Academic literature on the topic 'Stock market order submissions'

This study presents an agent-based computational cross market model for Chinese equity market structure, which includes both stocks and CSI 300 index futures. In this model, we design several stocks and one index future to simulate this structure. This model allows heterogeneous investors to make investment decisions with restrictions including wealth, market trading mechanism, and risk management. Investors’ demands and order submissions are endogenously determined. Our model successfully reproduces several key features of the Chinese financial markets including spot-futures basis distribution, bid-ask spread distribution, volatility clustering, and long memory in absolute returns. Our model can be applied in cross market risk control, market mechanism design, and arbitrage strategies analysis.

We investigate the behavior of limit order books (LOBs) on the meso-scale motivated by order execution scheduling algorithms. To do so, we carry out empirical analysis of the order flows from market and limit order submissions, aggregated from tick-by-tick data via volume-based bucketing, as well as various LOB depth and shape metrics. We document a nonlinear relationship between trade imbalance and price change, which however can be converted into a linear link by considering a weighted average of market and limit order flows. We also document a hockey-stick dependence between trade imbalance and one-sided limit order flows, highlighting numerous asymmetric effects between the active and passive sides of the LOB. To address the phenomenological features of price formation, we construct regression models to identify the most significant predictors, confirming the predictive power of limit order flows. Another finding is that the deeper LOB shape, rather than just the book imbalance, is more relevant on this timescale. The empirical results are based on analysis of six large-tick assets from Nasdaq.

We propose a limit order book (LOB) model with dynamics that account for both the impact of the most recent order and volume imbalance. To model these effects jointly we introduce a discrete Markov chain model. We then find the policy for optimal order choice and control. The optimal policy derived uses limit orders, cancellations and market orders. It looks to avoid non-execution and adverse selection risk simultaneously. Using ultra high-frequency data from the NASDAQ stock exchange we compare our policy with other submission strategies that use a subset of all available order types and show that ours significantly outperforms.

In modern electronic stock exchanges there is an opportunity to analyze event driven market microstructure data. This data is highly informative and describes physical price formation which makes it possible to find complex patterns in price dynamics. It is very time consuming and hard to find this kind of patterns by handcrafted rules. However, modern machine learning models are able to solve such issues automatically by learning price behavior which is always changing. The present study presents profitable trading system based on a machine learning model and market microstructure data. Data for the research was collected from Moscow stock exchange MICEX and represents a limit order book change log and all market trades of a liquid security for a certain period. Logistic regression model was used and compared to neural network models with different configuration. According to the study results logistic regression model has almost the same prediction quality as neural network models have but also has a high speed of response which is very important for stock market trading. The developed trading system has medium frequency of deals submission that lets it to avoid expensive infrastructure which is usually needed in high-frequency trading systems. At the same time, the system uses the potential of high quality market microstructure data to the full extent. This paper describes the entire process of trading system development including feature engineering, models behavior comparison and creation of trading strategy with testing on historical data.

While exchanges and regulators are able to observe and analyze the individual behavior of financial market participants through access to labeled data, this information is not accessible by other market participants nor by the general public. A key question, then, is whether it is possible to model individual market participants’ behaviors through observation of publicly available unlabeled market data alone. Several methods have been suggested in the literature using classification methods based on summary trading statistics, as well as using inverse reinforcement learning methods to infer the reward function underlying trader behavior. Our primary contribution is to propose an alternative neural network based multi-modal imitation learning model which performs latent segmentation of stock trading strategies. As a result that the segmentation in the latent space is optimized according to individual reward functions underlying the order submission behaviors across each segment, our results provide interpretable classifications and accurate predictions that outperform other methods in major classification indicators as verified on historical orderbook data from January 2018 to August 2019 obtained from the Tokyo Stock Exchange. By further analyzing the behavior of various trader segments, we confirmed that our proposed segments behaves in line with real-market investor sentiments.

Submission of Annual Financial Statements in Indonesia organized by theFinancial Services Authority (Otoritas Jasa Keuangan/OJK). All of the Company thatthe shares are traded on the Indonesia Stock Exchange (IDX) shall submit the AnnualFinancial Report. The Indonesian Capital Market Supervisory Agency Rule (2003), listedcompanies are required to submit the audited annual financial statement to BAPEPAMand Indonesian Stock Exchange (IDX) at the latest at the end of the third month after thedate of the statement.This research was conducted in order to test the effect of the characteristics of theaudit committees, predictions of bankruptcy and public ownership lag effect on the auditreport. The study was conducted at the manufacturing companies listed in Indonesia StockExchange in 2010-2014.The results showed that the characteristics of the audit committee and bankruptcyprediction have effect on audit report lag but public ownership has no effect on audit reportlag.Keywords: audit committees characteristics, audit report lag, bankruptcy prediction,manufacturing companies, public ownership.

AbstractAn optimal selection problem for bid and ask quotes subject to a stock inventory constraint is investigated, formulated as a constrained utility maximisation problem over a finite time horizon. The arrivals of buy and sell orders are governed by Poisson processes, and a diffusion approximation is employed on assuming the Poisson arrivals intensity is sufficiently large. Using the dynamic programming principle, we adopt an efficient numerical procedure to solve this constrained utility maximisation problem based on a successive approximation algorithm, and conduct numerical experiments to analyse the impacts of the inventory constraint on a dealer's terminal profit and stock inventory level. It is found that the stock inventory constraint significantly affects the terminal stock inventory level.

