High frequency trading

This paper investigates the technological barriers impeding the optimal growth and efficiency of algorithmic trading in India, concentrating on infrastructure and latency challenges. Despite substantial growth, India faces significant issues related to inadequate server quality, poorly located data centres, limited network bandwidth, and high latency. Through a comparative analysis with developed markets such as the United States and Europe, this paper highlights the gaps in India's current infrastructure and proposes potential enhancements. The findings underscore the need for advanced data centres, improved network connectivity, higher-quality market data, and a more supportive regulatory environment. Addressing these challenges is essential for boosting the efficiency and competitiveness of algorithmic trading in India, fostering innovation, attracting more participants, and enhancing the overall development of the Indian financial markets.

Donald Trump ha aperto un nuovo fronte di guerra: la Federal Reserve. Con il suo stile assai poco diplomatico, il Presidente Usa sembra pronto a sfidare l'istituzione che, da oltre un secolo, regola la politica monetaria statunitense. È solo una divergenza di vedute sulla corretta gestione della crisi economica/finanziaria oppure è l’inizio di una lotta contro l’élite finanziaria di Wall Street?

In today's technology world, financial trading instrument such as stock, currencies, futures, and acts is now done electronically via the internet due to technological improvements. Financial market trading traders apply technical and fundamental analyses to forecast the best price when buying or selling the finance instrument, and
they develop their trading tactics and strategies with technical analysis tools. The objective of this research is to figure out the level of competence that Malaysian retail traders have in Automated or Algorithmic Trading (AT) focusing on currency trading. The searches were conducted on a sample of Malaysian retail traders. The viewpoints of retail traders were investigated using a questionnaire designed specifically for this purpose. The data was analyzed using statistical software (SPSS). The findings of this study reveal that Malaysian traders have an excellent comprehension of AT. This study is useful for traders and researchers who want to design their own AT systems in the future.

This article, written in early 2020, explored the potential repetition of the 2008–2011 gold price pattern through both technical chart analysis and fundamental reasoning. The analysis anticipated a significant rally in gold driven by central bank liquidity injections and by systemic, cyberfinancial, and geopolitical tensions. While the projection of higher long-term targets proved conceptually and technically correct, the forecast was premature in terms of timing: the price consolidation phase lasted longer than expected before the subsequent rally unfolded.

Title: Why 97% of Retail Traders Lose to Institutions: Cases and Statistics
• Discipline: Finance / Economics
• Institution: Independent Researcher
      •Abstract
Most retail traders lose money in day trading, even though social media makes it look
easy. TikTok, YouTube, and online mentors always show profits but almost never
losses. The truth is that around 95–97% of retail traders lose money and most quit
quickly.
The main reason is that markets are controlled by large players — hedge funds, banks,
quant firms. They have data, infrastructure, and strategies that small traders don’t. In
this paper, I compare retail trading to institutional trading, use real statistics and
academic studies, and also include insights from industry professionals I talked to on
LinkedIn. My goal is to show why retail almost always loses and what this means for
the future of trading

Artificial intelligence (AI) has become integral to modern financial markets, particularly within the domain of highfrequency trading (HFT). Reinforcement learning (RL) agents, deep neural networks, and advanced optimization methods now execute trades at nanosecond scales, generating both extraordinary profits and systemic risks. However, these models often exploit spurious correlations-statistical regularities without causal meaning-which can lead to unstable strategies, flash crashes, and distorted market signals. Traditional statistical approaches to market prediction fail to disentangle correlation from

o mérito deste trabalho está muito mais na boa vontade desinteressada de um grupo considerável de pessoas e entidades que, com responsabilidade e seriedade, contribuíram para a perfeita realização do mesmo, do que nestas páginas por nós organizadas. Ao meu orientador Prof.Dr.Norberto Antonio Torres, quero agradecer de maneira especial. Pessoa que, a pelo menos uma dezena de anos, tive a oportunidade de conhecer no meio acadêmico e admirar por sua competência, seriedade e humanidade. A segurança e paciência com que sempre me orientou para esta monografia foram essenciais para o bom desempenho deste trabalho. Críticas sempre construtivas fizeram deste estudo algo de consistente e dirigido. A este grande professor e amigo, gostaria de deixar meus sinceros e carinhosos agradecimentos. Ao CAPES e CNPq, agradeço o apoio financeiro a este trabalho. A pesquisa referente ao mercado de capitais, devo agradecer a uma série de profissionais e entidades que serão citados no final destes agradecimentos. Quero mencionar o apoio incondicional do Prof.Dr.Jair Pereira dos Santos, professor de Métodos Quantitativos da EAESP/FGVno tocante a todo o desenvolvimento do modelo matemático desta monografia. Sua prestatividade, disponibilidade e apoio foram responsáveis por não desistir de incluir um modelo matemático nesta monografia, que tanto enriqueceu as conclusões e o todo do trabalho. Também agradeço, de maneira especial, os profissionais da IBM Brasil e IBM (USA), que muito ajudaram em entrevistas sobre novos produtos de informática. À minha família, meu marido Sérgio e meus filhos Raquel e Roberto, pela compreensão e apoio que deram durante os anos de estudo. Ofereço a eles, com todo o meu amor, estas páginas, fruto de muitas privações de convívio e lazer familiar. Agradeço à minha irmã, Ora.Ana Maria Sampaio Moreira, pela revisão do texto.

