Financial Times Stock Exchange Research Articles

Examining determinants of book-tax difference: Insights from Malaysian multinational corporations

This research investigates the factors affecting the book-tax differences of multinational corporations (MNCs) operating in Malaysia. The sample for this study consists of MNCs with subsidiaries in tax haven countries listed on the Financial Times Stock Exchange (FTSE) Bursa Malaysia Kuala Lumpur Composite Index (KLCI) and FTSE Bursa Malaysia Mid 70 Index. About 67 MNCs were selected after eliminating 32 companies with inadequate data and one with extreme outliers. A secondary data approach was employed, utilising multiple regression analysis. Selected corporate characteristics, including deferred tax liabilities and assets, taxable income, interest coverage ratio, return on assets, and effective tax rate, were used as independent variables. Descriptive analysis revealed evidence that corporations with a high return on assets (ROA) typically benefit from very high levels of book-tax differences (BTDs). Furthermore, the results indicate that a high interest coverage ratio may partly explain the outcomes leading to an increase in differences recorded between book income and taxable income. Thus, this supports the idea that a positive relationship exists between the interest coverage ratio and BTD. Hence, this study contributes to the tax literature and informs policymakers on factors influencing BTD, especially MNCs with subsidiaries in tax haven countries. The study aims to contribute to the existing body of knowledge by offering insights into the factors influencing MNCs' strategic tax planning decisions in the Malaysian context. Policymakers should address the unfair allocation of the worldwide tax base, where a reasonable portion of the earnings made by MNCs doing business in a nation should be subject to taxation in that nation.

Determinants of UK companies’ dividend policy

This study examines the major factors influencing UK companies listed on the Financial Times Stock Exchange (FTSE) 100 stock market's dividend policy (as determined by the dividend payout ratio) over 32 years, from 1990 to 2022. The dividend premium and free cash flow components make up the catering dividend. The outcomes of a wide range of panel data analysis regressions, such as Generalized Method of Moments (GMM) and Two-Stage Least Squares (2SLS) regressions, clearly show that the catering dividend significantly impacts UK firms' dividend policy. On the other hand, the dividend policy benefits from the dividend premium, which increases it by 12% to 17% on average. Free cash flow, on the other hand, has a negligible negative impact on the dividend policy by just 5%. It is crucial to mention that this outcome varies depending on the models and regression techniques used. Furthermore, this study emphasizes how important it is for a firm's size and profitability to play a key role in determining how it will implement its dividend policy. Financial leverage also becomes important since a company's dividend payment ratio decreases when it relies more heavily on debt in its capital structure. By using GMM and 2SLS regressions, this study carefully tackles the endogeneity issue, and the results hold up even when the endogeneity effect is reduced. Ultimately, this study emphasizes how important dividend catering components are in guiding UK companies' dividend policies, arguing that CEOs and legislators should pay more attention to this.

Global uncertainty and potential shelters: gold, bitcoin, and currencies as weak and strong safe havens for main world stock markets

This article investigates five safe-haven asset responses from 2014 to 2022, including the unprecedented COVID-19 crisis, Russian invasion of Ukraine, and sharp US interest rate increases of 2015 and 2022. We apply the unique approach of the multivariate factor stochastic volatility (MSV) model, which is extremely efficient for financial market analysis and allows us to conduct dynamic factor analysis of safe-haven relationships that cannot be observed directly. The research sample consists of five prospective safe-haven assets—gold, bitcoin, the euro, the Japanese yen, and the Swiss franc—and five primary world stock market indices—the S&P 500, Financial Times Stock Exchange (FTSE) 100, DAX, STOXX Europe 600, and Nikkei 225. Our findings are useful for investors searching for the best safe-haven assets among gold, bitcoin, and currencies to hedge against financial turmoil in global stock markets. Our unique findings suggest that safe-haven effects work differently for gold and the yen; that is, the Japanese yen acts as the strongest safe haven across all stock indices. Bitcoin is not a strong safe-haven currency since it has zero days of negative correlations with the considered stock indices, but it is a weak safe-haven during times of financial distress. Consequently, we state that strong and weak safe-haven properties vary across time and place. The novelty of our study lies in the methodological complexity of the MSV model (used for the first time to find the best safe-haven asset properties), dynamic factor analysis, a long-term research sample covering the Russian invasion of Ukraine in 2022, and an international investor perspective focusing on the world’s leading stock markets. We extend earlier studies by analyzing the interrelations of the world’s leading stock market indices with five potential safe-haven assets during the long period of 2014–2022 and using a unique dynamic factor analysis to show the differentiated behaviors of the Japanese yen and gold. Additionally, the main innovative contribution is a new framework of weak and strong safe-haven asset classifications not previously applied in the literature.