We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks (GANs). Our Stock-GAN model employs a conditional Wasserstein GAN to capture history dependence of orders. The generator design includes specially crafted aspects including components that approximate the market's auction mechanism, augmenting the order history with order-book constructions to improve the generation task. We perform an ablation study to verify the usefulness of aspects of our network structure. We provide a mathematical characterization of distribution learned by the generator. We also propose statistics to measure the quality of generated orders. We test our approach with synthetic and actual market data, compare to many baseline generative models, and find the generated data to be close to real data.

The stock market is an emerging market formed after China's reform and opening up. It plays a very important role in the development of our social economy. As an information intensive market, the stock price is very sensitive to the change of information. In order to make the stock market in a more fair and true state, relevant information needs to be transmitted to investors in a timely and accurate manner. However, in the actual stock market, relevant stock information cannot be disclosed to investors in a timely and accurate manner, which leads to an unfair stock investment environment and is not conducive to the sustainable and healthy development of the stock market. However, the gradual opening of the stock market in recent years has had an important impact on the manipulation of the stock market. Based on this, this paper chooses the impact of the opening of the stock market on market manipulation to study, in order to provide some reference for the follow-up research.

Doctor of Philosophy
The motivation for the research presented in this thesis stems from the recent availability of high frequency limit order book data, relative scarcity of studies employing such data, economic significance of transaction costs management, and a perceived potential of data mining for uncovering patterns and relationships not identified by the traditional top-down modelling approach. We analyse and build computational models for order submissions on the Australian Stock Exchange, an order-driven market with a public electronic limit order book. The focus of the thesis is on the trade implementation problem faced by a trader who wants to transact a buy or sell order of a certain size. We use two approaches to build our models, top-down and bottom-up. The traditional, top-down approach is applied to develop an optimal order submission plan for an order which is too large to be traded immediately without a prohibitive price impact. We present an optimisation framework and some solutions for non-stationary and non-linear price impact and price impact risk. We find that our proposed transaction costs model produces fairly good forecasts of the variance of the execution shortfall. The second, bottom-up, or data mining, approach is employed for trade sign inference, where trade sign is defined as the side which initiates both a trade and the market order that triggered the trade. We are interested in an endogenous component of the order flow, as evidenced by the predictable relationship between trade sign and the variables used to infer it. We want to discover the rules which govern the trade sign, and establish a connection between them and two empirically observed regularities in market order submissions, competition for order execution and transaction cost minimisation. To achieve the above aims we first use exploratory analysis of trade and limit order book data. In particular, we conduct unsupervised clustering with the self-organising map technique. The visualisation of the transformed data reveals that buyer-initiated and seller-initiated trades form two distinct clusters. We then propose a local non-parametric trade sign inference model based on the k-nearest-neighbour classifier. The best k-nearest-neighbour classifier constructed by us requires only three predictor variables and achieves an average out-of-sample accuracy of 71.40% (SD=4.01%)1, across all of the tested stocks. The best set of predictor variables found for the non-parametric model is subsequently used to develop a piecewise linear trade sign model. That model proves superior to the k-nearest-neighbour classifier, and achieves an average out-of-sample classification accuracy of 74.38% (SD=4.25%). The result is statistically significant, after adjusting for multiple comparisons. The overall classification performance of the piecewise linear model indicates a strong dependence between trade sign and the three predictor variables, and provides evidence for the endogenous component in the order flow. Moreover, the rules for trade sign classification derived from the structure of the piecewise linear model reflect the two regularities observed in market order submissions, competition for order execution and transaction cost minimisation, and offer new insights into the relationship between them. The obtained results confirm the applicability and relevance of data mining for the analysis and modelling of stock market order submissions.

The motivation for the research presented in this thesis stems from the recent availability of high frequency limit order book data, relative scarcity of studies employing such data, economic significance of transaction costs management, and a perceived potential of data mining for uncovering patterns and relationships not identified by the traditional top-down modelling approach. We analyse and build computational models for order submissions on the Australian Stock Exchange, an order-driven market with a public electronic limit order book. The focus of the thesis is on the trade implementation problem faced by a trader who wants to transact a buy or sell order of a certain size. We use two approaches to build our models, top-down and bottom-up. The traditional, top-down approach is applied to develop an optimal order submission plan for an order which is too large to be traded immediately without a prohibitive price impact. We present an optimisation framework and some solutions for non-stationary and non-linear price impact and price impact risk. We find that our proposed transaction costs model produces fairly good forecasts of the variance of the execution shortfall. The second, bottom-up, or data mining, approach is employed for trade sign inference, where trade sign is defined as the side which initiates both a trade and the market order that triggered the trade. We are interested in an endogenous component of the order flow, as evidenced by the predictable relationship between trade sign and the variables used to infer it. We want to discover the rules which govern the trade sign, and establish a connection between them and two empirically observed regularities in market order submissions, competition for order execution and transaction cost minimisation. To achieve the above aims we first use exploratory analysis of trade and limit order book data. In particular, we conduct unsupervised clustering with the self-organising map technique. The visualisation of the transformed data reveals that buyer-initiated and seller-initiated trades form two distinct clusters. We then propose a local non-parametric trade sign inference model based on the k-nearest-neighbour classifier. The best k-nearest-neighbour classifier constructed by us requires only three predictor variables and achieves an average out-of-sample accuracy of 71.40% (SD=4.01%)1, across all of the tested stocks. The best set of predictor variables found for the non-parametric model is subsequently used to develop a piecewise linear trade sign model. That model proves superior to the k-nearest-neighbour classifier, and achieves an average out-of-sample classification accuracy of 74.38% (SD=4.25%). The result is statistically significant, after adjusting for multiple comparisons. The overall classification performance of the piecewise linear model indicates a strong dependence between trade sign and the three predictor variables, and provides evidence for the endogenous component in the order flow. Moreover, the rules for trade sign classification derived from the structure of the piecewise linear model reflect the two regularities observed in market order submissions, competition for order execution and transaction cost minimisation, and offer new insights into the relationship between them. The obtained results confirm the applicability and relevance of data mining for the analysis and modelling of stock market order submissions.

