The evolution and dynamics of stocks on the Johannesburg Securities Exchange and their implications for equity investment management

[No subject] This thesis explores the dynamics of the Johannesburg Stock Exchange returns to understand how they impact stock prices. The introductory chapter renders a brief overview of financial markets in general and the Johannesburg Securities Exchange (JSE) in particular. The second chapter employs the fractal analysis technique, a method for estimating the Hurst exponent, to examine the JSE indices. The results suggest that the JSE is fractal in nature, implying a long-term predictability property. The results also indicate a logical system of variation of the Hurst exponent by firm size, market characteristics and sector grouping. The third chapter investigates the economic and political events that affect different market sectors and how they are implicated in the structural dynamics of the JSE. It provides some insights into the degree of sensitivity of different market sectors to positive and negative news. The findings demonstrate transient episodes of nonlinearity that can be attributed to economic events and the state of the market. Chapter 4 looks at the evolution of risk measurement and the distribution of returns on the JSE. There is evidence of fat tails and that the Student t-distribution is a better fit for the JSE returns than the Normal distribution. The Gaussian based Value-at-Risk model also proved to be an ineffective risk measurement tool under high market volatility. In Chapter 5 simulations are used to investigate how different agent interactions affect market dynamics. The results show that it is possible for traders to switch between trading strategies and this evolutionary switching of strategies is dependent on the state of the market. Chapter 6 shows the extent to which endogeneity affects price formation. To explore this relationship, the Poisson Hawkes model, which combines exogenous influences with self-excited dynamics, is employed. Evidence suggests that the level of endogeneity has been increasing rapidly over the past decade. This implies that there is an increasing influence of internal dynamics on price formation. The findings also demonstrate that market crashes are caused by endogenous dynamics and exogenous shocks merely act as catalysts. Chapter 7 presents the hybrid adaptive intelligent model for financial time series prediction. Given evidence of non-linearity, heterogeneous agents and the fractal nature of the JSE market, neural networks, fuzzy logic and fractal theory are combined, to obtain a hybrid adaptive intelligent model. The proposed system outperformed traditional models

Feature learning for stock price prediction shows a significant role of analyst rating

Data Availability Statement: The code is available from https://mkhushi.github.io/ (accessed on 1 February 2021). Dataset License: License under which the dataset is made available (CC0).Efficient Market Hypothesis states that stock prices are a reflection of all the information present in the world and generating excess returns is not possible by merely analysing trade data which is already available to all public. Yet to further the research rejecting this idea, a rigorous literature review was conducted and a set of five technical indicators and 23 fundamental indicators was identified to establish the possibility of generating excess returns on the stock market. Leveraging these data points and various classification machine learning models, trading data of the 505 equities on the US S&P500 over the past 20 years was analysed to develop a classifier effective for our cause. From any given day, we were able to predict the direction of change in price by 1% up to 10 days in the future. The predictions had an overall accuracy of 83.62% with a precision of 85% for buy signals and a recall of 100% for sell signals. Moreover, we grouped equities by their sector and repeated the experiment to see if grouping similar assets together positively effected the results but concluded that it showed no significant improvements in the performance—rejecting the idea of sector-based analysis. Also, using feature ranking we could identify an even smaller set of 6 indicators while maintaining similar accuracies as that from the original 28 features and also uncovered the importance of buy, hold and sell analyst ratings as they came out to be the top contributors in the model. Finally, to evaluate the effectiveness of the classifier in real-life situations, it was backtested on FAANG (Facebook, Amazon, Apple, Netflix & Google) equities using a modest trading strategy where it generated high returns of above 60% over the term of the testing dataset. In conclusion, our proposed methodology with the combination of purposefully picked features shows an improvement over the previous studies, and our model predicts the direction of 1% price changes on the 10th day with high confidence and with enough buffer to even build a robotic trading system.This research received no external funding

Tripartite analysis across business cycles in Turkey: A multi-timescale inquiry of efficiency, volatility and integration

AbstractIn the current era of globalization, deregulation and liberalization of markets have led to financial integration amongst developing and developed countries. The sudden massive inflow of capital into developing country's stock markets begs the question of whether or not the markets are sufficiently efficient to handle the increasing integration of markets. Furthermore, the relationship between the integration and efficiency of stock markets tends to be of greater importance during economic downturns. Taking Turkey as a case study owing to its economic growth and importance in two successful blocs, i.e. the EU and the OIC, we attempt to analyse the linkages between stock market efficiency and integration during the different phases of the economy. The findings of our study provide an interesting insight into the relative improvement in volatility, efficiency and integration across business cycles, in a multi time scale analysis

