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Global Standards and Ethical Stock Indexes: The Case of the Dow Jones Sustainability Stoxx Index
The increased scrutiny of investors regarding the non-financial aspects of corporate performance has placed portfolio managers in the position of having to weigh the benefits of ' holding the market' against the cost of having positions in companies that are subsequently found to have questionable business practices. The availability of stock indexes based on sustainability screening makes increasingly viable for institutional investors the transition to a portfolio based on a Socially Responsible Investment (SRI) benchmark at relatively low cost. The increasing share of socially responsible investments may play a role in providing incentives towards a continuous upgrading of sustainability standards to the extent that their performance is not systematically inferior to that of the other funds. This article examines whether these incentives have been so far detectable with particular reference to the Dow Jones Sustainability Stoxx Index (DJSSI) that focuses on the European corporations with the highest CSR scores among those included in the Dow Jones Stoxx 600 Index. The aim of the article is twofold. First, we analyse the performance of the DJSSI over the period 2001-2006 compared to that of the Surrogate Complementary Index (SCI), a new benchmark that includes only the components of the DJ Stoxx 600 that do not belong to the ethical index to evaluate more correctly the size of possible divergent performances. Second, we perform an event study on the same data set to analyse whether the stock market evaluation reacts to the inclusion (deletion) in the DJSSI. In both cases, the results suggest that the evaluation of the CSR performance of a firm is a significant criterion for asset allocation activities.
Journal Article
Energy Prices and Their Impact on US Stock Indices: A Wavelet- based Quantile-on-Quantile Regression Approach
This study delves into the effects of crude oil and gas prices on the United States’ (US) conventional, Islamic, and environmental, social, and governance (ESG) stock indices from January 2013 to December 2022. Decomposing original time series data to minimise inherent fluctuations and using the Quantile-on-Quantile (QQ) regression approach presents a nuanced view of how these energy prices impact different stock indices. The findings reveal that crude oil prices have a variable impact on the indices: high prices negatively influence the indices, low prices have a positive effect, and moderate prices yield a moderate positive impact. After data decomposition, this positive influence diminishes in higher quantiles, indicating an emerging neutral effect in stabilised conditions. In contrast, gas prices show a limited impact, with high prices slightly benefiting conventional and ESG indices but less so for the Islamic index. This suggests a more pronounced influence of oil prices on the indices, likely due to the dependence of many listed companies on oil. The study emphasises the importance of considering oil-related risks in investment strategies and highlights the asymmetric impact of crude oil prices on the US stock indices. These findings have significant implications for investors and policymakers. They underscore the need for careful consideration of oil price dynamics in investment decisions and the importance of staying vigilant against shifts in oil prices that could lead to market instability.
Journal Article
Predicting the Trend of Stock Market Index Using the Hybrid Neural Network Based on Multiple Time Scale Feature Learning
In the stock market, predicting the trend of price series is one of the most widely investigated and challenging problems for investors and researchers. There are multiple time scale features in financial time series due to different durations of impact factors and traders’ trading behaviors. In this paper, we propose a novel end-to-end hybrid neural network, a model based on multiple time scale feature learning to predict the price trend of the stock market index. Firstly, the hybrid neural network extracts two types of features on different time scales through the first and second layers of the convolutional neural network (CNN), together with the raw daily price series, reflect relatively short-, medium- and long-term features in the price sequence. Secondly, considering time dependencies existing in the three kinds of features, the proposed hybrid neural network leverages three long short-term memory (LSTM) recurrent neural networks to capture such dependencies, respectively. Finally, fully connected layers are used to learn joint representations for predicting the price trend. The proposed hybrid neural network demonstrates its effectiveness by outperforming benchmark models on the real dataset.
Journal Article
Analysis of market efficiency and fractal feature of NASDAQ stock exchange: Time series modeling and forecasting of stock index using ARMA-GARCH model
The multi-fractal analysis has been applied to investigate various stylized facts of the financial market including market efficiency, financial crisis, risk evaluation and crash prediction. This paper examines the daily return series of stock index of NASDAQ stock exchange. Also, in this study, we test the efficient market hypothesis and fractal feature of NASDAQ stock exchange. In the previous studies, most of the technical analysis methods for stock market, including K-line chart, moving average, etc. have been used. These methods are generally based on statistical data, while the stock market is in fact a nonlinear and chaotic system which depends on political, economic and psychological factors. In this research we modeled daily stock index in NASDAQ stock exchange using ARMA-GARCH model from 2000 until the end of 2016. After running the model, we found the best model for time series of daily stock index. In next step, we forecasted stock index values for 2017 and our findings show that ARMA-GARCH model can forecast very well at the error level of 1%. Also, the result shows that a correlation exists between the stock price indexes over time scales and NASDAQ stock exchange is efficient market and non-fractal market.
