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The purpose of this paper is to investigate the response of transaction volume in Hong Kong’s housing market to public land auctions.

An event study approach with the use of regression analyses was adopted for the empirical study.

Fewer pre-event transactions in the secondary housing spot market come with greater dispersion in the pre-event forecasts of land auction outcomes. Unexpected auction outcomes were also found to minify the post-event transaction volume in the secondary housing spot market, with negative unexpected outcomes exerting a stronger downward force.

These findings are contrary to the empirical evidence commonly found in most financial literature on stock transaction volume around corporate earnings announcements with an assumption of negligible transaction costs. Imperfect market structure, differences in sellers’ and buyers’ characteristics and short-sale restriction may explain the disparity.

Price in the secondary housing market is more sensitive to negative unexpected land auction outcomes. The analysis results of the current study attest that the impact exerted by the negative unexpected auction outcomes on transaction volume in the housing spot market is stronger than that of positive unexpected auction outcomes.

Unlike price and return, transaction volume has not received substantial academic attention in property research. In particular, within the existing small body of transaction volume research, the impact of information events on trading activities has been largely ignored.

This paper investigates if investors consider legal insider trading data while making investment decisions. If any investment decision is based on insider transactions, then it will result in abnormal stock characteristics. The purpose of this paper is to investigate if insider trading affects stock characteristics like price, return and volume. The paper further investigates the effect on stock characteristics after the trade of different types of insiders and the relationship between abnormal return and abnormal volume.

The study uses the event study method to measure the abnormal price, return and volume. Two-stage least square regression is used to investigate the relationship between abnormal return and abnormal volume.

The insider trades affect price, return and volume. The results are identical for both buy and sell transactions. The trades of different types of insiders have diverse effects on stock characteristics. The trades of substantial shareholders give rise to the highest abnormal price and return, whereas the promoters' trades result in the highest abnormal volume. No relationship is detected between abnormal return and volume.

A novel method to calculate the abnormal price is proposed. The effect of trading of all types of insiders on stock characteristics is analyzed. The relationship between abnormal return and abnormal volume, after an insider trade, is investigated.

The purpose of this paper is to examine how installed base information could help servitizing original equipment manufacturers (OEMs) forecast and support their industrial service sales, and thus increase OEMs’ understanding regarding the dynamics of their customers lifetime values (CLVs).

This work constitutes a constructive research aiming to arrive at a practically relevant, yet scientific model. It involves a case study that employs statistical methods to analyze real-life quantitative data about sales and the global installed base.

The study introduces a forecasting model for industrial service sales, which considers the characteristics of the installed base and predicts the number of active customers and their yearly volume. The forecasting model performs well compared to other approaches (Croston’s method) suitable for similar data. However, reliable results require comprehensive, up-to-date information about the installed base.

The study contributes to the servitization literature by introducing a new method for utilizing installed base information and, thus, a novel approach for improving business profitability.

OEMs can use the forecasting model to predict the demand for – and measure the performance of – their industrial services. To-the-point predictions can help OEMs organize field services and service production effectively and identify potential customers, thus managing their CLV accordingly. At the same time, the findings imply new requirements for managing the installed base information among the OEMs, to understand and realize the industrial service business potential. However, the results have their limitations concerning the design and use of the statistical model in comparison with alternative approaches.

The study presents a unique method for employing installed base information to manage the CLV and supplement the servitization literature.

The purpose of this paper is to provide a dating system for the Italian residential real estate market from 1927 to 2019 and investigate its interaction with credit and business cycles.

To detect the local turning point of the Italian residential real estate market, the authors apply the honeycomb cycle developed by Janssen et al. (1994) based on the joint analysis of house prices and the number of transactions. To this end, the authors use a unique historical reconstruction of house price levels by Baffigi and Piselli (2019) in addition to data on transactions.

This study confirms the validity of the honeycomb model for the last four decades of the Italian housing market. In addition, the results show that the severe downsizing of the housing market is largely associated with business and credit contraction, certainly contributing to exacerbating the severity of the recession. Finally, preliminary evidence suggests that whenever a price bubble occurs, it is coincident with the start of phase 2 of the honeycomb cycle.

To the best of the authors’ knowledge, this is the first time that the honeycomb approach has been tested over such a long historical period and compared to the cyclic features of financial and real aggregates. In addition, even if the honeycomb cycle is not a model for detecting booms and busts in the housing market, the preliminary evidence might suggest a role for volume/transactions in detecting housing market bubbles.