This thesis consists of three related essays that examine investors' order submission strategies during the pre-opening period on the Malta Stock Exchange. The pre-opening is a period of liquidity formation and price discovery characterised by the absence of trade execution. The three essays collectively examine the information content of the order book in relation to: the intensity of order submissions, the aggressiveness of investors' order placement strategy and the determination of returns generated over the pre-opening period.The first essay empirically investigates if public information concerning the current state of the order book impacts the duration between order arrivals. Utilizing an augmented ACD model, the research reveals that the information which can be inferred from the characteristics of incoming orders has a more significant impact on the intensity of buy order submissions as compared to sell order submissions during the pre-opening period. Furthermore, prospective buyers appear to be more responsive to liquidity provided by the sell side than the reverse. Locked or crossed order submissions tend to increases (decreases) the intensity of order flow on the own (opposite) side of the order book, corroborating Cao et al. (2000) that such order-types contain informative signals about the fundamental value of the asset.The second essay analyses the impact of limit order book information on the aggressiveness observed in the submission, revision and cancellation of limit orders during the market pre-opening period. The empirical results indicate that the aggressiveness of order submissions and forward price revisions react both to the existing and subsequent changes in the execution probability at market opening, driven in part by the depth on either side of the order book. The aggressiveness of order cancellations increases on both sides of the order book when the depth at the top of the ask order book increases. In addition, the results suggest that the order book height and size of the inside spread impacts the aggressiveness of order submissions, revisions and cancellations.The third essay studies the contribution of the pre-opening period to the daily price discovery process and the factors that impact the return generated over this period. The results indicate that approximately one third of daily price discovery occurs in the pre-opening period. In addition, the impact of relative depth and height of the overnight and opening order book are concentrated at the top of the order book. Furthermore, cumulative changes to relative depth attributable to order submissions most significantly impact the opening returns of less actively traded stocks. The results show a strong relationship between opening returns and cumulative changes in the relative height along the order book attributable to order submissions, cancellations and forward and backward price revisions over the pre-opening period.

This study develops an econometric model of limit-order completion time using survival analysis. Time-to-completion for both buy and sell limit orders is estimated using tick-by-tick UK order data. The study investigates the explanatory power of variables that measure order characteristics and market conditions, such as the limitorder price, limit-order size, best bid-offer spread, and market volatility. The generic results show that limit-order completion time depends on some variables more than on others. This study also provides an investigation of how the dynamics of the market are incorporated into models of limit-order completion. The empirical results show that time-varying variables capture the state of an order book in a better way than static ones. Moreover, this study provides an examination of the prediction accuracy of the proposed models. In addition, this study provides an investigation of the intra-day pattern of order submission and time-of-day effects on limit-order completion time in the UK market. It provides evidence showing that limit orders placed in the afternoon period are expected to have the shortest completion times while orders placed in the mid-day period are expected to have the longest completion times, and the sensitivities of limit-order completion time to the explanatory variables vary over the trading day.

This study examines the market reaction to rights offering announcements in Turkey. Even though the topic is extensively studied in the finance literature, there is still research going on for emerging markets. The first part of this study measures market reaction to rights offering announcements for six different information arrival dates. The results are significantly negative except for the case of the announcement of the rights offering period. Additionally, the sample is divided into two sub-periods as before and after the 2001 crisis. The results show that there is a significant difference in market reaction and this difference is attributed to the change in economic policy after the 2001 crisis. The second part of the study examines the determinants of this market reaction and the findings suggest that bonus issues are positively related and there is also evidence that firms time their equity issues. The third part analyzes the long term performance of equity issuing firms in two subgroups as financial and non-financial firms. The results provide evidence of a negative performance and this finding is consistent with the results of previous studies.

