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The purpose of this paper is to analyze a particular segment of the US “shadow banking” market and its revival since the recent credit crisis, namely, lending to the private Middle Market, defined as financings of $5-100 million to non-public, unrated operating entities or pools of assets with not more than $50 million in earnings before interest, taxes, depreciation and amortization.

The analysis includes a review survey of a segment of capital markets and primary evidence from direct participation in two examples of actual private, non-bank lending between 2011 and 2012 executed by a Middle Market US investment bank.

While there have been considerable challenges, historically, in providing credit for small-and mid-sized businesses in the USA, private Middle Market capital is (post the recent credit crisis) finding opportunities, notwithstanding, constraints imposed by market and other forces, including systemic crises, cyclical forces and changes in regulatory regimes.

Any generalization is limited due to the absence of disaggregated survey data for the US capital markets and the limited examples examined.

The capital markets segment and non-bank financial institutions examined in this paper are developing as an alternative source of credit/lending from commercial banks for mid-sized companies.

The mid-sized firms financed by the shadow credit market are a significant source of job creation in the US economy making non-bank credit a lifeline to job growth in the financial crisis.

Direct participation is unique to the firms studied. Value is in developing a general framework to analyze different segments of the capital market.

This paper aims to analyze the impact of price-limit hits by hit type and when such hits start and stop using intraday trades and quotes at a one-second frequency for firms included in the BIST-50 index during the 13-months starting with March 2008. Like the recent COVID-19 period, this period includes the heightened stress in global financial markets in September 2008.

Using intra-day trades and quotes at a one-second frequency, the authors examine the market effects of price limits for firms included in the BIST-50 index during the global financial crisis. The authors compare the values of various metrics for 60 min centered on price-limit hit periods. The authors conduct robustness tests using auto regressive integrated moving average (ARIMA) models with trade-by-trade and with 3-min returns.

The findings are supportive of the following hypotheses: magnet price effects, greater informational asymmetric effects of market quality and each version of price discovery. Results are robust using samples differentiated by cross-listed status, same-day quotes instead of transaction prices and equidistant and trade-by-trade returns.

The authors use intraday data to reduce measurement error that is particularly pronounced when daily data are used to assess price limits that start and/or stop during a trading session. The authors contribute to the micro-structure literature by using ARIMA models with trade-by-trade and 3-min returns to alleviate some bias due to the autocorrelations in returns around price-limit hits in the presence of a magnet effect. The authors include some recent regulation changes in various countries to illustrate the importance of circuit breakers using price limits during COVID-19.

This paper sets out to apply chaos theory to the prediction of stock returns using Greek and Turkish stock index data. The aim of the analysis is to empirically show whether the markets have informational efficiency, in a comparative perspective.

The research employs Grassberger and Procaccia's methodology in the time series analysis in order to estimate the correlation and minimum embedding dimensions of the corresponding strange attractor. To achieve out of the sample multistep ahead prediction, the paper gives the average for overall neighbours' projections of k‐steps into the future.

The results display the fact that the chaos theory is suitable to examine the time series of stock index returns. The empirical findings show that the stock markets are efficient in Greece, though in Turkey the market is predictable. The main practical implication of the findings is that the technical analysis works in Turkish markets and it is possible to beat the market, while in Greece the fundamental analysis works for equity trading.

The research results have both methodological and practical originality. On the theoretical side, the research shows how the chaos theory can be applied in financial time series analysis. The model is employed with data from Greece, as an EU member; and Turkey, as a candidate to the EU. The fact that the model works in Turkey implies that chaos theory can be used in emerging economies as a prediction model. On the practical side, the paper contributed to the previous literature by providing empirical evidence on market efficiency using a stochastic model.

The purpose of this paper is to consider whether asymmetric information about correlations between assets can induce financial contagion. Contagion, unjustified by fundamentals, would arise if participants react in one market to uninformative trades in the other market that actually convey no relevant information. The authors also consider whether the market accurately disseminates insider information about fundamental value correlations when such information is indeed present.

The authors employ experimental asset markets to answer the research questions. The experimental markets allow participants to simultaneously trade two assets for multiple rounds. In each round, a shock occurs, which either have an idiosyncratic effect on the shocked asset, or a systematic effect on both assets. Half of the time, there exist insiders who know the true nature of the shock and how it affects the value of the other asset. The other half of the time, no agent knows whether there is a correlation between the assets. In such cases, there is the potential for the appearance of information mirages. Uninformed traders, in either condition, do not know whether or not there exist insiders, but can try to infer this from the market activity they observe.

The results of the experiment show that when inside information about the nature of the correlation between assets does exist, it is readily disseminated in the form of market prices. However, when there is no private information (PI), mirages are common, demonstrating that financial contagion can arise in the absence of any fundamental relationship between assets. An analysis of individual behavior suggests that some unprofitable decisions appear to be related to an aversion to complex distributions of lottery payoffs.

