scholarly journals Intraday patterns of price clustering in Bitcoin

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
Donglian Ma ◽  
Hisashi Tanizaki
Keyword(s):  
The Other ◽  
Investor Behavior ◽  
Statistical Measures ◽  
Tick Data ◽  
Price Clustering ◽  
Japanese Yen ◽  
Intraday Patterns

AbstractIn 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.

scholarly journals What Can Machine Learning Tell Us About Intraday Price Patterns in a Frontier Stock Market?

2020 ◽  
Vol 11 (5) ◽  
pp. 205
Author(s):  
Dan Gabriel Anghel
Keyword(s):  
Stock Market ◽  
Spillover Effects ◽  
International Markets ◽  
Investor Behavior ◽  
State Variables ◽  
Different Types ◽  
Market State ◽  
Price Patterns ◽  
The One ◽  
Intraday Patterns

Quite a lot. On the one hand, it enables us to classify intraday patterns into 6 unique classes and to show how each class is related to several important market state variables. On the other hand, it enables us to identify the relevant set of variables and define a better model of the drivers of intraday patterns in a frontier stock market. Overall, our results show that intraday patterns in returns in the frontier stock market of Romania are mostly the result of risk, information flows, and spillover effects from more developed international markets. However, we find that low market efficiency and investor behavior also have a significant contribution. Among others, we identify signs of overreaction to information, irrational exuberance and “making the close” practices by different types of investors.

scholarly journals A Three-State Markov-Modulated Switching Model for Exchange Rates

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Idowu Oluwasayo Ayodeji
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Markov Switching ◽  
The Other ◽  
Markov Switching Model ◽  
Switching Model ◽  
Empirical Results ◽  
Proposed Model ◽  
Markov Modulated ◽  
Japanese Yen

Several authors have examined the long swings hypothesis in exchange rates using a two-state Markov switching model. This study developed a model to investigate long swings hypothesis in currencies which may exhibit ak-state(k≥2)pattern. The proposed model was then applied to euros, British pounds, Japanese yen, and Nigerian naira. Specification measures such as AIC, BIC, and HIC favoured a three-state pattern in Nigerian naira but a two-state one in the other three currencies. For the period January 2004 to May 2016, empirical results suggested the presence of asymmetric swings in naira and yen and long swings in euros and pounds. In addition, taking0.5as the benchmark for smoothing probabilities, choice models provided a clear reading of the cycle in a manner that is consistent with the realities of the movements in corresponding exchange rate series.

Comparing English and Russian humour perceptions through signature analysis

2020 ◽  
Vol 8 (2) ◽  
pp. 166
Author(s):  
Faisal L Kadri ◽  
Ekaterina N. Zakharenko
Keyword(s):  
Nuclear Physics ◽  
Age Groups ◽  
Statistical Technique ◽  
The Other ◽  
Particle Identification ◽  
Signature Analysis ◽  
Empirical Measures ◽  
Statistical Measures ◽  
Age Trends ◽  
Direct Measures

Signature analysis is a statistical technique introduced in the 1940s in order to identify groups of statistical measures to identify aircraft from radar reflections. Other applications include particle identification in nuclear physics and dark matter location in astrophysics. Humour appreciation, or funniness scores, are empirical measures of perceived humour. Two questionnaires, one in English, the other its translation into Russian, were made available online. Each had 96 humorous sentences or jokes. The sentences were classified empirically according to four age trends. Signatures of the four classes of sentences are calculated from participant scores in six age groups. The original scores will be available to researchers for verification and further investigation from either author. The use of signature analysis in this work involves the comparison of a sentence profile with the signature of its class or category; if the profile meets a strict criterion of errors then it can be described as a best predictor of its class. One notable finding from signature analysis is the existence of offsets: displacement of a sentence profile from its type signature. We suggest that offset values are direct measures of humorousness without reference to context. In this analysis, the profiles of the Russian and English sentences are compared to each other and their graphical differences are interpreted including offsets.

scholarly journals Towards a Comprehensive Assessment of Statistical Versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction

Water ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 2451
Author(s):  
Mohammad Zounemat-Kermani ◽  
Behrooz Keshtegar ◽  
Ozgur Kisi ◽  
Miklas Scholz
Keyword(s):  
Neural Network ◽  
Soft Computing ◽  
Input Data ◽  
Computational Models ◽  
The Other ◽  
Support Vector ◽  
P Value ◽  
Pan Evaporation ◽  
Statistical Measures ◽  
Computing Models

