FTSE100 Index Research Articles

Risk disclosure, income smoothing and firm risk

PurposeEvidence on whether firms with higher risk choose a more transparent or more opaque risk reporting strategy in their annual reports is mixed. A potential explanation is that firms choose an alternative reporting strategy to risk disclosure, namely income smoothing. The purpose of this paper is to investigate the association between both strategies in relation to firm risk levels.Design/methodology/approachThe authors use a balanced sample of 74 non-financial UK firms from the FTSE100 index over the period 2005–2015, examining the association between firm risk measures and both risk disclosure and income smoothing using a seemingly unrelated regression methodology.FindingsThe authors find that firm financial risk measures are positively associated with both risk disclosure and income smoothing, implying a complementary association. Furthermore, non-risk-related factors are associated with both lower levels of risk disclosure and higher income smoothing, implying a substitutive effect.Research limitations/implicationsThe authors do not consider other factors such as managerial optimism, managerial financial incentives and analysts' earnings forecasts which might influence the association between risk disclosure and income smoothing, and hence, this may be a limitation of the current study.Practical implicationsThese results are important to regulators, investors and boards of directors who are interested in understanding the alternative reporting strategies that managers select when faced with high risk. The findings signal a need for closer regulatory scrutiny on not only the level of risk disclosure but also the financial reporting choices.Originality/valueThe authors extend the literature on the reporting versus recognition decisions made by managers.

Comparison of the Global, Local and Semi-Local Chaotic Prediction Methods for Stock Markets: The Case of FTSE-100 Index

Chaotic prediction methods are classified as global, local and semi-local methods. In this paper, unlike the studies in the literature, it is aimed to compare all these methods together for stock markets in terms of prediction performance and to determine the best prediction method for stock markets. For this purpose, Multi-Layer Perceptron (MLP) neural networks from global methods, nearest neighbour method from local methods, radial basis functions from semi-local methods are used. The FTSE-100 index is selected to represent the stock market and applied the all methods to these data. The prediction performance is measured in term of root mean square error (RMSE) and normalized mean square error (NMSE). As a result of the analysis; it has been determined that the best prediction method for the FTSE-100 index is the semi-local method. While it is possible to make a maximum of 5 days prediction with global and local methods, it has been determined that up to 20 days prediction can be made with the semi-local prediction methods. The results show that semi-local prediction methods are successful in predicting the behaviour of stock market.

Stochastic Dominance on FTSE Index

Stochastic dominance is a method that refers to a set of relations, which may hold between a specific pair of distributions. However, the concept can be applied in many domains, but in particular in financial economic areas, where the considered distributions are usually those of random returns to different financial assets. The aim of this paper is to provide an implementation of a stochastic dominance algorithm that establish which of more risky indices is preferred more by investors who have an aversive risk profile. The study is performed on FTSE indices. The focus is to emphasis the imbalance between FTSE regional indices and FTSE sectorial indices. The analyzed period for regional indices is April 3, 2000 –September 12, 2014. As regards the sector indices, the analyzed period is January 3, 1994 – September 12, 2014.Its relevance consist in that, it offers a different perspective for investors when choosing between different financial assets. This approach together with Meyer algorithm has been proved that it is a useful tool in risk aversion analysis.

An incremental bundle method for portfolio selection problem under second-order stochastic dominance

In this paper, we propose an incremental bundle method with inexact oracle for solving the portfolio optimization with stochastic second-order dominance (SSD) constraints. We first relax the SSD problem as a stochastic semi-infinite programming (SIP) problem. For the particular case of SIP problem, we exploit the improvement function and the idea of incremental technique for dealing with the infinitely many constraints. In the stochastic model, as an adding-rules, the “inexact oracle” is introduced in this algorithm. Therefore, the algorithm does not need all the information about the constraints, but only needs the inexact information of one component function to update the bundle and produces the search direction. Our numerical results on solving the academic problems have shown advantages of the incremental bundle method over three existing algorithms. Finally, numerical results on a part of portfolio optimization problem are presented by using the FTSE100 Index.