A novel Deep Reinforcement Learning based automated stock trading system using cascaded LSTM networks

Deep Reinforcement Learning (DRL) algorithms have been increasingly used to construct stock trading strategies, but they often face performance challenges when applied to financial data with low signal-to-noise ratios and unevenness, as these methods were originally designed for the gaming community. To address this issue, we propose a DRL-based stock trading system that leverages Cascaded Long Short-Term Memory (CLSTM-PPO Model) to capture the hidden information in the daily stock data. Our model adopts a cascaded structure with two stages of carefully designed deep LSTM networks: it uses one LSTM to extract the time-series features from a sequence of daily stock data in the first stage, and then the features extracted are fed to the agent in the reinforcement learning algorithm for training, while the actor and the critic in the agent also use a LSTM network. We conduct experiments on stock market datasets from four major indices: the Dow Jones Industrial index (DJI) in the US, the Shanghai Stock Exchange 50 (SSE50) in China, S&P BSE Sensex Index (SENSEX) in India, and the Financial Times Stock Exchange 100 (FTSE100) in the UK. We compare our model with several benchmark models, including: (i) a model based on a buy-and-hold strategy; (ii) a Proximal Policy Optimization (PPO) model with Multilayer Perceptron (MLP) policy; (iii) some up-to-date models like the MLP model, LSTM model, Light Gradient Boosting Machine (LGBM) model, and histogram-based gradient boosting model; and (iv) an ensemble strategy model. The experimental results show that our model outperforms the baseline models in several key metrics, such as cumulative returns, maximum earning rate, and average profitability per trade. The improvements range from 5% to 52%, depending on the metric and the stock index. This indicates that our proposed method is a promising way to build an automated stock trading system.

A Comprehensive Analysis of the Drivers Behind the Effective Tax Rate of Financial Times Stock Exchange (FTSE) in Malaysia

This study examines corporate effective tax rates (ETR) of Malaysian multinational corporations (MNC) listed in the Financial Times Stock Exchange (FTSE) Bursa Malaysia Kuala Lumpur Composite Index (KLCI) and FTSE Bursa Malaysia Mid 70 Index. The main objective of this study is to examine the relationship between the ETR of MNCs with subsidiaries in tax haven countries, and selected corporate characteristics such as firm size, leverage, capital intensity, return on assets, inventory intensity, profit margin, and foreign directors. Using a multiple linear regression approach on a balanced panel sample of 68 companies (321 firm-years) spanning 2017 to 2021, the study provides evidence for the variability of corporate ETRs in which the average corporate ETRs falls below the STR for the financial year 2021 of 24%. The statistical results also reveal that lower ETRs are associated with highly leveraged companies, and greater investments in fixed assets. Further, a negative coefficient for return on assets and profit margin suggests that companies have benefited from tax incentives provided by the government. Hence, this study contributes to tax literature and policymakers on factors that influence the corporate ETR, especially MNC with subsidiaries in tax haven countries. Tax authorities should implement tight monitoring to avoid these MNCs extensively engaging in tax planning which will result in revenue loss to the Malaysian income tax collection.

