scholarly journals The COVID‐19 Pandemic and Consumption of Food away from Home: Evidence from High‐frequency Restaurant Transaction Data

2021 ◽  
Vol 29 (6) ◽  
pp. 73-94
Author(s):  
Chen Zhu ◽  
Rigoberto A. Lopez ◽  
Yuan Gao ◽  
Xiaoou Liu
Keyword(s):  
High Frequency ◽  
Transaction Data ◽  
Food Away From Home

Multivariate Hawkes process model of market participants behavior in the high frequency world

2021 ◽  
Vol 08 (01) ◽  
pp. 2050054
Author(s):  
Sugato Chakravarty ◽  
Kiseop Lee ◽  
Yang Xi
Keyword(s):  
Public Sector ◽  
High Frequency ◽  
Process Model ◽  
Hawkes Process ◽  
Transaction Data ◽  
The Public ◽  
Market Participants ◽  
Public Sector Banks ◽  
Average Return ◽  
Causality Relationship

We propose a multivariate Hawkes process to model the interaction between the non-high frequency traders (NHFTs) behavior (Buy and sell) and high frequency traders (HFTs) behavior (Buy and sell). We apply our model to the intraday transaction data of the public sector banks stock in India, which is sampled from March 2012 to June 2012. We find that the mutually-exciting NHFT and HFT behaviors benefit the stocks, which have better average return above the average return of the public sector bank index. We further identify the granger causality relationship for mutually exciting dominating stocks that HFTs activities cause the activities of NHFTs. In other words, NHFTs are market followers in those stocks.

scholarly journals Using High-Frequency Transaction Data to Estimate the Probability of Informed Trading

2009 ◽  
Vol 7 (3) ◽  
pp. 288-311 ◽  
Author(s):  
A. Tay ◽  
C. Ting ◽  
Y. K. Tse ◽  
M. Warachka
Keyword(s):  
High Frequency ◽  
Informed Trading ◽  
Transaction Data ◽  
Probability Of Informed Trading

scholarly journals Analisis Pola Pembelian Obat di Apotek Sekar Adi Menggunakan Metode Algoritma Apriori Depok

2020 ◽  
Vol 4 (1) ◽  
pp. 112
Author(s):  
Siti Awaliyah Rachmah Sutomo ◽  
Frisma Handayanna
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Association Rules ◽  
High Frequency ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Mining Methods ◽  
Using Data ◽  
Purchase Patterns

By using data mining methods can be processed to obtain information and assist in decision making, the amount of data on sales transactions in each drug purchase can cause a data accumulation and various problems, such as drug stock inventory, and sales transaction data, with Data mining techniques, the behavior of consumers in making transactions of drug purchase patterns can be analyzed, It can be known what drugs are commonly purchased by mostly people, the application of Apriori Algorithm is expected to help in forming a combination of itemset. The process of determining drug purchase patterns can be carried out by applying the Appriori algorithm method, determination of drug purchase patterns can be done by looking at the results of the consumer's tendency to buy drugs based on a combination of 3 itemset. By calculating the Analysis of High Frequency Patterns and the Formation of Association Rules, with a minimum of 30% support, there is a combination of 3 itemsset namely MOLAGIT PER TAB (M1), VIT C TABLET (V2), and PARACETAMOL 500 MG TABLET (P2) with 33.33 % support results obtained, and with minimum confidence of 65% there are 6 final association rules.

scholarly journals Hawkes Processes in Finance

2015 ◽  
Vol 01 (01) ◽  
pp. 1550005 ◽  
Author(s):  
Emmanuel Bacry ◽  
Iacopo Mastromatteo ◽  
Jean-François Muzy
Keyword(s):  
High Frequency ◽  
Point Processes ◽  
Order Book ◽  
Great Flexibility ◽  
Transaction Data ◽  
Hawkes Processes ◽  
Academic Literature ◽  
Optimal Execution ◽  
Multivariate Point ◽  
Multivariate Point Processes

