIA, SINGAPURA DAN INDEKS HARGA SAHAM SYARIAH JAKARTA ISLAMIC INDEX (JII) DI INDONESIA." ILTIZAM Journal of Shariah Economic Research 3, no. 2 (December 22, 2019): 102. http://dx.doi.org/10.30631/iltizam.v3i2.536.

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Penelitian ini bertujuan untuk mengetahui hubungan antara Indeks Harga Saham Syariah di Negara Malaysia, Singapura dan Indeks Harga Saham Syariah Jakarta Islamic Index (JII) di Indonesia. Memanfaatkan indeks harga saham syariah FTSE bursa Malaysia emas shariah index (FBMS), indeks harga saham syariah FTSE SGX Asia shariah 100 index (sgs 100) sebagai variabel independent dan indeks harga saham syariah Jakarta Islamic Index (JII) sebagai variabel dependent. Setelah dilakukan pengujian analisis regresi berganda diperoleh hasil konstanta bernilai Positif sebesar 274.463, variabel indeks harga saham syariah FBMS bernilai positif sebesar 0,418 dan variabel indeks harga saham syariah SGS 100 bernilai positif sebesar 0,156. Dan dari hasil pengujian secara parsial (Uji-t) indeks harga saham syariah FBMS bernilai sig. 0,000 ini menunjukkan terdapat pengaruh yang signifikan terhadap indeks harga saham JII.variabel indeks harga saham syariah SGS 100 secara parsial (Uji-t) tidak pengaruh signifikan terhadap indeks harga saham JII dengan nilai sig. 0,312. Dan dengan pengujian menggunakan Uji F indeks harga saham syariah FBMS dan SGS 100 secara bersama-sama mempengaruhi indeks harga saham syariah JII dengan nilai sig.0,000. Dibuktikan dengan uji R square variabel indeks harga saham syariah FBMS dan SGS 100 mempengaruhi JII sebesar 79,4%. Dan sisanya 20,6% dipengaruhi faktor lain.

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36

Astikawati, Yunita, and Avelius Dominggus Sore. "PENGARUH VOLUME PERDAGANGAN SAHAM DI MATURE MARKET TERHADAP VOLUME PERDAGANGAN SAHAM DI EMERGING MARKET." JURKAMI : Jurnal Pendidikan Ekonomi 4, no. 1 (May 13, 2019): 11–19. http://dx.doi.org/10.31932/jpe.v4i1.421.

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Abstract: Mature market is safer investment than emerging market. It’s became a references to emerging market in order to make investment decision particularly in stock exchange. Therefore, the analize to test of effect the stock trading volume in both mature market and emerging market year 2014 until 2018, is needed. The sample was determined by purposive sampling. Stock index which used in this research i.e. NYSE, NASDAQ, FTSE 100, SSEC, HANG SENG, Kopsi, STI, SENSEX, IHSG, JSE, Stock Exchange Thailand, and TSEC Taiwan. To analize the data is used multilinear regression. The result shows that’s mature market which representative by NYSE, Nasdaq, FTSE 100, SSEC, HANGSENG, Kopsi, and STI has a significant positive effect to IHSG, SET, and TSEC. Meanwhile, NYSE, Nasdaq, FTSE 100, SSEC, HANGSENG, Kopsi, STI hasn’t stock trading volume effect in SENSEX and JSE. It can be concluded that in developing countries, the decision to buy and sell stock in the mature market became fundamental consideration for investors to sell or buy it. Keyword: Emerging, Mature, VolumeAbstrak: Mature market memiliki keamanan investasi daripada emerging market. Mature market menjadi acuan bagi emerging market dalam membuat keputusan investasi terutama di pasar modal. Oleh karena itu perlu adanya analisis untuk menguji pengaruh dari volume perdaganan saham di mature market dan emerging market tahun 2014-2018. Sampel yang digunakan adalah purposive sampling. Indek saham yang digunakan dalam analisis ini adalah NYSE, Nasdaq, FTSE 100, SSEC, HANGSENG, Kopsi, STI, SENSEX, IHSG, JSE, Stock Exchange Thailand, dan TSEC taiwan. Analisis yang digunakan adalah regresi berganda. Hasil analisis menunjukan bawha mature market yang diwakili oleh indek NYSE, Nasdaq, FTSE 100, SSEC, HANGSENG, Kopsi, STI memiliki pengaruh positif signifikan terhadap indek saham IHSG, SET, TSEC. Sedangkan indek NYSE, Nasdaq, FTSE 100, SSEC, HANG SENG, Kopsi, STI tidak mempengaruhi volume perdagangan saham di indek SENSEX dan JSE. Disimpulkan bahwa keputusan jual beli saham di mature market menjadi dasar pertimbangan bagi investor untuk menjual atau membeli saham pada negara berkembangKata Kunci: Emerging, Mature, Volume

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37

Alghalith, Moawia, Christos Floros, and Ricardo Lalloo. "A note on dynamic hedging." Journal of Risk Finance 16, no. 2 (March 16, 2015): 190–96. http://dx.doi.org/10.1108/jrf-10-2014-0143.

