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Bitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model.

In this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm.

The proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively.

In this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN.

This paper aims to investigate factors associated with higher or lower television audience retention from one programme aired sequentially after another, referred to as lead-in audience retention. Lead-in is a primary determinant of television programme audience size.

The study models a series of factors linked to lead-in audience retention, such as rating of the second programme, genre match and competitor options. The hypothesised relationships are tested across over 1,000 pairs of programmes aired in the UK and Australia, using multivariate linear regression models.

The study finds the factors consistently related to significantly higher lead-in audience retention are the rating of the second programme in the pair and news genre match in programming. Factors consistently linked to lower audience retention include the rating of the initial programme and the number of competitor options starting at the same time as the second programme.

The findings help television networks understand drivers of lead-in audience retention. Knowledge that can be used to inform the design of tailored marketing plans for programmes based on schedule, timing and adjacent programming. Further, the findings help advertisers and media buyers with scheduling television advertising to achieve reach or frequency objectives.

No previous studies have comprehensively combined all four factors driving lead-in audience retention into a single model. The testing across multiple markets adds to the robustness of the findings. In particular, the discoveries about the impact of competitor network activities and genre build considerably on past research.

This study examines the lead-lag relationship between KOSPI200 and the volatility index based on the implied volatility from the KOSPI200 options. The sample period covers from January 2, 2003 to June 30, 2004. Both daily and minute-by-minute data were used for the lead-lag analysis. The study also determines whether the response of volatil ity index to KOSPI200 is symmetric or not. The most important findings may be summarized as follows.

First, there is no lead-lag relationship between the change in volatility index and the KOSPI200 returns on a daily basis. However, on a minute-by-minute basis, volatility index leads KOSPI200 for the group of largest increases in volatility index, and the opposite is true for the group of largest decreases and least changes in volatility index. The option market appears to react more quickly to volatility increases, while the stock market seems more sensitive to volatility decreases. Second, the volatility increase in response to the stock market decline is more severe than the volatility decrease in response to the stock market rise for daily data. This evidence of asymmetry suggests that volatility index plays a role of investors’fear gauge. Our results show no asymmetric response of volatility index to stock market movements for weekly data.

This study explores the Granger causal relationship between return and volume in the KOSPI200 spot and option markets for the period from December 13. 2002 to December 9. 2004. using minute-by-minute data. Specifically, we examine the lead-lag relationship among OPtion volume, option return, cash volume, and cash return to determine whether option volume and return impact cash return.

Our results show that option volume has no direct impact on cash return as cash return unilaterally leads option volume‘ While option volume impacts cash volume. cash return unilaterally leads cash volume. implying no indirect impact of option volume on cash return.

However, there is evidence that option return impacts cash return directly, given a bilateral causality between option return and casll return. Option return also impacts cash volume, but again cash volume has no impact on cash return. meaning no indirect impact of option return on cash return. Our findings were generally robust across days of the week and different maturities. Finally, we analyzed lead-lag relationship within the option market. and found a bilateral causality between option volume and option return. This implies that option volume may impact cash return indirectly via option return.

Although digital platforms have become important to organizations and society, little is known about how platforms evolve over time. This is particularly true for early-stage platforms provided by entrepreneurial firms competing in nascent markets. This study aims to investigate the relationship between a platform provider's mission and the evolution of its digital platform.

This study conducted an exploratory, multi-case study of startups in the emerging health/fitness wearables market over the period 2007 to 2016.

This study emerged two organizational mission constructs – consistency and specificity – and two evolutionary dynamics of digital platforms – unity and evolution rate. It also considered unity and evolution rate in terms of features created by the platform provider and features connected by external parties. This study found relationships between aspects of mission consistency and platform unity and identified relationships between aspects of mission specificity and platform evolution rates.

This study formalized findings into a set of theoretical propositions, thereby enriching the understanding of the relationship between organizational mission and digital platform evolution in nascent markets. This study provides new constructs and relationships that can be tested and refined in future research.

