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Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We show that conventional time series modeling using ARMA and ARMA GARCH run on a rolling basis produces better or comparable forecasting errors than those that machine learning techniques produce. The key to achieving a good forecast is to fit the correct AR and MA orders for each window. When we optimize the correct AR and MA orders for each window using ARMA, we achieve an MAE of 0.024 and an RMSE of 0.037. The RMSE is approximately 11.27% better, and the MAE is 10.7% better compared to those in the literature and is similar to or better than those of the machine learning techniques. The ARMA-GARCH model also has an MAE and an RMSE which are similar to those of ARMA.

This paper examines the characteristics of banks and their lending behavior in relation to Paycheck Protection Program (PPP) loans and commercial and industrial (C&I) loans to small businesses during the COVID-19 pandemic. Our findings show that lenders facing greater risk tended to lend more PPP loans, consistent with the risk-aversion theory. Specifically, banks with a higher loan–deposit ratio, lower overall profitability, poorer loan quality, and higher exposure to risks in business (C&I) loans are characterized by higher PPP loans. C&I loans to all businesses are negatively related to the loan–deposit ratio and loan loss allowance ratio, but are positively linked with the capital ratio. However, we find important differences in C&I lending to small businesses versus large businesses. Furthermore, there is evidence regarding the success of targeting PPP loans towards more productive sectors of the US economy. Using FDIC-defined banks’ lending specializations, we show that banks focused on international lending had a limited role in PPP lending.

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Purpose The purpose of this is paper is to pay a closer look at the 2008-2009 financial crisis (and its aftermath) and analyzes stock returns of nine major US oil companies as well as the oil and gas sector under daily data from January 1992 to April 2012. Design/methodology/approach The authors adopt the arbitrage pricing theory model to examine the relationship between stock returns and their influences including oil price return, yield spreads, and US dollar index return. The authors also provide a test for structural changes in each regression model of return series to capture for multiple breaks. To examine the asymmetric effect of oil price returns on stock returns, the authors separate oil price returns series into two series: positive changes in oil price and negative changes in oil price. Findings The authors find stock returns of oil companies as well as the oil and gas sector are positively affected by oil prices and have stronger effects in the downward direction. Interestingly, The authors find the effects of oil price movements on stock returns increase over time. The authors examine the possibility that investors wishing to hedge against a weakening USD invest in US oil companies and find that more than half of these companies benefit from a weaker USD against the JPY, while all strongly benefit from a weaker USD against major currencies. Originality/value The authors employ daily data for two-decade period including the last global financial crisis. Due to the long-term period covered in this study, sequential Bai-Perron tests are used to detect structural breaks of stock return series. In addition, the data-dependent procedures result in good specifications throughout with white-noise processes in almost all cases.

Purpose The purpose of this paper is to identify three (maturity, agency, and information) effects that help explain the change in idiosyncratic volatility after a firm initiates a dividend. Design/methodology/approach The paper uses a cross-sectional analysis where the standard errors are adjusted for heteroskedasticity. As for robustness check, the authors perform two-stage analysis to control for potential self-selection bias. The authors also control for 2003 Dividend Tax Cut effect, matching-firm volatility, and confounding events. Findings Using a sample of 688 dividend-initiating firms for a period of 1977 to 2010, the authors find evidence consistent with the hypotheses based on the maturity, agency, and information effects. The volatility changes upon the dividend initiation can be reliably explained by the changes in profit volatility and free cash flow per total assets, and whether the firm consummated a stock split prior to the dividend initiation. The information effect is also found to be economically significant. Originality/value By studying a firm’s decision to initiate a dividend and its impact on the change in its volatility, the research helps contribute to the payout policy and volatility literatures.

