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

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

블록체인, 가상화폐, 파생상품 시장을 위한 예측 모형

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 2. 이재욱.This dissertation aims to conduct the empirical analysis for the financial derivative and cryptocurrency market and to develop analytical techniques based on machine learning models suitable for prediction and estimation of each field. In the financial derivative market, a Markov chain Monte Carlo (MCMC) methods employ the candidate probability distribution nearest to the target probability distribution to acquire sample distributed from the posterior density. Choice of the candidate probability distribution affects the practical convergence speed of the MCMC methodology and the fitness of the sample. In this dissertation, we propose a MCMC framework possible to samples from the candidate distribution nearest to the target probability density without the specification of the candidate distribution. We confirm that the jump diffusion models and Bayesian neural networks have the best performance in estimating and predicting given the data of the recent day for the model estimation given S&P index options in 2012. Especially, the jump diffusion model has a very high performance in terms of domain adaptation between the American option and the European option. This difference is reflected in the fact that the jump diffusion model is based on the common asset of the American option and the European option. Based on this empirical precedent study, we proposed a machine learning model called generative Bayesian neural network (GBNN) to overcome the disadvantages of the machine learning model. GBNN maximizes posterior probability through the GBNN obtains prior information from the GBNN data learned up to the previous day, and learns likelihood probability from actual trading data of learning day. We identify that the GBNN model outperform other benchmark models in terms of model prediction. Bitcoin is a successful cryptocurrency, and it has been extensively studied in fields of economics and computer science. In this dissertation, we analyze the time series of Bitcoin price with a BNN using Blockchain information in addition to macroeconomic variables. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the Bitcoin price in Aug. 2017. In addition, we suggested the enhanced GRU model for correlation analysis between cryptocurrency markets. Assuming that the gate value obtained from the GRU model is the parameter of the VAR model, it makes possible to visualize the correlation between various alternative currencies in the cryptocurrency market. As a result, it is confirmed that there is a very significant correlation between the currencies separated from the existing currencies and the existing currencies.Chapter 1 Introduction 21 1.1 Financial derivative market analysis 21 1.2 Cryptocurrency market analysis 24 1.3 Aims of the Dissertation 26 1.4 Outline of the Dissertation 28 Chapter 2 Literature Review 29 2.1 Review of Financial Econometric Models 29 2.1.1 Time series models 29 2.1.2 Option pricing methods 34 2.2 Review of Statistical Machine Learning Models 39 2.2.1 Articial neural networks 39 2.2.2 Bayesian neural networks 39 2.2.3 Support vector regression 43 2.2.4 Gaussian process 45 Chapter 3 Predictive Models for the Derivatives Market 47 3.1 Chapter Overview 47 3.2 A Generative Model Sampler for Inference in State Space Model 51 3.2.1 Backgrounds 51 3.2.2 Proposed methods: generative model sampler 56 3.3 Machine Learning versus Econometric Models in Predictability of Financial Options Markets 59 3.3.1 Data description and experimental design 59 3.3.2 Estimation and prediction performance 62 3.3.3 Robustness and Domain Adaptation Performance of the Models 66 3.4 A Generative Bayesian Neural Networks Model for Risk-Neutral Option Pricing 70 3.4.1 Proposed method 70 3.4.2 Empirical Studies 74 3.5 Chapter Summary 86 Chapter 4 Predictive Models for Blockchain and Cryptocurrency Market 89 4.1 Chapter Overview 89 4.2 Economics of Bitcoin and Blockchain 91 4.3 An Empirical Study on Modeling and Prediction of Bitcoin Prices Based on Blockchain Information 93 4.3.1 Data Specication and Structure of the Experiment 93 4.3.2 Linear Regression Analysis 99 4.3.3 Estimation and Prediction Results of Bitcoin Price 104 4.4 Enhanced GRU Framework for Correlation Analysis of Cryptocurrency Market 111 4.4.1 Enhanced GRU Framework 111 4.4.2 Empricial Studies 113 4.5 Chapter Summary 115 Chapter 5 Conclusion 119 5.1 Contributions 119 5.2 Future Work 122 Bibliography 125 국문초록 161Docto

Fighting Poverty, Profitably: Transforming the Economics of Payments to Build Sustainable, Inclusive Financial Systems