Nationalism: Past as Prologue began as a single volume being compiled by Ad Akande, a scholar from South Africa, who proposed it to me as co-author about two years ago. The original idea was to examine how the damaging roots of nationalism have been corroding political systems around the world, and creating dangerous obstacles for necessary international cooperation. Since I (Bruce E. Johansen) has written profusely about climate change (global warming, a.k.a. infrared forcing), I suggested a concerted effort in that direction. This is a worldwide existential threat that affects every living thing on Earth. It often compounds upon itself, so delays in reducing emissions of fossil fuels are shortening the amount of time remaining to eliminate the use of fossil fuels to preserve a livable planet. Nationalism often impedes solutions to this problem (among many others), as nations place their singular needs above the common good. Our initial proposal got around, and abstracts on many subjects arrived. Within a few weeks, we had enough good material for a 100,000-word book. The book then fattened to two moderate volumes and then to four two very hefty tomes. We tried several different titles as good submissions swelled. We also discovered that our best contributors were experts in their fields, which ranged the world. We settled on three stand-alone books:” 1/ nationalism and racial justice. Our first volume grew as the growth of Black Lives Matter following the brutal killing of George Floyd ignited protests over police brutality and other issues during 2020, following the police assassination of Floyd in Minneapolis. It is estimated that more people took part in protests of police brutality during the summer of 2020 than any other series of marches in United States history. This includes upheavals during the 1960s over racial issues and against the war in Southeast Asia (notably Vietnam). We choose a volume on racism because it is one of nationalism’s main motive forces. This volume provides a worldwide array of work on nationalism’s growth in various countries, usually by authors residing in them, or in the United States with ethnic ties to the nation being examined, often recent immigrants to the United States from them. Our roster of contributors comprises a small United Nations of insightful, well-written research and commentary from Indonesia, New Zealand, Australia, China, India, South Africa, France, Portugal, Estonia, Hungary, Russia, Poland, Kazakhstan, Georgia, and the United States. Volume 2 (this one) describes and analyzes nationalism, by country, around the world, except for the United States; and 3/material directly related to President Donald Trump, and the United States. The first volume is under consideration at the Texas A & M University Press. The other two are under contract to Nova Science Publishers (which includes social sciences). These three volumes may be used individually or as a set. Environmental material is taken up in appropriate places in each of the three books. * * * * * What became the United States of America has been strongly nationalist since the English of present-day Massachusetts and Jamestown first hit North America’s eastern shores. The country propelled itself across North America with the self-serving ideology of “manifest destiny” for four centuries before Donald Trump came along. Anyone who believes that a Trumpian affection for deportation of “illegals” is a new thing ought to take a look at immigration and deportation statistics in Adam Goodman’s The Deportation Machine: America’s Long History of Deporting Immigrants (Princeton University Press, 2020). Between 1920 and 2018, the United States deported 56.3 million people, compared with 51.7 million who were granted legal immigration status during the same dates. Nearly nine of ten deportees were Mexican (Nolan, 2020, 83). This kind of nationalism, has become an assassin of democracy as well as an impediment to solving global problems. Paul Krugman wrote in the New York Times (2019:A-25): that “In their 2018 book, How Democracies Die, the political scientists Steven Levitsky and Daniel Ziblatt documented how this process has played out in many countries, from Vladimir Putin’s Russia, to Recep Erdogan’s Turkey, to Viktor Orban’s Hungary. Add to these India’s Narendra Modi, China’s Xi Jinping, and the United States’ Donald Trump, among others. Bit by bit, the guardrails of democracy have been torn down, as institutions meant to serve the public became tools of ruling parties and self-serving ideologies, weaponized to punish and intimidate opposition parties’ opponents. On paper, these countries are still democracies; in practice, they have become one-party regimes….And it’s happening here [the United States] as we speak. If you are not worried about the future of American democracy, you aren’t paying attention” (Krugmam, 2019, A-25). We are reminded continuously that the late Carl Sagan, one of our most insightful scientific public intellectuals, had an interesting theory about highly developed civilizations. Given the number of stars and planets that must exist in the vast reaches of the universe, he said, there must be other highly developed and organized forms of life. Distance may keep us from making physical contact, but Sagan said that another reason we may never be on speaking terms with another intelligent race is (judging from our own example) could be their penchant for destroying themselves in relatively short order after reaching technological complexity. This book’s chapters, introduction, and conclusion examine the worldwide rise of partisan nationalism and the damage it has wrought on the worldwide pursuit of solutions for issues requiring worldwide scope, such scientific co-operation public health and others, mixing analysis of both. We use both historical description and analysis. This analysis concludes with a description of why we must avoid the isolating nature of nationalism that isolates people and encourages separation if we are to deal with issues of world-wide concern, and to maintain a sustainable, survivable Earth, placing the dominant political movement of our time against the Earth’s existential crises. Our contributors, all experts in their fields, each have assumed responsibility for a country, or two if they are related. This work entwines themes of worldwide concern with the political growth of nationalism because leaders with such a worldview are disinclined to co-operate internationally at a time when nations must find ways to solve common problems, such as the climate crisis. Inability to cooperate at this stage may doom everyone, eventually, to an overheated, stormy future plagued by droughts and deluges portending shortages of food and other essential commodities, meanwhile destroying large coastal urban areas because of rising sea levels. Future historians may look back at our time and wonder why as well as how our world succumbed to isolating nationalism at a time when time was so short for cooperative intervention which is crucial for survival of a sustainable earth. Pride in language and culture is salubrious to individuals’ sense of history and identity. Excess nationalism that prevents international co-operation on harmful worldwide maladies is quite another. As Pope Francis has pointed out: For all of our connectivity due to expansion of social media, ability to communicate can breed contempt as well as mutual trust. “For all our hyper-connectivity,” said Francis, “We witnessed a fragmentation that made it more difficult to resolve problems that affect us all” (Horowitz, 2020, A-12). The pope’s encyclical, titled “Brothers All,” also said: “The forces of myopic, extremist, resentful, and aggressive nationalism are on the rise.” The pope’s document also advocates support for migrants, as well as resistance to nationalist and tribal populism. Francis broadened his critique to the role of market capitalism, as well as nationalism has failed the peoples of the world when they need co-operation and solidarity in the face of the world-wide corona virus pandemic. Humankind needs to unite into “a new sense of the human family [Fratelli Tutti, “Brothers All”], that rejects war at all costs” (Pope, 2020, 6-A). Our journey takes us first to Russia, with the able eye and honed expertise of Richard D. Anderson, Jr. who teaches as UCLA and publishes on the subject of his chapter: “Putin, Russian identity, and Russia’s conduct at home and abroad.” Readers should find Dr. Anderson’s analysis fascinating because Vladimir Putin, the singular leader of Russian foreign and domestic policy these days (and perhaps for the rest of his life, given how malleable Russia’s Constitution has become) may be a short man physically, but has high ambitions. One of these involves restoring the old Russian (and Soviet) empire, which would involve re-subjugating a number of nations that broke off as the old order dissolved about 30 years ago. President (shall we say czar?) Putin also has international ambitions, notably by destabilizing the United States, where election meddling has become a specialty. The sight of Putin and U.S. president Donald Trump, two very rich men (Putin $70-$200 billion; Trump $2.5 billion), nuzzling in friendship would probably set Thomas Jefferson and Vladimir Lenin spinning in their graves. The road of history can take some unanticipated twists and turns. Consider Poland, from which we have an expert native analysis in chapter 2, Bartosz Hlebowicz, who is a Polish anthropologist and journalist. His piece is titled “Lawless and Unjust: How to Quickly Make Your Own Country a Puppet State Run by a Group of Hoodlums – the Hopeless Case of Poland (2015–2020).” When I visited Poland to teach and lecture twice between 2006 and 2008, most people seemed to be walking on air induced by freedom to conduct their own affairs to an unusual degree for a state usually squeezed between nationalists in Germany and Russia. What did the Poles then do in a couple of decades? Read Hlebowicz’ chapter and decide. It certainly isn’t soft-bellied liberalism. In Chapter 3, with Bruce E. Johansen, we visit China’s western provinces, the lands of Tibet as well as the Uighurs and other Muslims in the Xinjiang region, who would most assuredly resent being characterized as being possessed by the Chinese of the Han to the east. As a student of Native American history, I had never before thought of the Tibetans and Uighurs as Native peoples struggling against the Independence-minded peoples of a land that is called an adjunct of China on most of our maps. The random act of sitting next to a young woman on an Air India flight out of Hyderabad, bound for New Delhi taught