Taiwan OTC market is an electronic, order driven, call market. The purpose of this paper is to gain understanding of whether trade size or number of transaction provides more information on explaining price volatility and market liquidity in this market. The paper also aims to investigate how market condition can affect the relationship between information type and trading activities.

The paper uses data from the Taiwan OTC market to run the empirical tests. It divides firms into five size groups based on their market capitalization. Regression equations are run to test: whether number of transactions has a more significant impact on price volatility on the Taiwan OTC market; the impact of market information on number of transactions; the relative impact of firm specific and market information on number of transactions; and the impact of number of transaction of bid‐ask spread.

Findings show that the larger the number of transactions, the higher the price volatility. Smaller firms on the Taiwan OTC market are traded based on firm‐specific information. This relation is further affected by market trends. Especially for the larger firms, when the market is up and the amount of market information increases, number of transactions increases. When the market is down and the amount of market information increases, number of transactions decreases. Finally, it is found spread size is more likely to be influenced by number of transactions, instead of trade size. Overall, based on these empirical results, the information content of number of transactions seems to be higher than that of trade size in the Taiwan OTC market.

Investors now understand that number of transaction actually carry more information than trade size does.

The relation between market information and number of transaction, also that between market information and trade size is influenced by market condition. The paper fills a gap in the literature to show that market condition has an impact on the relation between information type and trader's behavior. A number of transactions are identified that provide more information than trade size does. It is also shown that market conditions can further affect the impact of information on trading activities.

The purpose of this study is to propose a decentralized multi-party cross-trading scheme based on a certificate transaction mechanism for the transaction of excess consumption certificates (ECCs) of renewable energy. The aim is to address the problems associated with the existing centralized transaction mode and to promote the development of the green electricity industry.

The proposed scheme involves calculating the quotation difference for the same type of certificate transaction based on the quotations of all users of both buyers and sellers. The transaction volume is then determined based on the order of quotation difference from large to small, and the total interests of cooperation are calculated. The nucleolus method is adopted to allocate the total interests to each member of the alliance and calculate the final transaction price. The blockchain technology is used for the transaction to achieve accurate traceability and efficient supervision, and a corresponding smart contract is designed and simulated in the Ethereum consortium chain.

The results of the simulation show the rationality and effectiveness of the proposed scheme. The decentralized multi-party cross-trading scheme can overcome the problems associated with the existing centralized transaction mode, such as low transaction efficiency, difficulty in obtaining the optimal transaction strategy and efficient supervision. The proposed scheme can promote the development of the green electricity industry by stimulating users' demand potential for green electricity.

The proposed scheme is original in its use of a certificate transaction mechanism to facilitate the trading of ECCs of renewable energy. The scheme adopts a decentralized multi-party cross-trading approach that overcomes the problems associated with the existing centralized transaction mode. The use of the nucleolus method for the allocation of total interests to each member of the alliance is also original. Finally, the use of blockchain technology for accurate traceability and efficient supervision of the transaction is an original contribution to the field.

This study aims to empirically examine the impact of the price structure of two-sided markets on transaction volume and market share (MS) in the context of the Korean credit card industry. The Korean credit card market differs from those in the United States (U.S.) or Europe in terms of transaction structure (i.e. a three-party system in Korea vs a four-party system in the U.S. or Europe) and government policy. In addition to the merchant discount rate and the cardholder annual membership fee rate, the authors included and analyzed exogenous variables to eliminate any endogeneity. Based on the analysis results, the authors found that credit card usage performance (i.e. transaction volume) increases with an increase in the relative price ratio (merchant discount rate ÷ cardholder membership fee rate) paid by merchants and cardholders, provided that the total price (merchant discount rate + cardholder membership fee rate) paid by merchants and cardholders remains constant. Therefore, this study is the first to confirm that the Korean credit card market operated as the theoretical mechanism of a two-sided market during the analysis period. This effect can only be observed in specific cases such as the launch of the so-called “Chief Executive Officer(CEO)-designed card.” When a new CEO takes office in a credit card company and launches a “CEO-designed card,” there is a significant increase in not only card usage performance but MS as well owing to the price structure changes caused by expanding the benefits that customers derive from card use.

The objective of this paper is to identify and measure the perceived importance of barriers in the SME community to the adoption of internet business procurement and supply chain solutions.

This was a telephone survey of a sample of 173 Canadian small and medium‐sized enterprises (SMEs), stratified by size of company (small and medium) and according to whether they were distributors, retailers or manufacturers. The data were analyzed statistically through an analysis of variance approach.

