ATM Cash demand forecasting in an Indian Bank with chaos and deep learning

This paper proposes to model chaos in the ATM cash withdrawal time series of a big Indian bank and forecast the withdrawals using deep learning methods. It also considers the importance of day-of-the-week and includes it as a dummy exogenous variable. We first modelled the chaos present in the withdrawal time series by reconstructing the state space of each series using the lag, and embedding dimension found using an auto-correlation function and Cao's method. This process converts the uni-variate time series into multi variate time series. The "day-of-the-week" is converted into seven features with the help of one-hot encoding. Then these seven features are augmented to the multivariate time series. For forecasting the future cash withdrawals, using algorithms namely ARIMA, random forest (RF), support vector regressor (SVR), multi-layer perceptron (MLP), group method of data handling (GMDH), general regression neural network (GRNN), long short term memory neural network and 1-dimensional convolutional neural network. We considered a daily cash withdrawals data set from an Indian commercial bank. After modelling chaos and adding exogenous features to the data set, we observed improvements in the forecasting for all models. Even though the random forest (RF) yielded better Symmetric Mean Absolute Percentage Error (SMAPE) value, deep learning algorithms, namely LSTM and 1D CNN, showed similar performance compared to RF, based on t-test.Comment: 20 pages; 6 figures and 3 table

Can social microblogging be used to forecast intraday exchange rates?

The Efficient Market Hypothesis (EMH) is widely accepted to hold true under certain assumptions. One of its implications is that the prediction of stock prices at least in the short run cannot outperform the random walk model. Yet, recently many studies stressing the psychological and social dimension of financial behavior have challenged the validity of the EMH. Towards this aim, over the last few years, internet-based communication platforms and search engines have been used to extract early indicators of social and economic trends. Here, we used Twitter's social networking platform to model and forecast the EUR/USD exchange rate in a high-frequency intradaily trading scale. Using time series and trading simulations analysis, we provide some evidence that the information provided in social microblogging platforms such as Twitter can in certain cases enhance the forecasting efficiency regarding the very short (intradaily) forex.Comment: This is a prior version of the paper published at NETNOMICS. The final publication is available at http://www.springer.com/economics/economic+theory/journal/1106

Hokohoko: A comprehensive framework for evaluating artificial intelligence-based and statistical techniques for foreign exchange speculation

This thesis investigates the measurement of predictor performance as applied to foreign exchange speculation. It outlines the development of key ideas and techniques over the course of the last 120 years, and examines the datasets and metrics used within a representative sample of the academic corpus. In this examination two problems are identified: first, there is a lack of consistency in the datasets used to test researchers’ algorithms; and second, a large variety of metrics are used, most of which are either inappropriate for or inappropriately applied to FOREX speculation. To address these issues, this thesis presents two solutions: a Python library, Hokohoko, which provides a consistent dataset and interface for testing FOREX prediction algorithms; and a new metric, Speculative Accuracy, which it argues provides a more appropriate measure of usefulness with regards to speculation. Hokohoko is then used to test a series of hypotheses regarding the usefulness of various metrics, alongside Speculative Accuracy

An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods

MODEL NEURAL NETWORK BERBASIS PSO DALAM PREDIKSI NILAI TUKAR RUPIAH TERHADAP EURO

Bagi sebuah negara, nilai tukar mata uang merupakan indikator yang sangat penting bagi    perekonomian    mereka.    Tujuan    dalam    prediksi    kurs    mata    uang    yaitu  untuk mengetahui  nilai  tukar  mata  uang  di  masa  yang  akan  datang.  Setelah  hasil prediksi didapatkan, selanjutnya data tersebut akan digunakan dalam menentukan langkah-langkah strategis.Penelitian  ini  menghasilkan  sebuah model optimasi Neural Network berbasis Particle Swarm Optimization (PSO) yang memiliki kinerja yang paling akurat dalam prediksi nilai tukar Rupiah terhadap Euro dengan nilai RMSE sebesar 93.219 +/- 19.567. Kata  Kunci:  prediksi,  kurs,  Neural  Network, Particle  Swarm Optimizatio

