Monira Aloud

The author presents a simple data-driven intraday technical indicator trading approach based on Genetic Programming (GP) for return forecasting in the Bitcoin market. We use five trend-following technical indicators as input to GP for developing trading rules. Using data on daily Bitcoin historical prices from January 2017 to February 2020, our principal results show that the combination of technical analysis indicators and Artificial Intelligence (AI) techniques, primarily GP, is a potential forecasting tool for Bitcoin prices, even outperforming the buy-and-hold strategy. Sensitivity analysis is employed to adjust the number and values of variables, activation functions, and fitness functions of the GP-based system to verify our approach's robustness.

The author presents a simple data-driven intraday technical indicator trading approach based on Genetic Programming (GP) for return forecasting in the Bitcoin market. We use five trend-following technical indicators as input to GP for developing trading rules. Using data on daily Bitcoin historical prices from January 2017 to February 2020, our principal results show that the combination of technical analysis indicators and Artificial Intelligence (AI) techniques, primarily GP, is a potential forecasting tool for Bitcoin prices, even outperforming the buy-and-hold strategy. Sensitivity analysis is employed to adjust the number and values of variables, activation functions, and fitness functions of the GP-based system to verify our approach's robustness.

In this paper, we use an agent-based approach to model the trading activity in the Foreign Exchange (FX) market which is the most liquid financial market in the world. We focus on performing a systematic exploration of the market’s constituent elements and their impact on the dynamics of the market behaviour. In particular, our study explores and identifies the essential elements under which the statistical properties (stylized facts) of the high-frequency FX market are exhibited in the agentbased FX market (ABFXM). Our study suggests that among the key elements are the heterogeneity which has been embedded in our model in different ways, asynchronous trading time windows, initial activation conditions for the traders and the generation of limit orders. The dynamics of the FX market activity also depend on the number of agents. Furthermore, a trading strategy that is able to identify and respond to periodic patterns in the price time series appears to be effective in the FX market a...

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In this paper, we focus on studying the statistical properties (stylized facts) of the trading activity in the Foreign Exchange (FX) market which is the most liquid financial market in the world. We use a unique high-frequency dataset of anonymised individual traders ’ historical transactions on an account level provided by OANDA. To the best of our knowledge, this dataset can be considered to be the biggest available high-frequency dataset of the FX market individual traders ’ historical transactions. The established stylized facts can be grouped under three main headings: scaling laws, seasonality statistics and correlation behaviour. Our work confirms established stylized facts in the literature but also goes beyond those as we have discovered four new scaling laws and established six quantitative relationships amongst them, holding across EUR/USD and EUR/CHF transactions. The established stylized facts of the trading activity in high-frequency FX markets can offer insights into ...

One of the most critical design issues that developers face in electronic markets is that of the agents ’ trading strategies. In this paper, we aim to examine the impact of trading strategies on the high-frequency Foreign Exchange market. In particular, our goal is to explore the emergence of the stylized facts (statistical properties) in the trading activity when the market is populated with agents with three different strategies: a variation of the zero-intelligence with a constraint (ZI-CV) strategy; the zero-intelligence directional-change event (ZI-DCT0) strategy; and a genetic programming-based (GP) strategy. A series of experiments were conducted in an existing agent-based FX market with these three strategies and the results were compared against those of a high-frequency dataset from the FX market. Our results show that the ZI-DCT0 agents best reproduce and explain the properties and phenomena observed in the FX market real transactions data. Our study suggests that the obs...

One of the most critical issues that developers face in developing automatic systems or software agents for electronic markers is that of endowing the agents with appropriate trading strategies. In this paper, we examine the problem in the Foreign Exchange (FX) market and we use an agent-based FX market simulation to examine which trading strategies lead to market states in which the stylized facts (statistical properties) of the simulation match the stylised facts of the actual FX market transactions data. In particular, our goal is to explore the emergence of the stylized facts of the transactions data, when the simulated market is populated with agents using three different strategies: a variation of the zero-intelligence with a constraint (ZI-CV) strategy; the zero-intelligence directional-change event (ZI-DCT0) strategy; and a genetic programmingbased (GP) strategy. A series of experiments were conducted in an existing agent-based FX market with these three strategies and the r...

In this paper, we focus on studying the statistical properties (stylized facts) of the trading activity in the Foreign Exchange (FX) market which is the most liquid financial market in the world. We use a unique high-frequency dataset of anonymised individual traders’ historical transactions on an account level provided by OANDA. To the best of our knowledge, this dataset can be considered to be the biggest available high-frequency dataset of the FX market individual traders’ historical transactions. The established stylized facts can be grouped under three main headings: scaling laws, seasonality statistics and correlation behaviour. Our work confirms established stylized facts in the literature but also goes beyond those as we have discovered four new scaling laws and established six quantitative relationships amongst them, holding across EUR/USD and EUR/CHF transactions. The established stylized facts of the trading activity in high-frequency FX markets can offer insights into th...