We propose a model for market impact of algorithmic trades. Usually large orders cannot be executed immediately without significant trading costs. For optimized execution one relies on the help of a VWAP trading algorithm. We demonstrate that this VWAP algorithm is the optimal solution of the optimization problem using the presented market impact models. The purpose of this work is the empirical market impact analysis of a homogeneous set of algorithmic trades. The underlying data set contains trades resulting form a hedge fund trading strategy. We found the participation rate to be the most important description variable. Therefore we provide a linear model and also a concave power law model of the market impact dependent on the participation rate. The estimated parameters lead to interesting consequences to verifying certain aspects of the market microstructure theory. Our results also suggest different behavior of the various analyzed markets. On the one hand the market impact dependency on the participation rate behaves different for the Japanese market compared to the European, US, and Canadian. On the other hand, the individualized linear regression results suggest a dependency of the market impact on the tick size for the Japanese and the US market.

There is a long tradition of research using computational intelligence, i.e. methods from artificial intelligence (AI) and machine learning (ML), to automatically discover, implement, and fine-tune strategies for autonomous adaptive automated trading in financial markets, with a sequence of research papers on this topic published at major AI conferences such as IJCAI and in prestigious journals such as Artificial Intelligence: we show evidence here that this strand of research has taken a number of methodological missteps and that actually some of the reportedly bestperforming public-domain AI/ML trading strategies can routinely be out-performed by extremely simple trading strategies that involve no AI or ML at all. The results that we highlight here could easily have been revealed at the time that the relevant key papers were published, more than a decade ago, but the accepted methodology at the time of those publications involved a somewhat minimal approach to experimental evaluation of trader-agents, making claims on the basis of a few thousand test-sessions of the trader-agent in a small number of market scenarios. In this paper we present results from exhaustive testing over wide ranges of parameter values, using parallel cloud-computing facilities, where we conduct millions of tests and thereby create much richer data from which firmer conclusions can be drawn. We show that the best public-domain AI/ML traders in the published literature can be routinely outperformed by a "sub-zerointelligence" trading strategy that at face value appears to be so simple as to be financially ruinous, but which interacts with the market in such a way that in practice it is more profitable than the well-known AI/ML strategies from the research literature. That such a simple strategy can outperform established AI/ML-based strategies is a sign that perhaps the AI/ML trading strategies were good answers to the wrong question.

We describe a new public-domain open-source simulator of an electronic financial exchange, and of the traders that interact with the exchange, which is a truly distributed and cloud-native system that been designed to run on widely available commercial cloud-computing services, and in which various components can be placed in specified geographic regions around the world, thereby enabling the study of planetary-scale latencies in contemporary automated trading systems. The speed at which a trader can react to changes in the market is a key concern in current financial markets but is difficult to study latency issues using conventional market simulators, and is extremely difficult to study "in the wild" because of the financial and regulatory barriers to entry in conducting experimental work on real financial exchanges. Our simulator allows an exchange server to be launched in the cloud, specifying a particular geographic zone for the cloud hosting service; automated-trading clients which attach to the exchange can then also be launched in the cloud, in the same geographic zone and/or in different zones anywhere else on the planet, and those clients are then subject to the real-world latencies introduced by planetary-scale cloud communication interconnections. In this paper we describe the design and implementation of our simulator, called DBSE, which is based on a previous public-domain simulator, extended in ways that are partly inspired by the architecture of the real-world Jane Street Exchange. DBSE relies fundamentally on UDP and TCP network communications protocols and implements a subset of the FIX de facto standard protocol for financial information exchange. We show results from an example in which the exchange server is remotely launched on a cloud facility located in London (UK), with trader clients running in Ohio (USA) and Sydney (Australia). We close with discussion of how our simulator could be further used to study planetaryscale latency arbitrage in financial markets.