Forecast ranked tailored equity portfolios

We use a dynamic model averaging (DMA) approach to construct forecasts of individual equity returns for a large cross-section of stocks contained in the SP500, FTSE100, DAX30, CAC40 and SPX30 headline indices, taking value, momentum, and quality factors as predictor variables. Fixing the set of ‘forgetting factors’ in the DMA prediction framework, we show that highly significant return forecasts relative to the historic average benchmark are obtained for 173 (281) individual equities at the 1% (5%) level, from a total of 895 stocks. These statistical forecast improvements also translate into considerable economic gains, producing out-of-sample R2 values above 5% (10%) for 283 (166) of the 895 individual stocks. Equally weighted long only portfolios constructed from a ranking of the best 25% forecasts in each headline index can generate sizable returns in excess of a passive investment strategy in that index itself, even when transaction costs and risk taking are accounted for.

High frequency trading, price discovery and market efficiency in the FTSE100

This study examines the role of high frequency trading in price discovery and efficiency in the FTSE100 index tick changes. We find that there is no random walk when investors extract information at a millisecond to a second.

Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization

In the field of investment, how to construct a suitable portfolio based on historical data is still an important issue. The second-order stochastic dominant constraint is a branch of the stochastic dominant constraint theory. However, only considering the second-order stochastic dominant constraints does not conform to the investment environment under realistic conditions. Therefore, we added a series of constraints into basic portfolio optimization model, which reflect the realistic investment environment, such as skewness and kurtosis. In addition, we consider two kinds of risk measures: conditional value at risk and value at risk. Most important of all, in this paper, we introduce Gray Wolf Optimization (GWO) algorithm into portfolio optimization model, which simulates the gray wolf’s social hierarchy and predatory behavior. In the numerical experiments, we compare the GWO algorithm with Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA). The experimental results show that GWO algorithm not only shows better optimization ability and optimization efficiency, but also the portfolio optimized by GWO algorithm has a better performance than FTSE100 index, which prove that GWO algorithm has a great potential in portfolio optimization.

Desirability Index with Sector Analysis for Investment Decisions by Bernoulli Theorem: A Case of FTSE-100

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.

The impact of risk committee on financial performance of UK financial institutions

Following the recent financial crisis, Walker (2009) recommended that financial institutions should form a separate board level risk committee (RC) to manage various risks and prevent excessive risk taking. This research focuses on investigating how firms with separate risk committees differ from those that do not have one. The main research question we address is whether RCs have a fundamental influence on financial performance. We measure financial performance by ROA and ROE and we control for firm size, liquidity and gearing. Our sample consists of all listed financial institutions in FTSE-100 index from 2010 through 2014. Results indicate a negative relationship between risk committee characteristics (i.e., existence, size, independence, and meeting frequency) and financial performance. The results also indicate that firms without risk committee (RC) performed considerably well than firms with RC. The results are contradictory to Walker’s (2009) where RCs are recommended for their ability to mitigate and manage risks more expertly. However, we argue that establish strong RC constrain management ability to make excessive risk taking behaviour which may affect financial performance negatively. We contribute to the current research on the impact of risk committee governance attributes on financial performance after banking and governance reforms.

The impact of risk committee on financial performance of UK financial institutions

Following the recent financial crisis, Walker (2009) recommended that financial institutions should form a separate board level risk committee (RC) to manage various risks and prevent excessive risk taking. This research focuses on investigating how firms with separate risk committees differ from those that do not have one. The main research question we address is whether RCs have a fundamental influence on financial performance. We measure financial performance by ROA and ROE and we control for firm size, liquidity and gearing. Our sample consists of all listed financial institutions in FTSE-100 index from 2010 through 2014. Results indicate a negative relationship between risk committee characteristics (i.e., existence, size, independence, and meeting frequency) and financial performance. The results also indicate that firms without RC performed considerably well than firms with RC. The results are contradictory to Walker's (2009) where RCs are recommended for their ability to mitigate and manage risks more expertly. However, we argue that establishing strong RC constrains management ability to make excessive risk taking behaviour, which may affect financial performance negatively. We contribute to the current research on the impact of risk committee governance attributes on financial performance after banking and governance reforms.