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Purpose – The purpose of this paper is to empirically test dynamic hedging, using data from the FTSE-100 and Standard & Poor’s (S&P) 500 futures indices. Design/methodology/approach – The authors introduce a dynamic continuous-time hedging model in futures markets. The authors further relax the statistical-independence assumption between the spot price and basis risk. Findings – The authors show that the investors are, on average, quite risk averse. The authors find that a one unit increase in the price volatility reduces the hedged FTSE-100 (S&P 500) by 645.62 (777.07) units. Similarly, a one unit increase in basis risk reduces the hedged FTSE-100 (S&P 500) by 403.57 (378.54) units. The authors’ approach shows that risk-averse investors should decrease their hedge (i.e. increase their equity allocation) with an increase in index price risk. Practical implications – These findings are helpful to risk managers dealing with futures markets. Originality/value – The contribution of this paper is that it successfully introduces a dynamic continuous-time hedging model in futures markets.

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38

Draper, Paul, and Joseph K. W. Fung. "A Study of Arbitrage Efficiency Between the FTSE-100 Index Futures and Options Contracts." Journal of Futures Markets 22, no. 1 (January 2002): 31. http://dx.doi.org/10.1002/fut.2206.

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39

McCann, Rory, and Daniel Broby. "The Effect of “Brexit” Uncertainty on The FTSE 100 Index and The UK Pound." European Journal of Economics 1, no. 1 (September 2, 2021): 1–13. http://dx.doi.org/10.33422/eje.v1i1.42.

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We investigate the impact of the uncertainty surrounding the United Kingdom’s proposed departure from the European Community (“Brexit”) on financial assets. We conduct an event study around the November 14th 2018 draft withdrawal agreement. Our motivation was that the economic impact of the various political permutations that persisted throughout the negotiation period were both measurable and distinct. The probability of each Brexit scenario that was discussed varied over the political discourse. Using opinion poll data we investigate the event impact on both the FTSE 100 and the UK Pound. We found that, in accordance with existing academic evidence, asset prices discounted the weighted probabilistic economic impact of likely outcomes. We observe, however, that this impact was not as immediate as theory suggests. Interestingly, currency markets had the greater sensitivity. Our conclusions have important implications for the pricing of country risk premia in general and the European Union in particular. Key takeaways: 1) Asset prices were slow to discount the weighted probabilistic economic impact of Brexit risk. 2) Currency markets had the greater sensitivity to changes in Brexit risk. 3) Country risk premia can be impacted by perceived changes in custom union.

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40

Gregoriou, Andros, Jerome Healy, and Christos Ioannidis. "Hedging under the influence of transaction costs: An empirical investigation on FTSE 100 index options." Journal of Futures Markets 27, no. 5 (2007): 471–94. http://dx.doi.org/10.1002/fut.20257.

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41

McMillan, David G., and Alan E. H. Speight. "Intra-day periodicity, temporal aggregation and time-to-maturity in FTSE-100 index futures volatility." Applied Financial Economics 14, no. 4 (February 20, 2004): 253–63. http://dx.doi.org/10.1080/0960310042000201165.

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42

Butterworth, Darren, and Phil Holmes. "The Hedging Effectiveness of U.K. Stock Index Futures Contracts Using an Extended Mean Gini Approach: Evidence for the FTSE 100 and FTSE Mid250 Contracts." Multinational Finance Journal 9, no. 3/4 (December 1, 2005): 131–60. http://dx.doi.org/10.17578/9-3/4-1.

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43

Gwilym, Owain ap, Mike Buckle, and Stephen H. Thomas. "The Intraday Behavior of Bid-Ask Spreads, Returns, and Volatility for FTSE-100 Stock Index Options." Journal of Derivatives 4, no. 4 (May 31, 1997): 20–32. http://dx.doi.org/10.3905/jod.1997.407980.

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44

Holmes, Phil. "Ex ante hedge ratios and the hedging effectiveness of the FTSE-100 stock index futures contract." Applied Economics Letters 2, no. 3 (March 1995): 56–59. http://dx.doi.org/10.1080/135048595357564.

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45

Donmez, Cem Cagri, and Doruk Sen. "Desirability Index with Sector Analysis for Investment Decisions by Bernoulli Theorem: A Case of FTSE-100." International Journal of Computational Physics Series 1, no. 1 (March 1, 2018): 151–60. http://dx.doi.org/10.29167/a1i1p151-160.