me that the Tibetans had something to share with the Lakota, the Iroquois, and hundreds of other Native American states and nations in North America. Active resistance to Chinese rule lasted into the mid-nineteenth century, and continues today in a subversive manner, even in song, as I learned in 2018 when I acted as a foreign adjudicator on a Ph.D. dissertation by a Tibetan student at the University of Madras (in what is now in a city called Chennai), in southwestern India on resistance in song during Tibet’s recent history. Tibet is one of very few places on Earth where a young dissident can get shot to death for singing a song that troubles China’s Quest for Lebensraum. The situation in Xinjiang region, where close to a million Muslims have been interned in “reeducation” camps surrounded with brick walls and barbed wire. They sing, too. Come with us and hear the music. Back to Europe now, in Chapter 4, to Portugal and Spain, we find a break in the general pattern of nationalism. Portugal has been more progressive governmentally than most. Spain varies from a liberal majority to military coups, a pattern which has been exported to Latin America. A situation such as this can make use of the term “populism” problematic, because general usage in our time usually ties the word into a right-wing connotative straightjacket. “Populism” can be used to describe progressive (left-wing) insurgencies as well. José Pinto, who is native to Portugal and also researches and writes in Spanish as well as English, in “Populism in Portugal and Spain: a Real Neighbourhood?” provides insight into these historical paradoxes. Hungary shares some historical inclinations with Poland (above). Both emerged from Soviet dominance in an air of developing freedom and multicultural diversity after the Berlin Wall fell and the Soviet Union collapsed. Then, gradually at first, right wing-forces began to tighten up, stripping structures supporting popular freedom, from the courts, mass media, and other institutions. In Chapter 5, Bernard Tamas, in “From Youth Movement to Right-Liberal Wing Authoritarianism: The Rise of Fidesz and the Decline of Hungarian Democracy” puts the renewed growth of political and social repression into a context of worldwide nationalism. Tamas, an associate professor of political science at Valdosta State University, has been a postdoctoral fellow at Harvard University and a Fulbright scholar at the Central European University in Budapest, Hungary. His books include From Dissident to Party Politics: The Struggle for Democracy in Post-Communist Hungary (2007). Bear in mind that not everyone shares Orbán’s vision of what will make this nation great, again. On graffiti-covered walls in Budapest, Runes (traditional Hungarian script) has been found that read “Orbán is a motherfucker” (Mikanowski, 2019, 58). Also in Europe, in Chapter 6, Professor Ronan Le Coadic, of the University of Rennes, Rennes, France, in “Is There a Revival of French Nationalism?” Stating this title in the form of a question is quite appropriate because France’s nationalistic shift has built and ebbed several times during the last few decades. For a time after 2000, it came close to assuming the role of a substantial minority, only to ebb after that. In 2017, the candidate of the National Front reached the second round of the French presidential election. This was the second time this nationalist party reached the second round of the presidential election in the history of the Fifth Republic. In 2002, however, Jean-Marie Le Pen had only obtained 17.79% of the votes, while fifteen years later his daughter, Marine Le Pen, almost doubled her father's record, reaching 33.90% of the votes cast. Moreover, in the 2019 European elections, re-named Rassemblement National obtained the largest number of votes of all French political formations and can therefore boast of being "the leading party in France.” The brutality of oppressive nationalism may be expressed in personal relationships, such as child abuse. While Indonesia and Aotearoa [the Maoris’ name for New Zealand] hold very different ranks in the United Nations Human Development Programme assessments, where Indonesia is classified as a medium development country and Aotearoa New Zealand as a very high development country. In Chapter 7, “Domestic Violence Against Women in Indonesia and Aotearoa New Zealand: Making Sense of Differences and Similarities” co-authors, in Chapter 8, Mandy Morgan and Dr. Elli N. Hayati, from New Zealand and Indonesia respectively, found that despite their socio-economic differences, one in three women in each country experience physical or sexual intimate partner violence over their lifetime. In this chapter ther authors aim to deepen understandings of domestic violence through discussion of the socio-economic and demographic characteristics of theit countries to address domestic violence alongside studies of women’s attitudes to gender norms and experiences of intimate partner violence. One of the most surprising and upsetting scholarly journeys that a North American student may take involves Adolf Hitler’s comments on oppression of American Indians and Blacks as he imagined the construction of the Nazi state, a genesis of nationalism that is all but unknown in the United States of America, traced in this volume (Chapter 8) by co-editor Johansen. Beginning in Mein Kampf, during the 1920s, Hitler explicitly used the westward expansion of the United States across North America as a model and justification for Nazi conquest and anticipated colonization by Germans of what the Nazis called the “wild East” – the Slavic nations of Poland, the Baltic states, Ukraine, and Russia, most of which were under control of the Soviet Union. The Volga River (in Russia) was styled by Hitler as the Germans’ Mississippi, and covered wagons were readied for the German “manifest destiny” of imprisoning, eradicating, and replacing peoples the Nazis deemed inferior, all with direct references to events in North America during the previous century. At the same time, with no sense of contradiction, the Nazis partook of a long-standing German romanticism of Native Americans. One of Goebbels’ less propitious schemes was to confer honorary Aryan status on Native American tribes, in the hope that they would rise up against their oppressors. U.S. racial attitudes were “evidence [to the Nazis] that America was evolving in the right direction, despite its specious rhetoric about equality.” Ming Xie, originally from Beijing, in the People’s Republic of China, in Chapter 9, “News Coverage and Public Perceptions of the Social Credit System in China,” writes that The State Council of China in 2014 announced “that a nationwide social credit system would be established” in China. “Under this system, individuals, private companies, social organizations, and governmental agencies are assigned a score which will be calculated based on their trustworthiness and daily actions such as transaction history, professional conduct, obedience to law, corruption, tax evasion, and academic plagiarism.” The “nationalism” in this case is that of the state over the individual. China has 1.4 billion people; this system takes their measure for the purpose of state control. Once fully operational, control will be more subtle. People who are subject to it, through modern technology (most often smart phones) will prompt many people to self-censor. Orwell, modernized, might write: “Your smart phone is watching you.” Ming Xie holds two Ph.Ds, one in Public Administration from University of Nebraska at Omaha and another in Cultural Anthropology from the Chinese Academy of Social Sciences, Beijing, where she also worked for more than 10 years at a national think tank in the same institution. While there she summarized news from non-Chinese sources for senior members of the Chinese Communist Party. Ming is presently an assistant professor at the Department of Political Science and Criminal Justice, West Texas A&M University. In Chapter 10, analyzing native peoples and nationhood, Barbara Alice Mann, Professor of Honours at the University of Toledo, in “Divide, et Impera: The Self-Genocide Game” details ways in which European-American invaders deprive the conquered of their sense of nationhood as part of a subjugation system that amounts to genocide, rubbing out their languages and cultures -- and ultimately forcing the native peoples to assimilate on their own, for survival in a culture that is foreign to them. Mann is one of Native American Studies’ most acute critics of conquests’ contradictions, and an author who retrieves Native history with a powerful sense of voice and purpose, having authored roughly a dozen books and numerous book chapters, among many other works, who has traveled around the world lecturing and publishing on many subjects. Nalanda Roy and S. Mae Pedron in Chapter 11, “Understanding the Face of Humanity: The Rohingya Genocide.” describe one of the largest forced migrations in the history of the human race, the removal of 700,000 to 800,000 Muslims from Buddhist Myanmar to Bangladesh, which itself is already one of the most crowded and impoverished nations on Earth. With about 150 million people packed into an area the size of Nebraska and Iowa (population less than a tenth that of Bangladesh, a country that is losing land steadily to rising sea levels and erosion of the Ganges river delta. The Rohingyas’ refugee camp has been squeezed onto a gigantic, eroding, muddy slope that contains nearly no vegetation. However, Bangladesh is majority Muslim, so while the Rohingya may starve, they won’t be shot to death by marauding armies. Both authors of this exquisite (and excruciating) account teach at Georgia Southern University in Savannah, Georgia, Roy as an associate professor of International Studies and Asian politics, and Pedron as a graduate student; Roy originally hails from very eastern India, close to both Myanmar and Bangladesh, so he has special insight into the context of one of the most brutal genocides of our time, or any other. This is our case describing the problems that nationalism has and will pose for the sustainability of the Earth as our little blue-and-green orb becomes more crowded over time. The old ways, in which national arguments often end in devastating wars, are obsolete, given that the Earth and all the people, plants, and other animals that it sustains are faced with the existential threat of a climate crisis that within two centuries, more or less, will flood large parts of coastal cities, and endanger many species of plants and animals. To survive, we must listen to the Earth, and observe her travails, because they are increasingly our own.

