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A financial market is a trading system for financial assets such as derivatives, stocks, currencies, and bonds. Financial markets, which can be virtual or physical, are essential to the world economy. Financial forecasting has become an enormous difficulty due to the markets’ quick internationalization and the volume of data gathered from various sources growing at an accelerating rate. The study explores the creation of a model for stock market forecasting that leverages the power of big data analytics and quantum computing (QC). By handling multifaceted datasets and achieving high-accuracy forecasting, big data and QC improve market trends and insights. This is achieved by leveraging QC speed-driven problem-solving capabilities. In this study, we proposed a novel Hippopotamus Optimized Quantum Refined Support Vector Machine (HO-QRSVM) to predict the financial market. The financial dataset came from several places, such as economic databases and stock exchanges. The preprocessed data from the acquired data were cleaned and normalized. To remove dimensionality and extract features from preprocessed data, principle component analysis (PCA) is used. The capacity of QC to handle entanglement and superposition enables the simultaneous investigation of several potential market situations, leading to faster convergence and more precise forecasts. The result demonstrated the proposed method has significant improvements in prediction accuracy compared to other traditional algorithms. The performance evaluation techniques include the F1-score (96.3%), recall (98.2%), accuracy (98.5%), and precision (98.3%). The research, big data analytics, and QC combined can greatly improve financial market forecasts, giving decision-makers and strategic investors an advantage over their competition.

We describe a new model to simulate the dynamic interactions between market price and the decisions of two different kind of traders. They possess spatial mobility allowing to group together to form coalitions. Each coalition follows a strategy chosen from a proportional voting "dominated" by a leader's decision. The interplay of both kind of agents gives rise to complex price dynamics that is consistent with the main stylized facts of financial time series. The present model incorporates many features of other known models and is meant to be the first step toward the construction of an agent-based model that uses more realistic markets rules, strategies, and information structures.

In Ref. 1, a new model for the description of the financial markets dynamics has been proposed. Traders move on a two dimensional lattice and interact by means of mechanisms of mutual influence. In the present paper, we present results from large-scale simulations of the same model enhanced by the introduction of rational traders modeled as moving-averages followers. The dynamics now accounts for log-normal distribution of volatility which is consistent with some observation of real financial indexes⁷ at least for the central part of the distribution.

A three-state model based on the Potts model is proposed to simulate financial markets. The three states are assigned to "buy", "sell" and "inactive" states. The model shows the main stylized facts observed in the financial market: fat-tailed distributions of returns and long time correlations in the absolute returns. At low inactivity rate, the model effectively reduces to the two-state model of Bornholdt and shows similar results to the Bornholdt model. As the inactivity increases, we observe the exponential distributions of returns.

We have investigated the leverage effects of three major financial markets within a time frame from 2000 to 2012 throughout the 2008 financial crisis. First, dividing the considered time into four consecutive periods, we find the leverage effects of markets exhibiting similar pattern at various periods. Second, splitting the yield data into the positive-return and negative-return series, we find these two series always show anti-leverage effect. The anti-leverage effect of negative-return series usually dominates over the positive one, reflecting people at most times are more sensitive to bad news. However, we observe anomalous behavior in approaching the outbreak of crisis, where the positive-return series shows stronger anti-leverage effect, i.e. people become more sensitive to good news instead. Such phenomenology can persist till after the crisis for an immature market, as opposed to a mature market where it disappears before the end of crisis without external intervene. Our results afford insight into the micro-emotion of various financial markets swept through by the financial crisis.

In the folklore of emerging markets, there is a popular belief that bubbles are inevitable. In this paper, our objective is to estimate a state-space model for rational bubbles in selected Asian economies with the aid of the Kalman Filter. For each economy, we derive a possible picture of the bubble formation process that is implied by the state-space formulation. The estimation is based on the rational valuation formula for stock prices. Our results provide a possible way of defining the presence of rational bubbles in the stock markets of Taiwan, Singapore, Korea, and Malaysia.

This paper studies the effects of US quantitative easing (QE) on Asia by examining capital flows and financial markets. After the global financial crisis (GFC), Asian economies with more open and developed capital markets experienced greater swings in capital inflows. In particular, large capital flows manifest more in portfolio investment and other investment such as bank loans than in foreign direct investment. Empirical analysis shows QE, QE1 in particular, significantly contributed to the rebounding of capital inflows to the region after the onset of the crisis by lowering domestic yield rates as well as CDS premiums. Although the currency value responses differed across countries, it appears that economies with stable exchange rates roughly coincide with those in which house prices have been rising, suggesting that monetary easing of advanced countries have affected Asian countries through either appreciation of currency values or increases in the prices of housing.

