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On 29 January 2001, Euronext LIFFE introduced single security futures contracts on a range of global companies. The purpose of this paper is to examine the impact that the introduction of these futures contracts had on the behaviour of opening and closing UK equity returns.

The paper models the price discovery process using the Amihud and Mendelson partial adjustment model which can be estimated using a Kalman filter.

Empirical results show that during the pre‐futures period both opening and closing returns under‐react to new information. After the introduction of futures contracts opening returns over‐react. A rise in the partial adjustment coefficient also takes place for closing returns but this is not large enough to cause over‐reaction.

This is the first study to examine the impact of a single security futures contract on the speed of spot market price discovery.

This paper aims to provide evidence that market efficiency varies greatly across individual stock, and across market exchanges.

Three approaches, partial adjustment model, Dimson beta model and variance ratio test, are used on a large sample of US stocks.

This paper finds prices are closer to random walk benchmarks (i.e. more efficient) for stocks with better liquidity provision, frequent trading, greater return volatility, higher prices, larger market capitalizations and smaller trade sizes. These findings suggest that liquidity stimulates arbitrage activity, which, in turn, enhances market efficiency. Market efficiency also varies with information environment. The results show that stocks with greater information-based trading exhibit higher level of efficiency. Finally, market structure influences market efficiency. New York Stock Exchange stocks achieve higher level of efficiency than NASDAQ stocks do. The empirical results are robust and not driven by differences in stock attributes between the two markets.

Overall, these results indicate that liquidity provision, stock attributes and market structure exert a significant impact on the realization of market efficiency.

In addition, this paper is also relevant to both stock exchanges facing increased competition and to market regulators.

Prior studies offer little evidence on the speed at which new information is impounded into the price. There is also limited evidence regarding how liquidity provision and market structure affect market efficiency. Using a transformation of the speed of price adjustment and other measurements as proxies for individual stock efficiency, this study may shed further lights on our understanding of market efficiency.

This research intends to investigate the impact of economic policy uncertainty (EPU) on the firm's leverage and its adjustment speed.

This study applies dynamic panel data modeling by using a partial adjustment model. The study is based on secondary data of the non-financial firms that are listed on the Pakistan stock exchange. For the analysis purpose, the study applies the generalized method of moments (GMM) estimation technique and uses a newly developed text-based measure of economic policy uncertainty.

The results show the negative impact of EPU on leverage decisions but a positive impact of EPU on leverage speed of adjustment for both, short-run and long-run economic policy shocks. Additional analysis reveals that the negative influence of long-run policy shocks on leverage decisions is moderated through profitability, and the negative influence of short-run policy shocks on leverage is moderated through firm size, tangibility and available growth prospects. However, the significant positive impact of EPU on the leverage speed of adjustment in both short and long-term policy shocks indicates that the speed of adjustment for these firms is not affected by policy shocks.

This research contributes to the existing literature on capital structure dynamics,by investigating the impact of EPU on firm financing decisions and estimating the adjustment speed of capital structure in a developing market context. The study also extends the existing literature by applying the concept of long-run and short-run economic policy uncertainty in the capital structure dynamic framework. Additionally, the new news-based measure of EPU is used. Moreover, it also looks into the COVID-19 effect on the relationship.

This paper aims to study the leverage adjustment behavior of firms distinguished based on financial flexibility. Financial flexibility is one of the key strengths of the companies to borrow funds for long-term capital investment. The lack of extensive studies in this domain motivates the authors to delve into the significance of financial flexibility in making corporate capital structure decisions.

The data comprise a combination of firm-specific and macroeconomic variables for firms in eight manufacturing sectors from 2009 to 2020. The authors employ an advance estimator, dynamic panel fraction, on the partial adjustment model to investigate the diverse impact on capital structure's speed of adjustment (SoA) between the financially flexible and financially inflexible firms. Furthermore, the authors utilize the generalized method of moments and panel-corrected standard errors to establish the robustness.

