FTSE100 Index Research Articles

Integrating LSTM Networks with Mean-Variance Optimization for Enhanced Portfolio Construction: An Empirical Study on UK Stock Market

Portfolio optimization has long been a central theme in finance. With ongoing advancements in machine learning, there is a significant opportunity to integrate predictive methods into portfolio optimization. This paper proposes leveraging Long Short-Term Memory (LSTM) networks alongside the established Mean-Variance (MV) optimization framework to construct optimal portfolios. These portfolios aim to help financial investors effectively manage and mitigate risk while maximizing returns. The study meticulously screened the leading stocks of 12 prominent UK companies listed on the London Stock Exchange (LSE), known for their influence and visibility. Initially, the study applies the LSTM networks to predict stock price volatility and integrates these predictions into the MV model to allocate portfolio weights effectively. To underscore the superiority of the proposed approach, the study compares cumulative returns from portfolios optimized for maximum and minimum variance Sharpe ratios with real data against the FTSE100 index over the same period. The results demonstrate that the portfolio optimized with the maximum Sharpe ratio significantly outperforms both the conservative minimum variance portfolio and the FTSE100 index. This empirical evidence underscores the practical value of integrating advanced machine learning techniques with established financial theory, enabling more innovative and efficient investment strategies to meet the evolving needs of financial investors.

Index tracking via reparameterizable subset sampling in neural networks

Index tracking aims at replicating the benchmark index performance by constructing a tracking portfolio consisting of partial component stocks of the index. This is an NP-hard question because of the cardinality constraint which limits the number of component stocks in the tracking portfolios. This paper proposes a new method to solve this problem in an end-to-end way by using a continuous subset sampling relaxation which can provide differentiable gradients calculation in neural networks. The proposed approach is tested with the FTSE-100 index data and the S&P100 index data, and compared with widely used index tracking methods, such as the forward and backward selection and optimization solvers. According to the empirical results, our proposed approach achieves the best tracking performance for different evaluation criteria.

Research and Forecasting of the FTSE100 Index over Long Time Series

Research and Forecasting of the FTSE100 Index over Long Time Series

Risk Return Analysis of FTSE Listed Select Stocks with FTSE100 Index

This paper analyses the stock returns of FTSE 100 and a sample of selected companies within the index to determine the existence of interdependence and magnitude of the same. The companies are selectively chosen from certain primary sectors based on their influence on the economy. The idea is to understand if large firms have significant effect on the index. The study involves statistical analysis representing data over a 10-year period spanning 1st November 2011 to 31st October 2021), the data points include adjusted closing values of the index and the selected companies. Correlation and regression analysis is performed by taking the index as the dependent variable and the companies as the independent variable. The results are then analyzed from a variety of perspectives including Skewness, Kurtosis, Correlation Coefficients and Coefficient of Determination. Skewness and Kurtosis reveals the performance of returns as compared to the average. The results of the study reveal a net positive correlation is observed between the index and the companies. additionally, the regression analysis results showcase how certain sectors like defense have a significant relation with the index while others like supermarkets do not. Other interesting facts uncovered relate to how a certain cluster of firms compare as opposed to a single firm when analyzed in conjunction with the index. The results are summarized to come up with a conclusion on the relationship an index has with its constituents.

Asymmetric Wealth Effect between US Stock Markets and US Housing Market and European Stock Markets: Evidences from TAR and MTAR

Evidence of the asymmetric wealth effect has important implications for investors and continues to merit research attention, not least because much of the evidence based on linear models has been refuted. Indeed, stock and house prices are influenced by economic activity and react non-linearly to positive/negative shocks. This problem justifies our research. The objective of this study is to examine evidence of cointegrations between the US housing and stock markets and between the US and European stock markets, given the international relevance of these exchanges. Using data from 1989:Q1 to 2020:Q2, the Threshold Autoregression model as well as the Momentum Threshold Autoregression model were calculated by combining the US Freddie, DJIA, and SPX indices and the European STOXX and FTSE indices. The results suggest a long-term equilibrium relationship with asymmetric adjustments between the housing market and the US stock markets, as well as between the DJIA, SPX, and FTSE indices. Moreover, the wealth effect is stronger when stock prices outperform house prices above an estimated threshold. This empirical evidence is useful to portfolio managers in their search for non-perfectly related markets that allow investment diversification and control risk exposure across different assets.