Forecast Ranked Tailored Equity Portfolios

We use a dynamic model averaging (DMA) approach to construct forecasts of individual equity returns for a large cross-section of stocks contained in the SP500, FTSE100, DAX30, CAC40 and SPX30 headline indices, taking value, momentum, and quality factors as predictor variables. Fixing the set of ‘forgetting factors’ in the DMA prediction framework, we show that highly significant return forecasts relative to the historic average benchmark are obtained for 173 (281) individual equities at the 1% (5%) level, from a total of 895 stocks. These statistical forecast improvements also translate into considerable economic gains, producing out-of-sample R2 values above 5% (10%) for 283 (166) of the 895 individual stocks. Equally weighted long only portfolios constructed from a ranking of the best 25% forecasts in each headline index can generate sizable returns in excess of a passive investment strategy in that index itself, even when transaction costs and risk taking are accounted for.

THE COINTEGRATION RELATIONSHIP BETWEEN BITCOIN PRICES AND MAJOR WORLD STOCK INDICES: AN ANALYSIS WITH ARDL MODEL APPROACH

Purpose – Aiming to discover whether Bitcoin prices have an effect on investor decisions in stock market transactions sounds exciting. Therefore, among investment, money, payment system functionalities of the cryptocurrencies which are very popular on economic and financial agenda nowadays, only the investment function regarding to the market volume of Bitcoin (which is very popular) is taken into consideration in this study. Methodology - In the scope of the study, because similar analyses between cryptocurrencies and stock market indices do not exist in the literature, cointegration relation between them are examined. Thus, the cointegration between Bitcoin (since there are many cryptocurrencies and Bitcoin is very popular and has the highest proportion in all terms in general) and selected stock indices can be investigated by the ARDL boundary test method. Since the analysis method gives meaningful information in terms of different time periods, the price and index data of these variables have been analysed. Findings- Cointegration relationship between Bitcoin prices and leading US and Chinese stock market indices is observed. Within this context, it can be told that investors in these stock markets could be influenced by Bitcoin prices in their long-term investment decision process. Any relationship was not found with BIST100, FTSE100 and NIKKEI225 indices. Conclusion - Necessity to examine the relation among Bitcoin, cryptocurrencies and other investment instruments with payment systems, money, e-commerce figures and macroeconomic indicators in the light of the arguments and the results found in our analysis would add more value to the literature. In addition, other dimensions of this topic should be regulated and be analysed by new studies within the scope of other technological developments in the 4 th Industrial Revolution. It is also decided to analyse relationship among related Istanbul Stock Exchange sub-indices, the Turkish Lira, gold, money supply and other cryptocurrencies in the following/future studies.

Biogeography-Based Optimization of the Portfolio Optimization Problem with Second Order Stochastic Dominance Constraints

The portfolio optimization problem is the central problem of modern economics and decision theory; there is the Mean-Variance Model and Stochastic Dominance Model for solving this problem. In this paper, based on the second order stochastic dominance constraints, we propose the improved biogeography-based optimization algorithm to optimize the portfolio, which we called ε BBO. In order to test the computing power of ε BBO, we carry out two numerical experiments in several kinds of constraints. In experiment 1, comparing the Stochastic Approximation (SA) method with the Level Function (LF) algorithm and Genetic Algorithm (GA), we get a similar optimal solution by ε BBO in [ 0 , 0 . 6 ] and [ 0 , 1 ] constraints with the return of 1.174% and 1.178%. In [ - 1 , 2 ] constraint, we get the optimal return of 1.3043% by ε BBO, while the return of SA and LF is 1.23% and 1.26%. In experiment 2, we get the optimal return of 0.1325% and 0.3197% by ε BBO in [ 0 , 0 . 1 ] and [ - 0 . 05 , 0 . 15 ] constraints. As a comparison, the return of FTSE100 Index portfolio is 0.0937%. The results prove that ε BBO algorithm has great potential in the field of financial decision-making, it also shows that ε BBO algorithm has a better performance in optimization problem.

The one-trading-day-ahead forecast errors of intra-day realized volatility

Two volatility forecasting evaluation measures are considered; the squared one-day-ahead forecast error and its standardized version. The mean squared forecast error is the widely accepted evaluation function for the realized volatility forecasting accuracy. Additionally, we explore the forecasting accuracy based on the squared distance of the forecast error standardized with its volatility. The statistical properties of the forecast errors point the standardized version as a more appropriate metric for evaluating volatility forecasts.We highlight the importance of standardizing the forecast errors with their volatility. The predictive accuracy of the models is investigated for the FTSE100, DAX30 and CAC40 European stock indices and the exchange rates of Euro to British Pound, US Dollar and Japanese Yen. Additionally, a trading strategy defined by the standardized forecast errors provides higher returns compared to the strategy based on the simple forecast errors. The exploration of forecast errors is paving the way for rethinking the evaluation of ultra-high frequency realized volatility models.

