Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

The Consequences of Euronext integration on the French, Belgian and Dutch stock markets.

On 22 September 2000, the French, Belgian and Dutch stock exchanges merged and formed the Euronext N.V., the first pan-European ex change. The creation of Euronext was a response to changes in the political and econ omic environment in Europe. The benefits to market participants are easier access t o a wider range of financial products, increase in liquidity and lower transaction costs. Indeed, since its incorporation, Euronext has the second largest capitalization in E urope. The aim of this thesis is to investigate the conseq uences of Euronext integration on the French, Belgian and Dutch stock markets. It rai ses two questions: 1. has the merger improved the information efficiency of the m arkets; and 2. has the level of integration between the markets increased following the incorporation of Euronext? The study uses daily prices for the markets’ main i ndices for the period 01/01/1990 to 10/12/2010. The original sample is divided into thr ee periods: pre-integration, integration and post-integration period. Two types of returns are computed: log- returns and excess returns. A dummy variable and a control variable, the German main index DAX, are included in the analysis to acc ount for the effect of the introduction of the Euro. Unit root and stationarity tests show that prices s eries are integrated of the first order and that the returns series are stationary. Moreove r, the volatility of returns exhibits long-memory patterns. The data generating process o f all the returns series is captured with ARMA-GARCH models. The returns exhibit volatil ity clusters in all sub- periods. Hence, the information efficiency of the m arket has not increased following Euronext integration. However, GARCH models do not include an asymmetric component for the post-integration period, indicati ng that the returns do not display leverage effects after the creation of Euronext. Fi nally, a Euro dummy variable was significant only for the Belgian returns. Cointegration tests show that the three indices exp erience long-run equilibrium during the integration and the post-integration periods. M oreover, the conditional correlation between the markets increases and stabilises after 2000. Overall, the evidence supports wider financial integration between these markets. However, it is difficult to 4 determine to what degree this change can be attribu ted to the creation of Euronext as opposed to the introduction of the Euro or to a com bination of both. A Granger causality test shows that EMU has Granger caused ma rket financial integration. On the other hand, a system comprised of the three ind ices and the control variable, DAX30, does not display long-run equilibrium for th e post-integration period, highlighting the role of Euronext. These results ar e important for market participants

Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models

This work is a comparative study of different univariate and multivariate time series predictive models as applied to Bitcoin, other cryptocurrencies, and other related financial time series data. ARIMA models, long regarded as the gold standard of univariate financial time series prediction due to both its flexibility and simplicity, are used a baseline for prediction. Given the highly correlative nature amongst different cryptocurrencies, this work aims to show the benefit of forecasting with multivariate time series models—primarily focusing on a novel parameter optimization of VARIMA models outlined in this paper. These models are trained on 3 years of historical data, aggregated from different cryptocurrency exchanges by Coinmarketcap.com, which includes: daily average prices and trading volume. Historical time series data of traditional market data, including the stock Nvidia, the de facto leading manufacture of gaming GPU’s, is also analyzed in conjunction with cryptocurrency prices, as gaming GPU’s have played a significant role in solving the profitable SHA256 hashing problems associated with cryptocurrency mining and have seen equivalently correlated investor attention as a result. Models are trained on this historical data using moving window subsets, with window lengths of 100, 200, and 300 days and forecasting 1 day into the future. Validation of this prediction against the actually price from that day are done with following metrics: Directional Forecasting (DF), Mean Absolute Error (MAE), and Mean Squared Error (MSE)

The puzzle of long swings in equity markets: Which way forward?