Using weekly data from January 3, 2003 to March 27, 2015, we examine the responses of U.S. stock returns (S&P 500, DJIA, and NASDAQ) to monetary policy, controlling for WTI oil prices and the value of the U.S. dollar (USD) against major currencies. Based on differences between the federal funds rate and inflation expectations, U.S. real interest rates have become continuously negative since January 28, 2009. Vector auto-regressions (VARs) suggest stronger linkages more recently and vine copula models identify the structure of dependence across these markets, which can help investors optimize portfolio diversification.

Shocks transmitted from productivity leaders to lagging economies are systematic sources of risk. Global technology and knowledge diffusion leads to predictable patterns in productivity dynamics across countries and industries. Technology gaps determine the level of exposure to the systematic productivity shocks. Firms in a country-industry with larger technology gaps relative to the world leader are more dependent on the leader’s innovations compared to their own productivity improvements. They thus have higher loadings on the leader productivity shocks and higher average stock returns. For OECD panel data, a country-industry’s technology gap significantly predicts the stock returns of the country-industry: holding the quintile of country-industry portfolios with the largest gaps and shorting the quintile with the smallest gaps generates annual returns of 9.8% (6.7% after risk adjustment with standard factors). A factor representing the technological productivity gap explains country-industry portfolio returns substantially better than standard factor models. Loadings on leader-country productivity shocks have substantial correlation with technology gaps, and leader productivity shocks are more important for stock returns than idiosyncratic productivity shocks. These findings support that the technology gaps and associated higher average returns are indeed linked to systematic risk.

This study reports results on the ex ante predictability of stock returns using real-time stock market data in Vietnam, a frontier market, from June 2008 to June 2021. Countries classified as a frontier market are often known for currency manipulation, financial market illiquidity, and political instability. Despite the enormous risk usually posed by these inefficiencies, potential profits are large and achievable for many investors. This study provides evidence on existing a strategy to form out-of-sample long portfolios that generate statistically significant and positive mean monthly returns even in the presence of transaction costs. I also justify the magnitude of these returns by showing that they exceed those of VnIndex and MSCI Vietnam Index. The results reject the hypothesis that the stock prices in Vietnamese market follow random walks, thus oppose the stock market efficiency hypothesis. Evidence found in this study provides a better understanding of informational efficiency in a frontier equity market setting. Specifically, there are several implications on portfolio selection strategies, stock price patterns, and trading behavior bias related to Vietnamese stock market can be drawn from this study.

This study analyzes the wealth impact on M&A deals when the acquirers in the financial industry utilize external versus in-house advising services. A quasi-natural observatory setting is applied to investigate the costs and benefits of retaining a financial advisor. Based on agency theory, information asymmetry and conflict of interest both exist in the setting of M&A deals when acquirers use advisory services. We first find that almost 40% of financial acquirers are more likely to use in-house advising services, the frequency of which is significantly higher than that of non-financial acquisitions previously documented. Further, we find that in certain complex deals of greater information asymmetry, the frequency of retaining advisory services in-house is even higher. This finding suggests that for financial acquirers who possess expertise in the M&A market, the concern of conflict of interests (i.e., misaligned incentives) between the acquirers and their advisors are more salient than the concern of information asymmetry. More importantly, using the two-stage regressions method controlling the endogeneity of the choice between in-house versus external advisory services, this study finds that the three-day abnormal returns around the acquisition announcements are 4.5% higher for the acquirers retaining in-house advisory services, 18.7% higher for the corresponding target, and the combined merger gains are 2.2% higher. Overall, our findings provide direct evidence of the agency cost when an external advisor is hired and document the incremental values that the financial acquirers’ in-house advisory services may create.