This study explores the arbitrage profitability of box spread strategies to test the KOSPI200 options market efficiency. using minute-by-minute data for the December 2003 - June 2004 period. The sample consists of 39.445 and 38.318 observations for small discrepancy and large discrepancy in exercise prices. respectively.

In the case of credit box spreads, there were 681 (2%) and 2.293 (6%) arbitrage observations for small and large discrepancies, while debit box spreads showed 831 (2%) and 3.098 (8%) observations for small and large discrepancies. In general, mean profit and median profit were different, and the arbitrage profit varied over time. The time to option expiration did not impact the arbitrage profit.

Also, the arbitrage profit of box spreads was significantly higher on Fridays for large discrepancy, and it varied across weekdays except the case of small-discrepancy debit box spread. Both arbitrage opportunities and profits substantially decreased as execution time increased. Our overall results suggest that the KOSPI200 options market has been efficient.

Construction activities, particularly related to transportation, have a considerable impact on the environment and air quality. This paper aims to present a geographic information systems (GIS) and computer‐aided design (CAD)‐based approach for visualizing, communicating and analysing greenhouse gas (GHG) emissions resulting from construction activities.

A methodology using GIS is developed to graphically represent spatial aspects of construction. The approach adopted involves use of a 3D model developed in CAD environment, which was synchronized with a construction schedule stored in Excel spreadsheets. GIS environment is used to link spatial and scheduling information relevant to GHG emissions from construction activities. A baseline was created to enable effective monitoring of construction emissions.

The presented GIS model has the potential to enhance visualisation of distribution and dynamic variations of GHG emissions and could help stakeholders better analyse and understand how construction activities impact the environment.

This paper presents a novel method of graphically presenting GHG and other hazardous air emissions from construction activities using a GIS‐based approach. The paper presents the result of comparing the 3D surface representation of simulated estimated and actual construction emissions to show the impact of construction activities on the environment to support the engineering analysis and decision‐making process.

This paper investigates the impact on the price volatility of Australian 90 day Bank Accepted Bills (BABs) when the futures contracts written on BABs expire. Using the absolute value of log price changes as a measure of volatility, and appropriate non‐parametric tests, the analysis does not detect any expiration day price effect, nor reversal in volatility between expiration Fridays and the following Mondays for the period 1 November 1979 to 1 November 1993. These findings are consistent with the results of those overseas studies which do not find evidence for expiration day effects.

We examine the impact of the unique Japanese stock market microstructure on the pricing of stock index futures contracts. We use intraday transactions data for the Nikkei 225 Futures contracts in Osaka and the corresponding Nikkei 225 Index in Tokyo. Incorporating more realistic transaction-cost estimates and various institutional impediments in Japan, we find that the time-varying liquidity of some component shares of the index in Tokyo represents the most critical impediment to intraday arbitrage and often causes futures prices in Osaka to deviate significantly and persistently from their no-arbitrage boundary, especially for longer-lived contracts.

The purpose of this paper is to examine the price discovery role of the Korea Composite Stock Price Index 200 (KOSPI 200) stock index options market in contrast to other developed options markets.

The price discovery roles of the stock and options markets using the error‐correction model derived from the co‐integration relationship are examined. Various analyses are conducted. First, Heston's stochastic volatility option pricing model is employed to confirm its usefulness, and compare the results with the Black and Scholes (BS) model. Second, whether the out of the money (OTM) options purchased by individual investors have a stronger price discovery role than options with other moneyness is examined. Finally, whether options have a stronger price discovery role in bullish or bearish markets than in normal markets is tested.

It is found that stock index prices lead implied index prices estimated from option prices using both BS and Heston models. In regards to the OTM options, the lead‐effect of real stock index to implied index prices holds. Also it is shown that there is a weak rise in the lead effect of the options to the stock index, but the lead effect of stock index market rules over that of the options market.

The paper examines the price discovery role of the KOSPI 200 stock index options market in contrast to other developed options markets and the results indicate that the consensus on the Korean financial markets may be incorrect.