Few differences were found between SME internet adopters and non‐adopters. There is a need for education for all SME management on the benefits and drawbacks to using e‐business solutions. Inter‐organizational information systems that are required to link supply chain partners can be a serious barrier to online solutions. There is a significant dependency among supply chain partners in decisions on adopting online links. Flexibility, agility and ability of SMEs can help them to use partial e‐business solutions for low volumes of business, but this approach can be very ineffective when transaction volumes are large.

The results from this paper can help to direct future efforts to encourage SMEs to adopt e‐business solutions.

This study differs from other SME e‐business adoption studies, in that it includes relationships with supply chain partners that play a large role in the adoption of innovative e‐business solutions, transaction volumes which, for many SMEs that have not adopted e‐business, may be too small to justify automated supply chain linkages, and transaction volumes with a company's biggest customer or supplier.

Liquidity is an important aspect of market efficiency. The purpose of this paper is threefold: first, this paper aims to discuss indicators that provide information about liquidity in agricultural land markets. Second, this paper aims to reflect on determinants of market liquidity and analyze the relationship with land prices. Third, this paper aims to conduct an empirical analysis for Germany that illustrates these concepts and allows hypothesis testing.

This study reviews liquidity dimensions and measurement in financial markets and derives indicators applicable to farmland markets. In an empirical analysis, this study exhibits the spatial and temporal variability of land market liquidity in Lower Saxony, a German federal state with the highest agricultural production value. This study uses a rich dataset that includes 72,547 sale transactions of arable land between 1990 and 2018. The research focuses on volume-based (number of transactions, volume and turnover) and time-based (trading frequency and durations) measures. A panel vector autoregression and Granger causality tests are applied to investigate the relation between land turnover and land prices.

The paper confirms the thinness of farmland markets but also reveals regional and temporal heterogeneity of land market liquidity. This study finds that the relation between market liquidity and prices is ambiguous. This study concludes that a high demand from expanding farms absorbs supply shocks regardless of the current price level in agricultural land markets.

Even though the relevance of agricultural land markets’ thinness is widely acknowledged in the literature, this paper is one of the first attempts to measure liquidity in agricultural land markets and to explain its relationship with land prices.

This paper aims to gather together the minimum units of users’ identity in the Bitcoin network (i.e. the individual Bitcoin addresses) and group them into representations of business entities, what we call “super clusters”. While these clusters can remain largely anonymous, the authors are able to ascribe many of them to particular business categories by analyzing some of their specific transaction patterns (TPs), as observed during the period from 2009 to 2015. The authors are then able to extract and create a map of the network of payment relationships among them, and analyze transaction behavior found in each business category. They conclude by identifying three marked regimes that have evolved as the Bitcoin economy has grown and matured: from an early prototype stage; to a second growth stage populated in large part with “sin” enterprise (i.e. gambling, black markets); to a third stage marked by a sharp progression away from “sin” and toward legitimate enterprises.

Data mining.

Four primary business categories are identified in the Bitcoin economy: miners, gambling services, black markets and exchanges. Common patterns of transaction behavior between the business categories and their users are a “one-day” holding period for bitcoin transactions is somewhat typical. That is, a one-day effect where traders, gamblers, black market participants and miners tend to cash out on a daily basis. There seems to be a strong preference to do business within the bitcoin economy in round lot amounts, whether it is more typical of traders exchanging for fiat money, gamblers placing bets or black market goods being bought and sold. Distinct patterns of transaction behavior among the business categories and their users are flows between traders and exchanges average just around 20 BTC, and traders buy or sell on average every 11 days. Meanwhile, gamblers wager just 0.5 BTC on average, but re-bet often within the same day. Three marked regimes have evolved, as the Bitcoin economy has grown and matured: from an early prototype stage, to a second growth stage populated in large part with “sin” enterprises (i.e. gambling, black markets), to a third stage marked by a sharp progression away from “sin” and toward legitimate enterprises. This evolution of the Bitcoin economy suggests a trend toward legitimate commerce.

The authors propose a new theoretical framework that allows investigating and exploring the network of payment relationships in the Bitcoin economy. This study starts by gathering together the minimum units of Bitcoin identities (the individual addresses), and it goes forward in grouping them into approximations of business entities, what is called “super clusters”, by using tested techniques from the literature. A super cluster can be thought of as an approximation of a business entity in that it describes a number of individual addresses that are owned or controlled collectively by the same beneficial owner for some special economic purposes. The majority of these important clusters are initially unknown and uncategorized. The novelty of this study is given by the pure user group and the TP analyses, by means of which the authors are able to ascribe the super clusters into specific business categories and outline a map of the network of payment relationships among them.