In this paper we propose an overview of the recent academic literature devoted to the applications of Hawkes processes in finance. Hawkes processes constitute a particular class of multivariate point processes that has become very popular in empirical high-frequency finance this last decade. After a reminder of the main definitions and properties that characterize Hawkes processes, we review their main empirical applications to address many different problems in high-frequency finance. Because of their great flexibility and versatility, we show that they have been successfully involved in issues as diverse as estimating the volatility at the level of transaction data, estimating the market stability, accounting for systemic risk contagion, devising optimal execution strategies or capturing the dynamics of the full order book.

scholarly journals Realized Jump Risk and Equity Return in China

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Guojin Chen ◽  
Xiaoqun Liu ◽  
Peilin Hsieh ◽  
Xiangqin Zhao
Keyword(s):  
Risk Factor ◽  
High Frequency ◽  
Factor Model ◽  
Arrival Rate ◽  
Jump Risk ◽  
Asset Return ◽  
Transaction Data ◽  
Risk Factor Model ◽  
Jump Size ◽  
Stock Portfolios

We utilize the realized jump components to explore a new jump (including nonsystematic jump and systematic jump) risk factor model. After estimating daily realized jumps from high-frequency transaction data of the Chinese A-share stocks, we calculate monthly jump size, monthly jump standard deviation, and monthly jump arrival rate and then use those monthly jump factors to explain the return of the following month. Our empirical results show that the jump tail risk can explain the equity return. For the large capital-size stocks, large cap stock portfolios, and index, one-month lagged jump risk factor significantly explains the asset return variation. Our results remain the same even when we add the size and value factors in the robustness tests.

The Impact of the COVID-19 Pandemic on Consumption: Learning from High-Frequency Transaction Data

2021 ◽  
Vol 111 ◽  
pp. 307-311
Author(s):  
Haiqiang Chen ◽  
Wenlan Qian ◽  
Qiang Wen
Keyword(s):  
High Frequency ◽  
Quick Response ◽  
Difference In Difference ◽  
Response Code ◽  
Transaction Data ◽  
Quick Response Code ◽  
Bank Card ◽  
The Impact

We use daily transaction data in 214 cities to study the impact of COVID-19 on consumption after China's outbreak in late January 2020. Based on difference-in-difference estimation, daily offline consumption--via bank card and mobile Quick Response code transactions--fell by 32 percent, or 18.57 million renminbi (RMB) per city, during the 12-week period. The effect is prevalent across cities and is more pronounced in the dining-and-entertainment and travel categories. We infer that China's offline consumption decreased by over 1.22 trillion RMB, or 1.2 percent of China's 2019 GDP, in the 3-month postoutbreak period.

scholarly journals Data of Sectoral Financial Flows as a High-Frequency Indicator of Economic Activity

2021 ◽  
pp. 28-49
Author(s):  
Natalia Turdyeva ◽  
◽  
Anna Tsvetkova ◽  
Levon Movsesyan ◽  
Alexey Alexey ◽  
...  
Keyword(s):  
Economic Activity ◽  
High Frequency ◽  
Economic Activities ◽  
Financial Flows ◽  
Transaction Data ◽  
Financial Transaction ◽  
Russian Economy ◽  
Crisis Events ◽  
Research Problems ◽  
Short Term Forecasting

In times of crisis, events are moving fast and standard macroeconomic statistics published with a lag cannot quite keep pace with the changing situation. During such periods, there is an increasing need to use high-frequency indicators that allow virtually real-time monitoring of economic activity. In many countries, this is achieved by using financial transaction data. In this paper, we present a methodology for the current analysis of sectoral financial flows in the Russian economy based on data from the Bank of Russia payment system. We use the information on the dynamics of average daily payments for each class of OKVED 2 (the Russian National Classifier of Economic Activities) to develop high- frequency indicators of economic activity, which have been published on the Bank of Russia website since April 2020. We also tentatively discuss the potential of financial transaction data in terms of improving the tools for short-term forecasting of business activity dynamics and solutions to other research problems.

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