Audit committee characteristics as determinants of non-audit fees in UK FTSE 350 companies

Purpose This study aims to examine trends in audit committee characteristics of companies and associates characteristics subject to major change with a fee-based proxy for audit committee effectiveness. Design/methodology/approach The research adopts an empirical approach. Using descriptive and inferential statistics, observations for 253 Financial Times Stock Exchange 350 companies’ audit committee characteristics gathered from annual reports at the beginning and end of a five-year period are evaluated against averaged non-audit fees (NAF) as a proportion of total audit fees. Findings Audit committee composition shows an increased incidence of female membership and of members with previous audit experience. The increase in members with previous audit experience is more marked where this is gained with the incumbent auditor. An increase is also shown in chief financial officers with previous audit experience. Previous audit experience is associated with reduced NAF as a proportion of total fees. This is marked where audit experience has been gained with the incumbent auditor. These results suggest that the benefits of financial expertise gained from audit experience outweigh impairments to independence due to social ties. Nevertheless, other studies indicate concerns about independence are still well-founded. Originality/value This paper’s original contribution is to evaluate the potential effect of previous audit experience on those involved in audit committees in light of concerns raised in the literature and by regulators that external auditor independence should be maintained. The innovative fee-based proxy for audit committee effectiveness facilitates an evaluation as to which influence prevails.

Public Disclosure of Tax Strategies and Firm’s Actual Tax Policy

We use a sample of 248 tax strategies published by U.K. companies listed on the Financial Times Stock Exchange (FTSE) 100 and FTSE 250 to examine (a) how companies present themselves—more as “responsible taxpayers” who view taxes as a meaningful contribution to society, or more as “tax planners” who view taxes primarily as a cost, and (b) whether these presentations correspond to actual tax avoidance behavior. Our results show that, on average, firms tend to portray themselves as “responsible taxpayers,” but that this portrayal is consistent with firms’ tax avoidance behavior only when firms are subject to above-average external monitoring by financial analysts. The results suggest that firms manage the content of qualitative tax disclosures to sway public opinion as long as the probability of detecting misstatements is sufficiently low. This raises doubts as to whether mandatory qualitative information provides added value for stakeholders if it is not under external review. JEL Classification: H25; H20; M40; M48

Voluntary Gender Diversity Targets and Their Impact on Firm Performance and Firm Value

ABSTRACT Regulators in the United Kingdom recommend that 33 percent of board members of large U.K. firms be female by 2020. We use this setting to investigate whether greater female representation and any associated financial benefits can be achieved without establishing quotas. Using data between 2012 and 2018 for the Financial Times Stock Exchange (FTSE) 350 firms, we find a significant increase in female representation on boards in recent years, and the rate of increase in gender diversity is higher in these years. Our results also indicate that board gender diversity is positively associated with both financial performance and firm value. However, this association exists only in recent years. Finally, capital market participants value nonexecutive female board members more than executive female board members. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G30; G38; M41.

A Private and Efficient Triple-Entry Accounting Protocol on Bitcoin

The ‘Big Four’ accountancy firms dominate the auditing market, auditing almost all the Financial Times Stock Exchange (FTSE) 100 companies. This leads to people having to accept auditing results even if they may be poor quality and/or for inadequate purposes. In addition, accountants may provide different auditing results with the same financial data. These issues are hard for regulators such as the Financial Reporting Council to identify because of insufficient resources or inconsistent compliance. In this paper, we proposed a triple-entry accounting protocol to allow users to report Bitcoin transactions to a third-party auditor to comply with regulations such as the travel rule. It allows the auditor to easily detect anomalies and identify the non-compliant parties, whilst the blockchain itself provides a transparent and immutable record of these anomalies. Despite building on a public ledger, our solution preserves privacy and offers an interoperability layer for information exchange. Merkle proofs were used to record non-compliant transactions whilst allowing compliant transactions to be pruned from an auditor’s active database.

Portfolio selection model using teaching learning-based optimization approach

<p>Portfolio selection is among the most challenging processes that have recently increased the interest of professionals in the area. The goal of mean-variance portfolio selection is to maximize expected return with minimizing risk. The Markowitz model was employed to solve the linear portfolio selection problem. However, due to numerous constraints and complexities, the problem is so critical that traditional models are insufficient to provide efficient solutions. Teaching learning-based optimization (TLBO) is a powerful population-based nature-inspired approach to solve optimization problems. This article presents a portfolio selection model using the TLBO approach to maximize the portfolio's Sharpe Ratio. The Sharpe ratio combines both expected return and risk. This algorithm model the natural teaching process of the classroom with two main phases, viz., teaching and learning. Performance analysis has been undertaken to investigate the suitability of TLBO based solution approach by comparing it with genetic algorithm (GA) and particle swarm optimization (PSO) on the real datasets, Deutscher Aktienindex (DAX) 100, Hang Seng 31, Standard and Poor’s (S&P) 100, financial times stock exchange (FTSE) 100, and Nikkei 225. The empirical results verify the superiority of the TLBO over GA and PSO.</p>