The Gates Foundation's Financial Services for the Poor program (FSP) believes that effective financial services are paramount in the fight against poverty. Nonetheless, today more than 2 billion people live outside the formal financial sector. Increasing their access to high quality, affordable financial services will accelerate the well-being of households, communities, and economies in the developing world. One of the most promising ways to deliver these financial services to the poor -- profitably and at scale -- is by using digital payment platforms.These are the conclusions we have reached as the result of extensive research in pursuit of one of the Foundation's primary missions: to give the world's poorest people the chance to lift themselves out of hunger and extreme poverty.FSP conducted this research because we believe that there is a gap in the fact base and understanding of how payment systems can extend digital services to low income consumers in developing markets. This is a complex topic, with fragmented information and a high degree of country-by-country variability. A complete view across the entire payment system has been missing, limiting how system providers, policy makers, and regulators (groups we refer to collectively as financial inclusion stakeholders) evaluate decisions and take actions. With a holistic view of the payment system, we believe that interventions can have higher impact, and stakeholders can better understand and address the ripple effects that changes to one part of the system can have. In this report, we focus on the economics of payment systems to understand how they can be transformed to serve poor people in a way that is profitable and sustainable in aggregate

2018 SDSU Data Science Symposium Program

Table of Contents: Letter from SDSU PresidentLetter from SDSU Department of Mathematics and Statistics Dept. HeadSponsorsGeneral InformationKeynote SpeakersInvited SpeakersSunday ScheduleWorkshop InformationMonday ScheduleAbstracts| Invited SpeakersAbstracts | Oral PresentationsPoster PresentationCommittee and Volunteer

Analysis of Recurrent Neural Networks for Henon Simulated Time-Series Forecasting

Forecasting of chaotic time-series has increasingly become a challenging subject. Non-linear models such as recurrent neural networks have been successfully applied in generating short term forecasts, but perform poorly in long term forecasts due to the vanishing gradient problem when the forecasting period increases. This study proposes a robust model that can be applied in long term forecasting of henon chaotic time-series whilst reducing the vanishing gradient problem through enhancing the models ability in learning of long-term dependencies. The proposed hybrid model is tested using henon simulated chaotic time-series data. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the generated forecasts. Performance evaluation results confirm that the proposed recurrent model performs long term forecasts on henon chaotic time-series effectively in terms of error metrics compared to existing forecasting models

Enhancing Option Pricing and Stock Return Predictions: Integrating Machine Learning with Firm Characteristics and Option Greeks

This thesis explores the use of machine learning in financial derivatives, particularly stock options, to improve understanding and prediction of option pricing. It includes three empirical chapters. Chapter 1 evaluates machine learning models that integrate various firm characteristics to predict stock option prices. It introduces two semi-parametric models: a variant of Andreou, Charalambous, and Martzoukos (2010) generalized parametric function model (GPF) and Lajbcygier and Connor (1997)’s hybrid model (HBD), applied to U.S. stock options from 1996 to 2021. The GPF model consistently outperforms the HBD model, with specific firm characteristics emerging as key predictors of option prices. Chapter 2 explores the predictive capacity of option market characteristics, especially implied volatility and Greeks, in forecasting extreme stock returns of the underlying assets. The study employs the LightGBM algorithm, which significantly outperforms traditional logistic regression in predicting stock market trends, emphasizing the value of a comprehensive approach to option dynamics. Chapter 3 builds upon the insights from Chapter 1, focusing on an in-depth analysis of option Greeks and specific firm characteristics within three semi-parametric frameworks: GPF, HBD, and AFFT (Almeida, Fan, Freire, and Tang, 2023). This chapter also explores how these three frameworks maintain consistency with various input features throughout the Pandemic period. Notably, the GPF framework shows exceptional resilience and adaptability when integrated with option Greeks and firm characteristics during the Pandemic. Overall, this thesis underscores the efficacy of incorporating firm characteristics and option Greeks in option pricing and stock return prediction, highlighting the superiority and adaptability of machine learning models in volatile market scenarios

Multilayer perceptron network optimization for chaotic time series modeling

Chaotic time series are widely present in practice, but due to their characteristics—such as internal randomness, nonlinearity, and long-term unpredictability—it is difficult to achieve high-precision intermediate or long-term predictions. Multi-layer perceptron (MLP) networks are an effective tool for chaotic time series modeling. Focusing on chaotic time series modeling, this paper presents a generalized degree of freedom approximation method of MLP. We then obtain its Akachi information criterion, which is designed as the loss function for training, hence developing an overall framework for chaotic time series analysis, including phase space reconstruction, model training, and model selection. To verify the effectiveness of the proposed method, it is applied to two artificial chaotic time series and two real-world chaotic time series. The numerical results show that the proposed optimized method is effective to obtain the best model from a group of candidates. Moreover, the optimized models perform very well in multi-step prediction tasks.This research was funded in part by the NSFC grant numbers 61972174 and 62272192, the Science-Technology Development Plan Project of Jilin Province grant number 20210201080GX, the Jilin Province Development and Reform Commission grant number 2021C044-1, the Guangdong Universities’ Innovation Team grant number 2021KCXTD015, and Key Disciplines Projects grant number 2021ZDJS138