Can cash hold its own? International comparisons: Theory and evidence

A number of papers predict the imminent demise of currency use in transactions while some make a case for its continued use due to its distinctive feature of anonymity. Notwithstanding the latter, this paper shows on both theoretical and empirical grounds, that cash use is sustainable for the foreseeable future because of the cost competitiveness of ATM networked cash to the consumer relative to electronic POS card substitutes. Indeed, since the mid-1990s, Finland, Canada and France which are countries in the vanguard of EFTPOS development, have experienced a resurgence of ATM cash use as measured by its expenditure share.

Did inflation really soar after the euro cash changeover? Indirect evidence from ATM withdrawals

The introduction of the euro notes and coins in the first two months of 2002 was followed by a lively debate on the alleged inflationary effects of the new currency. In Italy, as in the rest of the euro area, survey-based measures signaled a much sharper rise in inflation than measured by the official price indices, whose quality was called into question. In this paper we gather indirect evidence on the behavior of prices from the analysis of cash withdrawals from ATM and their determinants. Since these data do not rely on official inflation statistics, they provide an independent check for the latter. We present a model in which the relationship between aggregate ATM withdrawals and aggregate expenditure is not homogenous of degree one in the price level, a prediction which is strongly supported by the data. This feature allows us to test the hypothesis that, after the introduction of the euro notes and coins, consumer prices underwent an increase not recorded by official inflation statistics. We do not find evidence in support of this hypothesis.banknotes, currency, euro, inflation.

Technological change and the demand for currency: An analysis with household data

Advances in transaction technology allow agents to economize on the cost of cash management. We argue that accounting for the impact of new transaction technologies on currency holding behaviour is important to obtain theoretically consistent estimates of the demand for money. We modify a standard inventory model to study the effect of withdrawal technology on the demand for currency. An empirical specification for households’ demand schedule is suggested, in which both the level of currency holdings and the interest rate elasticity of demand depend on the withdrawal technology available to agents (e.g. ATM card ownership or a high/low density of bank branches, ATMs). The theoretical implications are tested using a unique panel of Italian household data (on currency holdings, deposit interest rates, consumption, development of banking services, etc.) for the period 1989-2004.money demand, inventory models, technological change

Can cash hold its own? International comparisons: Theory and evidence

A number of papers predict the imminent demise of currency use in transactions while some make a case for its continued use due to its distinctive feature of anonymity. Notwithstanding the latter, this paper shows on both theoretical and empirical grounds, that cash use is sustainable for the foreseeable future because of the cost competitiveness of ATM networked cash to the consumer relative to electronic POS card substitutes. Indeed, since the mid-1990s, Finland, Canada and France which are countries in the vanguard of EFTPOS development, have experienced a resurgence of ATM cash use as measured by its expenditure share

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

Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks

Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time interval. One expressive mathematical tool for modeling event is point process. The intensity functions of many point processes involve two components: the background and the effect by the history. Due to its inherent spontaneousness, the background can be treated as a time series while the other need to handle the history events. In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics. The whole model with event type and timestamp prediction output layers can be trained end-to-end. Our approach takes an RNN perspective to point process, and models its background and history effect. For utility, our method allows a black-box treatment for modeling the intensity which is often a pre-defined parametric form in point processes. Meanwhile end-to-end training opens the venue for reusing existing rich techniques in deep network for point process modeling. We apply our model to the predictive maintenance problem using a log dataset by more than 1000 ATMs from a global bank headquartered in North America.Comment: Accepted at Thirty-First AAAI Conference on Artificial Intelligence (AAAI17

Comparison study of transfer function and artificial neural network for cash flow analysis at Bank Rakyat Indonesia

The cash flow analysis is essential to examine the economic flows in the financial system. In this paper, the financial dataset at Bank Rakyat Indonesia was used, it recorded the sources of cash inflow and outflow during a particular period. The univariate time series model like the autoregressive and integrated moving average is the common approach to build the prediction based on the historical dataset. However, it is not suitable to estimate the multivariate dataset and to predict the extreme cases consisting of nonlinear pairs between independent-dependent variables. In this study, the comparison of using two types of models i.e., transfer function and artificial neural network (ANN) were investigated. The transfer function model includes the coefficient of moving average (MA) and autoregressive (AR), which allows the multivariate analysis. Furthermore, the artificial neural network allows the learning paradigm to achieve optimal prediction. The financial dataset was divided into training (70%) and testing (30%) for two types of models. According to the result, the artificial neural network model provided better prediction with achieved root mean square error (RMSE) of 0.264897 and 0.2951116 for training and testing respectively