The empirical analysis reveals that the SoA of financially flexible firms lies between 19.75% and 35.38% and the SoA of financially inflexible firms lies between 11.66% and 25.81%. Due to their conserved debt capabilities, financially flexible firms can rely on leverage to stay near the target whenever they move away from it. Furthermore, financially inflexible firms exhibit a low adjustment speed due to their incompetence to borrow funds to benefit from new growth opportunities. The existence of a target ratio among the studied firms is identified from the positive coefficient of lagged dependent variable, and the relevance of trade-off theory is proved by the quick adjustment speeds in most sectors.

The sectoral distinction in the backdrop of the financial flexibility component adds to the research novelty and managerial implications.

This study aims to investigate the dynamic aspects in the capital structure decisions of firms in Indonesia, offering an extension to the existing literature on Indonesia via a dynamic model, including the existence of target capital structure, the influencing factors, the speed of adjustments and the supporting theories to explain the findings.

This study uses a dynamic partial adjustment model estimated based on a generalized method of moments.

Indonesian firms do practice target capital structure and are influenced by firm-specific factors like profitability, business risk, firm size, liquidity and share price performance due to time-varying factors. A rapid adjustment toward target leverage is detected, thus supporting the existence of the dynamic trade-off theory (TOT). The pecking order theory (POT) also has significant influence, particularly after the new reformation of financing policy, where retained earnings are also preferred as a source of financing apart from merely external financing through bank loans. There are also traces of market timing influences where firms also seem to time their equity issuance.

Despite relatively utilizing recent data and bigger sample firms compared to the previous limited studies on Indonesia, the results of this study, however, need to be cautiously interpreted. First, the sample chosen focused on listed firms, hence may not be generalized to all Indonesian firms, listed and unlisted. Second, the study does not separate firms by sectors and their leverage positions, that is under-levered and over-levered, so as to note that financial decisions may also be affected by the sector in which the firms operate and their leverage positions. These are to be considered in future research.

There is strong evidence that the corporate financing behavior of Indonesian firms is governed by the POT and TOT. Both are dealing with the function of debt. The financial sector reformation does have a positive impact on the banking sector, but not the local corporate bond market. Therefore, regulators and policymakers should bear in mind that banking as well as private bond market in Indonesia must be tailored in such a way that both could act as intermediaries of debt financing, as bond market represents an important component of a diversified financial sector.

This study fills the gap by providing an extension to the existing literature and a deeper insight of the capital structure of Indonesian firms using a more robust dynamic model.

The purpose of this study is to investigate the dynamics of capital structure for businesses in China and India. Whether and how they adjust their capital structures to witness the trade-off behaviour in the light of different macro-level factors.

Firms listed on the National Stock Exchange and Shanghai Stock Exchange over the period of 2009-2018 are used for the study. System generalized method of moments proposed by Blundell and Bond (1998) is deployed due to the use of dynamic short panel data.

Indian firms revert to their target leverage ratios at a higher rate as compared to Chinese firms (30 and 20 per cent, respectively). Further, the inflation rate, bond market and stock market development are significant factors impacting leverage in the case of India, whereas bond market development significantly impacts leverage in the case of China. These results are robust across various definitions of leverage and other firm and institutional control variables.

This study has implications for various stakeholders. The study highlights that development in financial markets and economy impact the financing decisions and should be a cause for concern for the financial managers and policymakers. Thus, managers can use the findings of the study if they desire to maintain their target capital structures for better firm valuation and the policymakers can support them in achieving the same. Even, the investors can make informed investment decisions considering macro-level factors impacting firms’ financing choices.

It is believed to be the first piece of research effort to consider the novel paradigm of the macro-level factors impacting the target leverage to estimate the adjustment speed. Secondly, it is a pioneering study, which attempts to compare the trade-off behaviour of the top two emerging economies of the world.

This paper aims to test the hypothesis that the effect of production slowdown on labour demand can be muted by labour hoarding.

This study adopts a production function approach, using data from Malta, a small state in the EU.

The results confirm the hypothesis and indicate that firms are normally prepared to employ and dismiss more workers in the long run than in the short run.

This finding has important implications for developed countries, including that labour hoarding can be of certain relevance in times of economic slowdown as shocks are absorbed by internal flexibility.