Monetary Policy Shocks and Unemployment in Emerging Market Economies in Africa

ABSTRACT: Unemployment remains a global challenge that requires additional ways of dealing with it. Therefore, the role of Reserve or Central Banks in reducing unemployment is of recent seeking more attention. This paper examines monetary policy shocks and unemployment nexus in the emerging market economies in Africa. The models of this study are designed within the panel vector autoregressive (PVAR) framework. The models majorly rely on the New Keynesian model. Given the nominal rigidities of the New Keynesian model, the monetary policy can therefore, serve as an important tool which can have a reasonable leverage on real variables. As claimed by Bernanke (2007) that the stance of monetary policy was appropriate to prevent deflation and high unemployment and as it has been empirically tested for developed economies, this paper provides a quantitative evaluation of an aspect of such statement in the context of emerging economies of Africa. We used annual data series on five emerging economies in Africa over the period 1991 to 2020, making 150 balanced panel data observations. The countries include, Egypt, Mauritius, Morocco, Nigeria and South Africa. The selection was based on those African countries identified as emerging markets by various international bodies such as the International Monetary Fund (IMF), James O'Neill, Financial Times Stock Exchange (FTSE), S&P Global Ratings, EM Bond Index, Dow Jones, Russell, Columbia University and Cornell University. The findings of the study revealed that unemployment falls for at most four periods in response to contractionary monetary policy shocks while the response dissipates afterwards. Individually, unemployment rate in Mauritius and Nigeria rose in response to contractionary monetary policy shocks while in South Africa, Egypt and Morocco, it falls in response to contractionary monetary policy shocks. By and large, the results seem to show credence to the effectiveness of monetary policy in each country to achieve desired macroeconomic targets. The main implication of the results is that, expansionary monetary and fiscal policy undertaken by policymakers of emerging economies in Africa can enhance the labor market to recover from the downturns recently experienced in these economies.

Carbon emissions, carbon disclosure and organizational performance

This study draws on the multi-perspectives of organizational legitimacy theory to investigate the simultaneous association between corporate carbon emissions, carbon disclosure and organizational performance. Based on a sample of 62 UK Financial Times Stock Exchange (FTSE) 100 firms in carbon-sensitive sectors during 2010–2017, we find that carbon emissions are negatively associated with organizational performance, but firms with higher carbon emissions tend to employ more disclosure as a communicative legitimacy process to manage the legitimacy threat in order to conform to institutional pressures and protect the firm value. Cumulatively, carbon disclosure plays a mediating role in the relationship between carbon emissions and organizational performance. Most importantly, this legitimacy effect is more pronounced following the introduction of the UK mandatory carbon reporting regulation in 2013, as a result of the increased corporate capability of carbon disclosure. This study fills the literature gap in the value-protective attribute from resource-based view of organizational legitimacy by providing important insights into corporate carbon disclosure strategy over time.

Efficient Market Hypothesis: An Exploratory Study of FTSE-100 Stock Market

The weak form of the efficient market hypothesis posits that stock prices reflect all publicly available information in the market but not undisclosed or private information. To empirically validate the weak form of an efficient market hypothesis, ten years of data from 2012 to 2020 was collected for the (FTSE) Financial Times Stock Exchange Group -100 index. Ordinary least square (OLS) regression was used through SPSS software for statistical tests. The regression tests' findings reveal that the beta coefficient's value is greater than or less than 0 for the lag value of t-1, t-2, t-3, t-4, t-5, t-6, t-7, t-8, t-9, and t-10. Since the value of beta coefficients for all lagged returns of t-1 to t-10 periods is not equal to 0, the efficient market hypothesis is rejected. Thus, this invalidates the efficient market hypothesis. Hence, it can be determined that investors can beat the market by analyzing and evaluating past securities or market index trends. Since past trends predict the future, it can be assumed that stocks or indices will perform similarly.

