Empirical Finance

Penelitian ini memaparkan model optimasi struktural berbasis pendekatan semiotika matematis untuk memetakan perilaku psikologis pasar makro di Indonesia (IHSG dan LQ45) sepanjang periode krusial 2026. Dengan mengintegrasikan parameter parsimoni b = 45 yang merepresentasikan 45 saham penggerak likuiditas utama sekaligus siklus waktu 45 minggu di dalam ruang kalender objektif 52 minggu, model ini memproyeksikan dinamika krisis dan titik balik pemulihan secara murni (pure forecasting). Melalui formulasi fungsi kuadrat dan transformasi koordinat Day of the Year, ditemukan sinkronisasi presisi di mana magnitudo perubahan nilai fungsi kuadrat (∆f = -8, 857) secara matematis berkorespondensi mutlak dengan kenaikan nilai pertumbuhan bursa sebesar +8,85% pada fase krusial Juni 2026. Pendekatan ini membuktikan kemampuan bahasa simbol semiotika dalam menangkap struktur tersembunyi pasar melampaui keterbatasan kausalitas empiris konvensional. Pembaruan Oktober 2026: Analisis diperluas untuk mengintegrasikan guncangan struktural akibat sentimen MSCI Standalone Market yang memvalidasi batas volatilitas bawah model.

We review and construct consistent in-sample specification and out-of-sample model selection tests on conditional distributions and predictive densities associated with continuous multifactor (possibly with jumps) and (non)linear discrete models of the short term interest rate. The results of our empirical analysis are used to carry out a "horserace" comparing discrete and continuous models across multiple sample periods, forecast horizons, and evaluation intervals. Our evaluation involves comparing models during two distinct historical periods, as well as across our entire weekly sample of Eurodollar deposit rates from 1982-2008. Interestingly, when our entire sample of data is used to estimate competing models, the "best" performer in terms of distributional "fit" as well as predictive density accuracy, both in-sample and out-of-sample, is the three factor Chen (CHEN: 1996) model examined by Andersen, . Just as interestingly, a logistic type discrete smooth transition autoregression (STAR) model is preferred to the "best" continuous model (i.e. the one factor Cox, Ingersoll, and Ross (CIR: 1985) model) when comparing predictive accuracy for the "Stable 1990s" period that we examine. Moreover, an analogous result holds for the "Post 1990s" period that we examine, where the STAR model is preferred to a two factor stochastic mean model. Thus, when the STAR model is parameterized using only data corresponding to a particular sub-sample, it outperforms the "best" continuous alternative during that period. However, when models are estimated using the entire dataset, the continuous CHEN model is preferred, regardless of the variety of model specification (selection) test that is carried out. Given that it is very difficult to ascertain the particular future regime that will ensue when constructing ex ante predictions, thus, the CHEN model is our overall "winning" model, regardless of sample period.

We review and construct consistent in-sample specification and out-of-sample model selection tests on conditional distributions and predictive densities associated with continuous multifactor (possibly with jumps) and (non)linear discrete models of the short term interest rate. The results of our empirical analysis are used to carry out a "horserace" comparing discrete and continuous models across multiple sample periods, forecast horizons, and evaluation intervals. Our evaluation involves comparing models during two distinct historical periods, as well as across our entire weekly sample of Eurodollar deposit rates from 1982-2008. Interestingly, when our entire sample of data is used to estimate competing models, the "best" performer in terms of distributional "fit" as well as predictive density accuracy, both in-sample and out-of-sample, is the three factor Chen (CHEN: 1996) model examined by Andersen, . Just as interestingly, a logistic type discrete smooth transition autoregression (STAR) model is preferred to the "best" continuous model (i.e. the one factor Cox, Ingersoll, and Ross (CIR: 1985) model) when comparing predictive accuracy for the "Stable 1990s" period that we examine. Moreover, an analogous result holds for the "Post 1990s" period that we examine, where the STAR model is preferred to a two factor stochastic mean model. Thus, when the STAR model is parameterized using only data corresponding to a particular sub-sample, it outperforms the "best" continuous alternative during that period. However, when models are estimated using the entire dataset, the continuous CHEN model is preferred, regardless of the variety of model specification (selection) test that is carried out. Given that it is very difficult to ascertain the particular future regime that will ensue when constructing ex ante predictions, thus, the CHEN model is our overall "winning" model, regardless of sample period.