Vectorial model for progressive adaptation for purchase and sale of shares using stock market indicators

Las acciones son consideradas como parte fundamental del mercado de renta variable, ya que sus valores cambian con el tiempo como consecuencia de la oferta y la demanda, y por efecto de la volatilidad de los mercados. Esta volatilidad hace que la negociación de acciones en un mercado bursátil sea una tarea extremadamente difícil. Es por esto que en este artículo se desarrolla y analiza un sistema para la negociación automática de acciones, el cual incorpora una serie de modelos vectoriales por aprendizaje progresivo inspirado en la estructura de una máquina de vector soporte. Para la configuración de la estructura general del modelo, se utilizaron una serie de indicadores bursátiles utilizados por los inversionistas para fijar posiciones de compra y venta, mientras que el aprendizaje el modelo utilizó una estrategia negociación secuencial sobre cinco acciones diferentes inscritas en la bolsa de valores de Colombia, y en donde el aprendizaje estuvo guiado por las posiciones de compra y venta que iban fijando cada uno de los indicadores bursátiles de entrada, Los resultados arrojados por el sistema, mostraron la rentabilidad que el modelo iba logrando en la negociación como consecuencia del avance en el aprendizaje que cada uno de los modelos iba logrando a lo largo de la secuencia de acciones utilizadas para este estudio, haciendo el sistema cada vez más robusto, lo que lo hace ideal para la negociación de acciones basada en indicadores bursátilesThe shares are considered as a fundamental part of the equity market, as their values change over time as a result of offer and demand, and the effect of market volatility. This volatility makes the trading of shares on a stock exchange is an extremely difficult task. That is why in this article develops and analyzes a system for automatic trading shares, which incorporates a series of progressive learning vector models inspired by the structure of a support vector machine. For the configuration of the overall structure of the model, a number of stock market indicators used by investors to establish positions for buying and selling, were used while learning the model used a sequential negotiation strategy on five different shares listed on the stock exchange of Colombia, and where learning was guided by buying and selling positions that were setting each input stock market indicators. Results from the system showed the profitability that the model was achieved in the negotiation as a result of progress in learning that each of the models was achieved along the sequence of actions used for this study, making the system each more robust time, which makes it ideal for trading shares based on stock indexe

Recent Trends and Applications of Soft Computing: A Survey

Abstract: This paper is survey on the development of soft computing applications in various domains. Specifically, it briefly reviews main approaches of soft computing (in the wide sense) , the more recent development of soft computing, and finalise by presenting a panoramic view of applications: from the most abstract to the most practical ones. Within this context, fuzzy logic (FL), genetic algorithms (GA) and artificial neural networks (ANN), as well as their fusion are reviewed in order to examine the capability of soft computing methods and techniques to effectively address various hard-to-solve design tasks and issues. This paper presents applications of using different Soft Computation methods in both industrial, biological processes, in engineering design, in investment and financial Trading. It analyses the literature according to the style of soft computing used, the investment discipline used, the successes demonstrated, and the applicability of the research to real world trading

CNN-based multivariate data analysis for bitcoin trend prediction

Bitcoin is the most widely known blockchain, a distributed ledger that records an increasing number of transactions based on the bitcoin cryptocurrency. New bitcoins are created at a predictable and decreasing rate, which means that the demand must follow this level of inflation to keep the price stable. Actually, the price is highly volatile, because it is affected by many factors including the supply of bitcoin, its market demand, the cost of the mining process, as well as economic and political world-class news. In this work, we illustrate a novel approach for bitcoin trend prediction, based on the One-Dimensional Convolutional Neural Network (1D CNN). First, we propose a methodology for building useful datasets that take into account social media data, the full blockchain transaction history, and a number of financial indicators. Moreover, we present a cloud-based system characterized by a highly efficient distributed architecture, which allowed us to collect a huge amount of data in order to build thousands of different datasets, using the aforementioned methodology. To the best of our knowledge, this is the first work that uses 1D CNN for bitcoin trend prediction. Remarkably, an efficient and low-cost implementation is feasible due to the simple and compact configuration of 1D CNN models that perform one-dimensional convolutions (i.e., scalar multiplications and additions). We show that the 1D CNN model we implemented, trained, validated and tested using the aforementioned datasets, allow one to predict the bitcoin trend with higher accuracy compared to LSTM models. Last but not least, we introduce and simulate a trading strategy based on the proposed 1D CNN model, which increases the profit when the bitcoin trend is bullish and reduces the loss when the trend is bearish

