Profitability of Directional Change Based Trading Strategies: The Case of Saudi Stock Market

An event-based framework of directional changes and overshoots maps financial market 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 directional changes (DC) event approach into three automated trading strategies for investments in the financial markets: ZI- 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 strategies for the financial market. Keywords: directional changes, financial forecasting, automated trading, financial markets, simulation. JEL Classifications: G11, G14, G1

Time Series Analysis Indicators under Directional Changes: The Case of Saudi Stock Market

We introduce a set of time series analysis indicators under an event based framework of directional changes and overshoots.  Our aim is to map continuous financial market price data into the so-called Directional-Change (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. Keywords:  directional changes, financial forecasting, automated trading, financial markets, Saudi Stock Market. JEL Classifications: G11, G14, G

Stylized Facts of the FX Market Transactions Data: An Empirical Study

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

Modeling the High-Frequency FX Market: An Agent-Based Approach

The development of computational intelligence‐based strategies for electronic markets has been the focus of intense research. 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 article, we develop an agent‐based model of the foreign exchange (FX) market, which is the market for the buying and selling of currencies. Our agent‐based model of the FX market comprises heterogeneous trading agents that employ a strategy that identifies and responds to periodic patterns in the price time series. We use the agent‐based model of the FX market 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 that result in the emergence of the stylized facts similarly to the real market data, and formulate a model that closely approximates the stylized facts. We use a unique high‐frequency data set of historical transactions data that enables us to run multiple simulation runs and validate our approach and draw comparisons and conclusions for each market setting

Exploring Trading Strategies and Their Effects in the Foreign Exchange Market

One of the most critical issues that developers face in developing automatic systems for electronic markets is that of endowing the agents with appropriate trading strategies. In this article, we examine the problem in the foreign exchange (FX) market, and we use an agent‐based market simulation to examine which trading strategies lead to market states in which the stylized facts (statistical properties) of the simulation match those of the FX market transactions data. Our goal is to explore the emergence of the stylized facts, when the simulated market is populated with agents using different strategies: a variation of the zero intelligence with a constraint strategy, the zero‐intelligence directional‐change event strategy, and a genetic programming‐based strategy. A series of experiments were conducted, and the results were compared with those of a high‐frequency FX transaction data set. Our results show that the zero‐intelligence directional‐change event agents best reproduce and explain the properties observed in the FX market transactions data. Our study suggests that the observed stylized facts could be the result of introducing a threshold that triggers the agents to respond to periodic patterns in the price time series. The results can be used to develop decision support systems for the FX market

First Car

The story of how I spend my winter break and what I did to buy and actually take possession of my first car