The main purpose of this dissertation is to determine which class of models -- bubble or Imperfect Knowledge Economics (IKE) -- provides the better account of short-term stock price fluctuations -- and thus long-swings -- on the basis of empirical evidence. However, it is not clear how to test the bubble models\u27 implication that pure psychological and technical momentum-related factors are the primary driver of stock price movements. Moreover, IKE models\u27 implication that fundamentals are the primary drivers of stock price movements -- but that changes in this relation are non-routine -- is also problematic. This thesis addresses these difficulties in two main ways. One is to construct a novel dataset based on Bloomberg News\u27 end-of-the-day equity market wrap stories. The textual data provides unambiguous support for IKE models over the bubble models. They indicate that fundamental factors are the primary driver of price fluctuations and that this relation changes at times and in ways that would be difficult to adequately capture with any overarching rule. Psychological considerations are also found to be quite important, but their impact is almost always tethered to a fundamental factor. The bubble models\u27 implication that pure psychological and technical momentum-related considerations are the main drivers of stock prices receives little support. The thesis also relies on formal econometric analysis to reexamine the connection between stock prices and fundamental factors. It employs recursive structural change tests and cointegration and out-of-sample fit analyses. The results support those obtained with the Bloomberg data: short-term stock price fluctuations are related to fundamentals but the relationship between prices and fundamentals is temporally unstable at times and in ways that cannot be fully foreseen. Beyond shedding new light on the empirical validity of bubble and IKE models, the thesis examines the question of what circumstances cause market participants to pay attention to certain fundamentals over others when forecasting market outcomes. Analyses combining both the Bloomberg data and formal econometrics suggest that the frequency with which certain fundamentals merit the attention of market participants is a function of the recent variation of such factors as well as deviations of fundamentals away from estimates of common benchmark levels

International stock market linkages: the case of Zimbabwe and South Africa

Thesis submitted in fulfillment of the requirements for the degree of Master of Management in Finance & Investment Faculty of Commerce, Law and Management Wits Business School University of Witwatersrand 2017The aim of this paper is twofold. First, it aims to investigate whether or not there are both short run and long run bilateral linkages between the Zimbabwe Stock Exchange (ZSE) and the Johannesburg Stock Exchange (JSE) markets. Secondly, it aims to find out whether or not the extent of linkages between the two markets has been changing over time. The results of the study can be stated simply: - correlation coefficients calculated for the two sub-periods 1980(1)–1990(12) (apartheid in South Africa and independence in Zimbabwe, but still some controls on the economy) and 1991(1)–1999(12) (death of apartheid in South Africa and financial liberalization in Zimbabwe) show that they were not constant overtime. The extent of the linkage has been increasing overtime. Bivariate co-integration tests indicate that there is a common trend linking the Zimbabwe Stock Exchange and the Johannesburg Stock Exchange stock price indices in the period 1991–1999, but none was found for the period 1980-1990. The results suggest that the interrelations between the two markets have increased overtime. They are in line with macroeconomic trends that have taken place since 1991, which were sufficient to strengthen the linkages between the markets, including capital market liberalization, securitization of national markets and a significant increase in cross - listing of stocks of multinational and national companies. This paper thus provides new empirical evidence on international stock market linkages between the Zimbabwe Stock Exchange and the Johannesburg Stock ExchangeMT 201

Every crypto breath in the world : the current global position of the cryptocurrency market and future prediction