Recurrent error-based ridge polynomial neural networks for time series forecasting

Time series forecasting has attracted much attention due to its impact on many practical applications. Neural networks (NNs) have been attracting widespread interest as a promising tool for time series forecasting. The majority of NNs employ only autoregressive (AR) inputs (i.e., lagged time series values) when forecasting time series. Moving-average (MA) inputs (i.e., errors) however have not adequately considered. The use of MA inputs, which can be done by feeding back forecasting errors as extra network inputs, alongside AR inputs help to produce more accurate forecasts. Among numerous existing NNs architectures, higher order neural networks (HONNs), which have a single layer of learnable weights, were considered in this research work as they have demonstrated an ability to deal with time series forecasting and have an simple architecture. Based on two HONNs models, namely the feedforward ridge polynomial neural network (RPNN) and the recurrent dynamic ridge polynomial neural network (DRPNN), two recurrent error-based models were proposed. These models were called the ridge polynomial neural network with error feedback (RPNN-EF) and the ridge polynomial neural network with error-output feedbacks (RPNN-EOF). Extensive simulations covering ten time series were performed. Besides RPNN and DRPNN, a pi-sigma neural network and a Jordan pi-sigma neural network were used for comparison. Simulation results showed that introducing error feedback to the models lead to significant forecasting performance improvements. Furthermore, it was found that the proposed models outperformed many state-of-the-art models. It was concluded that the proposed models have the capability to efficiently forecast time series and that practitioners could benefit from using these forecasting models

Improved relative discriminative criterion using rare and informative terms and ringed seal search-support vector machine techniques for text classification

Classification has become an important task for automatically classifying the documents to their respective categories. For text classification, feature selection techniques are normally used to identify important features and to remove irrelevant, and noisy features for minimizing the dimensionality of feature space. These techniques are expected particularly to improve efficiency, accuracy, and comprehensibility of the classification models in text labeling problems. Most of the feature selection techniques utilize document and term frequencies to rank a term. Existing feature selection techniques (e.g. RDC, NRDC) consider frequently occurring terms and ignore rarely occurring terms count in a class. However, this study proposes the Improved Relative Discriminative Criterion (IRDC) technique which considers rarely occurring terms count. It is argued that rarely occurring terms count are also meaningful and important as frequently occurring terms in a class. The proposed IRDC is compared to the most recent feature selection techniques RDC and NRDC. The results reveal significant improvement by the proposed IRDC technique for feature selection in terms of precision 27%, recall 30%, macro-average 35% and micro- average 30%. Additionally, this study also proposes a hybrid algorithm named: Ringed Seal Search-Support Vector Machine (RSS-SVM) to improve the generalization and learning capability of the SVM. The proposed RSS-SVM optimizes kernel and penalty parameter with the help of RSS algorithm. The proposed RSS-SVM is compared to the most recent techniques GA-SVM and CS-SVM. The results show significant improvement by the proposed RSS-SVM for classification in terms of accuracy 18.8%, recall 15.68%, precision 15.62% and specificity 13.69%. In conclusion, the proposed IRDC has shown better performance as compare to existing techniques because its capability in considering rare and informative terms. Additionally, the proposed RSS- SVM has shown better performance as compare to existing techniques because it has capability to improve balance between exploration and exploitation