For more than a decade Vytelingum's Adaptive-Aggressive (AA) algorithm has been recognized as the best-performing automated auction-market trading-agent strategy currently known in the AI/Agents literature; in this paper, we demonstrate that it is in fact routinely outperformed by another algorithm when exhaustively tested across a sufficiently wide range of market scenarios. The novel step taken here is to use large-scale compute facilities to brute-force exhaustively evaluate AA in a variety of market environments based on those used for testing it in the original publications. Our results show that even in these simple environments AA is consistently outperformed by IBM's GDX algorithm, first published in 2002. We summarize here results from more than one million market simulation experiments, orders of magnitude more testing than was reported in the original publications that first introduced AA. A 2019 ICAART paper by Cliff claimed that AA's failings were revealed by testing it in more realistic experiments, with conditions closer to those found in real financial markets, but here we demonstrate that even in the simple experiment conditions that were used in the original AA papers, exhaustive testing shows AA to be outperformed by GDX. We close this paper with a discussion of the methodological implications of our work: any results from previous papers where any one trading algorithm is claimed to be superior to others on the basis of only a few thousand trials are probably best treated with some suspicion now. The rise of cloud computing means that the compute-power necessary to subject trading algorithms to millions of trials over a wide range of conditions is readily available at reasonable cost: we should make use of this; exhaustive testing such as is shown here should be the norm in future evaluations and comparisons of new trading algorithms.

I introduce parameterised response zero intelligence (PRZI), a new form of zero intelligence (ZI) trader intended for use in simulation studies of the dynamics of continuous double auction markets. Like Gode and Sunder's classic ZIC trader, PRZI generates quote prices from a random distribution over some specified domain of discretely valued allowable quote prices. Unlike ZIC, which uses a uniform distribution to generate prices, the probability distribution in a PRZI trader is parameterised in such a way that its probability mass function (PMF) is determined by a real-valued control variable s in the range [-1.0, +1.0] that determines the strategy for that trader. When s = 0 , a PRZI trader behaves identically to the ZIC strategy, with a uniform PMF; but when s ≈ ±1 the PRZI trader's PMF becomes maximally skewed to one extreme or the other of the price range, thereby making it more or less "urgent" in the prices that it generates, biasing the quote price distribution towards or away from the trader's limit price. To explore the co-evolutionary dynamics of populations of PRZI traders that dynamically adapt their strategies, I show initial results from long-term market experiments in which each trader uses a simple stochastic hill-climber algorithm to repeatedly evaluate alternative s-values and choose the most profitable at any given time. In these experiments the profitability of any particular s-value may be non-stationary because the profitability of one trader's strategy at any one time can depend on the mix of strategies being played by the other traders at that time, which are each themselves continuously adapting. Results from these market experiments demonstrate that the population of traders' strategies can exhibit rich dynamics, with periods of stability lasting over hundreds of thousands of trader interactions interspersed by occasional periods of change. Python source code for PRZI traders, and for the stochastic hill-climber, have been made publicly available on GitHub.

Since the inception of Pakistan, there remained two forms of government namely autocratic and democratic. Both types of government keep on taking their turns in an almost consistent manner in terms of time duration. This study endeavors to comprehensively compare and contrast the performance of autocracy with democracy in Pakistan. Considering the significant duration of autocratic rule, spanning 32 out of 75 years, Pakistan's geostrategic position has subjected it to various internal and externally-driven crises. By analyzing 15 carefully selected socio-economic indicators over a span of 54 years , this research aims to elucidate the disparities between autocracy and democracy. Employing rigorous statistical methods such as independent t-tests and logistic regression, this study seeks to discern the distinct effects and efficacy of both forms of government on Pakistan's socioeconomic landscape. The outcomes of this research endeavor to shed light on the implications and performance of differing governance systems in Pakistan. Results have indicated that out of these 15 indicators, eight have shown significant differences in the performance of both forms of government while the other seven showed no significant differences (could not reject the null hypothesis). Overall, the performance of autocratic governments has remained better with respect to economic indicators. While the political governments have an edge over social indicators although majority of the results are insignificant. The study will promote the understanding of authorities and concerned individuals to accelerate the process of better economic and social reforms. The successful models of past irrespective of the type of government be followed to bring sustainability in the policies. Furthermore, this study will pave the way for further research to deeply analyze the causes of better economic performance under autocratic regimes and establishment of credible policies to attain the better results under democratic regimes as well.