The study focusses on one of the triggers of unjustified financial contagion, namely, asymmetric information. The authors have studied financial contagion in a controlled experimental setting where the authors can carefully control information, and specify the fundamental interdependence between assets traded in different markets.

Without precedent, green bonds confront, for the first time since their emergence, a twofold crisis context, namely the Covid-19-Russian–Ukrainian crisis period. In this context, this paper aims to investigate the connectedness between the two pioneering bond market classes that are conventional and treasury, with the green bonds market.

In their forecasting target, authors use a Support Vector Regression model on daily S&P 500 Green, Conventional and Treasury Bond Indexes for a year from 2012 to 2022.

Authors argue that conventional bonds could better explain and predict green bonds than treasury bonds for the three studied sub-periods (pre-crisis period, Covid-19 crisis and Covid-19-Russian–Ukrainian crisis period). Furthermore, conventional and treasury bonds lose their forecasting power in crisis framework due to enhancements in market connectedness relationships. This effect makes spillovers in bond markets more sensitive to crisis and less predictable. Furthermore, this research paper indicates that even if the indicators of the COVID-19 crisis have stagnated and the markets have adapted to this rather harsh economic framework, the forecast errors persist higher than in the pre-crisis phase due to the Russian–Ukrainian crisis effect not yet addressed by the literature.

This study has several implications for the field of green bond forecasting. It not only illuminates the market participants to the best market forecasters, but it also contributes to the literature by proposing an unadvanced investigation of green bonds forecasting in Crisis periods that could help market participants and market policymakers to anticipate market evolutions and adapt their strategies to period specificities.

Introduction: The insurance industry is one of the lucrative sectors of the economy. However, it is volatile because of the large chunk of data generated by the transactions taking place daily. However, every bit of it is responsible for creating market trends for stock investors to predict the returns. The specialised data mining techniques act as a solution for decision-making, reducing uncertainty in decision-making.

Purpose: There are limited studies that have examined the efficiency and effectiveness of data mining techniques across the companies in the insurance industry to date. To enable the companies to take exact benefit of data mining techniques in insurance, the present study will focus on investigating the efficiency of artificial neural network (ANN) and support vector machine SVM across insurance companies of CNX 500.

Method: For predictive models, various technical indicators were considered independent variables, and change in return, i.e. increase and decrease, was deemed a dependent variable. The indicators were transformed from daily raw data of insurance company’s stock values spanning four years. We formed 90 data sets of varied periods for building the model – specifically six months, one year, two years, and four years for selected six insurance companies.

Findings: The study’s findings revealed that ANN performed best for the ICICIPRULI data model in terms of hit ratio. Whereas the performance of SVM was observed to be the best for the ICICIGI data model. In the case of pairwise comparison among the six selected Indian insurance companies from CNX 500, the extracted data evaluated and concluded that there were eight significantly different pairs based on hit ratio in the case of ANN models and nine significantly different pairs based on hit ratio for SVM models.

This study examines the impact of debt maturity structure on stock price crash risk (SPCR) in Asian economies and the moderating effect of firm age on this relationship.

The study utilized annual data from 432 nonfinancial firms publicly listed in six Asian countries: China, Hong Kong, Japan, Singapore, Pakistan and India. The observation period covers 14 years, from 2007 to 2020. The sample was categorized into three groups: the entire sample and one group each for developing and developed Asian economies. A generalized least squares panel regression method was employed to test the research hypotheses.

The results suggest that long-term debt has a significant negative influence on SPCR in Asian economies, indicating that firms with high long-term debt experience lower future SPCR. Moreover, firm age negatively moderates this relationship, implying that older firms may experience a more pronounced reduction in SPCR due to high long-term debt. Finally, firms in developed Asian economies with high long-term debt are more effective in mitigating the risk of a significant drop in their stock prices than firms in developing Asian economies.

This study contributes to the literature in several ways. To the best of the researcher’s knowledge, this is the first of such efforts to investigate the relationship between debt maturity structure and crash risk in Asia. Additionally, it reveals that long-term debt influences SPCR directly and indirectly in Asia through the moderating role of firm age. Lastly, it is likely one of the first studies by a research team in Asia to compare the nonfinancial markets of developed and developing Asian countries.

The purpose of this paper is to study the influence of different quantitative (traditionally used) and qualitative variables, such as the possible negative effect in determined periods of certain socio-political factors on share price formation.

We first analyse descriptively the evolution of the Ibex-35 in recent years and compare it with other international benchmark indices. Bellow, two techniques have been compared: a classic linear regression statistical model (GLM) and a method based on machine learning techniques called Extreme Gradient Boosting (XGBoost).

XGBoost yields a very accurate market value prediction model that clearly outperforms the other, with a coefficient of determination close to 90%, calculated on validation sets.

According to our analysis, individual accounts are equally or more important than consolidated information in predicting the behaviour of share prices. This would justify Spain maintaining the obligation to present individual interim financial statements, which does not happen in other European Union countries because IAS 34 only stipulates consolidated interim financial statements.