This paper evaluates six soft computational models along with three statistical data-driven models for the prediction of pan evaporation (EP). Accordingly, improved kriging—as a novel statistical model—is proposed for accurate predictions of EP for two meteorological stations in Turkey. In the standard kriging model, the input data nonlinearity effects are increased by using a nonlinear map and transferring input data from a polynomial to an exponential basic function. The accuracy, precision, and over/under prediction tendencies of the response surface method, kriging, improved kriging, multilayer perceptron neural network using the Levenberg–Marquardt (MLP-LM) as well as a conjugate gradient (MLP-CG), radial basis function neural network (RBFNN), multivariate adaptive regression spline (MARS), M5Tree and support vector regression (SVR) were compared. Overall, all the applied models were highly capable of predicting monthly EP in both stations with a mean absolute error (MAE) < 0.77 mm and a Willmott index (d) > 0.95. Considering periodicity as an input parameter, the MLP-LM provided better results than the other methods among the soft computing models (MAE = 0.492 mm and d = 0.981). However, the improved kriging method surpassed all the other models based on the statistical measures (MAE = 0.471 mm and d = 0.983). Finally, the outcomes of the Mann–Whitney test indicated that the applied soft computational models do not have significant superiority over the statistical ones (p-value > 0.65 at α = 0.01 and α = 0.05).

scholarly journals Coherent uncertainty analysis of aerosol measurements from multiple satellite sensors

2013 ◽  
Vol 13 (2) ◽  
pp. 4637-4685 ◽  
Author(s):  
M. Petrenko ◽  
C. Ichoku
Keyword(s):  
Statistical Approach ◽  
The Other ◽  
South East Asia ◽  
Mean Square ◽  
Sampling System ◽  
Statistical Measures ◽  
Bright Surface ◽  
Satellite Sensors ◽  
Using Data ◽  
Aerosol Properties

Abstract. Aerosol retrievals from multiple spaceborne sensors, including MODIS (on Terra and Aqua), MISR, OMI, POLDER, CALIOP, and SeaWiFS – altogether, a total of 11 different aerosol products – were comparatively analyzed using data collocated with ground-based aerosol observations from the Aerosol Robotic Network (AERONET) stations within the Multi-sensor Aerosol Products Sampling System (MAPSS, http://giovanni.gsfc.nasa.gov/mapss/ and http://giovanni.gsfc.nasa.gov/aerostat/). The analysis was performed by comparing quality-screened satellite aerosol optical depth or thickness (AOD or AOT) retrievals during 2006–2010 to available collocated AERONET measurements globally, regionally, and seasonally, and deriving a number of statistical measures of accuracy. We used a robust statistical approach to detect and remove possible outliers in the collocated data that can bias the results of the analysis. Overall, the proportion of outliers in each of the quality-screened AOD products was within 12%. Squared correlation coefficient (R2) values of the satellite AOD retrievals relative to AERONET exceeded 0.6, with R2 for most of the products exceeding 0.7 over land and 0.8 over ocean. Root mean square error (RMSE) values for most of the AOD products were within 0.15 over land and 0.09 over ocean. We have been able to generate global maps showing regions where the different products present advantages over the others, as well as the relative performance of each product over different landcover types. It was observed that while MODIS, MISR, and SeaWiFS provide accurate retrievals over most of the landcover types, multi-angle capabilities make MISR the only sensor to retrieve reliable AOD over barren and snow/ice surfaces. Likewise, active sensing enables CALIOP to retrieve aerosol properties over bright-surface shrublands more accurately than the other sensors, while POLDER, which is the only one of the sensors capable of measuring polarized aerosols, outperforms other sensors in certain smoke-dominated regions, including broadleaf evergreens in Brazil and South-East Asia.

scholarly journals AN EMPIRICAL STUDY OF PRICE CLUSTERING ON THE NAIROBI SECURITIES EXCHANGE

2017 ◽  
Vol 2 (2) ◽  
pp. 23
Author(s):  
Lugongo Maurice Wafula ◽  
Dr. Sifunjo E Kisaka
Keyword(s):  
Empirical Study ◽  
Regression Model ◽  
Stock Price ◽  
Multivariate Regression ◽  
The Other ◽  
Multivariate Regression Model ◽  
Stock Volatility ◽  
Market Capitalization ◽  
Secondary Sources ◽  
Price Clustering