Traders make trade decisions specifying entry, exit, and stop loss prices. Technicians often decide on entry, exit, and stop loss prices based on a predefined set of technical rules. In this paper, we employ a method based on grammatical evolution to coevolve technical rules for entry, exit, and stop loss for trading a London Stock Exchange (LSE) based stock in high frequency. We consider the case of two class of investors with risk averse, and loss preferences and build a partial trading frontier given the preferences considered. The performance of the rules evolved is compared to a publicly available trading system called the turtle trading system (TTS) and the best rules produced by our method outperforms TTS.

We introduce a new high-frequency foreign exchange dataset from EBS (Electronic Broking Service) that includes trading volume in the global interdealer spot market, data not previously available to researchers. The data also gives live transactable quotes, rather than the indicative quotes that have been used in most previous high frequency foreign exchange analysis. We describe intraday volume and volatility patterns in euro-dollar and dollar-yen trading. We study the effects of scheduled U.S. macroeconomic data releases, first confirming the finding of recent literature that the conditional mean of the exchange rate responds very quickly to the unexpected component of data releases. We next study the effects of data releases on trading volumes. News releases cause volume to rise, and to remain elevated for a longer period. However, in contrast to the result for the level of the exchange rate, even if the data release is entirely in line with expectations, we find that there is still typically a large pickup in trading volume.

Quantitative finance – also known as mathematical finance – applies advanced mathematical models and statistical techniques to financial markets. This field underpins the pricing of securities, risk management, and investment strategies in modern finance. Over the past decades, quantitative finance has evolved from the foundational Black–Scholes formula for option pricing to a broad discipline encompassing complex derivatives, interest rate models, credit risk, and algorithmic strategies. In this systematic review, we provide a comprehensive overview of the field’s development, highlight key theoretical advances and practical innovations, identify persistent research gaps, and propose future research directions. The review draws on foundational texts like Musiela and Rutkowski’s Martingale Methods in Financial Modelling and the collected volume Advances in Mathematical Finance, alongside supporting literature, to chart the trajectory of quantitative finance from its classical roots to the latest frontiers.

Regulators impose price limits on daily price movements to protect investors from excessive volatility, but several empirical studies have cast serious doubt on the benefits of such mechanisms. Using a large cross-sectional sample combined with intraday data from the Tokyo Stock Exchange, this study finds evidence that partially supports conventional criticisms that price limits spread out volatility, delay price discovery, and interrupt trading activities. More importantly, the transaction data analysis reveals that price limits help to reduce order imbalance and improve information asymmetry, justifying the existence of price limits on the Tokyo Stock Exchange.

This study examines the impact of increasing pre-trade transparency using intraday data from the Taiwanese stock market, which has experienced a gradual increase in transparency. Specifically, we analyze the influence of transparency on information content of the limit order book, and its effect on order placement strategies. Concerning the limit order book information, we show that the bid disclosures are more informative than ask disclosures, the disclosing quotes are more informative than disclosing depth, and the institutional traders' unmatched orders provide more information content than individual traders. In addition, the best quote of unexecuted orders for individual traders always have more information content than the average quotes from steps 2 to 5, whereas this is not true for institutional investors. With regard to order strategies, though most of the explanatory power of the book concentrates on the best quotes and depth, however, the book beyond the best disclosures also matters in explaining the order aggressiveness of investors. Investors are more aggressive as the book is more crowded on the same side of the market and as the book is more disperse in the opposite side of the market.

SummaryIn this paper, we focus on the French cancel order tax implemented on 1 August 2012. We question the effectiveness of the modified tax with no exemptions and we analyze its impact on market quality, measured by liquidity, volatility and efficiency. Additionally, this paper raises the question whether this tax leads to a reduction of high‐frequency trading (HFT) activities and a decline in trading volume. Based on our findings we report that introduction of cancel order tax only slightly reduces HFT activities, but it significantly affects market liquidity, increases market volatility and leads to deteriorating market efficiency. We conclude that it is difficult to dissuade investors from entering into unproductive trades and eliminate negative outputs of HFT (such as price manipulations) through tax, without altering the benefits of HFT like liquidity provision and efficient price discovery.