This study was motivated by the breakthrough of cryptocurrencies in 2018. The other main reasons behind the motivation are the total market capitalisation of one trillion-dollar diversification possibilities and the lack of preceding scientific research to identify the portfolio diversification possibilities of cryptocurrencies from many angles. Four empirical studies were conducted to provide a holistic view of cryptocurrency as an investment tool. The first study investigated the portfolio diversification possibilities between cryptocurrencies and traditional financial markets. A quantitative method was employed with Cointegration, ARDL bound testing approach, causality, and co-movement testing. Applying Modern portfolio theory to identify the diversification possibilities between the aforementioned markets enabled the study to highlight how investors can reap the benefits of cryptocurrencies. The second study extended the investigation of the portfolio diversification possibilities of cryptocurrency by including precious metals and cryptocurrencies in the same investment basket. Investors switch from traditional investment assets, such as equity and debt market instruments, to precious metal markets to reap benefits. Therefore, this study investigates how cryptocurrency can be an alternative source of investment to include in an investment portfolio. The daily precious metal and cryptocurrency data from 2017 to 2022 was utilised through an ARDL framework to obtain the Cointegration between cryptocurrency, precious metal and across cryptocurrencies. Modern portfolio theory is used to identify the diversification possibilities in this study with different portfolio diversification strategies. The third study clarified the cryptocurrency stakeholders to identify the global perception of cryptocurrency investments. A qualitative method was employed with sentiment analysis, followed by data extractions from the global databases using machine learning algorithms. The study identified the percentage of stakeholder groups' positive, negative, and neutral perceptions of cryptocurrency. The main obstacles hindering cryptocurrency investment growth are the fear of current scams, lack of definitional issues and the absence of a legal framework in some countries. The fourth study included the findings from the first, second and third studies to develop a cryptocurrency predictive model by factoring in macroeconomic variables. Panel data regression with fixed and dynamic effects was employed to analyse the data from 2017 to 2002. The findings suggest the impact of each macroeconomic variable selected in the study for the cryptocurrency price changes while adding more significance to technological variables. The overall findings provide strong support for the portfolio diversification possibilities of cryptocurrencies. Inclusions of the wide range of investment classes, exploring stakeholder perception and highlighting the macroeconomic variables' influence on the cryptocurrency price prediction generate new insights and valuable comparisons about cryptocurrency markets for academia, crypto issuers, investors, government, policymakers, and fund managers to use as an investment and decision-support tools. Keywords: Cryptocurrency, ARDL, Financial Markets, Cointegration, Causality, Portfolio diversification, Precious Metals, Predictive model.Doctor of Philosoph

The macroeconomic drivers of economic growth in SADC countries

This study empirically investigates the key macroeconomic determinants of economic growth in three Southern African Development Community countries, namely: Malawi, Zambia, and South Africa, using annual data for the period 1970-2013. The study uses the recently developed Autoregressive Distributed Lag bounds-testing approach to co-integration and error correction model. In Malawi, the study finds that investment, human capital development, and international trade are positively associated, while inflation is negatively associated with economic growth in the short run. In the long run, the results reveal that investment, human capital development, and international trade are positively and significantly associated, while population growth and inflation are negatively and significantly associated with economic growth. In Zambia, the short-run results reveal that investment and human capital development are positively and significantly associated, while government consumption, international trade, and foreign aid are negatively and significantly associated with economic growth. The long-run results reveal that investment and human capital development are positively and significantly associated, while foreign aid is negatively and significantly associated with economic growth. In South Africa, the study results show that in the short run, investment is positively and significantly associated, while population growth and government consumption are negatively and significantly associated with economic growth. In the long run, the results reveal that economic growth is positively and significantly associated with investment, human capital development, and international trade, but negatively and significantly associated with population growth, government consumption, and inflation. These results all have significant policy implications. It is recommended that Malawian authorities should focus on strategies that attract investment: in addition there is a need to improve the quality of education, encourage export diversification, reduce population growth, and ensure inflation stability. Similarly Zambian authorities should focus on creation of incentives that attract investment, provision of quality education: moreover they need to improve government effectiveness, encourage international trade and ensure the effectiveness of development aid. South African authorities are recommended to focus on policies that attract investments, the provision of quality education, and trade liberalisation: concomitantly there is also a need to reduce population growth, government consumption and inflation.EconomicsPh.D. (Economics

Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model

A common issue when analyzing real-world complex systems is that the interactions between their elements often change over time. Here we propose a new modeling approach for time-varying interactions generalising the well-known Kinetic Ising Model, a minimalistic pairwise constant interactions model which has found applications in several scientific disciplines. Keeping arbitrary choices of dynamics to a minimum and seeking information theoretical optimality, the Score-Driven methodology allows to extract from data and interpret the presence of temporal patterns describing time-varying interactions. We identify a parameter whose value at a given time can be directly associated with the local predictability of the dynamics and we introduce a method to dynamically learn its value from the data, without specifying parametrically the system's dynamics. We extend our framework to disentangle different sources (e.g. endogenous vs exogenous) of predictability in real time, and show how our methodology applies to a variety of complex systems such as financial markets, temporal (social) networks, and neuronal populations

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio