Intraday patterns of price clustering in Bitcoin

In this study, an investigation is conducted into the phenomenon of price clustering in Bitcoin (BTC) denominated in the Japanese yen (JPY). It answers two questions using tick-by-tick data. The first is whether price clustering exists in BTC/JPY transactions, and the other is how the scale of price clustering varies throughout a trading day. With the assistance of statistical measures, the last two digits of BTC price were discovered to cluster at the numbers that end with ’00’. In addition, the scales of BTC/JPY clustering at ’00’ tended to decline at the specific hour intervals. This study contributes to the emerging literature on price clustering and investor behavior.

Analysis and implementation of realtime stock prediction using reinforcement frameworks

Real time stock prediction is interesting research topic due to the risk involved with volatile scenarios. Modelling of the stocks by reducing the overestimation in ANN model, due to rapid fluctuations in the market guide fund managers risky decisions while building stock portfolio. This paper builds real time framework for stock prediction using deep reinforcement learning to buy, sell or hold the stocks. This paper models the transformed stock tick data and technical indicators using Transformed Deep-Q Learning. Our framework is cost reduced and transaction time optimized to get real time stock prediction using GPU and Memory containers. Stock predictor is architected using GRPC based clean architecture which has the benefits of easy updates, addition of new services with reduced integration costs. Data archive features of the cloud will give benefit of reduced cost of the new stock predictor framework.

Empirical Estimation of Intraday Yield Curves on the Italian Interbank Credit Market e-MID

This paper introduces a major novelty: the empirical estimation of spot intraday yield curves based on tick-by-tick data on the Italian electronic interbank credit market (e-MID). To analyze the consequences of the recent financial crisis, we split the data into four periods, which include events before, during, and after the recent financial crisis starting in 2007. Our first result is that, from a practical point of view, the intraday yield curve can be modeled by standard models for yield curves providing advantages for intraday trading on intraday interbank credit markets. Moreover, the estimates show that the systematic dynamics in the intraday yield curves during the turmoil were highly noticeable, resulting in a significantly better goodness-of-fit. Based on this fact, we infer that investors in the interbank credit market base their investment decisions on the effects of the intraday dynamics of intraday interest rates more intensively during a financial crisis. Therefore, the systematic impact on the e-MID appears to be stronger and econometric modeling of the intraday interest rate curve becomes even more attractive during a turmoil.

Analysis of intra-day fluctuations in the Mexican financial market index

In this paper, a statistical analysis of high frequency fluctuations of the IPC, the Mexican Stock Market Index, is presented. A sample of tick--to--tick data covering the period from January 1999 to December 2002 was analyzed, as well as several other sets obtained using temporal aggregation. Our results indicates that the highest frequency is not useful to understand the Mexican market because almost two thirds of the information corresponds to inactivity. For the frequency where fluctuations start to be relevant, the IPC data does not follows any $\alpha$-stable distribution, including the Gaussian, perhaps because of the presence of autocorrelations. For a long range of lower-frequencies, but still in the intra-day regime, fluctuations can be described as a truncated L\'evy flight, while for frequencies above two-days, a Gaussian distribution yields the best fit. Thought these results are consistent with other previously reported for several markets, there are significant differences in the details of the corresponding descriptions.

Intraday Variability and Trading Volume: Evidence from National Stock Exchange

In this article, we investigate patterns in returns, volume and volatility and analyse the volume–return relationship using tick-by-tick data from the Indian equity market. Based on descriptive measures and regression frameworks, we document three important findings. First, we report unusually high volatility, trading volume and number of trades during the opening and closing minutes of the market depicting a ‘U’-shaped curve, implying high market activity during these periods. Second, while accounting for trading volume, we observe that volatility is not significantly different between mid-day period and evening period as compared to the normal ‘U’ curve. Finally, we document a significant positive relationship between intraday volume and price movements controlling for microstructure effects. The impact of positive returns on trading volume is higher than the impact of negative returns, implying the presence of return–volume asymmetry in the Indian market. JEL Codes: G12, G15

Long memory investigation during demonetization in India

Long-range dependence (LRD) in financial markets remains a key factor in determining whether there is market memory, herding traces, or a bubble in the economy. Usually referred to as ‘Long Memory’, LRD has remained a key parameter even today since the mid-1970s. In November 2016, a sudden and drastic demonetization measure took place in the Indian market, aimed at curbing money laundering and terrorist funding. This study is an attempt to identify market behavior using long-range dependence during those few days in demonetization. Besides, it tries to identify nascent traces of bubble and embedded herding during that time. Auto Regressive Fractionally Integrated Moving Average (ARFIMA) is used for three consecutive days around the event. Tick-by-tick data from CNX Nifty High Frequency Trading (CNX Nifty HFT) is used for three consecutive days around demonetization (approximately, 5000 data points from morning trading sessions on each of the three days). The results show a clear and profound presence of herd behavior in all three data sets. The herd intensity remained similar, indicating a unique mixture of both ‘Noah Effect’ and ‘Joseph Effect’, proving a clear regime switch. However, the results on the event day show stable and prominent herding. Mandelbrot’s specified effects were tested on an uncertain and sudden financial event in India and proved to function perfectly.