The descriptive analysis allows us to see how the Ibex-35 has moved away from international trends, especially in periods in which some relevant socio-political events occurred, such as the independence referendum in Catalonia, the double elections of 2019 or the early handling of the Covid-19 pandemic in 2020.

Compared to other variables, the XGBoost model assigns little importance to socio-political factors when it comes to share price formation; however, this model explains 89.33% of its variance.

El propósito de este artículo es estudiar la influencia de diferentes variables cuantitativas (tradicionalmente usadas) y cualitativas, como la posible influencia negativa en determinados períodos de ciertos factores sociopolíticos, sobre la formación del precio de.

Primero analizamos de forma descriptiva la evolución del Ibex-35 en los últimos años y la comparamos con la de otros índices internacionales de referencia. A continuación, se han contrastado dos técnicas: un modelo estadístico clásico de regresión lineal (GLM) y un método basado en el aprendizaje automático denominado Extreme Gradient Boosting (XGBoost).

XGBoost nos permite obtener un modelo de predicción del valor de mercado muy preciso y claramente superior al otro, con un coeficiente de determinación cercano al 90%, calculado sobre las muestras de validación.

De acuerdo con nuestro análisis, la información contable individual es igual o más importante que la consolidada para predecir el comportamiento del precio de las acciones. Esto justificaría que España mantenga la obligación de presentar estados financieros intermedios individuales, lo que no ocurre en otros países de la Unión Europea porque la NIC 34 solo obliga a realizar estados financieros intermedios consolidados.

El análisis descriptivo permite ver cómo el Ibex-35 se ha alejado de las tendencias internacionales, especialmente en periodos en los que se produjo algún hecho sociopolítico relevante, como el referéndum de autodeterminación de Cataluña, el doble proceso electoral de 2019 o la gestión inicial de la pandemia generada por el Covid-19.

En comparación con otras variables, el modelo XGBoost asigna poca importancia a los factores sociopolíticos cuando se trata de la formación del precio de las acciones; sin embargo, este modelo explica el 89.33% de su varianza.

This study aims to address gaps and limitations in the literature on corporate governance and stock liquidity. It explores the potential benefits of increasing female representation in corporate leadership, which has been a subject of debate and policy intervention in recent years.

Based on prior empirical studies and by integrating the insights of different theories, this study links gender diversity to stock liquidity and uses a multivariate panel regression approach.

The results show that gender diversity, both on the board and in executive positions, positively and consistently affects stock liquidity across different business cycles. The findings reinforce the notion that diverse executive leadership is crucial and influential irrespective of the prevailing economic conditions.

This study has practical implications for investors, managers and policymakers who are interested in the benefits of gender diversity in corporate leadership. It suggests that increasing the percentage of female executives and board members can improve stock market liquidity, which is a key indicator of market efficiency and firm value.

This study advocates for gender equality and diversity in corporate leadership, which can benefit society. It demonstrates that the presence of women directors can enhance financial stability and thus benefit the stakeholders and the community.

This study contributes to the academic literature by examining the impact of gender diversity on board and executive levels on stock liquidity in the US market. Previous research on this topic has mainly relied on French or Australian data. Moreover, this study extends previous work through examining the case of executives’ gender diversity. To the best of the authors’ knowledge, this study is the first to analyze the relationship between gender diversity and stock liquidity across different business cycles, providing a nuanced understanding of how economic contexts affect this relationship.

Introduction: The insurance industry has unprecedented growth, and the demand for insurance has outgrown in the recent past due to the prevailing pandemic. The companies have a large base of the data set at their disposal, and companies must appropriately handle these data to come out with valuable solutions. Data mining enables insurance companies to gain an insightful approach to map strategies and gain competitive advantage, thus strengthening the profits that will allow them to identify the effectiveness of back-propagation neural network (BPNN) and support vector machines (SVMs) for the companies considered under study. Data mining techniques are the data-driven extraction techniques of information from large data repositories, thus discovering useful patterns from the voluminous data (Weiss & Indurkya, 1998).

Purpose: The present study is performed to investigate the comparative performance of BPNNs and SVMs for the selected Indian insurance companies.

Methodology: The study is conducted by extracting daily data of Indian insurance companies listed on the CNX 500. The data were then transformed into technical indicators for predictive model building using BPNN and SVMs. The daily data of the selected insurance companies for four years, that is, 1 April 2017 to 21 March 2021, were used for this. The data were further transformed into 90 data sets for different periods by categorising them into biannual, annual, and two-year collective data sets. Additionally, the comparison was made for the models generated with the help of BPNNs and SVMs for the six Indian insurance companies selected under this study.

Findings: The findings of the study exhibited that the predictive performance of the BPNN and SVM models are significantly different from each other for SBI data, General Insurance Corporation of India (GICRE) data, HDFC data, New India Assurance Company Ltd. (NIACL) data, and ICICIPRULI data at a 5% level of significance.