This is the first of three chapters that review the factors that drive the demand for, supply of, and the incentives to introduce new products. It explores the determination of the demand for newly launched products, with emphasis upon intertemporal development. Parallels are drawn with the literature on the diffusion of new technologies and it is emphasized how learning, differences between buyers, stock effects, order, and other effects impact upon the demand. The issue of new suppliers offering further products on the market is explored with a distinction between new to market and new to firm products and between horizontal and vertical innovations. The demand for a product innovation may change over time as products, knowledge, and the number of suppliers changes. One might expect that prices (and price expectations) play a major role in the determination of demand, but many other factors also come into play.

This book provides a thorough analysis of Brazilian business law for investors and their legal advisers, focusing on topics relevant to business transactions and disputes that can arise in the aftermath of the signing or performance of deals. The essence of investment and negotiation processes is risk evaluation and allocation. Examining Brazilian law, the book focuses on the legal risks, which are higher in Brazilian law than elsewhere, particularly in comparison with contract, partnership, and company (LLC) laws governing international business transactions in the US and UK. However, whilst Brazilian contract law remains a risk factor as a result of its over-ambitious and consequently interventionist approach, Brazilian law in the areas of stock corporation, capital market, antitrust, and public procurement are state-of-the-art when compared to the US and leading European laws in Germany, Switzerland, and France. The book is divided into eight chapters: the introduction provides an overview of the economic and legal framework for doing business in Brazil, focusing on features of the Brazilian legal and economic order that are unusual to international practitioners from a comparative perspective. The other seven chapters analyze those fields of substantive law that impact most investments and cross-border transactions in Brazil. The book focuses on the interpretation of statutory law by the Brazilian Superior Court of Justice and regulatory agencies, and also provides insights into the economic and business rationale of some of the legal solutions offered.