Investigating Causal Spillovers among International Stock Markets

Recognizing how financial shocks are transmitted to national economies will enable policymakers to take appropriate fiscal and monetary policy action. Such actions will be able to prevent or reduce the intensity of shocks to critical macroeconomic variables. Observing the financial crises in countries of the European Union and Argentina, highlights the main similarities and differences they present in the context of their course in international economic conditions. This study investigates the relation among international stock indices of major importance and stock indices of less developed economies during normal periods and the Covid-19 pandemic by employing a Vector Autoregression (VAR) framework. Econometric outcomes indicate that the German DAX30 and the British FTSE100 indices are positively influential towards the Greek ATX and the Portuguese PSI20 indices. Notably, this impact is revealed to fade out as more lags are considered. Moreover, the French CAC40 index is found to exert negative effects on the Greek and Portuguese indices and its impacts also diminish as time passes. Notably, the Argentinian stock index is found to be both a transmitter of purely negative effects towards the Italian and the Spanish stock indices while impacts on the Greek and the Portuguese indices turn into positive with the evolution of time. Nevertheless, its influence on stock indices of more advanced economies (DAX30, FTSE100) is found to be weaker. This paper serves as a compass for interested investors in order to better allocate their resources during normal periods as well as during crises.

A systematic literature review on solution approaches for the index tracking problem

Abstract Accepted by: Giorgio Consigli The passive management approach offers conservative investors a way to reduce risk concerning the market. This investment strategy aims at replicating a specific index, such as the NASDAQ Composite or the FTSE100 index. The problem is that buying all the index’s assets incurs high rebalancing costs, and this harms future returns. The index tracking problem concerns building a portfolio that follows a specific benchmark with fewer transaction costs. Since a subset of assets is required to solve the index problem this class of problems is NP-hard, and in the past years, researchers have been studying solution approaches to obtain more practical tracking portfolios. This work brings an analysis, spanning the last three decades, of the advances in mathematical approaches for index tracking. The systematic literature review covered important issues, such as the most relevant research areas, solution methods and model structures. Special attention was given to the exploration and analysis of metaheuristics applied to the index tracking problem.

KÜRESEL KRİZLERİN GELİŞMEKTE OLAN PİYASALAR ÜZERİNDEKİ ETKİSİ

Example example example example example example example example example example example example example example In this study, the emergence of the global crisis and the response of emerging markets to the crisis environment are investigated. For this purpose, the countries defined as the ‘Fragile Five’ (Turkey, India, Brazil, Indonesia and South Africa) by Morgan Stanley have been selected as the subject of the study. In order to measure the impact of global negativity on the Fragile Five markets, the period between January 2, 2020 and July 21, 2022, when COVID-19 was effective, has been chosen. TARCH and EGARCH models are used for the estimation of the indices subject to the study. As a result of the TARCH model estimation, it is determined that the coefficient showing the asymmetric effect for the SNSX and FTSE indices is significant. As a result of the EGARCH model estimation, the coefficient showing the asymmetric effect in BIST100, BVSP and JKSE indices is negative and significant. According to these results, the study argues that a negative shock in global markets has a significant effect on volatility.

Expected vs. real growth of companies listed on the London Stock Exchange

The aim of this paper is related to the growth opportunity measures representing the expected growth and their relationship with the real growth of companies. The motivation for this research is based on the evidence that growth opportunity measures may fail to predict future growth as measured by earnings per share. In the presented paper the companies listed on LSE are divided for two groups depending on the market they are traded: FTSE100 index and AIM all-share index. It was found that in both samples the growth opportunity measures relationship with the real growth is significant. The difference between companies listed on the main and alternative markets may influence the efficiency of growth opportunity measures predicting the future growth, and therefore the samples should be analyzed separately. The methods applied in this paper include the correlation analysis between growth opportunity and real growth variables, panel data OLS models with fixed effects and the differences between the correlation and regression tests. It can be concluded that growth opportunity measures predict the growth of companies as measured by EPS.