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Due to complex and sophisticated nature of financial markets, many features including psychological, political and economic can influence the routine behaviour and considered by characteristic nonlinearities. There have been many efforts about the stock market prediction by using technical analysis methods. Concerning the application area of entropy into economics which could be referred as econophysics; there are two approaches that could be investigated within. These two could be named as ontological and metaphorical. As the Second Law of Thermodynamics has a critical role in the concept of ontological approach, the economy is considered as processes of physics and biology that are brought by energy while the metaphorical approach is more concerned with the application of finance and equilibrium theory on the info design of entropy. This research takes the metaphorical approach to the investigation of Oil & Gas Producers industry sector of the London Stock Exchange’s FTSE-100 index. First, we observed the necessity to analyse each stock with the Bernoulli Theorem to compute an index for the desirability of each to express the complexity of the investment decisions. Then, an analysis is conducted for the sector with the computation. During the computation phase, macroeconomic figures are introduced to the Bernoulli Theorem, as inflation. In addition to this, other stock movements in the sector are introduced for precise prediction as well as the parameter of the trading volume of each stock. With the suggested approach, the desirability index is formed as an unconventional theoretical approach within econophysics for expression of the complex nature of investment environment.

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46

McMillan, David G., and Alan E. H. Speight. "Nonlinear dynamics and competing behavioral interpretations: Evidence from intra-day FTSE-100 index and futures data." Journal of Futures Markets 26, no. 4 (2006): 343–68. http://dx.doi.org/10.1002/fut.20203.

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47

Brooks, Chris, and Ian Garrett. "Can we explain the dynamics of the UK FTSE 100 stock and stock index futures markets?" Applied Financial Economics 12, no. 1 (January 2002): 25–31. http://dx.doi.org/10.1080/09603100110087996.

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48

Wang, Yaw-Huei, and Yu-Jen Hsiao. "The Impact of Non-trading Periods on the Measurement of Volatility." Review of Pacific Basin Financial Markets and Policies 13, no. 04 (December 2010): 607–20. http://dx.doi.org/10.1142/s0219091510002098.

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Based upon the theory of the "arrival of news", the main purpose of this paper is to investigate the impact of non-trading periods on the measurement of volatility for the S&P 500, FTSE 100, and TAIEX indices. Using an adaptation of the GJR (1,1) model, we find that both weekday holiday periods and half-day trading periods have significant impacts on the estimation of volatility for the S&P 500 and FTSE 100 indices. On the other hand, weekends have significant impacts for the TAIEX index. Our findings imply that for the UK and US markets, much less relevant information is produced during weekends, while more relevant information continues to be produced during other types of non-trading periods. However, the weekend volatility of the Taiwan market is specially driven because the US macro news is announced on Fridays and the trading time of the US market is later than that of the Taiwan market without any overlapping.

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Agrawal, Tarunika Jain, Sanjay Sehgal, and Rahul Agrawal. "Disruptive Innovations, Fundamental Strength and Stock Winners: Implications for Stock Index Revisions." Vision: The Journal of Business Perspective 24, no. 3 (June 14, 2020): 356–70. http://dx.doi.org/10.1177/0972262920928890.

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Globally, disruptions driven by technological advancements are visible in the form of unicorns and declining lifespan of the index constituents. Sectors such as information technology, financial services, energy, consumer goods and automobile are found to be more prone to disruptive innovation. Assessing the financial strength of the incumbents is crucial to assess their strength to endure disruption. We construct a fundamental strength index (FSI) using 11 financial performance measures covering 7 key attributes, namely profitability, efficiency, solvency, liquidity, net investments, pursuance of innovation and entry barriers, over the 5-year period 2014–2019. FSI helps in categorizing stocks of National Stock Exchange (NSE) 200 universe as ‘A’ being the fundamentally strongest and ‘C’ being the weakest. Potential crossovers can take place between ‘C’ category stock in Nifty 50 (Next 50) and ‘A’ category stock belonging to the Next 50 (Nifty Midcap 100). The results show that the disruptor’s portfolio (Next 50 stocks) outperforms the incumbent’s portfolio (Nifty 50 constituents) with a return of 1.61 per cent vs 0.47 per cent. A similar observation holds true for the Next 50 and Nifty Midcap 100, with the disruptor’s portfolio surpassing the incumbent’s portfolio (return of 2.59% vs 0.44%). The study has significant implications for the policymakers, investors, companies and academicians.

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Busso, Donatella, Alain Devalle, and Fabio Rizzato. "A comparative analysis of the board evaluation in European entities: Italy vs UK." Corporate Ownership and Control 14, no. 1 (2016): 578–87. http://dx.doi.org/10.22495/cocv14i1c4art4.

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Board evaluation is an evaluation of the performance of the board of directors and its committees, as well as their size, composition and operation. The aim of this paper is to investigate how entities do the evaluation of the performance of the board and how they disclose the self-assessment. We analysed the largest forty constituents of both Italy’s FTSE MIB index and the UK’s FTSE 100 index. The results show that although Corporate Governance Codes’ requirements are similar, implementation of these requirements and the related disclosure continue to show significant differences. The UK companies seem to have a stronger “forward-looking” approach compared to Italian companies. Disclosure provided by Italian companies is too often not enough to enable stakeholder understanding of the process and its outcome. This research contributes to the literature by providing results on the evaluation of boards of directors: regulators, practitioners and researchers must deal with this topic in order to strengthen the rules of corporate governance.

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