We investigate the relationships among credit default swap (CDS), government bond yield, foreign investors’ ownership, and exchange rate by conducting Vector Error Correction Model and Vector Auto Regression. We compare the relationships in the period of 2008 to 2013 and in the sub period when financial crisis happened in 2008. Using daily data from April 28, 2008 to December 31, 2013 in Indonesian government bond market, we find government bond yield plays a significant role; and drives the movement of the other variables. However, during the 2008 crisis, the yield loses its dominance and tends to follow the movement of CDS.

The BRICS (Brazil, Russia, India, China and South Africa) acronym was created by the International Monetary Foundation (IMF)–Group of Seven (G7) to represent the bloc of developing economies which crucially impact on the global economy by their potential economic growth. Most of the foreign direct investment are considering the stock markets of BRICS as the most attractive destination for foreign portfolio investment. This study aims to identify the relationship between macroeconomic variables and the stock market index values of BRICS and generate accurate predictions for index values by performing linear regression and artificial neural network hybrid models. Monthly data from January 2003 to December 2019 are used for the empirical study. The results indicate that a strong correlation exists between the stock market and macroeconomic variables in BRICS over time. The hybrid model is observed very accurate for index value prediction where the mean absolute percentage error (MAPE) value is 0.714% for the whole data set covering all BRICS countries data during the study period. Additionally, MAPE values for each of the BRICS countries are, respectively, obtained as 0.083%, 2.316%, 0.116%, 0.962% and 0.092%. Thus, the main findings of this study show that while neural network-integrated models have high performances for volatile stock market prediction, macroeconomic stabilization should be the priority of monetary policy to prevent the high volatility of stock markets.

Financial market is a complex system whose characteristic behaviors can be caught in corresponding time series. Analyzing such time series by appropriate methods will aid in making inferences and predictions. Here phase synchronization approach is used for visual pattern recognition of crises. Based on Empirical Mode Decomposition and the Hilbert transform, phase evolution of various rhythmic components exiting in the market is extracted. Then the concept of synchronization can be successfully applied to crises detection. Unlike other approaches, this detection distinguishes crises from normal state according to variations of interaction among rhythmic components. The empirical results mentioned here convince us of the fact that financial crises take place at the time when the adjustment processes of other quasi-periodic oscillations and the trend are out of synchronization. On the contrary, when other rhythmic oscillations can be synchronized with the trend, the market will develop healthily. The presence and duration of synchronization reflect dynamics of financial market. All these results will enlighten people to disclose its reasons and probe methods for controlling its pathological rhythms.

In this paper, we further study a financial market model established in our earlier paper. The model dynamics is driven by a two-dimensional piecewise linear discontinuous map, which is investigated analytically and numerically for one-sided fixed points being flip saddle and two-sided fixed points being attractors. The existence of chaotic orbit is explained by using the theory of homoclinic intersection between stable and unstable manifolds of the flip saddle invariant set. The structure of chaotic attractor is disclosed. It consists of finite segments rooted on both sides of the $x$-axis which are unstable manifolds of flip saddle invariant set. The basins and their structural changes of bounded attractors and coexisting attractors are presented by contact bifurcation theory and numerical simulations. The border collision bifurcation (BCB for short) curves are calculated and coexisting multiattractors are disclosed by overlapping periodicity regions. The results can deepen our understanding of financial markets and dynamical systems.

By adding trend followers, we extend the model given by Tramontana et al. from one-dimensional ($1$D) piecewise linear discontinuous (PWLD) map to a new 2D PWLD map. Using this map in financial markets, we describe the bifurcation mechanisms associated with the appearance/disappearance of cycles, which may be related to several cases: border collision bifurcations; Poincaré equator collision bifurcations; degenerate flip bifurcations in both supercritical and subcritical cases. We investigate the multistability regions in the parameter plane and related basins of multiattractors to uncover the reason for the unpredictability of the internal law of price fluctuations in financial market.

Finding strategies to profit from the stock markets is a topic of immense interest to both institutional and retail investors. Possible methods range from machine learning, time series prediction, and network science to game theory. Game theory is a useful mathematical tool to model the relationship between agents and the stocks. In this paper, we ask an important question: Why do some (non-optimal) traders end up winning despite alternating between losing stocks? Our empirical observations reveal that by using a switching strategy abstractly derived from the switching in Parrondo’s paradox, we can demonstrate that losing stocks can be combined in a time-based switching scheme to achieve a winning outcome. This is akin to an automatic investment plan. Several stocks within a given time period are being analyzed. Our analysis may provide a descriptive explanation for why some traders end up winning despite switching between losing stocks. However, it is not the focus of this paper to provide any normative advice or support for switching between stocks.