We introduce a set of time series analysis indicators under an event based framework of directional changes (DC) and overshoots. Our aim is to map continuous financial market price data into the so-called DC Framework - A state based discretization of basically dissected price time series. The DC framework analysis relied on understanding the price time series as an event-based process, as an alternative of focusing on their stochastic character. Defining a scheme for state reduction of DC Framework, we show that it has a dependable hierarchical structure that permits for analysis of financial data. We show empirical examples within the Saudi Stock Market. The new DC indicators represent the foundation of a completely new generation of financial tools for studying volatility, risk measurement, and building advanced forecasting and automated trading models

The development of computational-intelligence based strategies for electronic markets has been the focus of intense research. In order to be able to design efficient and effective automated trading strategies, one first needs to understand the workings of the market, the strategies that traders use and their interactions as well as the patterns emerging as a result of these interactions. In this paper, we develop an agent-based model of the FX market which is the market for the buying and selling of currencies. Our agent-based model of the FX market (ABFXM) comprises heterogeneous trading agents which employ a strategy that identifies and responds to periodic patterns in the price time series. We use the ABFXM to undertake a systematic exploration of its constituent elements and their impact on the stylized facts (statistical patterns) of transactions data. This enables us to identify a set of sufficient conditions which result in the emergence of the stylized facts similarly to the...

In this paper, we focus on studying the statistical properties (stylized facts) of the transactions data in the Foreign Exchange (FX) market which is the most liquid financial market in the world. We use a unique high-frequency dataset of anonymised individual traders’ historical transactions on an account level provided by OANDA. To the best of our knowledge, this dataset can be considered to be the biggest available high-frequency dataset of the FX market individual traders’ historical transactions. The established stylized facts can be grouped under three main headings: scaling laws, seasonality statistics and correlation behaviour. Our work confirms established stylized facts in the literature but also goes beyond those as we have discovered four new scaling laws and established six quantitative relationships amongst them, holding across EUR/USD and EUR/CHF transactions.

An event-based framework of directional changes (DC) and overshoots maps financial market (FM) price time series into the so-called intrinsic time where events are the time scale of the price time series. This allows for multi-scale analysis of financial data. In the light of this, this paper formulates DC event approach into three automated trading strategies for investments in the FMs: ZI-Directional Change Trading (DCT0), DCT1, and DCT2. The main idea is to use intrinsic time scale based on DC events to learn the size and the direction of periodic patterns from the asset price historical dataset. Using simulation models of Saudi Stock Market, we evaluate the returns of the automated DC trading strategies. The analysis revealed interesting results and evidence that the proposed strategies can indeed generate effective trading for investors with a high rate of returns. The results of this study can be used further to develop decision support systems and autonomous trading agent str...

Designing a profitable trading strategy plays a critical role in algorithmic trading, where the algorithm can manage and execute automated trading decisions. Determining a specific trading rule for trading at a particular time is a critical research problem in financial market trading. However, an intelligent, and a dynamic algorithmic trading driven by the current patterns of a given price time-series may help deal with this issue. Thus, Reinforcement Learning (RL) can achieve optimal dynamic algorithmic trading by considering the price time-series as its environment. A comprehensive representation of the environment states is indeed vital for proposing a dynamic algorithmic trading using RL. Therefore, we propose a representation of the environment states using the Directional Change (DC) event approach with a dynamic DC threshold. We refer to the proposed algorithmic trading approach as the DCRL trading strategy. In addition, the proposed DCRL trading strategy was trained using t...

One of the most critical design issues that developers face in electronic markets is that of the agents’ trading strategies. In this paper, we aim to examine the impact of trading strategies on the high-frequency Foreign Exchange market. In particular, our goal is to explore the emergence of the stylized facts (statistical properties) in the trading activity when the market is populated with agents with three different strategies: a variation of the zero-intelligence with a constraint (ZI-CV) strategy; the zero-intelligence directional-change event (ZI-DCT0) strategy; and a genetic programming-based (GP) strategy. A series of experiments were conducted in an existing agent-based FX market with these three strategies and the results were compared against those of a high-frequency dataset from the FX market. Our results show that the ZI-DCT0 agents best reproduce and explain the properties and phenomena observed in the FX market real transactions data. Our study suggests that the obse...