Based on the first results, the French government estimates that the tax on cancelled orders, considered as tax on High Frequency Trading (HFT), generated no revenue in 2012. Our paper question the effectiveness of a modified cancelled order tax with no exemptions, all orders cancelled or modified within half-second time span are taxed. Our study has important implications for the regulation of HFT; we provide recommendations for regulators in relation to market rules which have yet to be introduced, using an artificial market framework. This paper addresses the question of whether this tax leads to a reduction in HFT activities and, as a result, to deterioration or amelioration of market quality. The evidence we provide should help market regulators to better understand the role played by HFT firms as liquidity suppliers. We show that HFT liquidity is short-lived. With the implementation of tax, decreased HFT activities do not have a statistically significant impact on market volatility and market liquidity measured by bid/ask spreads, but decrease dollar volumes as a liquidity measure. In addition, reduced HFT activities lead to less efficient markets as the deviation from fundamentals increases.

In this paper we focus on the traders that purely rely on algorithms in their decision making and their impact on market quality during moments of instability. We describe an agent-based framework that successfully reproduces main aspects of flash crash. We simulate the effect of a large liquidity shock generated by a very aggressive market order. We show that, despite the absence of market makers, the electronic order-book architecture favors market resiliency and recovery.

Automated trading systems on developed and emerging capital markets are studied in this paper. The standard for developed market is automated trading system with 40-days simple moving average. We tested it for the index SIX Industrial for 1000 and 730 trading days. The Buy and Hold trading system was 7.80 times more profitable than this etalon trading system for active trading. Simple taking of profitable standard trading system from a developed capital market does not lead to optimal results on the emerging capital markets. We then studied optimized standard trading system based on the simple moving average. The parameter of optimization was the number of weeks. We took the range from 1 to 100 weeks. An optimal system was that with 5 weeks. This trading system has some of its characteristics comparable with the etalon trading system on the NYSE Composite Index. The emerging market is more risky than the developed market. The profit on the emerging market is also higher. The range of optimized system parameter is quite robust. Observed was increase of number of trades in the range from the 21 weeks to the 25 weeks. This indicates creation of a new optimal middle range trading system. Results of testing for liquid shares are quantitatively similar.

An immediate consequence of the Efficient Market Hypothesis (EMH) is the absence of autocorrelation of the return series of the financial prices and the exclusion of excess profitability made by any (active) trading strategy. However, the precondition for the validity of EMH, which assumes that all market participants can promptly receive and rationally react to the relevant information affecting the prices, might be (approximately) true for a long time horizon, but not for a short time horizon. By examining local long-range dependence (measured by the rolling Rescaled Range estimates of the Hurst index) of an empirical example, the local market inefficiency is inferred, and excess profitability of a simple trend-following trading strategy is observed. Moreover, the significant positive cross-correlation between the local Hurst index estimates and the returns of the trend-following trading strategies implies the potential for constructing a more profitable trading system by incorporating the former into the latter.

This paper investigates market microstructure in an experimental artificial futures market using the data generated by the submitted trading agents in two laboratory experiments. Since our previous analyses reveal that the price series successfully replicate so-called "stylized facts" in some regards, the aim of this study is to check whether such phenomena are also observed at microlevel. Our empirical results confirm that although there are locally mispricing effects of several trading agents, in most cases the market liquidity improves over the rounds. Future perspectives of agent-based computational finance and experimental economics are also discussed.

This paper utilizes agent-based simulation to explore market making strategy for high frequency traders (HFTs) and tests its performance under competition environments. After proposing a model representing HFTs' activities in financial market when they act as market makers, we carry out simulations to explore how order price and order quantity affect HFTs' profits and risks. As the result, we find that offering prices around last trading price, as well as taking advantage of order imbalance, increases HFTs' returns. On the other hand, our results show utilizing adaptive order size based on previous order execution rate and setting a net threshold based on average trading volume helps to control the risks of end-of-day inventory. In addition, we introduce the competition environments of increased competitors and decreased latency, so as to see how these factors affect the performance of market making strategy.

Trading imbalances reflect the quality of market information and may contain more information than the number of trades or trading volume. In order to better understand how trading imbalances play a role different from traditional variables (i.e., number of trades and trading volume) in explaining volatility, we use intraday data to examine the dynamic relations among return volatility, trading imbalances, and traditional variables for E-mini S&P 500 futures and Japanese Yen futures contracts, respectively. The Granger-causality tests indicate strong feedback effects between volatility and trading variables, confirming the informationbased and hedging-based trading. We also compare the results of the traditional volumes and trading imbalances through variance decomposition and impulse responses analysis. It is shown that the sequential arrival of private information through trading imbalance is more important in explaining return volatility than the traditional variables, which are a proxy for the public information.