We measure the performance of probabilistic models from a decision-theoretic perspective along the lines of Friedman and Sandow [6]. In particular, we adopt the point of view of an investor who evaluates models based on the test-sample averaged utility of the expected-utility-optimal strategies that the models suggest in the horse race setting. In this paper, we relax the assumptions of Friedman and Sandow [6]: we omit the notion of a "true" measure and we allow our investor to withhold or borrow cash, which widens the range of possible applications. We show that, in this setting, our relative model performance measure is odds-ratio independent if and only if the investor has a generalized logarithmic utility function, in which case it essentially reduces to the likelihood ratio. We also show that for horse races with nearly homogeneous returns, our relative performance measure is approximately equal to the likelihood ratio.

Recently, an information theoretic inspired concept of transfer entropy has been introduced by Schreiber. It aims to quantify in a nonparametric and explicitly nonsymmetric way the flow of information between two time series. This model-free based on Shannon entropy approach in principle allows us to detect statistical dependencies of all types, i.e., linear and nonlinear temporal correlations. However, we always analyze the transfer entropy based on the data, which is discretized into three partitions by some coarse graining. Naturally, we are interested in investigating the effect of the data discretization of the two series on the transfer entropy. In our paper, we analyze the results based on the data which are generated by the linear modeling and the ARFIMA modeling, as well as the dataset consists of seven indices during the period 1992–2002. The results show that the higher the degree of data discretization get, the larger the value of the transfer entropy will be, besides, the direction of the information flow is unchanged along with the degree of data discretization.

The modelling of financial markets presents a problem which is both theoretically challenging and practically important. The theoretical aspects concern the issue of market efficiency which may even have political implications (Cuthbertson, 1996), whilst the practical side of the problem has clear relevance to portfolio management (Elton and Gruber, 1995) and derivative pricing (Hull, 1997). Up till now all market models contain "smart money" traders and "noise" traders whose joint activity constitutes the market (De Long et al., 1990; Bak et al., 1997). On a short time scale this traditional separation does not seem to be realistic, and is hardly acceptable since all high-frequency market participants are professional traders and cannot be separated into "smart" and "noise". In this paper we present a "microscopic" model with homogenuous quasi-rational behaviour of traders, aiming to describe short time market behaviour. To construct the model we use an analogy between "screening" in quantum electrodynamics and an equilibration process in a market with temporal mispricing (Ilinski, 1997; Dunbar, 1998). As a result, we obtain the time-dependent distribution function of the returns which is in quantitative agreement with real market data and obeys the anomalous scaling relations recently reported for both high-frequency exchange rates (Ghashghaie et al., 1996, S&P500 (Mantegna and Stanley, 1994) and other stock market indices (Bouchaud and Sornette, 1994; Matacz, 1997).

Investors with different trading strategies can be viewed as different "species" in financial markets. Since the asset price is ultimately determined by the individual trading decisions, the combination and evolution of different trader species in financial market ecology will have great impact to the price dynamics. Considering the limitations and shortcomings of traditional analytical approaches in financial economics in dealing with this issue, an agent-based computational model is introduced in this paper. With the co-existence of 3-type trader species that make different decisions based on their own beliefs and constrains, it is found that although rational speculation destabilizes the price process with the presence of positive feedback strategy, as suggested in the literature, introducing extra noise trading behavior to the market will make the price process back to a more stationary situation, meaning that the market will be healthier if more diversified trader species co-exist in the markets.

A parsimonious percolation model for stock market is proposed, of which the avalanche dynamics agree with the real-life one as well. We have also investigated how the interaction parameter p affects the price dynamics. Simulation results about the formation of the bullish/bearish market and corresponding avalanche taking place in the market indicate that the magnified "herd behavior" resulting from the evolution of p may be the origin of the observed avalanche phenomena.

This study reviews how climate change could be considered an additional source of financial market risk using a bibliometric methodology. We find that the primary impetus for academics’ research into these concerns has come from significant international climate change events, e.g., the adoption of the Paris Climate Agreement. Ecological Economics, Energy Policy, and Energy Economics emerge as the major journals of the existing research output. The bibliographic coupling analysis of the corpus further reveals the existence of five major themes. The first theme evaluates the climate risk and explores mechanisms by which can financial risks be impacted by climate change. The second theme talks about the losses brought on by climate change. The third theme talks about how climate finance products, such as green bonds, green securities, and green insurance, are created and issued, as well as how they help to address and mitigate climate risks. The fourth theme examines how the current financial policy frameworks and instruments can be optimized and adapted in light of the considerable degree of uncertainty that surrounds climate change. The fifth theme discusses the climate-related financial risk modeling. We provide a summary of the development of these themes as well as the future direction to be explored.

The systematic importance of US Treasuries for the global financial market stems from their attribute of fundamental collateral. This paper attempts to use the concept of fundamental collateral, as well as its basic features and functions, to explain how US Treasuries have strengthened the international status of the US dollar, and examines the prospects of a diversifying global fundamental collateral market.