Many investors seek a trading strategy in order to maximize their profit. In the light of this, this paper derived a new trading strategy (DCT1) based on the Zero-Intelligence Directional Change Trading Strategy ZI-DCT0, and found that the resulting strategy outperforms the original one. We enhanced the conventional ZI-DCT0 by learning the size and direction of periodic fixed patterns from the price history for EUR/USD currency pairs. To evaluate DCT1, experiments were carried out using the bid and ask prices for EUR/USD currency pairs from the OANDA trading platform over the year 2008. We compared the resulting profits from ZI-DCT0 and DCT1. The analysis revealed interesting results and evidence that the proposed DCT1 investment strategy can indeed generate effective electronic trad- ing investment returns for investors with a high rate of return. The results of this study can be used further to develop decision support systems and autonomous trading agent strategies for the FX mar...

The authors present a simple data-driven decision support system for stock market trading using multiple technical indicators, decision trees, and genetic algorithms (GAs). It assembles technical indicators set into a decision tree based on stock trading rules and generates buy, hold, and sell classes that represent trading decisions. The main contribution of this study is the use of GAs based on a two-step classification method. This allows for selecting the relevant inputs and adapting them to the market dynamic. The GAs are used at the data input selection step and the weight selection step. Classifiers of different technical indicators are trained in the first step and combined into the trading rules in the second step. Random sampling and data input selection techniques were used to ensure the required variety of technical indicators in the first step. An evaluation shows that the proposed algorithm improved forecasting accuracy from 73.6% to 81.78%.

How important are social constraints and information gaps in explaining the low rates of female labor force participation (FLFP) in conservative societies that are undergoing social change? To answer this question, we conducted a field experiment embedded in a survey of female university students at a large public university in Saudi Arabia. We randomly provided one subset of individuals with information on the labor market and aspirations of their female peers (T1), while another subset was provided with this information along with a prime that made the role of parents and family more salient (T2). We find that expectations of working among those in the Control group are quite high, yet students underestimate the expected labor force attachment of their female peers. We show that information matters: relative to the Control group, expectations about own labor force participation are significantly higher in the T1 group. We find little evidence that dissemination of information is counteracted by local gender norms: impacts for the T2 group are significant and often larger than those for T1 group. However, T2 leads to higher expectations of working in Education - a sector that is socially more acceptable for women.

Purpose The purpose of this paper is to investigate the way that Saudi universities are engaging their audience via social media platforms by means of the five meaningful themes: visibility, branding, authenticity, commitment, and engagement. The study will answer the questions: how do Saudi universities exploit social media platforms to engage their target audience? What are the recommendations for Saudi universities toward maximizing the value of social media engagement? Design/methodology/approach A content analysis approach was used to study all Saudi universities (26 public, 11 private). Facebook, YouTube, LinkedIn and Twitter were the anticipated social media platforms in this study. Findings The results showed that Twitter is the most frequently used platform to communicate with audiences. While visibility in the anticipated social media platforms was high, the engagement was lacking. On the other hand, authenticity and branding in the anticipated social media platforms were ...

The development of computational intelligence based trading strategies for financial markets has been the focus of research over the last few years. To develop efficient and effective automated trading strategies, we need to understand the workings of the market and the patterns emerging as a result of the traders interactions. In this paper, we develop an adaptive Genetic Programming (GP) agent-based trading system under Intraday Seasonality Model (ISM), which is abbreviated as GPISM trading system. ISM is used for creating maps and visualizing the dynamic price evolution of the asset during the day. This new model permits the recognition of periodic patterns and seasonalities in the price time series and hence eliminates any unnecessary data input. We use a high-frequency dataset of historical price data from Saudi Stock Market, which enables us to run multiple market simulation runs and draw comparisons and conclusions for the developed trading strategies. The goal of our work is to develop automated computational intelligence-based strategies for real markets, and this study facilitates a more thorough understanding of a specific market’s workings and constitutes the basis for further exploration into such strategies designed for the stock market. We evaluate the intelligence of the GP-ISM trading system through agent-based simulation market index trading. For comparison, we also include four other types of trading agents in this contest, namely, zero-intelligence agents, Buy-and-Hold agents, fundamental agents and technical analysis agents. As a result, GP-ISM performs the best, which provides a general framework for the further development of automated trading strategies and decision support systems.