Journal Article
Neural Network-Based Predictive Models for Stock Market Index Forecasting
The stock market, characterised by its complexity and dynamic nature, presents significant challenges for predictive analytics. This research compares the effectiveness of neural network models in predicting the S&P500 index, recognising that a critical component of financial decision making is market volatility. The research examines neural network models such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and Gated Recurrent Unit (GRU), taking into account their individual characteristics of pattern recognition, sequential data processing, and handling of nonlinear relationships. These models are analysed using key performance indicators such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy, a metric considered essential for prediction in both the training and testing phases of this research. The results show that although each model has its own advantages, the GRU and CNN models perform particularly well according to these metrics. GRU has the lowest error metrics, indicating its robustness in accurate prediction, while CNN has the highest directional accuracy in testing, indicating its efficiency in data processing. This study highlights the potential of combining metrics for neural network models for consideration when making decisions due to the changing dynamics of the stock market.
Journal Article
Revisiting Stock Market Index for the Helsinki Stock Exchange 1912–1981
Stock market indices play a central role in portfolio and risk management and performance evaluation, as well as academic research. This paper presents a fully updated and extended stock market index for the Finnish stock market using new and updated historical databases that cover the period from the establishment of the Helsinki Stock Exchange in October 1912 to the end of 1981. In addition to the all-share market index, four industry indices are presented for the first time. The observed geometric mean market return is 1.034 percent per month (13.14% p.a.). Of the industry indices, the banking sector performed the worst as it was found to have clearly lagged behind in the market, whereas the paper and forest and the metal and manufacturing industries performed the best during the sample period. The results also highlight the importance of taking into account corporate capital actions—which are, historically, often the hardest information to obtain—as they can have a material effect on the index performance.
Journal Article
Galformer: a transformer with generative decoding and a hybrid loss function for multi-step stock market index prediction
The prediction of stock market fluctuations is crucial for decision-making in various financial fields. Deep learning algorithms have demonstrated outstanding performance in stock market index prediction. Recent research has also highlighted the potential of the Transformer model in enhancing prediction accuracy. However, the Transformer faces challenges in multi-step stock market forecasting, including limitations in inference speed for long sequence prediction and the inadequacy of traditional loss functions to capture the characteristics of noisy, nonlinear stock history data. To address these issues, we introduce an innovative transformer-based model with generative decoding and a hybrid loss function, named “Galformer,” tailored for the multi-step prediction of stock market indices. Galformer possesses two distinctive characteristics: (1) a novel generative style decoder that predicts long time-series sequences in a single forward operation, significantly boosting the speed of predicting long sequences; (2) a novel loss function that combines quantitative error and trend accuracy of the predicted results, providing feedback and optimizing the transformer-based model. Experimental results on four typical stock market indices, namely the CSI 300 Index, S&P 500 Index, Dow Jones Industrial Average Index (DJI), and Nasdaq Composite Index (IXIC), affirm that Galformer outperforms other classical methods, effectively optimizing the Transformer model for stock market prediction.
Journal Article
Are Sustainability Indices Infected by the Volatility of Stock Indices? Analysis before and after the COVID-19 Pandemic
Considering the growing importance of sustainable investments worldwide, we explore the volatility transmission effects between the EURO STOXX Sustainability Index and the stock market indexes of its stocks. Using daily index return data, during 2000–2022, covering the COVID-19 pandemic, Multivariate Generalized Auto-Regressive Conditional Heteroskedasticity (MGARCH) models are used to explore if volatility effects of the stock indices felt during the pandemic implied any evolution in the effects already felt between the volatilities existing in these stock indices and the effects of stock market indices’ volatility over the sustainability index. Results point to the great dependence that the sustainability index has on stock index movements. The volatility felt in stock indices during the pandemic period did not become decisive in reversing a previous correlation trajectory between the stock market and sustainability indexes. Provided that sustainability is not observed exclusively in financial and economic terms, but in a triple bottom line context (including the social and environmental sides), we should not verify a high influence of stock market indexes over the sustainability index, as the results point out. Policymakers and investors should be aware of the high influence and take measures to turn the sustainability index more independent.
Journal Article
Hedging the Risks of MENA Stock Markets with Gold: Evidence from the Spectral Approach
In this paper, we contribute to the old debate on the dynamic correlation between gold and stock markets by considering a spectral approach within the framework of portfolio hedging. Specifically, we consider eight MENA stock markets (Tunisia, Egypt, Morocco, Jordan, UAE, Saudi Arabia, Qatar, and Oman) and examine the optimal composition between gold and the stock market index, with a minimum portfolio risk and a high expected return. Based on the spectral approach, we propose seven portfolio structures and evaluate them through a comparison with the conventional DCC-GARCH method and the most best 10 portfolios constructed by using wavelet approach. The main results show that the spectral-based approach outperforms the DCC-GARCH and the wavelet methods. In fact, the optimal gold-stock composition depends on the spectral density of each stock market index, where a stock market index with a stable spectral density requires more investments in gold than a stock market index with an unstable spectral density.
Journal Article