We show that firms' idiosyncratic volatility obeys a strong factor structure and that shocks to the common idiosyncratic volatility (CIV) factor are priced. Stocks in the lowest CIVbeta quintile earn average returns 5.4% per year higher than those in the highest quintile. The CIV factor helps to explain a number of asset pricing anomalies. We provide new evidence linking the CIV factor to income risk faced by households. Our findings are consistent with an incomplete markets heterogeneous agent model. In the model, CIV is a priced state variable because an increase in idiosyncratic firm volatility raises the average household's marginal utility. The calibrated model matches the high degree of comovement in idiosyncratic volatilities, the CIV-beta return spread, and several other asset price moments.

The objective of this paper is to investigate the use of tick-by-tick data for market risk measurement. We propose an Intraday Value at Risk (IVaR) at different horizons based on irregularly time-spaced high-frequency data by using an intraday Monte Carlo simulation. An UHF-GARCH model extending the framework of Engle ( ) is used to specify the joint density of the marked-point process of durations and high-frequency returns. We apply our methodology to transaction data for the Royal Bank and the Placer Dome stocks traded on the Toronto Stock Exchange. Results show that our approach constitutes reliable means of measuring intraday risk for traders who are very active on the market. The UHF-GARCH model performs well out-ofsample for almost all the time horizons and the confidence levels considered even when normality is assumed for the distribution of the error term, provided that intraday seasonality has been accounted for prior to the estimation.

We examine a sample of 54 management buyouts (MBO) in Japan from January 2001 to March 2009 and document an average buyout premium of 49.0% and a post-MBO investor average return of 54.6%. We find no evidence of operating performance improvements; rather, operating cash flow is worse after MBOs in Japan. However, MBO firms do sell off significant amounts of assets post-MBO. The median equity ownership of pre-buyout managers is 29.36% increasing to 59.82% post buyout. Our MBO sample firms have high equity book to market ratios compared to industry and sizematched control firms indicating that MBO firms are undervalued relative to their peers. MBO firms have higher levels of stock repurchase activity in the years prior to the buyout compared to control firms. MBO firms have less financial visibility relative to the control firms. The evidence suggests undervaluation and not the reduction of agency problems as the motivation for MBOs in Japan.

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This paper provides new evidence on native-migrant differences in financial behavior by analyzing the role of noncognitive and cognitive skills. We make use of data from the Health and Retirement Survey (HRS) which is a longitudinal household survey of the older U.S. population containing detailed information about demographic characteristics, financial assets and personality traits of household members. In line with previous studies, we find a substantial gap in stockholding between immigrant and native households. Estimates from a random effects model suggest that cognitive and non-cognitive skills, including personality concepts and economic preferences, are important drivers of stockholding and explain part of the differences between natives and immigrants. These findings are supported by results from a Blinder-Oaxaca decomposition analysis. Our paper therefore delivers first evidence that differences in non-cognitive and cognitive skills contribute to the explanation of the financial market participation gap between natives and immigrants.

In the context of improving financial performance of companies throughout the world, commercial incentives provided to finance managers with respect to enhanced employee performance are of paramount importance. Commercial incentives is able to align employee goals with organizational objectives, inspire employees, encourage development, and offer constructive feedback. However, this paper is based on the findings of the publicly listed companies at the Colombo stock exchange. This paper aims to ascertain the adequacy and the level of commercial inducements of finance managers required to enhance the monetary growth of publicly listed companies in Sri Lanka. For the purpose of investigation, a quantitative study with the use of the deductive method, using a stratified random sampling technique consisting of a sample of 200 Public Listed Companies out of a population of 306 was used. Both primary data sourced through questionnaires from the representative sample of the population and secondary data available in the annual reports of listed firms within the last 5 years were used to conduct multiple correlation and regression analyses. In the analysis of multiple correlations between corporate inducements and financial performance, salary, share options, allowances, fringe benefits, bonuses and overtime were considered to ascertain the relationship with ROA, ROE, ROCE, GPM and NPM.
The obtained results were relatively according to the literature developed in the study as expounded by Fredrick Herzberg under Two Factor Theory and also by Alderfer under ERG theory. The results indicated corporate incentives have a strong effect on financial performance and a strong relationship between corporate incentives and with financial performance of listed firms. Corporate incentives in the context of factors of motivation were more effective than hygiene factors as explained by Herzberg and also by Alderfer which the theoretical framework was based upon in this study.
This study recommended that publicly listed companies in the Colombo Stock Exchange should focus on intrinsic corporate incentives (factors of motivation) as emphasized by Herzberg than extrinsic corporate incentives (hygiene factors). This study implied that Human Resource practitioners, theorists, researchers and remuneration policymakers to consider the requisite level of corporate incentives to formulate remuneration policies and procedures to mitigate, avoid and prevent discrepancies in incentive anomalies to motivate finance managers to gain successful financial growth.

This study analyses the performance of prediction of returns as per four Fama-French factors (market risk premium, size, value, and momentum) on six Indian portfolios (SV, SN, SG, BV, BN, BG). The study employs models based on advanced machine learning (ML) assembling techniques, namely XGBoost, LightGBM, Random Forest and Support Vector Regression (SVR) along with linear models, Ridge, and Lasso. The outputs from these models are compared with those derived from traditional OLS regression. The purpose is to evaluate the performance of factor-based models using advance machine learning algorithmic approaches. The relative contribution of each factor across models is highlighted using feature importance and SHAP value analysis. This study demonstrates that machine learning based models enhance the prediction accuracy of returns from factor portfolios, in general. However, for the large cap portfolios (BV, BN, BG), linear models like Ridge, Lasso and OLS regression perform better in explaining the variance. For the majority of models, market risk premium emerges as a consistently strong predictor. The study contributes to the existing body of literature by applying asset price theories to modern data-driven modelling in emerging markets.

Banking stability has become an important issue in the economy, especially after the 1998 Asian economic crisis and the 2008 global crisis which showed that the economic stability of a country is strongly influenced by the stability of its financial system. This study aims to analyze the influence of macroeconomic factors such as inflation, interest rate (BI Rate), and rupiah exchange rate on banking stability in Indonesia. Using a quantitative approach and multiple regression analysis, it was found that interest rates (BI Rate) and the rupiah exchange rate have a significant influence on banking stability, while inflation was not found to have a significant influence. Interest rates affect stability through the cost of funds incurred by banks, while a stable rupiah exchange rate increases investor confidence and lowers exchange rate risk, strengthening financial system stability. The non-significant effect of inflation may be due to the banking sector's ability to adjust interest rates and effective risk management strategies. The results emphasize the importance of effective monetary policy and exchange rate risk management to maintain banking stability in Indonesia. This research provides valuable insights for regulators in formulating policies that support banking stability and sustainable economic growth.

This study investigates the effect of exchange rate volatility on the competitiveness of Small and Medium Enterprises (SMEs) in Nakonde District, Zambia's principal northern commercial border town. Operating within a multi-currency trade corridor, Nakonde-based enterprises face acute macroeconomic exposure due to fluctuations between the Zambian Kwacha and regional currencies. The primary objective is to evaluate how currency instability impacts import procurement costs, operational cash flows, and market pricing strategies. Utilizing a mixed-methods research design, primary data was gathered through structured questionnaires administered to 120 registered cross-border SMEs, alongside key informant interviews with trade association leaders and commercial bank officials. The findings reveal that sharp exchange rate depreciations drive significant import cost inflation, compress profit margins by up to 35%, and restrict long-term business planning due to a lack of accessible hedging instruments. The paper concludes with targeted policy and strategic recommendations, advocating for enhanced financial risk training, micro-hedging financial products, and streamlined clearance processes at the One-Stop Border Post (OSBP) to bolster border-town enterprise resilience.

Extant research shows that stock returns of investable firms are highly sensitive to foreign market and global information shocks, suggesting that having foreign investors might insulate investable firms from shocks to local fundamentals. Examining 24 emerging markets, we find that both investable and non-investable firms are sensitive to local monetary policy shocks. This allays the concern that emerging-market opening reduces the efficacy of local monetary policy. We also find that in 11 countries (46% of our country-sample), investable firms are more sensitive to local shocks than noninvestable firms. Differences in leverage, stock liquidity, size, domestic product-market exposure, or industry cyclicality do not drive this finding.

Behavioural Finance is a nascent field that examines the impact of human behaviour on financial decision making. Corporate behavioural finance is a niche that studies the actions of individuals especially CXO's, management and board in the context of corporate financial decision making. Overconfidence is an erroneous, dysfunctional and excessively optimistic assessment that individuals have of their own ability or knowledge. It is one of the most common but powerful and significant cognitive behavioural bias prevalent in managerial decision making. Research in various disciplines indicates that managerial overconfidence leads to detrimental consequences for firm performance such as destruction in value and subpar investment behaviours. In contrast literature also provides evidence of positive effect of managerial overconfidence due to its impact on innovation of pioneering products, processes and services. Past studies have investigated the effect of managerial overconfidence on firm performance outside India, but the literature for Indian Companies remains scarce. This research seeks to contribute to the broader understanding of how cognitive biases influence managerial behaviour and correlates with firm performance within the unique context of an emerging economy. By exploring managerial overconfidence within the Indian corporate landscape, this study aim to provide insights that may aid strategic decision-making and improve organizational performance.

The study examined the impact of monetary and fiscal policies on the stock market capitalization of 5 selected African economies with highest market capitalization namely; Egypt, Morocco, Kenya, Nigeria and South Africa. Notwithstanding the positive development in the African stock market, it still encounters problems such as high inflation and uncertainty in the business environment which erode purchasing power, the aim of the study was to establish the relationship between the monetary and fiscal policies and stock market capitalization in these countries. The data employed in this study are annual panel data spanning from 2000 to 2022. The study utilized panel VEC of the underlying panel VAR model to distil insightful results. The panel cointegration results indicate that both the monetary and fiscal policies and stock market capitalization have long run equilibrium association. Specifically, money supply (MSG), interest rate (INT),and gross domestic product growth (GDPG) rate have long run effects on stock market capitalization (SMC) of the 5 selected African economies. In the short run, all the variables are statistically insignificant. The policy implication of this results is that policy stakeholders in these understudied economies should be more concerned with long term policies than the policies of short term. That is, the implementable monetary and fiscal policies should be a long-term program rather than short term.

This study provides new evidence about short sellers' superior skills in processing industry information. In industries with the highest aggregate shorted values, the most shorted stocks earn -2.76% abnormal returns over the next six months. These results are likely driven by short sellers' focus on complex industries with the highest profit potentials. In the targeted industries, firm level shorting predicts increase in default risk, suggesting that short sellers successfully identify firms with fundamental issues. Overall, in addition to aiding information discovery at the firm level, short sellers also reduce information complexity and improve economic efficiency at the industry level.

The purpose of this paper is to investigate whether the Italian index option contract Ž . Ž . MIBO30 , recently introduced in the Italian Derivatives Market IDEM , is efficient. Two different methods are used in the analysis. First, we tested on the Italian index option market the validity of the put-call parity conditions, extended to account of transaction costs associated with replicating and establishing a short hedge on the index. We find that the significant deviations from put-call parity are not exploitable when all transaction costs are accounted for. Since the put-call parity is just a weak test for market efficiency, we further investigate the possibility to generate profitable positions through the simulation of an ex-post volatility hedging strategy. This strategy does not allow for systematic abnormal returns, supporting the hypothesis that option prices are consistent with market efficiency.

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An association between increased index futures mispricing and concurrent index volatility has been reported within several prior studies; in the present study, we argue that expected volatility over an arbitrage horizon also adversely affects the ability and willingness of traders to engage in arbitrage, leading to larger and more persistent futures mispricing. Using the CBOE VIX and its innovation on the concurrent spot volatility as proxies for expected volatility, we present evidence of an increase in S&P 500 index futures mispricing with expected volatility. The impact of the VIX grows exponentially across the distribution of conditional mispricing levels, which suggests that with expectations of heightened future volatility, arbitrageurs will demand considerably higher risk premiums; however, the influence of expected volatility is found to have been reduced during the global financial crisis period, during which concurrent volatility overwhelmingly dominated the magnitude of mispricing.

Mean-variance optimization fails out-of-sample due to estimation error. We propose a 4-step walk-forward backtesting framework: 1) rolling windows, 2) strict benchmark vs 1/N naive, 3) transaction costs, 4) code replication. We test this on 8 ETFs from 2010-2025. Results show 1/N naive diversification outperforms Markowitz optimization in 3 of 10 periods, with 3 ties. This confirms that estimation error, not model sophistication, determines real-world performance. Before trading any strategy, it must survive this test.

Leveraged Buyouts (LBOs) and Management Buyouts (MBOs) have garnered increasing interest since the late 1980s, characterised by the acquisition of firms primarily financed through debt. LBOs act as a distinctive external control mechanism. By acquiring firms predominantly through debt financing, LBOs impose financial discipline by limiting managerial discretion over free cash flows, as a substantial portion is allocated to servicing debt obligations. While LBOs are well-documented in developed markets, their applicability and effectiveness in emerging economies, particularly the MENA region, remain insufficiently explored. The primary objective of the study is to explore the impact of ownership structure on the financial performance of Leveraged Buyout (LBO) transactions in the MENA region, a key emerging market region. Drawing on agency theory by Jensen & Meckling and the capital structure theory of Modigliani and Miller, the study investigates how different shareholder configurations, particularly managerial equity participation, influence LBO outcomes. The data for this study were sourced from the CAPITALIQ and Crunchbase platforms, complemented by financial reports published by the target companies. Based on a sample of 233 transactions conducted between 2000 and 2023, the research adopts a quantitative methodology grounded in a hypothetico-deductive approach. The analysis focuses on the interactions between managerial ownership, leverage, target firm size, and operational performance. The descriptive statistics for the variables Management Buyout (MBO), Indebtness, EBITDA/Enterprise Value (EV), Size, and Multiple, derived from a sample of 211 leveraged buyout (LBO) transactions, provide a comprehensive overview of the financial and operational characteristics of the studied firms, shedding light on their post-LBO value creation dynamics. The findings support the agency theory premise that managerial ownership aligns interests and enhances performance, showing a positive relationship between managerial equity stakes and financial outcomes. Conversely, the effect of leverage, central to Modigliani and Miller’s propositions, proves more nuanced, reflecting the region’s unique financial constraints and market imperfections. Firm size, meanwhile, shows no direct correlation with performance improvement. These insights underscore the complex mechanisms behind LBO success in the MENA context and offer practical and theoretical implications, particularly regarding governance practices and institutional frameworks. The study also highlights avenues for future research, including a deeper examination of regional governance dynamics and the moderating role of institutional factors—such as investor protection, financial market maturity, and political stability—to better understand the conditions driving LBO success across different national contexts.

When war breaks out on a weekend, stock exchanges are closed but Bitcoin is not. This paper studies what happens in that gap, and in the weeks that follow. We examine five discrete conflict escalations across three ongoing wars: Russia's full-scale invasion of Ukraine (February 2022), the Hamas attack on Israel (October 2023), two rounds of Iranian strikes on Israel (April and October 2024), and the US-Israeli strikes on Iran that began Operation Epic Fury on 28 February 2026. Using event study methodology across short [-2,+2], medium [-5,+30], and long [-30,+180] windows, we find that Bitcoin drops 5-8% at or around conflict onset in most events and recovers within days, behaving nothing like a safe haven in the short run yet outperforming gold over medium horizons in three of five events. The January 2024 spot ETF approval marks a structural break: post-ETF shocks are roughly 3 percentage points smaller and recovery time fell from 18 days to 4. Perhaps the clearest finding is on-chain rather than price-based: USDT transfer volumes on conflict-zone exchanges surged between 200% and 700% across events, dwarfing Bitcoin's role; people under fire want dollars, not Bitcoin. VAR analysis confirms that geopolitical risk shocks depress Bitcoin returns by around 0.42% per standard deviation, with oil prices accounting for approximately 43% of that transmission. As a robustness check we trained XGBoost and LSTM classifiers to predict next-day Bitcoin return direction during conflict windows. XGBoost underperformed random (AUC = 0.41), while LSTM marginally exceeded it (AUC = 0.54), suggesting some sequential structure in conflict-period returns that tree-based models cannot capture but that is too weak to generate exploitable signals at daily frequency. E5 data run through 15 March 2026: Bitcoin is up approximately 5.9% from its pre-strike level at $70,982, trading above equities (S&P 500 down 3.1% year-to-date) with Brent crude at $101.68/barrel and the Strait of Hormuz blockaded.

This paper provides a time-series test for the Differences-of-Opinion theory proposed by in the aggregate market, thus extending Chen, Hong, and Stein's (2001) crosssectional test for this theory across individual stocks. An autoregressive conditional density model with a skewed-t distribution is used to estimate the effects of past trading volume on return asymmetry. Using NYSE and AMEX data from 1962 to 2000, we find that the prediction of the Hong-Stein model that negative skewness will be most pronounced under high trading volume conditions is not supported in our time-series analysis with market data.

Behavioural Finance is a nascent field that examines the impact of human behaviour on financial decision making. Corporate behavioural finance is a niche that studies the actions of individuals especially CXO's, management and board in the context of corporate financial decision making. Overconfidence is an erroneous, dysfunctional and excessively optimistic assessment that individuals have of their own ability or knowledge. It is one of the most common but powerful and significant cognitive behavioural bias prevalent in managerial decision making. Research in various disciplines indicates that managerial overconfidence leads to detrimental consequences for firm performance such as destruction in value and subpar investment behaviours. In contrast literature also provides evidence of positive effect of managerial overconfidence due to its impact on innovation of pioneering products, processes and services. Past studies have investigated the effect of managerial overconfidence on firm performance outside India, but the literature for Indian Companies remains scarce. This research seeks to contribute to the broader understanding of how cognitive biases influence managerial behaviour and correlates with firm performance within the unique context of an emerging economy. By exploring managerial overconfidence within the Indian corporate landscape, this study aim to provide insights that may aid strategic decision-making and improve organizational performance.

In this study, within the selected timeframe, we found statistically significant size factor (SMB), profitability factor (RMW), and investment factor (CMA) for Amazon within a four-factor model. Despite the theoretical appeal of a five-factor model, our findings indicate that it does not improve the explanation of average returns beyond the four-factor model that excludes HML. This suggests that the average return attributed to HML is already captured by its exposures to other factors. Consequently, within the five-factor model, HML becomes redundant in explaining average returns, resulting in a dilution of statistical significances. However, the market index MSCI World remains the primary component and explains the most variance. The four-factor model with SMB/RMW/CMA provides an additional and significant explanatory contribution, enhancing the model. Compared to the single-factor CAPM, the explanatory contribution (adjusted R-squared) has increased from 37.48% to 59.38%. In academic research, the existence of factors is difficult to refute as they cannot be tested in random experiments.

In this paper researchers portray a bibliometric analysis of empirical study in the domain of behavioral finance. This classical review of empirical studies aims to guide future research by identifying gaps in the literature and emphasizing the evolving nature of this field. The analysis revealed the concentration of empirical authors regarding the selected variables during the study period (2016-2023). Such variables consist of keywords, author's origin country, and journals in which the empirical study was published. The study also highlights the significance of understanding psychological factors that influence financial decision-making. The bibliometric analysis has been executed by bibliographic and analysis using "Visualization of Similarities (VOS) viewer" software. Using the themes generated modal, the empirical study has been discussed in detail, providing a comprehensive overview of the research trends in behavioral finance. The study has found that over the 8 years most of the research in the field of behavioral finance done by developed country "United States of America".

This paper examines the time-series relations among expected return, risk, and bookto-market (B/M) at the portfolio level. I "nd that B/M predicts economically and statistically signi"cant time-variation in expected stock returns. Further, B/M is strongly associated with changes in risk, as measured by the Fama and French (1993) (Journal of Financial Economics, 33, 3}56) three-factor model. After controlling for risk, B/M provides no incremental information about expected returns. The evidence suggests that the three-factor model explains time-varying expected returns better than a characteristics-based model.

A Copula-based Dynamic Conditional Correlation (DCC) model is developed to capture time-varying correlations and non-linear dependencies among financial assets, integrating Copula functions with multivariate GARCH frameworks. The model employs Sklar's theorem to separate marginal distributions from joint dependencies, enabling robust estimation of tail dependencies and dynamic correlations through maximum likelihood methods. It is applied to daily returns of equities, bonds, and commodities from 2013 to 2023, including the 2020 COVID-19 market crash, to assess risk metrics under varying market conditions. The results show that the Copula-DCC model outperforms traditional models, delivering more accurate Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) estimates with fewer backtesting violations during market stress.

This paper analyzes the causal linkage between transaction costs and financial volatility under two methodological improvements over the existing literature. First, we use panel data in which exogenous transaction cost differences in the French stock market are induced by price level dependent minimum price variation rules (tick size rules). Unlike in previous studies based on one-time regulatory tick size changes (like the U.S. decimalization), we can separately identify and control for marketwide volatility changes. Second, we avoid the pitfalls of biased volatility measurement across regimes by using the range as a tick size robust volatility metric. Panel regressions controlling for marketwide volatility effects show at high levels of statistical significance that the hourly range volatility of individual stocks increases by more than 30% for a 20% exogenous increase in transaction costs due to tick size variations in the French trading system. In the light of this evidence, higher transaction costs in general, and security transaction taxes in particular, should be considered as volatility increasing. (JEL: F3, G1, G14)

This study examines the short-term stock market reaction to share buyback announcements by companies listed on the National Stock Exchange (NSE) of India during the year 2024. Using event study methodology, the research analyzes abnormal returns surrounding buyback announcements over a five-day event window (-2 to +2), with the estimation window spanning 250 trading days prior to the announcement. The sample consists of 34 buyback events after filtering for data availability and NSE listing. The findings indicate that share buyback announcements are associated with statistically significant positive abnormal returns, suggesting that such announcements are interpreted by the market as signals of undervaluation or efficient capital allocation. These results support theories such as signaling, undervaluation, and agency cost mitigation. The study contributes to the growing body of literature on corporate buybacks in emerging markets and provides practical insights for investors and corporate managers on the implications of buyback decisions in the Indian context.

Using two newly available ultrahigh-frequency datasets, we investigate empirically how frequently one can sample certain foreign exchange and U.S. Treasury security returns without contaminating estimates of their integrated volatility with market microstructure noise. Using volatility signature plots and a recently-proposed formal decision rule to select the sampling frequency, we …nd that one can sample FX returns as frequently as once every 15 to 20 seconds without contaminating volatility estimates; bond returns may be sampled as frequently as once every 2 to 3 minutes on days without U.S. macroeconomic announcements, and as frequently as once every 40 seconds on announcement days. With a simple realized kernel estimator, the sampling frequencies can be increased to once every 2 to 5 seconds for FX returns and to about once every 30 to 40 seconds for bond returns. These sampling frequencies, especially in the case of FX returns, are much higher than those often recommended in the empirical literature on realized volatility in equity markets. We suggest that the generally superior depth and liquidity of trading in FX and government bond markets contributes importantly to this di¤erence.

We study the risk dynamics and pricing in international economies through a joint analysis of the timeseries returns and option prices on three equity indexes underlying three economies: the S&P 500 Index of the United States, the FTSE 100 Index of the United Kingdom, and the Nikkei-225 Stock Average of Japan. We develop an international capital asset pricing model, under which the return on each equity index is decomposed into two orthogonal jump-diffusion components: a global component and a country-specific component. We apply separate stochastic time changes to the two components so that stochastic volatility can come from both global and country-specific risks. For each economy, we assign separate market prices for the two return risk components and the two volatility risk components. Under this specification, we obtain tractable option pricing solutions. Model estimation reveals several interesting insights. First, global and country-specific return and volatility risks show different dynamics. Global return movements contain a larger discontinuous component, and global return volatility is more persistent than the country-specific counterparts. Second, investors charge positive prices for global return risk and negative prices for volatility risk, suggesting that investors are willing to pay positive premiums to hedge against downside global return movements and upside volatility movements. Third, the three economies contain different risk profiles and also price risks differently. Japan contains the largest idiosyncratic risk component and smallest global risk component. Investors in the Japanese market also price more heavily against future volatility increases than against future market downfalls.

This study examines whether a firm's subsidiary operations in offshore financial centers (OFCs) affect loan syndicate structure. We find that as borrowers engage more aggressively in operations in OFCs, lead lenders of a loan syndicate hold a larger percentage of loans to such firms and the number of lenders participating in a loan syndicate becomes smaller. This finding is robust to various robustness tests including propensity score matching analysis and quasi-natural experiment. Furthermore, we find that lead lender reputation and prior lending relationship with the borrower can attenuate the positive relation between OFC operations and loan syndicate concentration.

This study examines whether a firm's subsidiary operations in offshore financial centers (OFCs) affect loan syndicate structure. We find that as borrowers engage more aggressively in operations in OFCs, lead lenders of a loan syndicate hold a larger percentage of loans to such firms and the number of lenders participating in a loan syndicate becomes smaller. This finding is robust to various robustness tests including propensity score matching analysis and quasi-natural experiment. Furthermore, we find that lead lender reputation and prior lending relationship with the borrower can attenuate the positive relation between OFC operations and loan syndicate concentration.

Microcredit system, which was put into practice in the last quarter of the XX. century, is rapidly growing. On the one hand, the microfinance system contributes to the economic growth of the countries, while on the other hand it makes a significant contribution to the social development. The current interest rates affect the microcredit system, so thus causing deviations in varying degrees at targets intended by microcredit institutions. This study aimed to analyze the effect of efficiency on interest rate in microfinance system of sampled transition economies in the period between 2000 and 2014. The data was collected from 215 financial institutions operating in 11 countries of sampled transition economies and analyzed by panel data method. The findings of this study indicate that the cost effectiveness of micro-credit institutions operating in transition economies have a significant effect on the interest rates.

We analyze the determinants of average individual inflation uncertainty and the cross-sectional variance of point forecasts (“disagreement”) based on data from the European Central Bank's Survey of Professional Forecasters. We empirically confirm the implication from a theoretical variance decomposition that disagreement is an incomplete approximation to overall uncertainty. Both measures are associated with macroeconomic conditions and indicators of monetary policy, but the relations differ qualitatively. In particular, average individual inflation uncertainty is higher during periods of expansionary monetary policy, whereas disagreement rises during contractionary periods. This implies that conclusions based on disagreement as a single indicator of ex ante uncertainty are incomplete and potentially misleading.

A large literature debates the link between news and investor decision making. Relying on unique U.S. firm-level news data between 1979 and 2016, we document a crosssectional difference in the speed of diffusion of the information contained in news. We distinguish news articles as being either slowly or quickly incorporated into contemporaneous stock prices. The return spread between these two types of news yields a statistically significant profitability (139 basis points per month) and this effect cannot be explained by other well-known risk factors. By employing novel attention data (Google Search Volume Index and Bloomberg News Readerships Index), we find that this news-induced anomaly can be attributed to limited-attention theory where firmspecific news is not read by investors. Our research refines the role of news regarding information dissemination in the financial markets.

This work was carried out in collaboration between both authors. Author CPK provided the generalized normal distribution and the information relative risk framework. Author TLT performed all the mathematical computations. Both authors read and approved the final manuscript.

This paper provides a comprehensive review of momentum and trend reversal phenomena from a quantitative finance perspective. We explore the seminal works on momentum strategies, including both cross-sectional and time-series approaches, and delve into the behavioral finance theories that seek to explain these market anomalies. The paper further investigates the phenomenon of momentum crashes, which are characterized by sudden and severe reversals, and their relationship with market volatility and investor sentiment. Finally, we examine contrarian and mean-reversion strategies as potential tools for navigating and capitalizing on trend reversals. Our analysis synthesizes key findings from the academic literature, providing a structured overview of the quantitative methods and theoretical frameworks used to understand and model these persistent market dynamics.

Evidence on the impacts of dividend taxation on dividend distributions and investment is still unsettled and based mainly on the behaviour of large companies that are listed on the stock exchange. This paper utilises a large, register-based, panel data set, where the large majority of firms are small and medium sized enterprises, to examine the responses to the Finnish dividend tax increase of 2005. This reform creates a useful opportunity to measure enterprise behaviour, since it involves exogenous variation in the tax treatment of different types of firms. The results, based on differences-in-differences estimation and matching methods, indicate that dividends dropped somewhat in closely held corporations that faced a tax increase, perhaps for timing reasons, while investments did not decline. These findings are probably more in line with the new rather than the old view of dividend taxation.