Purpose: The purpose of this study was to empirically investigate price clustering phenomenon on the Nairobi Securities Exchange for the period 2009 to 2013.Materials and methods: The study used secondary sources of data obtained from the Nairobi Securities exchange. The study revealed that there has been a preference by investors for stock whose prices end with the digit 5 and this accounted for 67.88 percent of all the stocks examined and was followed by stocks whose prices ended with the digit 0 which accounted for 4.55 percent. In order to establish the determinants of this observed behavior a multivariate regression model used by Harris (1991) was adopted where price clustering was regressed against stock volatility, number of trades, market capitalization, and own stock price.Results: The regression results indicated that the number of trades as well as Market Capitalization was positive and significantly related to price clustering. The study also found the stock price to be negative and significantly related to price clustering. On the other hand, Stock volatility was established to be an insignificant predictor of price clustering. The multivariate regression model was found to be significant in explaining the observed relationship and that 15.4 percent of the variance in price clustering was explained by number of trades, stock volatility, own stock price and the market capitalization. The study finds that there is a tendency of prices to cluster around certain numbers as evidenced by the 67.88 percent of numbers clustering around the number 5 and that price clustering is positively related to number of tradesRecommendations: It is thus recommended that if firms are to increase the number of trades of their shares they should consider pricing their shares according to the preferences of investors who prefer shares or stocks whose prices ends with 5 or 0.

EFFECTS OF THE SHAPE OF PERIODIC FORCES ON STOCHASTIC RESONANCE

2008 ◽  
Vol 18 (07) ◽  
pp. 2073-2088 ◽  
Author(s):  
V. M. GANDHIMATHI ◽  
S. RAJASEKAR ◽  
J. KURTHS
Keyword(s):  
Time Series ◽  
Stochastic Resonance ◽  
Residence Time ◽  
Sine Wave ◽  
The Other ◽  
Resonance Phenomenon ◽  
Potential Wells ◽  
Statistical Measures ◽  
Time Series Plot ◽  
Similarities And Differences

We study the influence of the shapes of three different external periodic forces on the stochastic resonance phenomenon in multiple potential well systems with Gaussian noise. We consider as external periodic forces the sine wave, the modulus of sine wave and the rectified sine wave. The systems of our interest are two coupled overdamped anharmonic oscillators and the Duffing oscillator. For fixed values of the parameters, when the intensity D of the external noise is varied, the systems with these periodic forces separately are found to exhibit stochastic resonance. Certain similarities and differences are found in the characteristics of these statistical measures such as signal-to-noise ratio (SNR), response amplitude (Q), time series plot, mean residence time τMR in the potential wells and the distribution P of the normalized residence time for these different forces. Especially, the time series plot at the maximum SNR shows an almost periodic switching between the potential wells for the sine force which is not observed for the other two forces. In the noise-induced intermittent dynamics, τMR is the same in different wells for the sine force, whereas it is different in different wells for the other two forces for each value of the noise intensity D. Further, variation of τMR with D, the value of τMR at the resonance and the distribution P show different features for the different types of forces. We present a detailed comparative study and explanation for the similarities and differences observed in the stochastic resonance dynamics.

scholarly journals Arts and Humanities and the others: Why can’t we measure arts and humanities

2020 ◽  
Vol 33 ◽  
pp. 02003
Author(s):  
Zehra Taşkın ◽  
Güleda Doğan
Keyword(s):  
Web Of Science ◽  
Ease Of Use ◽  
The Other ◽  
Disciplinary Differences ◽  
Arts And Humanities ◽  
Statistical Measures ◽  
Subject Categories ◽  
Science Subject

The use of numbers (publications and citations) to evaluate research/er performances are widespread since ease of use. However, disciplinary differences must be considered to evaluate research/ers accurately without misjudgments in tenures and incentives. The most different filed from others in terms of publications and citation patterns is Arts & Humanities. The main aim of this study is to reveal the main differences between Arts & Humanities and the other fields by considering publications, citations, and collaboration. For this aim, the main statistics for 59,728,700 papers published between 1980-2018 are gathered from InCites in terms of the 251 Web of Science subject categories. The data confirmed that Arts & Humanities is considerably different from other fields. We showed the degree of these differences using statistical measures. The huge difference found out that underline the indispensability for evaluating Arts & Humanities separately from the others.

scholarly journals Forecasting COVID-19 in Pakistan

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242762
Author(s):  
Muhammad Ali ◽  
Dost Muhammad Khan ◽  
Muhammad Aamir ◽  
Umair Khalil ◽  
Zardad Khan
Keyword(s):  
Time Series ◽  
Prediction Interval ◽  
Arima Model ◽  
The Other ◽  
Time Series Models ◽  
Cumulative Number ◽  
Arima Models ◽  
Forecasting Accuracy ◽  
Statistical Measures ◽  
Better Than

