Computer simulation of the Cont-Bouchaud percolation model of financial markets

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Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis

This paper applies the vector AR-DCC-FIAPARCH model to eight national stock market indices' daily returns from 1988 to 2010, taking into account the structural breaks of each time series linked to the Asian and the recent Global financial crisis. We find significant cross effects, as well as long range volatility dependence, asymmetric volatility response to positive and negative shocks, and the power of returns that best fits the volatility pattern. One of the main findings of the model analysis is the higher dynamic correlations of the stock markets after a crisis event, which means increased contagion effects between the markets. The fact that during the crisis the conditional correlations remain on a high level indicates a continuous herding behaviour during these periods of increased market volatility. Finally, during the recent Global financial crisis the correlations remain on a much higher level than during the Asian financial crisis.

Euphoria in financial markets: how Indian companies generate value in their cross-border acquisitions

In this paper, we investigate the effect of euphoria on returns derived by Indian companies in their cross-border acquisitions. Cognitive legitimacy generated at the country level facilitated firms in deriving higher value from internationalization. In addition, overoptimism after the legitimacy-building event led to euphoria in financial markets and short-term abnormal returns. Hence we argue that the springboard effect created by legitimacy is short-lived, as euphoria fades away over time. Using cross-border and domestic acquisitions by Indian companies during 1999-2009, and controlling for fundamental factors, both financial and non-financial, we find support for our euphoria hypothesis. Because of overoptimism, Indian companies experienced short-term abnormal returns in their cross-border acquisitions in the few years following the legitimation process, but not in later years.

Financial Futures Markets and Their Impact on the Real Economy in Middle and Low Income Countries

This project analyses the influence of the futures market on middle and low income countries. In it, I attempt to show that investments made by large investment funds in this market, as well as by certain pension plans, bring major consequences whose effects are more evident in less developed countries. The cornerstones of the work are as follows; to attempt to see the existing relationship between the commodity futures market and its underlying assets; analysing products such as wheat, rice and corn in-depth, because these are the most basic foodstuffs at a global level; to determine how an increase in trading in these markets can affect the lives of people in the poorest countries; to analyse investor concern regarding the consequences that their investments may have. Throughout the project we will see how large speculators use production forecasting models to determine the shortage of a commodity in order to take a position in the futures market to profit from it. In addition we will see how an increase in trading in this market causes an increase in the price of the underlying asset in the spot market. As for investor concern, I can say it is negligible, but the idea of running pension plans or investment funds that follow some social criteria has been welcomed by those interviewed, which makes me think that different legislation is possible. This legislation will only come into existence if it is demanded by the people. A fact that now becomes complicated because without a minimum financial basis, they cannot even know how the large investment funds trade with hunger in the world. The day when most people understand how large speculators profit from famine will be the day to put pressure on governments to begin to put limits on speculation. This makes financial awareness necessary in order to achieve a curb in excessive speculation.

Europe’s Untapped Capital Market: Rethinking integration after the great financial crisis. Final report of the European Capital Markets Expert Group. Task Force Report, February 2016

In December 2014, ECMI and CEPS formed the European Capital Markets Expert Group (ECMEG) with the aim of providing a long-term contribution to the debate on the Capital Markets Union (CMU) project, proposed by the European Commission. After an intensive, year-long research effort and in-depth discussions with ECMEG members, this final report aims to rethink financial integration policies in the European Union and to devise an EU-wide plan to remove the barriers to greater capital markets integration. It offers a methodology to identify and prioritise cross-border barriers to capital markets integration and provides a set of policy recommendations to improve its key components: price discovery, execution and enforcement of capital markets transactions.

Is financial instability male-driven? Gender and cognitive skills in experimental asset markets

The hypothesis that price stability would reliably increase with the fraction of women operating in financial markets has been frequently suggested in policy discussions. To test this hypothesis we conducted 10 male-only, 10 female-only and 10 mixed-gender experimental asset markets, and compared the effects of gender composition, confidence, risk attitude and cognitive skills. Male and female markets have comparable volatility and deviations from fundamentals, whereas mixed-gender markets are substantially more stable. On the other hand, higher average cognitive skills of the group are associated with reduced market volatility. Individual-level analysis shows that subjects with higher cognitive skills trade at prices closer to fundamental values and earn significantly higher profits; similarly, mixed markets exhibit lower mispricing, particularly for traders with lower cognitive skills. Our results are demonstrated to hold in other experimental asset market studies, suggesting that a mixed-gender composition reduces mispricing across different types of asset markets.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

Financial literacy and pension investment decisions

The call for enhanced financial literacy amongst consumers is a global phenomenon, driven by the growing complexity of financial markets and products, and government concerns about the affordability of supporting an ageing population. Worldwide, defined benefit pensions are giving way to the risk and uncertainty of defined contribution superannuation/pension funds where fund members now make choices and decisions that were once made on their behalf. An important prerequisite for informed financial decision-making is adequate financial knowledge and skills to make competent investment decisions. This paper reports the findings of an online survey of the members of a large Australian public sector-based superannuation fund and shows that although respondents generally understand basic financial matters, on average, their understanding of investments concepts, such as the relationship between risk and returns, is inadequate. These results highlight the need for education programs focusing specifically on developing fund members’ investment knowledge and skills to facilitate informed retirement savings decisions.