This paper deals with a stochastic order-driven market model with waiting costs, for orderbooks with heterogenous traders. Offer and demand of liquidity drives price formation and traders anticipate future evolutions of the orderbook. The natural framework we use is mean field game theory, a class of stochastic differential games with a continuum of anonymous players. Several sources of heterogeneity are considered including the mean size of orders. Thus we are able to consider the coexistence of Institutional Investors and High Frequency Traders (HFT). We provide both analytical solutions and numerical experiments. Implications on classical quantities are explored: orderbook size, prices, and effective bid/ask spread. According to the model, in markets with Institutional Investors only we show the existence of inefficient liquidity imbalances in equilibrium, with two symmetrical situations corresponding to what we call liquidity calls for liquidity. During these situations the transaction price significantly moves away from the fair price. However this macro phenomenon disappears in markets with both Institutional Investors and HFT, although a more precise study shows that the benefits of the new situation go to HFT only, leaving Institutional Investors even with higher trading costs.

This paper deals with a stochastic order-driven market model with waiting costs, for orderbooks with heterogenous traders. Offer and demand of liquidity drives price formation and traders anticipate future evolutions of the orderbook. The natural framework we use is mean field game theory, a class of stochastic differential games with a continuum of anonymous players. Several sources of heterogeneity are considered including the mean size of orders. Thus we are able to consider the coexistence of Institutional Investors and High Frequency Traders (HFT). We provide both analytical solutions and numerical experiments. Implications on classical quantities are explored: orderbook size, prices, and effective bid/ask spread. According to the model, in markets with Institutional Investors only we show the existence of inefficient liquidity imbalances in equilibrium, with two symmetrical situations corresponding to what we call liquidity calls for liquidity. During these situations the transaction price significantly moves away from the fair price. However this macro phenomenon disappears in markets with both Institutional Investors and HFT, although a more precise study shows that the benefits of the new situation go to HFT only, leaving Institutional Investors even with higher trading costs.

As financial markets become increasingly complex, the demand for stock price forecasting is growing. To capture both linear trends and volatility in sequences as well as nonlinear dependencies, this paper proposes an ARIMA-GARCH-LSTM hybrid model. First, ARIMA is used to extract linear factors, followed by GARCH to express residual volatility conditions, and finally LSTM to capture deep nonlinear features. Based on the closing prices of the Shanghai Composite Index over 1,027 trading days from 2021 to 2025, RMSE, MAE, and MAPE were used for moving forecasts and multi-indicator estimates. The experiments show that the hybrid model outperforms individual ARIMA, GARCH, or LSTM models in all metrics, confirming its accuracy and robustness. Additionally, the hybrid model demonstrates strong adaptability during periods of high volatility.

This paper addresses the little investigated topic of the relationship between the speed of exchange servers, an absolute reference for the system, and trading speed, considered relative to the former. This is a major issue, as trading speed overwhelming the capability of the server to cope with the incoming orders might jeopardise the orderly functioning of the markets. It will be shown how, by raising the speed of trading and increasing the number of the agents operating in the market, it is possible to generate a crisis, no matter how performing the exchange server is. The paper presents a theoretical framework and then verifies its occurrence by analysing audit trail data. The theoretical framework shows a scenario in which under certain, heavy but by no means uncommon, conditions, the excess speed of the trading agents with respect to servers is capable of exacerbating price volatility, leading to vicious feedback loops capable of potentially creating a financial crisis. The empirical part analyses data taken from a particularly volatile day and compares them with much less volatile days. It results that, because of excessive speed, one of the most widely used techniques for minimising risk, order churning, can cause a major crisis.

The relatively recent phenomenon of high-frequency trading has had a profound impact on the micro-structure of financial markets. Several authors hailed it as a provider of liquidity and a mechanism for controlling volatility, two highly welcome features, especially beneficial to retail traders, whereas other authors view the situation generated by algorithmic trading as damaging for both small and institutional traders, and the orderly functioning of the markets. This paper analyzes the impact of high-frequency trading in respect of the main parameters affecting market quality: volatility, transaction costs, liquidity, price discovery, penalization of slower traders, and impact on sudden financial crises, the notorious flash crashes. As often happens within the financial community, different views stand to each other and no conclusive agreement on the value of most parameters has been reached as yet. A section on the apparently falling profits of high-frequency traders, as denounced in recent times, completes the review.