The results of this study add on to the existing literature in two ways. First, this study compares two industries –manufacturing and financial services– for which the former sector received support to hoard labour after the financial turmoil of 2008. Consequently, the dominance of labour hoarding in manufacturing relative to financial services is uncovered and the effect of hoarding practices on labour demand is estimated. Second, Malta is an interesting case because it is one of the smallest economies in the world and faces a high degree of vulnerability because of constraints associated with small size and insularity. As a result, firms adopt policy-induced measures to minimise adjustment costs.

This study aims to understand how quickly Japanese banks readjust their capital ratios (leverage, regulatory capital, tier-I capital and common equity) following an economic shock.

This study uses a two-step system GMM framework to test the study's hypotheses using the annual data of Japanese commercial and cooperative banks ranging from 2005 to 2020.

The findings show that banks adjust their leverage ratio faster than regulatory capital, tier-I capital and common equity ratios. In addition to that, the results reveal that the speed of capital adjustment is higher for commercial banks than for cooperative banks, suggesting higher economic costs and implications for commercial banks. Furthermore, it is worth noting that well-capitalised (under-capitalised) banks tend to prioritise the adjustments to common equity (leverage) before considering the adjustments to leverage (common equity). According to the results, high-liquid (low-liquid) banks alter their regulatory capital and tier-I capital ratios (leverage) more quickly (more slowly) than low-liquid (high-liquid) banks.

The findings suggest that when formulating and implementing new banking regulations, particularly in assessing and adjusting specific capital requirements under Pillar II of Basel III, management (including bankers, regulators and policymakers) should consider the heterogeneity observed in the rate of capital adjustment across various bank characteristics. Additionally, bank managers should also consider the speed of adjustment when determining optimal half-life and target capital structures.

To the author's knowledge, this study represents a pioneering investigation into the rate of adjustment of capital ratios (leverage, regulatory, tier-I and common equity) within Japan's banking sector. The study employs a comprehensive dataset encompassing both commercial and cooperative banks to facilitate this analysis. A notable contribution to the existing body of literature, this study offers a detailed analysis and emphasises the varying degrees of adjustment in capital ratios. The study also highlights the heterogeneous nature of the adjustment rate in these ratios by categorising the data into well-capitalised, under-capitalised, highly liquid and low-liquid banks.

Financial sector reform measures, which were initiated in 1991, have provided some degree of maturity and integration of different segments of India's financial markets. The purpose of this paper is to articulate the impact of financial sector reform measures on integration of various segments of financial markets in India.

The paper surveys various methodologies for measurement of financial integration and uses the recently developed technique of co‐integration in a VAR framework to assess the extent of integration of various segments of India's financial markets.

The paper concludes that the financial market integration is inconclusive in India. Only a few segments of money market, Gilt market and foreign exchange market are integrated. Interest rate parity does not hold in India's case, which indicates poor evidence in support of international integration of domestic financial markets. Similarly, the analysis of the relationship between domestic saving and domestic investment does not support international integration. The study of co‐integration of Nasdaq and Bombay sensitive index (BSE), also revealed absence of international integration.

Owing to non‐availability of time series data, the paper could not consider the mutual fund market, pension market and various derivatives markets in the overall process of assessment of financial integration. However, the impact on the findings is minimal, as these markets are not so far developed in India.

The findings have significant practical implications particularly in the formulation of policies on management and interventions in the money market, foreign exchange and equity markets and in the overall formulation of monetary policy for the economy.

This paper presents a quite comprehensive research study on financial integration in India and is original, particularly in the area of application of the co‐integration technique for assessment of financial integration.

The adoption of a model‐building approach to marketing is today inevitable, due to improvements in hardware and software and the increased professionalisation of marketing and its techniques. Aggregate response models are focused upon, particularly the issues of which responses are realistic and should be modelled, how the response can be expressed and how a choice can be made between options available. The traditional model‐building process is described, and the inclusion of correct variables found to be critical, the primary means of doing this being statistical analysis. Simple expressions perform as effectively as more complex ones, and should be used if able to give operationally meaningful results. Cross‐correlation analysis and biased estimation techniques provide good guides to usable variables and their effects.