The effect of the US Dollar Index and foreign stock indexes on Jakarta Composite Index (JKSE)

Many theories explain that the Jakarta Composite Index (JKSE) can be influenced by stock indices or currencies of other countries. With the recent events of the war between Russia and Ukraine, this might be the perfect opportunity to prove that statement. The purpose of this study is to see whether the United States Dollar Index, Dow Jones Industrial Average (DJIA), NASDAQ 100 Index, Shanghai Composite Index (SSEC), Nikkei 225 Index (N225), Australian All Ordinaries Index (AORD), FTSE Index 100, against the Jakarta Composite Index (JKSE). This study uses the documentation method with secondary data in the form of daily closing prices for foreign stock indices and USD indices spread from 4 January 2022 to 25 February 2022. The hypothesis is tested with a partial significant test (t-test), simultaneous significant test (F test), and coefficient of determination. The test results show that the USD, DJIA, NASDAQ, SSEC, N225, AORD, and FTSE indices do not affect the JKSE. But simultaneously the USD Index, DJIA, NASDAQ, SSEC, N225, AORD, and FTSE affect JKSE. This may be because, during the study period, there were no foreign companies listed on the Indonesian Stock Exchange. Companies listed on the Indonesia Stock Exchange are domestic companies denominated in Rupiah, so the investment that occurs is purely local investment using Rupiah. At this time the world is also in a period of economic recovery after previously declining due to the COVID-19 pandemic. This causes investors from other countries to prefer investing in their own country. So, investment from abroad to Indonesia also decreased. This research is limited to using independent and dependent variables used and the research is limited to only 2 months. For further research, it is recommended to add more varied independent variables and extend the observation period so that it can increase better data distribution.

Predict FTSE100 Stock Movements Using Business News Sentiment and Machine Learning

In order for investors to maximize their benefit by having better forecasts of the complex dynamics of the stock market, there are many factors that affect the stock market, from a company's financial ratios to investor sentiment and reactions to financial news. This project aims to collect UK business news from the Guardian and uses NLP techniques to transform unstructured text data into usable structured sentiment data to predict the movement of the FTSE100 index. The program uses two different libraries TEXTBLOB and VADER to extract sentiments from both the headlines and main bodies of the business news articles. Four machine learning algorithms including Logistic Regression, Naive Bayes, K-Nearest Neighbours and Support Vector Machines and a voting classifier were used to predict FTSE100 index movement given the business news sentiments of the previous day.

Coarse Graining on Financial Correlation Networks

Community structure detection is an important and valuable task in financial network studies as it forms the basis of many statistical applications such as prediction, risk analysis, and recommendation. Financial networks have a natural multi-grained structure that leads to different community structures at different levels. However, few studies pay attention to these multi-part features of financial networks. In this study, we present a geometric coarse graining method based on Voronoi regions of a financial network. Rather than studying the dense structure of the network, we perform our analysis on the triangular maximally filtering of a financial network. Such filtered topology emerges as an efficient approach because it keeps local clustering coefficients steady and it underlies the network geometry. Moreover, in order to capture changes in coarse grains geometry throughout a financial stress, we study Haantjes curvatures of paths that are the farthest from the center in each of the Voronoi regions. We performed our analysis on a network representation comprising the stock market indices BIST (Borsa Istanbul), FTSE100 (London Stock Exchange), and Nasdaq-100 Index (NASDAQ), across three financial crisis periods. Our results indicate that there are remarkable changes in the geometry of coarse grains.