Some academic research has identified the possibility of High-Frequency Trading (HFT) creating a two tier market, in which the fast traders mostly deal with each other at most favourable prices and spread, leaving the slower investors to share the least profitable deals. Yet, although intriguing, this view has been seldom quantitatively investigated -and that is the gap found in previous research. A computer simulation has been produced to mimic the behaviour of both slow and fast traders, each category showing characteristics consistent with their behaviour on the markets. In particular, High-Frequency (HF) traders show their preference for aggressive orders when the bid-ask spread is tight and are less aggressive when spread is wide. The Low-Frequency (LF) traders are then forced to live with the remaining deals, hoping to profit from longer term price movements. The purpose of this piece of research is to verify whether HF traders (HFTs) tend to deal with each other and, something not investigated by previous studies, if LF traders also mainly restrict their trading with other slow traders. The results show some tendency toward markets actually splitting into two-tiers: they confirm previous findings of HF traders' tendency to deal with their similar counterparts in case of thin bid-ask spread (which means higher probability of profits for the aggressive side), leaving mostly LF traders to deal amongst themselves when the spread is wider, so aiming at long-term gain (which makes trading inherently riskier).

The global financial system has evolved rapidly in the digital age, leading to increased interest in foreign exchange (forex) trading as both a speculative endeavor and a professional career path. With the rise of new technologies and data-driven platforms such as Pylongy, traders now have access to powerful tools that can enhance performance and decision-making. This paper provides an introductory overview of the forex market, highlights the significance of trading psychology, and critically examines the utility of Pylongy in modern trading.

In the following paper we analyze the strategic competition between fast and slow traders. The model of Kyle (1985) is adapted to analyze the effect of speed in such a model. A High Frequency Trader (HFT) is defined as a trader that has the ability to react to information faster than other informed traders and as a consequence can trade more than other traders. This trader benefits from low latency compared to slower trader. In such a setting, we prove the existence and the unicity of an equilibrium with fast and slow traders. We find that the speed advantage of High Frequency Traders (HFTs) has a beneficial effect on market liquidity as well as price efficiency. The positive effect on liquidity is present only if there are 2 or more HFTs. However, despite those effects slower traders are at a disadvantage as they are not able to trade on their private information as many times as their HFTs counterpart . When they are able to trade on it, most of their private information has been incorporated into prices due to the latency of HFTs. This implies that slower traders are worse off when HFTs are present. The speed differential benefits HFTs as they earn higher expected profits than their slower counterparts and also benefits liquidity traders. We find the existence of an optimal level of speed for HFT.

In the following paper we analyze the strategic competition between fast and slow traders. A fast or High Frequency Trader (HFT) is defined as a trader that has the ability to react to information faster than other informed traders and as a consequence can trade more than other traders. This trader benefits from low latency compared to slower trader. In such a setting, we prove the existence and the unicity of an equilibrium with fast and slow traders. We find that the speed advantage of HFTs has a beneficial effect on market liquidity as well as price efficiency. The positive effect on liquidity is present only if there are 2 or more HFTs. However, despite those effects slower traders are at a disadvantage as they are not able to trade on their private information as many times as their HFTs counterpart. Once they can, most of their private information has been incorporated into prices due to the lower latency of HFTs. This implies that slower traders are worse off when HFTs are present. The speed differential benefits HFTs as they earn higher expected profits than their slower counterparts and also benefits liquidity traders. We find the existence of an optimal level of speed for HFT.

The six months moving average series for monthly M OD measured based on cluster structure in Figure . Bottom: The six months moving average return for S&P 500. The two plots behave similarly for many subperiods, indicating that M OD can be a strong indicator for the cycle of economy.

The six months moving average series for monthly M OD measured based on cluster structure in Figure . Bottom: The six months moving average return for S&P 500. The two plots behave similarly for many subperiods, indicating that M OD can be a strong indicator for the cycle of economy.

To understand the impact of high frequency trading (HFT) systems on financial market dynamics, a series of controlled real-time experiments involving humans and automated trading agents were performed. These experiments fall at the interdisciplinary boundary between the more traditional fields of behavioural economics (human-only experiments) and agent based computational economics (agent-only simulations). Experimental results demonstrate that: (a) faster financial trading agents can reduce market efficiency-a worrying result given the race towards zerolatency (ever faster trading) observed in real markets; and (b) faster agents can lead to market fragmentation, such that markets transition from a regime where humans and agents freely interact, to a regime where agents are more likely to trade between themselves-a result that has also been observed in real financial markets. It is also shown that (c) realism in experimental design can significantly alter market dynamics-suggesting that, if we want to understand complexity in real financial markets, it is finally time to move away from the simple experimental economics models first introduced in the 1960s.

