The sectoral effects of Brexit on the British economy: early evidence from the reaction of the stock market

Ramiah, Vikash; Pham, Huy; Moosa, Imad A.

doi:10.1080/00036846.2016.1240352

Cited by 101 publications

(90 citation statements)

References 4 publications

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“…Schiereck, Kiesel and Kolaric () focus more narrowly on the financial sector and show that stock prices of banks dropped sharply after the referendum, particularly for EU banks. Ramiah, Pham and Moosa () look at a much wider range of sectors and discuss whether the observed price reactions are in line with prior expectations. They do not regress abnormal returns on explanatory variables, however, and thus cannot formally test hypotheses about differential sector‐level impacts advanced in the pre‐referendum literature.…”

Section: Introductionmentioning

confidence: 71%

The Economic Effects of Brexit: Evidence from the Stock Market

Breinlich

Leromain²,

Novy

et al. 2018

Fiscal Studies

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We study stock market reactions to the Brexit referendum on 23 June 2016 in order to assess investors’ expectations about the effects of leaving the European Union on the UK economy. Our results suggest that initial stock price movements were driven by fears of a cyclical downturn and by the sterling depreciation following the referendum. We also find tentative evidence that market reactions to two subsequent speeches by Theresa May (her Conservative party conference and Lancaster House speeches) were more closely correlated with potential changes to tariffs and non‐tariff barriers on UK–EU trade, indicating that investors may have updated their expectations in light of the possibility of a ‘hard Brexit’. We do not find a correlation between the share of EU immigrants in different industries and stock market returns.

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Section: Introductionmentioning

confidence: 71%

The Economic Effects of Brexit: Evidence from the Stock Market

Breinlich

Leromain²,

Novy

et al. 2018

Fiscal Studies

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“…This DV is multiplied by the market risk premium to form the interaction variable. Based on Ramiah et al ()'s study, the model to be estimated is expressed as follows:

{\overset{r}{true}}_{italicit} - {\overset{r}{true}}_{italicft} = β_{i}^{0} + β_{i}^{1} [{\overset{r}{true}}_{italicmt} - {\overset{r}{true}}_{italicft}] + β_{i}^{2} [{\overset{r}{true}}_{italicmt} - {\overset{r}{true}}_{italicft}] * DV + β_{i}^{3} {DV}_{t} + {\overset{ε}{true}}_{italicit}

where

{\overset{false~}{r}}_{it}

is sector i 's return at time t,

{\overset{false~}{r}}_{ft}

is the risk‐free rate at time t ,

{\overset{false~}{r}}_{mt}

is the returns of stocks within each sector at time t , DV is a DV that takes the value of one on the first day of trading following the election/Inauguration Day and zero otherwise,

β_{i}^{0}

is the intercept of the regression equation [E (

β_{i}^{0}

) = 0],

β_{i}^{1}

corresponds to the average short‐term systematic risk of the industry,

β_{i}^{2}

corresponds to the change in the industry risk, and

β_{i}^{3}

measures the intercept of Equation (),

{\overset{false~}{ε}}_{it}

is the error term. The Equation () is estimated to identify the short‐term change in systematic risk of the US disaggregated stock markets.…”

Section: Methodsology and Datamentioning

confidence: 56%

“…To evaluate the disaggregated US stock market responses to the 2016 US presidential election result and the Inauguration Day, we follow Ramiah, Pham, and Moosa () and Bouoiyour and Selmi (, b) by adjusting daily returns to obtain the ex‐post ARs where adjustment is approximated by the Capital Asset Pricing Model (CAPM). The ARs are, thereafter, grouped into sectors to determine the disaggregated average ( D ) at time t , (AR Dt ) is denoted as

{AR}_{D t i t} = {false\sum}_{i = 1}^{n} \ln ()(, {normalP}_{italicit}, /P_{i t - 1}) - E false(R_{it} false)

where

E (R_{italicit})

is calculated from the following equation:

E false(R_{it} false) = β_{0_{it}} + β_{1_{it}} false({\overset{false~}{r}}_{m t_{US}} - {\overset{false~}{r}}_{f t_{US}} false)

With P it denoting the adjusted price of sector i at time t,

E (R_{italicit})

denoting the expected return on sector i at time t ,

{\overset{false~}{r}}_{m t_{US}}

denoting the returns of stocks within each sector and

{\overset{false~}{r}}_{f t_{US}}

referring to the US risk‐free rate proxied by the US three‐month Treasury Bill (T‐Bill).…”

Section: Methodsology and Datamentioning

confidence: 99%

“…To evaluate the disaggregated US stock market responses to the 2016 US presidential election result and the Inauguration Day, we follow Ramiah, Pham, and Moosa (2016) and Bouoiyour and Selmi (2018a, b) by adjusting daily returns to obtain the ex-post ARs where adjustment is approximated by the Capital Asset Pricing Model (CAPM). The ARs are, thereafter, grouped into sectors to determine the disaggregated average (D) at time t, (AR Dt ) is denoted as where E(R it ) is calculated from the following equation:…”

Section: Me Thodology and Datamentioning

confidence: 99%

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The financial costs of political uncertainty: Evidence from the 2016 US presidential elections

Selmi

Bouoiyour

2019

Scottish J Political Eco

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The victory of Mr. Donald Trump came as a surprise to a wide range of market participants. Some of the elements of his economic plan were envisaged to affect all US sectors. This paper assesses the reactions of disaggregated US stock market to the 2016 US presidential election results, and possible deregulation that is to follow after his inauguration. We find that the different US sectors were significantly and varyingly influenced by the election result, and were greatly reactive during the days after the inauguration. This underscores that uncertainty tends to persist and even rises since the President‐elect took office.

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“…The British economy was the center of the storm during the referendum as it witnessed severe losses. The Brexit produce varying effects on different industries, banks, and financial services were the most affected, with a cumulative abnormal return (CAR) of almost −15% for the banking sector (Ramiah et al 2016).…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Performance of Exchange Traded Funds During the Brexit Referendum: An Event Study

Alkhatib

Harasheh

2018

SSRN Journal

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In today's interrelated economies, financial information travel at speed of light to reach investors around the globe. Global financial markets experience regular shocks that transmit negative waves to other equity markets and different asset classes. Given the unique characteristics of exchange-traded funds (ETFs), this paper examines how different ETFs that are traded on London Financial center reacted to the Brexit event in 23 June 2016. The unexpected referendum result the day after is viewed as the next significant financial event since 2008. The paper employs an event study market model on daily and abnormal returns of the selected ETFs with respect to FTSE 250 around the event date. Contrary to what is expected, the world equities fund experienced significant positive abnormal return on the event day. Emerging markets again proved to be a preferred investment destination in times of financial turmoil; the emerging equities fund gained 3% while enjoying an 11.5% positive significant abnormal returns. The US T-Bond fund recorded a 9% return with a significant 7.2% abnormal return. The gold fund soared as much as 4% as investors seeks refuge from Brexit, and the oil fund retraced 1% amid concerns of slowing global demand.

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The sectoral effects of Brexit on the British economy: early evidence from the reaction of the stock market

Cited by 101 publications

References 4 publications

The Economic Effects of Brexit: Evidence from the Stock Market

The Economic Effects of Brexit: Evidence from the Stock Market

The financial costs of political uncertainty: Evidence from the 2016 US presidential elections

Performance of Exchange Traded Funds During the Brexit Referendum: An Event Study

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