This chapter presents two higher order neural networks (HONN) for efficient prediction of stock market behavior. The models include Pi-Sigma, and Sigma-Pi higher order neural network models. Along with the traditional gradient descent learning, how the evolutionary computation technique such as genetic algorithm (GA) can be used effectively for the learning process is also discussed here. The learning process is made adaptive to handle the noise and uncertainties associated with stock market data. Further, different prediction approaches are discussed here and application of HONN for time series forecasting is illustrated with real life data taken from a number of stock markets across the globe.

The model has some similarities and many differences with the life cycle savings hypothesis (LCSH). The major similarity is that both models assume that the young will save in order not to starve when elderly and that the old will often dissave either directly by spending more than their income or indirectly though annuities or final-salary pension schemes. Its major difference is that Modigliani implicitly assumes that the way companies behave has no bearing on savings. His model has no corporate veil. The major concern of the LCSH is to explain the level of savings, while this book’s model explains the stationarity of the equity return and, by examining the trade-off between the risks and returns of bonds and equities, to explain how this affects the leverage of companies and the ratio of debt assets to equities in household portfolios. Both models are consistent with the evidence that saving rates are higher where growth is higher, but the explanations differ. Modigliani assumes that it follows from the behaviour of individuals, while this model demonstrates the importance of corporate savings. In other respects, the LCSH fails when tested; for instance the claim that the ratio of wealth to income rises and falls with the rate of growth of the economy is inconsistent with the evidence.

In order to finance World War I, the British government sold war bonds to millions of investors and savers, thereby prompting a wider interest in financial securities including stocks and shares during the interwar period. Faced with a large intake of investment newcomers, the City of London was anxious of ‘amateur’ involvement in the market. The largest securities market, the London Stock Exchange, restricted access to small investors where possible, which pushed much of the new retail activity to the market fringes. Here, ‘outside brokers’ and ‘bucket shops’ catered for investment newcomers, the more gullible of which fell prey to fraudulent share pushers. Scholars have entirely overlooked this vibrant grey market for financial securities. But it was here—and not just at the organized exchanges—that ever more people made their first experiences with the ups and the downs of the stock market, most prominently in the great crash of 1929. This new perspective brings a sharper contour on some fundamental challenges that Britain’s financial landscape was facing in the interwar period: a large intake of new investors, a resurgence of financial fraud, and a new struggle over the distinction between speculation and gambling. The City’s response to these challenges can be described as financial paternalism. After a surge in political democratization, there was very little appetite to enfranchise ordinary people in the stock market. Instead, institutions like the Stock Exchange deliberately took a conservative stance on the ‘democratisation of investment’.

The challenges generated by climate change have led to a greater involvement of companies in promoting the principles of sustainable development, one of the tools used being social responsibility programs. International organizations have launched various initiatives or principles to support companies in this complex process of transition to the green economy. The authors focused their analysis on the involvement of stock exchanges in the process of promotion of corporate social responsibility. The objective of this chapter is to identify the main tools used by stock exchanges in order to model the behavior of listed companies. In particular, the activity of the Bucharest Stock Exchange was analyzed. Even if it is an emerging market, the efforts made by this stock exchange and the results obtained can be used as a benchmark by stock exchanges in the region.

This paper aims to test the efficient market hypothesis, in its weak form, in the stock markets of BOTSWANA, EGYPT, KENYA, MOROCCO, NIGERIA and SOUTH AFRICA, in the period from September 2, 2019 to September 2, 2020. In order to achieve this analysis, we intend to find out if: the global pandemic (Covid-19) has decreased the efficiency, in its weak form, of African stock markets? The results therefore support the evidence that the random walk hypothesis is not supported by the financial markets analyzed in this period of global pandemic. The values of variance ratios are lower than the unit, which implies that the yields are autocorrelated in time and, there is reversal to the mean, and no differences were identified between the stock markets analyzed. The authors consider that the results achieved are of interest to investors looking for opportunities for portfolio diversification in these regional stock markets.