The financial services cultural orientation matrix

With saturation within domestic marketplaces and increased growth opportunities overseas, many financial service providers are investing in foreign markets. However, cultural attitudes towards money can present market entry challenges to financial service providers. The industry would therefore benefit from a strategic model that helps to align financial marketing mixes with the cultural dimensions of a foreign market. The Financial Services Cultural Orientation (FSCO) Matrix has therefore been designed, with three cultural dimensions identified which influence preference for financial products; preference for cash, aversion to debt and savings orientation. Based on a combination of these dimensions and their relative strength within a culture, eight different consumer segments for financial products are identified, and marketing strategies for each consumer segment are then proposed. Three cultural clusters from the GLOBE Project House et al. (2002) are used to highlight possible geographic markets for each of these consumer segments. In particular, this paper focuses on GLOBE’s Confucian Asia, Southern Asia and Anglo cultural clusters, as these clusters represent the most well established financial markets in the world and the fastest growing financial markets for the future. The FSCO Matrix provides the financial services industry with an innovative and practical tool for addressing cross-cultural challenges and developing successful marketing strategies for entry into foreign markets.

Cortisol shifts financial risk preferences

Risk taking is central to human activity. Consequently, it lies at the focal point of behavioral sciences such as neuroscience, economics, and finance. Many influential models from these sciences assume that financial risk preferences form a stable trait. Is this assumption justified and, if not, what causes the appetite for risk to fluctuate? We have previously found that traders experience a sustained increase in the stress hormone cortisol when the amount of uncertainty, in the form of market volatility, increases. Here we ask whether these elevated cortisol levels shift risk preferences. Using a double-blind, placebo-controlled, cross-over protocol we raised cortisol levels in volunteers over eight days to the same extent previously observed in traders. We then tested for the utility and probability weighting functions underlying their risk taking, and found that participants became more risk averse. We also observed that the weighting of probabilities became more distorted among men relative to women. These results suggest that risk preferences are highly dynamic. Specifically, the stress response calibrates risk taking to our circumstances, reducing it in times of prolonged uncertainty, such as a financial crisis. Physiology-induced shifts in risk preferences may thus be an under-appreciated cause of market instability.

An experimental analysis of information acquisition in prediction markets

We study which factors in terms of trading environment and trader characteristics determine individual information acquisition in experimental asset markets. Traders with larger endowments, existing inconclusive information, lower risk aversion, and less experience in financial markets tend to acquire more information. Overall, we find that traders overacquire information, so that informed traders on average obtain negative profits net of information costs. Information acquisition and the associated losses do not diminish over time. This overacquisition phenomenon is inconsistent with predictions of rational expectations equilibrium, and we argue it resembles the overdissipation results from the contest literature. We find that more acquired information in the market leads to smaller differences between fundamental asset values and prices. Thus, the overacquisition phenomenon is a novel explanation for the high forecasting accuracy of prediction markets.

The economic consequences of corporate financial reporting on Twitter

The use of social media has spread into many different areas including marketing, customer service, and corporate disclosure. However, our understanding of the timely effect of financial reporting information on Twitter is still limited. In this paper, we examine the timely effect of financial reporting information on Twitter in the Australian context, as reflected in the follow-up stock market reaction. With the use of event methodology and comparative setting, we find that financial reporting disclosure on Twitter reduces the information asymmetry level. This is evidenced by reduction of bid-ask spread and increase of share trading volume. The results of this study imply that financial reporting disclosure on social media assists the dissemination of information and the stock market response to this information

Intraday Dynamics of International Equity Markets

The increased availability of high frequency data sets have led to important new insights in understanding of financial markets. The use of high frequency data is interesting and persuasive, since it can reveal new information that cannot be seen in lower data aggregation. This dissertation explores some of the many important issues connected with the use, analysis and application of high frequency data. These include the effects of intraday seasonal, the behaviour of time varying volatility, the information content of various market data, and the issue of inter market linkages utilizing high frequency 5 minute observations from major European and the U.S stock indices, namely DAX30 of Germany, CAC40 of France, SMI of Switzerland, FTSE100 of the UK and SP500 of the U.S. The first essay in the dissertation shows that there are remarkable similarities in the intraday behaviour of conditional volatility across European equity markets. Moreover, the U.S macroeconomic news announcements have significant cross border effect on both, European equity returns and volatilities. The second essay reports substantial intraday return and volatility linkages across European stock indices of the UK and Germany. This relationship appears virtually unchanged by the presence or absence of the U.S stock market. However, the return correlation among the U.K and German markets rises significantly following the U.S stock market opening, which could largely be described as a contemporaneous effect. The third essay sheds light on market microstructure issues in which traders and market makers learn from watching market data, and it is this learning process that leads to price adjustments. This study concludes that trading volume plays an important role in explaining international return and volatility transmissions. The examination concerning asymmetry reveals that the impact of the positive volume changes is larger on foreign stock market volatility than the negative changes. The fourth and the final essay documents number of regularities in the pattern of intraday return volatility, trading volume and bid-ask spreads. This study also reports a contemporaneous and positive relationship between the intraday return volatility, bid ask spread and unexpected trading volume. These results verify the role of trading volume and bid ask quotes as proxies for information arrival in producing contemporaneous and subsequent intraday return volatility. Moreover, asymmetric effect of trading volume on conditional volatility is also confirmed. Overall, this dissertation explores the role of information in explaining the intraday return and volatility dynamics in international stock markets. The process through which the information is incorporated in stock prices is central to all information-based models. The intraday data facilitates the investigation that how information gets incorporated into security prices as a result of the trading behavior of informed and uninformed traders. Thus high frequency data appears critical in enhancing our understanding of intraday behavior of various stock markets’ variables as it has important implications for market participants, regulators and academic researchers.