Efficacy of judicial independence in explaining financial markets in emerging markets

The relationship between institutional quality, financial development, and economic growth has been well documented in the literature. However, much less work has been done the effect of judicial independence (JI) on financial market development (FM) in particular. This paper fills that gap using the JI score and FM in the context of 23 emerging markets (as identified by the Financial Times Stock Exchange Group) over the period 1980–2021. Overall, the results suggest that, in selected emerging markets JI fosters FM. Interestingly, the results across various quantiles of JI show that low JI hampers FM, but a medium and high level of JI boosts FM in these markets. These results are robust to several specifications. To the best of our knowledge, this is the first study to examine the direct impact of JI on FM and confirms the theories on new institutional economics as well as law and finance in emerging markets.

Novel optimization approach for realized volatility forecast of stock price index based on deep reinforcement learning model

Accurately predicting volatility has always been the focus of government decision-making departments, financial regulators and academia. Therefore, it is very crucial to precisely predict the realized volatility (RV) of the stock price index. In this paper, we take the RV sequences of Shanghai Stock Exchange Composite Index (SSEC), Standard & Poor 500 index (SPX) and Financial Times Stock Exchange Index (FTSE) as the research objects, and propose a predictive model based on optimized variational mode decomposition (VMD), deep learning models including deep belief network (DBN), long short-term memory network (LSTM) and gated recurrent unit (GRU), and reinforcement learning Q-learning algorithm. Firstly, the original RV sequence is decomposed by using the VMD ideal parameters optimized by grey wolf optimizer (GWO) to obtain the intrinsic mode functions (IMFs). Then, DBN, LSTM and GRU are used to predict same IMF simultaneously. Finally, the optimal weights of the above three models are determined by the Q-learning algorithm to construct an integrated model, and the final results are obtained after accumulating the predicted values of each IMF. The predictive performance of the model was evaluated by four loss functions: the mean average error (MAE), mean squared error (MSE), heterogeneous mean average error (HMAE), heterogeneous mean squared error (HMSE) and modified Diebold and Mariano test (MDM). The experimental results show that the constructed GVMD-Q-DBN-LSTM-GRU method has better performance that the comparison model in both emerging and developed markets.

GARCHNet: Value-at-Risk Forecasting with GARCH Models Based on Neural Networks

This paper proposes a new GARCH specification that adapts the architecture of a long-term short memory neural network (LSTM). It is shown that classical GARCH models generally give good results in financial modeling, where high volatility can be observed. In particular, their high value is often praised in Value-at-Risk. However, the lack of nonlinear structure in most approaches means that conditional variance is not adequately represented in the model. On the contrary, the recent rapid development of deep learning methods is able to describe any nonlinear relationship in a clear way. We propose GARCHNet, a nonlinear approach to conditional variance that combines LSTM neural networks with maximum likelihood estimators in GARCH. The variance distributions considered in the paper are normal, t and skewed t, but the approach allows extension to other distributions. To evaluate our model, we conducted an empirical study on the logarithmic returns of the WIG 20 (Warsaw Stock Exchange Index), S&P 500 (Standard & Poor’s 500) and FTSE 100 (Financial Times Stock Exchange) indices over four different time periods from 2005 to 2021 with different levels of observed volatility. Our results confirm the validity of the solution, but we provide some directions for its further development.

Development and validation of a performance measurement system based on Islamic principles

The infamous malfeasance of corporate giants and the collapse of a financial system with global reverberations have further pressured the need for greater ethical discipline and consideration in business and finance. This study explored the motivations within firms as driven by their performance measurement systems (P.M.). Then, the study developed a newly proposed P.M.S. with greater ethical emphasis based on Islamic principles to be a foundation for augmented Sharia-compliant screening criteria for Islamic equities. Discourse analysis of the Islamic religious texts was used, followed by validation through interviews with scholars and practitioners. Results indicate that improving the current Sharia screening criteria is possible by expanding the qualitative and quantitative assessments to include indicators that consider shareholders, the board of directors and top management, business dealings and products, employee relations, community issues and environmental protection. This study has implications for regulators such as Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI), Islamic Financial Services Board (IFSB) and Sharia-compliant screening criteria users such as the Dow Jones Islamic Market (DJIM), Financial Times Stock Exchange (FTSE) and Standard and Poor's (S&P) that may consider expanding their current equity screening criteria which mainly depends on the issuer's business activity and narrow quantitative measures. This version: June 28, 2022.