The Volatility Transmission Between Cryptocurrency And Global Stock Market Indices: Case Of Covid-19 Period

The uncertainty originated by the COVID-19 pandemic and the unpredictability of both real and financial market indicators have increased the volatility of global financial markets. As a result of globalization, the determination of risk and information transfer between financial markets has gained importance during the pandemic process. In this context, the spread of volatility between the cryptocurrency market and the global stock markets was analyzed by considering the pandemic process. Bitcoin, which represents 42% of the total market cap, was used to represent the cryptocurrency market in the analysis. S&P500, FTSE100, SSEC and NIKKEI indices, which are among the world's leading indices in terms of market cap, were used to represent the global stock market. Constant Conditional Correlation Multivariate GARCH model was used for the analysis of volatility transmission. Daily closing prices covering the date range from 1st December 2019 to 1st July 202 were used for the analyses. The model results were positive and significant for all predicted conditional correlation parameters. In this context, there is volatility transmission and information transfer between BTC and stock returns. The model findings are expected to be a supporting element for financial market participants to make the right decision in the optimal portfolio allocation process.

An Analysis of the Dynamic Linkages of Pakistani Stock Market with the World’s Leading Stock Markets

Using the theoretical framework of International Portfolio Diversification (IPD), the study explores the long-run relationship between the Pakistan Stock Exchange (PSX) and the world's six leading stock exchanges of Canada, China, France, Japan, United Kingdom, and the United States. These markets are represented by the indices such as KSE-100 index (Pakistan), TSX (Canada), SSE Composite (China), CAC-40 Index (France), Nikkie-225 (Japan), FTSE-100 index (UK) and DJIA (US). An extended sample period of 27 years, has been studied ranging from 1991 to 2018, using daily, weekly, and monthly frequencies. This paper investigates the existence of cointegration among the markets by employing Johansen and Juselius bi-variate and multivariate co-integration techniques. The Augmented Dicky Fuller (ADF) and Phillip and Perron (P-P) test indicate that all the series are non stationary at level, whereas they are stationary at the first difference form. In addition, the results of the bi-variate co-integration test show that Pakistani market is weakly linked with the rest of the six markets. In addition, the findings of the multivariate co-integration test confirm two co-integrating equations—France and the UK stock exchanges are integrated on the daily data, while China and Canada stock exchanges are integrated on monthly data. An essential implication deduced from this result is that these markets are loosely connected with each other over the period investigated. Hence, international investors seeking to reduce risk can diversify their portfolio risk by including these markets in their pool of investment.

Cross hedging with stock index futures

This paper examines the cross hedging effectiveness between UK FTSE100 and world stock index futures from developed and emerging markets: the US, Australia, Brazil, Japan, Hong Kong, Korea and Malaysia. Our daily dataset spans from August 2002 through November 2019. We apply the OLS, VECM and Maximal Overlap Discrete Wavelet Transform (MODWT) to calculate the optimal cross hedge ratio and cross hedging effectiveness. Our empirical results show that the US E-Mini DJIA$5 futures contract is the best cross hedging instrument for the UK FTSE100, followed by the Australia S&P/ASX 200. The multiscale cross hedging effectiveness results from the MODWT estimation suggest the optimal hedging period of more than 256 days with the US E-Mini DJIA$5 and Australia S&P/ASX 200 futures.

SKEW NORMAL AND SKEW STUDENT-T DISTRIBUTIONS ON GARCH(1,1) MODEL

The Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) type models have become important tools in financial application since their ability to estimate the volatility of financial time series data. In the empirical financial literature, the presence of skewness and heavy-tails have impacts on how well the GARCH-type models able to capture the financial market volatility sufficiently. This study estimates the volatility of financial asset returns based on the GARCH(1,1) model assuming Skew Normal and Skew Student-t distributions for the returns errors. The models are applied to daily returns of FTSE100 and IBEX35 stock indices from January 2000 to December 2017. The model parameters are estimated by using the Generalized Reduced Gradient Non-Linear method in Excel’s Solver and also the Adaptive Random Walk Metropolis method implemented in Matlab. The estimation results from fitting the models to real data demonstrate that Excel’s Solver is a promising way for estimating the parameters of the GARCH(1,1) models with non-Normal distribution, indicated by the accuracy of the estimation of Excel’s Solver. The fitting performance of models is evaluated by using log-likelihood ratio test and it indicates that the GARCH(1,1) model with Skew Student-t distribution provides the best fitting, followed by Student-t, Skew-Normal, and Normal distributions.