Critical Market Crashes

This review is a partial synthesis of the book ``Why stock market crash'' (Princeton University Press, January 2003), which presents a general theory of financial crashes and of stock market instabilities that his co-workers and the author have developed over the past seven years. The study of the frequency distribution of drawdowns, or runs of successive losses shows that large financial crashes are ``outliers'': they form a class of their own as can be seen from their statistical signatures. If large financial crashes are ``outliers'', they are special and thus require a special explanation, a specific model, a theory of their own. In addition, their special properties may perhaps be used for their prediction. The main mechanisms leading to positive feedbacks, i.e., self-reinforcement, such as imitative behavior and herding between investors are reviewed with many references provided to the relevant literature outside the confine of Physics. Positive feedbacks provide the fuel for the development of speculative bubbles, preparing the instability for a major crash. We demonstrate several detailed mathematical models of speculative bubbles and crashes. The most important message is the discovery of robust and universal signatures of the approach to crashes. These precursory patterns have been documented for essentially all crashes on developed as well as emergent stock markets, on currency markets, on company stocks, and so on. The concept of an ``anti-bubble'' is also summarized, with two forward predictions on the Japanese stock market starting in 1999 and on the USA stock market still running. We conclude by presenting our view of the organization of financial markets.Comment: Latex 89 pages and 38 figures, in press in Physics Report

A similarity of multivariate time series in stocks network analysis

Correlation-based network as a model for financial markets, especially stock market, is a complex system has received much attention. There have been a lot of studies which deals with stocks network analysis, where each stock is represented by a univariate time series of its closing price, and then the similarity between two stocks are quantified by using Pearson correlation coefficient (PCC) on the logarithmic returns. However, in daily stock market activity, stock is represented by a multivariate time series during its opening, highest, lowest, and closing prices. The solely used of the information from closing price may cause the loss of information from other prices. In this thesis, all four prices are considered. The notion of multivariate time series similarity among stocks are developed. The use of Escoufier vector correlation (EVC), a multivariate generalization of PCC, is proposed to measure the similarity between stocks. Then the EVC coefficients are used to construct the stocks network in multivariate setting based on minimal spanning tree (MST). In the case study on BURSA MALAYSIA, the topological properties of stocks in EVC-based MST and in PCC-based MST are different. The total path lengths among stocks in the economic sector according to EVC-based MST is generally smaller than according to PCC-based MST. It means that with the approach of EVC-based MST, the stocks are strongly connected with other stocks in the same sector. Moreover, EVC is proposed to define the similarity between economic sectors, where each sector is represented by a multivariate time series of p components and each component is a univariate time series of stock’s closing price. To the best of our knowledge, there is no previous studies which deals with the similarity between economic sectors using this approach. The methodology for economic sectors network analysis is formulated in this thesis. The current practice of using Kruskal’s or Prim’s algorithm is to obtain MST, and then sub-dominant ultrametric (SDU) from the MST. It will consume a lot of time when the number of stocks is large. Therefore to solve this problem, an efficient algorithm is developed based on fuzzy relation approach. A comparison study based on the empirical and simulated data shows that the proposed algorithm is faster. The proposed algorithm provides not only MST and SDU, but also the forest of all MSTs

The dynamics of market efficiency: testing the adaptive market hypothesis in South Africa

A thesis submitted to the School of Economic and Business Sciences, Faculty of Commerce, Law and Management, University of the Witwatersrand in fulfilment of the requirements for the degree of Doctor of Philosophy (Ph/D). Johannesburg, South Africa June 2016In recent years, the debate on market efficiency has shifted to providing alternate forms of the hypothesis, some of which are testable and can be proven false. This thesis examines one such alternative, the Adaptive Market Hypothesis (AMH), with a focus on providing a framework for testing the dynamic (cyclical) notion of market efficiency using South African equity data (44 shares and six indices) over the period 1997 to 2014. By application of this framework, stylised facts emerged. First, the examination of market efficiency is dependent on the frequency of data. If one were to only use a single frequency of data, one might obtain conflicting conclusions. Second, by binning data into smaller sub-samples, one can obtain a pattern of whether the equity market is efficient or not. In other words, one might get a conclusion of, say, randomess, over the entire sample period of daily data, but there may be pockets of non-randomness with the daily data. Third, by running a variety of tests, one provides robustness to the results. This is a somewhat debateable issue as one could either run a variety of tests (each being an improvement over the other) or argue the theoretical merits of each test befoe selecting the more appropriate one. Fourth, analysis according to industries also adds to the result of efficiency, if markets have high concentration sectors (such as the JSE), one might be tempted to conclude that the entire JSE exhibits, say, randomness, where it could be driven by the resources sector as opposed to any other sector. Last, the use of neural networks as approximators is of benefit when examining data with less than ideal sample sizes. Examining five frequencies of data, 86% of the shares and indices exhibited a random walk under daily data, 78% under weekly data, 56% under monthly data, 22% under quarterly data and 24% under semi-annual data. The results over the entire sample period and non-overlapping sub-samples showed that this model's accuracy varied over time. Coupled with the results of the trading strategies, one can conclude that the nature of market efficiency in South Africa can be seen as time dependent, in line with the implication of the AMH.MT201

The Financial Economics of Gold - a survey

We review the literature on gold as an investment. We summarize a wide variety of literature. We begin with a review of how the gold markets operate, including the under researched leasing market; we proceed to examine research on physical gold demand and supply, gold mine economics and move onto analyses of gold as an investment. Additional sections provide context on gold market efficiency, the issue of gold market bubbles, gold’s relation to inflation and interest rates, and the very nascent literature on the behavioural aspects of gold