Purpose This study aims to compare the impact of board characteristics on the performance of listed non-financial firms to the impact of board characteristics on the performance of listed financial firms (commercial banks) in Ghana. Design/methodology/approach The fixed and random effects models with generalized least square specifications are used in estimating regressions to correct for heteroscedasticity and serial correlation. Additionally, this study uses lagged models of the board variables to address the possibility of the presence of endogeneity and to generate robust estimates. Findings The empirical results show some similarities and differences on the impact of board characteristics on the performance of listed non-financial firms and banks. On similarities, for both non-financial firms and banks, board size is seen to have a significant non-linear impact on Tobin’s q. Also, the proportion of foreign board members shows a positively significant relationship with firm performance for both listed non-financial firms and banks. The effect of the proportion of board members with higher educational qualifications on firm performance appears to be negative and statistically significant for both sample of firms. On the other hand, the impact of board composition and board gender diversity on firm performance differs from listed banks and non-financial firms. Research limitations/implications The panel regressions for the listed banks were run on 63 observations because of the small sample size for the listed banks. Though enough for estimation purposes, inferences from results should be made with caution. Originality/value This paper, unlike most corporate governance – firm performance studies, focuses not only on listed non-financial firms but also on listed banks. From a multi-theoretical perspective, this paper provides a comparative analysis on the impact of board characteristics on financial performance of listed non-financial firms and banks.

The Real Estate Investment Trust (REIT) market has become an increasingly important vehicle for alternative investment for equity investors. While existing research examining the cross-section of REIT returns usually employs standard risk factors in the in-sample models, it can only show the ex-post performance of REIT portfolios. The goal of our paper is to examine the ex-ante performance of REIT portfolios (i.e., the ability of investors to earn abnormal returns in real time). We employ the out-of-sample methodology of Cooper, Gutierrez, and Marcum (2005), and show that ex-ante performance of REIT portfolios is rather weak. For about half of our 19-year sample over the period of 1999 to 2017, the portfolio performances of REITs chosen ex-ante do not beat the performances of the FTSE-NAREIT or the CRSP Equal-Weighted index. After adjusting for transaction costs, the REIT portfolios significantly further underperform their benchmarks. Overall, our findings suggest that the market is relatively efficient in the REIT sector, and it is difficult for investors to devise trading strategies that improve the ex-ante performance of REIT portfolios, based on standard risk factors.

The behaviour of the Sterling/European Currency Unit (ECU) exchange rate is examined both during the time before Britain joined the European exchange rate mechanism (ERM) and during the time of Britain's membership. During the latter period, a GARCH (1, 1) model fits the data well but during the pre-ERM period there is evidence of significant non-linear - possibly chaotic - structure in the GARCH residuals. Analysis of the dominant Lyapunov exponents and correlation dimension for the pre-ERM period suggests that the data generation process may be chaotic and this is reinforced by the highly significant BDS statistics obtained for this sample period. © 1997, Taylor & Francis Group, LLC.

Recent studies have documented that it is only very recently that the Emerging Market Hedge Funds (EMHFs) have started mimicking the performance pattern of regular Hedge Funds. These findings therefore motivate us to analyze the market timing and security selection skills of EMHF managers. Rolling regression technique is employed to analyze the above mentioned issues on a time-varying dimension. The rolling market timing regression results suggest that the EMHF managers do not exhibit consistently superior security selection or market timing skills even in an up-market scenario. The static market timing models however, indicate significant outperformance due to superior security selection and significant underperformance due to perverse market timing for the EMHFs in general. Multifactor asset class regressions, using fund-level data, reaffirm the notion that the EMHFs mimic the performance pattern reported for mutual funds in the mutual fund literature. © 2012, Banking and Finance Review.

This paper examines early impacts of the COVID-19 outbreak on stock returns of 11 sectors using the firm-level stock price data from 10 countries. Results show that investors across sectors and countries respond differently during the outbreak. Communication Services, Consumer Staples, Health Care, Information Technology, and Utilities sectors consistently perform relatively well, compared to other sectors, in all countries except Italy, Japan and U.S. Conversely, Energy sector suffers greatest abnormal negative returns, among all sectors, in countries including Canada, Italy, U.K. and U.S. Furthermore, Japan and U.S. have highest numbers of abnormally and negatively affected sectors. © 2021 Informa UK Limited, trading as Taylor & Francis Group.