Value at Risk, Legislative Framework, Crises, and Procyclicality: what goes wrong?

This study highlights some deficiencies of the stock markets’ risk legislation framework, and particularly the CESR (2010) guidelines. We show that the current legislative framework fails to offer incentives to financial management companies to invest in advanced models for more representative Value at Risk (VaR) estimations, and for this reason, in many cases conventional VaR models are applied. We use data from the DAX, CAC 40, FTSE, FTSEMIB and IBEX indices, and then we apply them to the widely accepted Delta Normal VaR model. The empirical findings show that the conventional VaR models not only fail to provide information for the upcoming financial crises, but also contribute to such phenomena as procyclicality and overreaction in the stock market. We suggest additional tests and we empirically show how these tests could reduce the procyclicality issue and promote a more sustainable investment environment. Even though this study is mainly focused on CESR (2010) guidelines, it could be useful for any similar legislative framework, such as the Basel Accords.

Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization.

In the field of asset allocation, how to balance the returns of an investment portfolio and its fluctuations is the core issue. Capital asset pricing model, arbitrage pricing theory, and Fama–French three-factor model were used to quantify the price of individual stocks and portfolios. Based on the second-order stochastic dominance rule, the higher moments of return series, the Shannon entropy, and some other actual investment constraints, we construct a multiconstraint portfolio optimization model, aiming at comprehensively weighting the returns and risk of portfolios rather than blindly maximizing its returns. Furthermore, the whale optimization algorithm based on FTSE100 index data is used to optimize the above multiconstraint portfolio optimization model, which significantly improves the rate of return of the simple diversified buy-and-hold strategy or the FTSE100 index. Furthermore, extensive experiments validate the superiority of the whale optimization algorithm over the other four swarm intelligence optimization algorithms (gray wolf optimizer, fruit fly optimization algorithm, particle swarm optimization, and firefly algorithm) through various indicators of the results, especially under harsh constraints.

All on Board? New Evidence on Board Characteristics from a Large Panel of UK FTSE Indices

This study analyses whether the board characteristics (diversity attributes, competitive capital, time commitment) of companies listed on the Financial Times Stock Exchange FTSE100 exhibit a different performance compared to those associated with conventional benchmark indices (FTSE250, FTSE SMALLCAP (small capitalisation), FTSE Fledgling, and FTSE All-Shar). Using multivariate analysis and unbalanced panel data over the period 2000−2014, we provide novel evidence on how the UK stock market indices react to diversity in board characteristics. Our findings reveal that different aspects of board characteristics are significantly correlated with the performance of FTSE indices. This study further confirms that boards with high levels of diversity maintain better and more effective levels of governance, in particular for companies listed on FTSE100 and FTSE250. Interestingly, we report that the competitive capital and busyness associated with boards of companies listed on FTSE100 have professional, well-educated, and socially connected boards, and show a clear pattern whereby boards become significantly less competitive as firm size decreases.

Realized Measures to Explain Volatility Changes over Time

We studied (i) the volatility feedback effect, defined as the relationship between contemporaneous returns and the market-based volatility, and (ii) the leverage effect, defined as the relationship between lagged returns and the current market-based volatility. For our analysis, we used daily measures of volatility estimated from high frequency data to explain volatility changes over time for both the S&P500 and FTSE100 indices. The period of analysis spanned from January 2000 to June 2017 incorporating various market phases, such as booms and crashes. Based on the estimated regressions, we found evidence that the returns of S&P500 and FTSE100 indices were well explained by a specific group of realized measure estimators, and the returns negatively affected realized volatility. These results are highly recommended to financial analysts dealing with high frequency data and volatility modelling.