This paper presents an autonomous effective trading system devoted to the support of decision-making processes in the financial market domain. Genetic programming (GP) has been used effectively as an artificial intelligence technique in the financial field, especially for forecasting tasks in financial markets. In this paper, GP is employed as a means of automatically generating short-term trading rules on financial markets using technical indicators and fundamental parameters. The majority of forecasting tools use a fixed physical timescale, which makes the flow of price fluctuations discontinuous. Therefore, using a fixed physical timescale may expose investors to risks, due to their ignorance of some significant activities. Instead of using fixed timescales for this purpose, the trading rules are generated under a directional-change (DC) event framework.We examine the profitability of the trading systems for the Saudi Stock Exchange, and evaluate the GP forecasting performance under a DC framework through agent-based simulation market index trading. The performance of the forecasting model is compared with a number of benchmark forecasts, namely the buy-and-hold and technical analysis trading strategies. Our numerical results show that the proposed GP model under a DC framework significantly outperforms other traditional models based on fixed physical timescales in terms of portfolio return.

In this paper, we focus on studying the behaviour of the trading activity in the Foreign Exchange (FX) market which is the most liquid financial market in the world. We use an agent-based modelling (ABM) approach to model the trading activity in this high-frequency FX market. The aim is to identify the essential elements under which the statistical properties (stylized facts) of the high-frequency FX market trading activity are exhibited in an agent-based market. The initial part of this study is to establish stylized facts of the trading activity in the FX market using a unique high-frequency dataset of anonymised OANDA individual traders' historical transactions on an account level spanning 2.25 years. To the best of our knowledge, this dataset is the biggest available high-frequency dataset of the FX market individual traders' historical transactions. Using the identified stylized facts, we evaluate the trading activity generated from the agent-based FX market in resembli...

In this paper, we study in depth an agent-based market which models the Foreign Ex-change (FX) market. We focus on performing a systematic exploration of the constituent elements of the market and their impact on the trading activity. Our study explores the range of parameters in the agent-based financial market and identifies the essential elements un-der which the stylized facts of high-frequency FX market trading activities are exhibited in the agent-based market. The key elements are zero-intelligence directional-change event trading agents, the role of heterogeneity, asynchronous trading time windows, initial acti-vation conditions and the generation of limit orders. We also show that the dynamics of the market trading activity depends on the number of agents one considers. Our analysis emphasises the need to relate the macroscopic analysis of actual traders' behaviour to the trading agents' behaviour. Keywords — agent-based modelling, agent-based simulation, electronic...

Financial markets witness high levels of activity at certain times but remain calm at others. This makes the flow of physical time discontinuous. Therefore, to use physical time scales for studying financial time series runs the risk of missing important activities. An alternative approach is to use an event-based time scale that captures periodic activities in the market. In this paper, the authors use a special type of event, called a directional-change event, and show its usefulness in capturing periodic market activities. The study confirms that the length of the price-curve coastline, as defined by directional-change events, turns out to be a long one.

ABSTRACT In this paper, we show that a minimal agent-based model for the Foreign Exchange (FX) market is capable of reproducing, to a certain extent, FX market trading activity. The model is minimal in that it has the advantage of having the minimum set of elements necessary for modelling the FX market in order to reproduce the FX market trading activity. The key elements are zero-Intelligence directional-change events traders, historical prices, actual FX traders' behaviour, limit orders, FX market trading sessions, market holidays, and the activation of the initial condition. All of these play a fundamental role. Most importantly, the simulation output is evaluated by contrast against the microscopic behavioural analysis of high-frequency data of individual traders' transactions on an account level provided by OANDA LTD. The results of this comparison prove that the trading agents' behaviour reproduces the FX market trading activity. Overall, the model leads to the identification of the key elements that may be responsible for the emergence of FX market trading activity in an agent-based model.

In this chapter, the authors use an Agent-Based Modeling (ABM) approach to model trading behavior in the Foreign Exchange (FX) market. They establish statistical properties (stylized facts) of the traders' trading behavior in the FX market using a high-frequency dataset of anonymised OANDA individual traders' historical transactions on an account level spanning 2.25 years. Using the identified stylized facts of real FX market traders' behavior, the authors evaluate the collective behavior of the trading agents in resembling the collective behavior of the FX market traders. The study identifies the conditions under which the stylized facts of trading agents' collective behaviors resemble those for the real FX market traders' collective behavior. The authors perform an exploration of the market's features in order to identify the conditions under which the stylized facts emerge.

We use an agent-based approach to model trading behaviour in high-frequency markets. This study focuses on the Foreign Exchange (FX) market. The initial part of this study is to observe the micro-behaviour of traders to define the stylized facts of their trading activities. This is performed using a high- frequency dataset of anonymised individual traders' historical transactions on an account

The Foreign Exchange (FOREX) market is the largest and most complex financial market in the world. With the advent of behavioural and micro-structural studies, many properties of the market have been revealed. However, previous studies contain only aggregate transaction data that do not distinguish between the activities of the different participants, formulating the market collective behavior. Understanding the FOREX market