Exploration and incorporation of virtual data positions for efficient forecasting of financial time series

Variations occur in the trends of financial time series data due to several reasons. Such random fluctuations lead to a sudden fall after a steady increase or a sudden rise after a gradual fall in the trend of financial time series data; this makes it difficult to predict. This research work explores the impact of virtual data positions (VDPs) on financial time series forecasting using three different data exploration techniques with artificial neural network (ANN) trained by back propagation. To train and validate these models, the daily closing prices of BSE, DJIA, NASDAQ, FTSE100, S&P500, TAIEX and SSE indices were used. Since none of the exploration techniques were found to be noticeably superior compared with the others, additional experiments were conducted using genetic algorithm (GA) to train ANN; this resulted in better accuracy.

The impact of monetary policy shocks on the equity risk premium before and after the Quantitative Easing in the United Kingdom

The authors investigate the impact of structural monetary policy shocks on ex-post equity risk premium (ERP) of aggregate and sectoral FTSE indices and 25 Fama-French style value-weighted portfolios. They find that monetary policy shocks negatively affect the ERP but at the sectoral level, the magnitude of the response is heterogeneous. Further, monetary policy shocks have a significant negative (positive) impact on the ERP before (after) the implementation of quantitative easing (QE). The empirical evidence provided in the paper sheds light on the equity market’s asymmetric response to the BoE’s policy before and after the monetary stimulus. Keywords: monetary policy, equity risk premium, quantitative easing, monetary policy shocks, structural vector autoregression, Bank of England, Taylor monetary policy rule, unconventional monetary policy, output gap, inflation gap, Okun’s law. JEL Classification: E5, E30, G0, G1

Revisiting the returns–volume relationship: Time variation, alternative measures and the financial crisis

Following its introduction in the seminal study of Osborne (1959), a voluminous literature has emerged examining the returns–volume relationship for financial assets. The present paper revisits this relationship in an examination of the FTSE100 which extends the existing literature in two ways. First, alternative daily measures of the FTSE100 index are used to create differing returns and absolute returns series to employ in an examination of returns–volume causality. Second, rolling regression analysis is utilised to explore potential time variation in the returns–volume relationship. The findings obtained depict a hitherto unconsidered complexity in this relationship with the type of returns series considered and financial crisis found to be significant underlying factors. The implications of the newly derived results for both the understanding of the nature of the returns–volume relationship and the development of theories in connection to it are discussed.

A time-varying copula approach for modelling dependency: New evidence from commodity and stock markets

This paper examines the time-varying conditional dependency between commodity markets and stock markets by applying the rolling-sample technique on the dependence parameter of copula. The dataset consists of the closing prices of twelve commodities and the SP500, CAC40, DAX30 and FTSE100 indices during the period from July 7, 1992 to February 17, 2015. To date precisely the breakpoints in the dynamics of the copula parameter of dependence, we employ Bai and Perron’s (BP, 1998, 2003) structural break-testing procedure.Our empirical findings show that among the seven copulas investigated, the Student’s t-copula is more appropriate for modelling dependency. Moreover, the BP procedure shows strong evidence of time-varying behaviour in the parameter of dependence. The results show that the dates of breaks correspond to economic and financial events, such as the global financial crisis and crude oilprice fluctuations.

Measuring Risk with COGARCH(p,q) Models

In this paper we introduce a multivariate Independent Component COGARCH(p,q) model for financial time series. We determine optimal portfolio weights obtained as a solution of different static asset allocation problems. Empirical analysis is conducted on two datasets. The first is composed by 154 European hedge funds tracking the performance of the FTSE100 Index while the second contains the members of FTSE100. The performances of different strategies are investigated from an out-of-sample perspective.

Modelling and trading the English stock market with novelty optimization techniques

The scope for this paper is to introduce short term adaptive models to trade the FTSE100 index. There are five major innovations on this paper which include the introduction of an input selection criteria when utilising an expansive universe of inputs, adaptive sliding window modelling, a hybrid combination of PSO and RBF algorithms, the application of a PSO algorithm to a traditional ARMA model, and finally the introduction of a multi-objective algorithm to optimise statistical and trading performance when trading an equity index.