The authors concentrate their attention on the performance evaluation of stock markets. The markets evaluation and selection is the important part of investment decision making. In order to develop a conceptual framework for investment decisions in financial markets, it is important to establish a logical model for market selection. The main purpose of the article – to propose the scheme of stock market evaluation and selection for investment portfolio formation. The authors propose the scheme, according to that, it is possible to analyse the issue of the market value and to select markets that may potentially generate a sustainable investment return for investor, taking into account that sustainable investment return is the stable investment return for a long period. According to the analysis of selected stock markets and their evaluation using three-dimension utility function, the authors identified the most stable markets to investors for investment portfolio formation.

This study uses a hybrid high order Markov Chains Model to predict direction of exchange rate, gold price and stock market returns with the Artificial Neural Network Algorithm as an estimator of transition probability matrix. Many forecasting techniques are used to examine the direction of returns forecasting in the literature such as Markov Chains Model and Artificial Neural Network Algorithm. In this study, it is aimed to combine these two techniques and to utilize the predict values of the Artificial Neural Network Algorithm for calculate transition probabilities matrix. Calculations show that the hybrid model gives high correct classification probabilities besides of well approximated transition probabilities. Returns series of USD/TRY exchange rate, closing price of Borsa Istanbul Stock Exchange and gold prices cover the period of 01/01/2003 and 31/01/2016. All series are obtained from database of Central Bank of Turkey. As a result, although the transition probabilities almost equal to 0.5 and so estimation of these series are not easy, the transition probabilities and correct classification probabilities gained from the hybrid model provide substantial information related to direction of returns forecasting. Besides, estimated model provide valuable information to individual investors and companies, and could help them to take position against to risks.

The pandemic (Covid-19) has affected the global economy, and the impact on financial markets seems inevitable. In view of these events, this essay aims to analyse the shocks between the stock market indices of Brazil (BOVESPA), China (SSEC) India (SENSEX), Russia (IMOEX) and oil (WTC), in the period from January 2, 2019 to May 29, 2020. In order to carry out this analysis, different approaches were undertaken with a view to gauging whether (i) the global pandemic has accentuated the shocks between the BRIC financial markets and the WTC? The daily yields do not have normal distributions, show negative asymmetries, leptokurtic, and exhibit conditional heteroscedasticity. In general, we find evidence that the WTC causes the markets of Russia and India, China does not cause any market, and Brazil is not caused by any market analysed. On the other hand, short-term market shocks are relevant and create some arbitrage opportunities. However, our study did not analyse anomalous returns in these financial markets. These findings also open space for market regulators to take action to ensure better information between international financial markets.

In this research the technology of complex Markov chains is applied to predict financial time series. The main distinction of complex or high-order Markov Chains and simple first-order ones is the existing of after-effect or memory. The technology proposes prediction with the hierarchy of time discretization intervals and splicing procedure for the prediction results at the different frequency levels to the single prediction output time series. The hierarchy of time discretizations gives a possibility to use fractal properties of the given time series to make prediction on the different frequencies of the series. The prediction results for world’s stock market indices are presented.

This paper explores the effects of weather-induced rural-urban migration on urban labor and housing markets in Brazil. In order to identify causal effects, it uses weather shocks to the rural municipalities of origin of migrants. We show that larger migration shocks led to an increase in employment growth and a reduction in wage growth of 4 and 5 percent, respectively. The increased migration flows also affected the housing market in destination cities. On average, it led to 1 percent faster growth of the housing stock, accompanied by 5 percent faster growth in housing rents. These effects vary sharply by housing quality. We find a substantial positive effect on the growth rates of the most precarious housing units (with no effect on rents) and a negative effect on the growth of higher-quality housing units (with a positive effect on rents). This suggests that rural immigration growth slowed down housing-quality upgrading in destination cities.

One of the well-known results of the theory of management is the fact, that multi-stage hierarchical organization of management is unstable. Hence, the ideas expressed in a number of works by Don Tapscott on advantages of network organization of businesses over vertically integrated ones is clear. While studying the basic tendencies of business organization in the conditions of globalization, computerization and internetization of the society and the results of the financial activities of the well-known companies, the authors arrive at the conclusion, that such companies, as IBM, Boeing, Mercedes-Benz and some others companies have not been engaged in their traditional business for a long time. Their partner networks performs this function instead of them. The companies themselves perform the function of system integrators. The Tapscott’s idea finds its confirmation within the framework of a new powerful direction of the development of the modern interdisciplinary science – the theory of the complex networks (CN) [2]. CN-s are multifractal objects, the loss of multifractality being the indicator of the system transition from more complex state into more simple state. We tested the multifractal properties of the data using the wavelet transform modulus maxima approach in order to analyze scaling properties of our company. Comparative analysis of the singularity spectrumf(®), namely, the difference between maximum and minimum values of ® (∆ = ®max ¡ ®min) shows that IBM company is considerably more fractal in comparison with Apple Computer. Really, for it the value of ∆ is equal to 0.3, while for the vertically integrated company Apple it only makes 0.06 – 5 times less. The comparison of other companies shows that this dependence is of general character. Taking into consideration the fact that network organization of business has become dominant in the last 5-10 years, we carried out research for the selected companies in the earliest possible period of time which was determined by the availability of data in the Internet, or by historically later beginning of stock trade of computer companies. A singularity spectrum of the first group of companies turned out to be considerably narrower, or shifted toward the smaller values of ® in the pre-network period. The latter means that dynamic series were antipersistant. That is, these companies‘ management was rigidly controlled while the impact of market mechanisms was minimized. In the second group of companies if even the situation did changed it did not change for the better. In addition, we discuss applications to the construction of portfolios of stock that have a stable ratio of risk to return.