Off-exchange trading, which tends to attract uninformed trades, accounts for about 35 percent of total trading volume today. Taking uninformed trades off exchanges harms liquidity but improves price discovery, indicated by a decline in intraday price inefficiency (deviation of price changes from a random walk). We examine whether it also decreases an accounting inefficiency: investor underreaction to quarterly earnings news. Our results, based on investigation of a large panel of US firms and a natural experiment created by the SEC's Tick Size Pilot program, suggest the opposite conclusion: off-exchange trading increases underreaction to earnings news. The negative effects of off-exchange trading on liquidity (less depth and wider spreads) likely increase arbitrage costs, thereby reducing arbitrage activity that partially corrects underreaction. We find that the positive relation between off-exchange trading and underreaction remains strong even when we control for a host of observable arbitrage cost measures. Levels of off-exchange trading appear to serve as a useful proxy for hard-to-measure arbitrage costs.

The prediction of financial time series is a very complicated process. If the efficient market hypothesis holds, then the predictability of most financial time series would be a rather controversial issue, due to the fact that the current price contains already all available information in the market. This paper extends the Adaptive Neuro Fuzzy Inference System for High Frequency Trading which is an expert system that is capable of using fuzzy reasoning combined with the pattern recognition capability of neural networks to be used in financial forecasting and trading in high frequency. However, in order to eliminate unnecessary input in the training phase a new event based volatility model was proposed. Taking volatility and the scaling laws of financial time series into consideration has brought about the development of the Intraday Seasonality Observation Model. This new model allows the observation of specific events and seasonalities in data and subsequently removes any unnecess...

Due to the high efficiency and low switching losses of resonant power convertors in comparison with switching convertors, nowadays there is a growing trend towards these convertors. However, because of the high frequency of switching in such convertors, it is quite difficult to present an efficient control method. In this paper, based on the state feedback method and using pole placement technique a control strategy is developed which controlling delay time in the converter’s switching to minimize losses in the induction and hardening furnaces. In the propose method, the input voltage is isolated from output load. Moreover, fewer number of elements are employed in the control circuit which will be caused lower costs and small dimensions for control system. As a result, the proposed controller is more economical in comparison with the conventional ones. To show the effectiveness of propose controller, simulation circuits using PSIM software and an experimental setup are provided and ...

The practices of high-frequency trading (HFT) are dependent on automated financial markets, especially those produced by securities exchanges electronically interconnected with competing exchanges. How did this infrastructural and organizational state of affairs come to be? Employing the conceptual distinction between fixed-role and switch-role markets, we analyse the discourse surrounding the design and eventual approval of the Securities and Exchange Commission's Regulation of Exchanges and Alternative Trading Systems (Reg ATS). We find that the disruption of the exchange industry at the hands of automated markets was produced through an interweaving of both technological and political change. This processual redefinition of the 'exchange', in addition, may provide a suggestive precedent for understanding contemporary regulatory crises generated by other digital marketplace platforms.

Ogni qualvolta accadono tracolli sui mercati finanziari, i commentatori si affannano a fornire spiegazioni, ovviamente sempre ex-post, portando acqua al proprio mulino. Se le cause reali delle discese in Borsa fossero davvero interpretabili come un semplice rapporto lineare di causa-effetto, forse le discese sarebbero anche prevedibili prima che capitino e non soltanto spiegabili dopo che si sono verificate. Data la molteplicità delle possibili cause e la complessità del reale, ogni spiegazione semplice risulta necessariamente anche semplicistica, col rischio di cadere nella fallacia logica del post hoc, ergo propter hoc: la conseguenza temporale non è mai garanzia, di per sé, di un nesso causale.

Artificial Intelligence (AI) technology is everywhere today, greatly impacting every aspect of human life. This is particularly evident in the world of finance, where AI investments are growing exponentially. The main areas where AI is used are retail banking, trading, and fraud prevention. The task of investing or maybe borrowing money allows you to compare available products on the market. Lots of choice is hard to make, especially since products vary in thousands of parameters. On the bank side, this means that it is hard to service such a large number of consumer loans and other banking products. Investment in AI solves this problem.

The financial crisis of 2008 led to new international regulatory controls for the governance, risk and compliance of financial services firms. Information systems play a critical role here as political, functional and social pressures may lead to the deinstitutionalization of existing structures, processes and practices. This research examines how an investment management system is introduced by a leading IT vendor across eight client sites in the post-crisis era. Using institutional theory, it examines changes in working practices occurring at the environmental and organizational levels and the ways in which technological interventions are used to apply disciplinary effects in order to prevent inappropriate behaviours. The results extend the constructs of deinstitutionalization and identify empirical predictors for the deinstitutionalization of compliance and trading practices within financial organizations.