Firm’s exploration-exploitation capabilities. Do diversity and empowerment matter?

PurposeThis paper aims to explore the effect of gender diversity and female empowerment on a firm’s exploration and exploitation capabilities.Design/methodology/approachThis is an empirical paper. This study investigated the research question in this paper using data on UK Financial Times Stock Exchange 100 firms and OLS regression analysis.FindingsThis study found a positive association between senior management gender diversity in the workforce and exploitation and exploration capabilities. Also, female empowerment positively moderates senior management gender diversity’s impact on exploration capability.Practical implicationsThis study advises firms aspiring ambidextrous to establish gender diversity – especially at the senior management level – and focus on female empowerment in their organisations.Originality/valueThis paper argued for the role of females in making the organisations ambidextrous by impacting both exploitation and exploration capabilities, which did not receive adequate research attention to date.

Disclosure of auditor risk assessments in expanded audit reports

PurposeThis rich descriptive study examines auditors' client risk assessment (i.e. “key audit matters”/critical audit matters) disclosures in expanded audit reports of 328 Financial Times Stock Exchange (FTSE) 350 companies. The study compares auditor-identified client risks with corporate risk disclosures identified in audit committee reports, in terms of number and type of risks. The research also compares variation in auditor-identified client risks between individual Big 4 audit firms. In addition, the study examines auditor ranking of their client risks disclosed.Design/methodology/approachThe study manually content analyses disclosures in audit reports and audit committee reports of a sample of 328 FTSE-350 companies with 2015 year-ends.FindingsAudit committees identify more risks than auditors (23% more risks). However, auditor-identified client risks and audit-committee-identified risks are similar (80% similar), as are auditor-identified client risks between the individual Big 4 audit firms. Only ten (3%) audit reports rank the importance of auditor-identified client risks.Research limitations/implicationsSample is restricted to one year, one jurisdiction, large-listed companies and companies audited by Big 4 auditors.Practical implicationsThe study provides important insights for regulators, auditors and users of financial statements by identifying influences on disclosure of auditor-identified client risks.Originality/valueThe paper mobilises institutional theory to interpret the findings. The findings suggest that auditor-identified client risks in expanded audit reports may demonstrate mimetic behaviour in terms of similarity with audit-committee-identified risks and similarity between individual Big 4 audit firms. The study provides important insights for regulators, auditors and users of financial statements by identifying influences on disclosure of auditor-identified client risks.

Prediction of KLCI Index Through Economic LASSO Regression Model and Model Averaging

The Financial Times Stock Exchange (FTSE) Bursa Malaysia KLCI Index is a key component in the development of Malaysia's economic growth and the complexity in terms of identifying the factors that have a substantial impact on the Malaysian stock market has always been a contentious issue. In this study, the macroeconomic factors of exchange rate, interest rate, gold price, consumer price index, money supply M1, M2, and M3, industrial production, and oil price were discussed by using economic LASSO regression and Bayesian Model Averaging (BMA) with monthly average and monthly end time-series data spanning from January 2015 to June 2021, with a total of 78 observations by using the R Studio. The findings demonstrate that month-end data is better suited for stock market prediction than month-average data and that the BMA model is more suitable than the LASSO model, as seen by lower Mean Square Error of Prediction, MSE(P) and Residual Mean Square Error of Prediction, RMSE(P) values. The exchange rate, gold price, and money supply have a negative association with the dependent variables, while the consumer price index has a positive relationship associated with the dependent variables. The consumer price index is the most significant contributing factor, whereas gold price is the least significant. The result depicted that the KLCI index has no significant relationship with the variables interest rate, money supply M2, M1, industrial production index, and oil price. In conclusion, investors could specifically focus on the positive contributor and put lesser attention on improving their portfolio return.