Objectives Forecasting epidemics like COVID-19 is of crucial importance, it will not only help the governments but also, the medical practitioners to know the future trajectory of the spread, which might help them with the best possible treatments, precautionary measures and protections. In this study, the popular autoregressive integrated moving average (ARIMA) will be used to forecast the cumulative number of confirmed, recovered cases, and the number of deaths in Pakistan from COVID-19 spanning June 25, 2020 to July 04, 2020 (10 days ahead forecast). Methods To meet the desire objectives, data for this study have been taken from the Ministry of National Health Service of Pakistan’s website from February 27, 2020 to June 24, 2020. Two different ARIMA models will be used to obtain the next 10 days ahead point and 95% interval forecast of the cumulative confirmed cases, recovered cases, and deaths. Statistical software, RStudio, with “forecast”, “ggplot2”, “tseries”, and “seasonal” packages have been used for data analysis. Results The forecasted cumulative confirmed cases, recovered, and the number of deaths up to July 04, 2020 are 231239 with a 95% prediction interval of (219648, 242832), 111616 with a prediction interval of (101063, 122168), and 5043 with a 95% prediction interval of (4791, 5295) respectively. Statistical measures i.e. root mean square error (RMSE) and mean absolute error (MAE) are used for model accuracy. It is evident from the analysis results that the ARIMA and seasonal ARIMA model is better than the other time series models in terms of forecasting accuracy and hence recommended to be used for forecasting epidemics like COVID-19. Conclusion It is concluded from this study that the forecasting accuracy of ARIMA models in terms of RMSE, and MAE are better than the other time series models, and therefore could be considered a good forecasting tool in forecasting the spread, recoveries, and deaths from the current outbreak of COVID-19. Besides, this study can also help the decision-makers in developing short-term strategies with regards to the current number of disease occurrences until an appropriate medication is developed.

The role of house money effect and availability heuristic in investor behavior

2017 ◽  
Vol 55 (8) ◽  
pp. 1598-1612 ◽  
Author(s):  
Chieh-Shuo Chen ◽  
Jia-Chi Cheng ◽  
Fang-Chi Lin ◽  
Chihwei Peng
Keyword(s):  
Stock Market ◽  
Electronics Industry ◽  
The Other ◽  
Investor Behavior ◽  
January Effect ◽  
Availability Heuristic ◽  
Content Type ◽  
House Money Effect ◽  
Bonus Payments

Purpose The house money effect is proposed to describe that people appear to consider large or unexpected wealth gains to be distinct from the rest of their wealth, and are thus more willing to gamble with such gains than they ordinarily would be. On the other hand, the availability heuristic describes that people tend to have a cognitive and systematic bias due to their reliance on easily available or associational information. The purpose of this paper is to employ these behavioral perspectives in an empirical model regarding the January anomaly to explore investor behavior in Taiwanese stock market with bonus culture and well-known electronics industry. Design/methodology/approach This study uses the conventional and standard dummy variable regression model, as employed in prior studies, and further includes some control variables for firm, industry and macro-economic level factors. Moreover, 19 industrial indices for Taiwanese stock market over the period January 1990 to December 2014 are included in this study to examine the hypotheses, except for the 1997 Asian financial crisis and the global financial crisis period of 2007-2009 to avoid the potential effect. On the other hand, the authors also use the entire sample period of 1990-2014 for understanding whether the magnitude of January effect is different. Findings The empirical results indicate that Chinese bonus payments in January induce a strong January effect in the Taiwanese stock market, especially when most listed firms have positive earnings growth in the preceding year, suggesting a house money effect. Moreover, this study further provides some preliminary evidence that the higher January returns due to bonus culture are apparent only in the electronics industry when both Chinese New Year and bonus payments are in January, implying the role of availability heuristic based on the electronics stocks in investor behavior before the impending stock exchange holidays. Some robust tests show qualitative support. Research limitations/implications The major contribution of this study is to extend the existing research by incorporating cultural and industrial factors with behavioral finance, thus enriching the literature on the causes of seasonality for Asian stock markets. Practical implications This study also has behavioral implications of investments for investors in the Taiwanese stock market, especially for foreign institutional investors which pay close attention to this market. Originality/value This study first applies and examines the culture bonus hypothesis with regard to how employees who receive culture bonuses in January can change their attitudes toward risk and induce the January effect from the concept of mental accounting. Moreover, this study further proposes and examines the extended culture bonus hypothesis related to how the January effect due to culture bonus is different for the electronics and non-electronics industries when taking into account the stock market holidays from the concept of availability heuristic.

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