Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

Stock market prediction using weighted inter-transaction class association rule mining and evolutionary algorithm

Evolutionary computation and data mining are two fascinating fields that have attracted many researchers. This paper proposes a new rule mining method, named genetic network programming (GNP), to solve the prediction problem using the evolutionary algorithm. Compared with the conventional association rule methods that do not consider the weight factor, the proposed algorithm provides many advantages in financial prediction, since it can discover relationships among the attributes of different transactions. Experimental results on data from the New York Exchange Market show that the new method outperforms other conventional models in terms of both accuracy and profitability, and the proposed method can establish more important and accurate rules than the conventional methods. The results confirmed the effectiveness of the proposed data mining method in financial prediction

An application of deep learning for exchange rate forecasting

This paper examines the performance of several state-of-the-art deep learning techniques for exchange rate forecasting (deep feedforward network, convolutional network and a long short-term memory). On the one hand, the configuration of the different architectures is clearly detailed, as well as the tuning of the parameters and the regularisation techniques used to avoid overfitting. On the other hand, we design an out-of-sample forecasting experiment and evaluate the accuracy of three different deep neural networks to predict the US/UK foreign exchange rate in the days after the Brexit took effect. Of the three configurations, we obtain the best results with the deep feedforward architecture. When comparing the deep learning networks to time-series models used as a benchmark, the obtained results are highly dependent on the specific topology used in each case. Thus, although the three architectures generate more accurate predictions than the time-series models, the results vary considerably depending on the specific topology. These results hint at the potential of deep learning techniques, but they also highlight the importance of properly configuring, implementing and selecting the different topologies

Automated Trading Systems Statistical and Machine Learning Methods and Hardware Implementation: A Survey

Automated trading, which is also known as algorithmic trading, is a method of using a predesigned computer program to submit a large number of trading orders to an exchange. It is substantially a real-time decision-making system which is under the scope of Enterprise Information System (EIS). With the rapid development of telecommunication and computer technology, the mechanisms underlying automated trading systems have become increasingly diversified. Considerable effort has been exerted by both academia and trading firms towards mining potential factors that may generate significantly higher profits. In this paper, we review studies on trading systems built using various methods and empirically evaluate the methods by grouping them into three types: technical analyses, textual analyses and high-frequency trading. Then, we evaluate the advantages and disadvantages of each method and assess their future prospects

Design Analysis and Implementation of Stock Market Forecasting System using Improved Soft Computing Technique

In this paper, a stock market prediction model was created utilizing artificial neural networks. Many people nowadays are attempting to predict future trends in bonds, currencies, equities, and stock markets. It is quite challenging for a capitalist and an industry to forecast changes in stock market prices. Due to the numerous economic, political, and psychological aspects at play, forecasting future value changes on the stock markets is quite challenging. In addition, stock market forecasting is a difficult endeavor because it relies on a wide range of known and unknown variables. Many approaches, including technical analysis, fundamental analysis, time series analysis, and statistical analysis are used to attempt to predict the share price; however, none of these methods has been demonstrated to be a consistently effective prediction tool. Artificial neural networks (ANNs), a subfield of artificial intelligence, are one of the most modern and promising methods for resolving financial issues, such as categorizing corporate bonds and anticipating stock market indexes and bankruptcy (AI). Artificial neural networks (ANN) are a prominent technology used to forecast the future of the stock market. In order to understand financial time series, it is often essential to extract relevant information from enormous data sets using artificial neural networks. An outcome prediction neural network with three layers is trained using the back propagation method. Analysis shows that ANN outperforms every other prediction technique now available to academics in terms of stock market price predictions. It is concluded that ANN is a useful technique for predicting stock market movements globally

Grammatical evolution-based ensembles for algorithmic trading

The literature on trading algorithms based on Grammatical Evolution commonly presents solutions that rely on static approaches. Given the prevalence of structural change in financial time series, that implies that the rules might have to be updated at predefined time intervals. We introduce an alternative solution based on an ensemble of models which are trained using a sliding window. The structure of the ensemble combines the flexibility required to adapt to structural changes with the need to control for the excessive transaction costs associated with over-trading. The performance of the algorithm is benchmarked against five different comparable strategies that include the traditional static approach, the generation of trading rules that are used for single time period and are subsequently discarded, and three alternatives based on ensembles with different voting schemes. The experimental results, based on market data, show that the suggested approach offers very competitive results against comparable solutions and highlight the importance of containing transaction costs.The authors would like to acknowledge the nancial support of the Spanish Ministry of Science, Innovation and Universities under project PGC2018-646 096849-B-I00 (MCFin)

SISTEM EVALUASI ROBOT TRADING DENGAN METODE ELECTRE BERBASIS REAL-TIME WEB SERVICE PADA PASAR VALAS

Penelitian ini bertujuan mengoptimalkan keuntungan perdagangan valas secara otomatis menggunakan robot trading namun tetap mempertimbangkan tingkat akurasi dan drawdown. Sistem evaluasi mengelompokkan kinerja robot trading berdasarkan sesi pasar perdagangan (Sydney, Tokyo, London dan New York) untuk menentukan robot trading yang tepat untuk digunakan pada sesi pasar tertentu. Sistem evaluasi ini berbasis web dengan perhitungan metode ELECTRE yang berinteraksi secara real-time dengan robot trading melalui web service dan mampu menyajikan grafik kinerja secara real-time pada dashboard dengan komunikasi protokol web socket. Aplikasi web diprogram menggunakan teknologi NodeJs. Pada periode pengujian, semua robot trading disimulasikan 24 jam di semua sesi pasar selama tiga bulan, robot trading terbaik dinilai berdasarkan kriteria laba, akurasi dan drawdown yang dihitung menggunakan metode ELECTRE berbasis web. Ide dari penelitian ini adalah membandingkan robot trading terbaik pada periode pengujian dengan kinerja kolaborasi empat robot trading terbaik di setiap sesi pasar. Penelitian ini menggunakan data historis pergerakan mata uang EURO terhadap USD sebagai periode pengujian dan 3 bulan berikutnya sebagai data validasi. Dari hasil penelitian, kinerja kolaborasi empat robot trading terbaik yang dikelompokkan berdasarkan sesi pasar dapat meningkatkan persentase keuntungan secara konsisten dengan tetap menjaga tingkat akurasi dan drawdown. Kata Kunci: Kinerja, Sistem Evaluasi, Valas, Robot Trading, ELECTRE This research aims to optimize forex trading profit automatically using EA but its still keep considering accuracy and drawdown levels. The evaluation system will classify EA performance based on trading market sessions (Sydney, Tokyo, London and New York) to determine the right EA to be used in certain market sessions. This evaluation system is a web-based ELECTRE methods that interact in real-time with EA through web service and are able to present real-time charts performance dashboard using web socket protocol communications. Web applications are programmed using NodeJs technology. In the testing period, all EAs had been simulated 24 hours in all market sessions for three months, the best EA is valued by its profit, accuracy and drawdown criterias that calculated using web-based ELECTRE method. The ideas of this research is to compare the best EA on testing period with collaboration performances of each best classified EA by market sessions. This research uses three months historical data of EUR against USD as testing period and other 3 months as validation period. As a result, performance of collaboration four best EA classified by market sessions can increase profits percentage consistently in testing and validation periods and keep securing accuracy and drawdown levels. Keywords: Performance, Evaluation System, Forex, EA, ELECTR