Firm characteristics that drive the momentum pattern in the UK stock market

Previous studies have estimated the company characteristics of previous winners and losers to explore the momentum effect. Using UK data, this study focuses on the characteristics of companies that actually generate the momentum pattern. These are previous winners who keep performing well (WW) and past losers who consistently perform poorly (LL). This study illustrates that WW and LL firms may exhibit market-based characteristics similar to those of young, low-priced, small capitalisation companies, but that there are significant differences. Accounting and fundamental signals (e.g. profitability, value/growth) tend to distinguish winners from losers. Based on firm characteristics, we further develop investment strategies that can outperform significantly the profitability of the momentum strategy.     

The determinants of market-wide issue cycles for initial public offerings

This paper identifies the determinants of market-wide issue cycles for initial public offerings (IPOs) using an autoregressive conditional count model. We consider whether IPO volume is related to business conditions, investor sentiment, and time variation in adverse selection costs caused by asymmetric information between managers and investors. We provide evidence indicating that time variation in business conditions and investor sentiment are important determinants of monthly issue activity. By contrast, time variation in adverse selection costs does not significantly affect IPO volume.     

Venture capital funds and the public equity market

This paper considers the relationship between the public equity market and the returns to venture investing using a dataset which is derived from the records of two large limited partners who have been investing in venture capital for almost 30 years. Evidence is found to suggest that market conditions over the investment cycle, and exit conditions at the time of exit in particular, are an important determinant. This paper also investigates whether any other aspects of the venture investment process respond to events in the broader market. While general trends are evident in the venture investment cycle, only the intensity of the investment process is found to respond to events in the public equity market.     

Feasible momentum strategies: Evidence from the Swiss stock market

While there is little controversy on the profitability of momentum strategies, their implementation is afflicted with many difficulties. Most important, chasing momentum can generate high turnover. Though there are already several attempts to make momentum strategies less expensive with respect to transaction costs, we go a step further in the simplification of momentum strategies. By restricting our sample to Switzerland’s largest blue-chip stocks and choosing only one winner and one loser stock, we find average returns to our momentum arbitrage portfolios of up to 44% p.a. depending on the formation and holding periods. While unconditional risk models are at odds with momentum profits, stock market predictability and time-varying expected returns explain a large part of the momentum payoffs, including the post-holding period behavior of the winner and loser stocks (overreaction and subsequent price correction).

Insider trading,growth opportunities and the market reaction to new financing announcements*

Abstract

This paper explores the profitability of momentum strategies, by investigating if a momentum strategy is superior to a benchmark model once the effects of data-snooping have been accounted for. Two data sets are considered. The first set of data consists of US stocks and the second one consists of Swedish stocks. For the US data strong evidence is found of a momentum effect and hence the hypothesis of weak market efficiency is rejected. Splitting the sample in two parts, it is found that the overall significance is driven by events in the earlier part of the sample. The results for the Swedish data indicate that momentum strategies based on individual stocks generate significant profits. A very weak or no momentum effect can be found when stocks are sorted into portfolios. Finally, and perhaps most importantly, results show that data-snooping bias can be very substantial. Neglecting the problem would lead to very different conclusions.     

The determination and international transmission of stock market volatility

This paper extends the literature on low-frequency analysis of the causes and transmission of stock market volatility. It uses end-monthly data on stock market returns, interest rates, exchange rates, inflation, and industrial production for five countries (Britain, France, Germany, Japan, and the US) from July 1973 to December 1994. Efficient portfolios of world, European, and Japanese/US equity are first constructed, the existence of multivariate cointegrating relationships between them is demonstrated, and the transmission of conditional volatility between them is described. The transmission of conditional volatility from world equity markets and national business cycle variables to national stock markets is then modeled. Among the main findings are: first, world equity market volatility is caused mostly by volatility in Japanese/US markets and transmitted to European markets, and second, changes in the volatility of inflation are associated with changes of the opposite sign in stock market volatility in all markets where a significant effect is found to exist. To the extent that the volatility of inflation is positively related to its level, this implies that low inflation tends to be associated with high stock market volatility.     

CEO power,product market competition and the acquisition motive for going public

This study finds that CEO power and product market competition differentially influence post‐IPO growth strategy and its economic consequences. Powerful CEOs are more likely to prefer acquisition growth over internal investment. Further, while CEO power is positively related to the likelihood, frequency and size of post‐IPO acquisitions, it is unrelated to the post‐IPO performance of acquisitive firms. In contrast, product market rivalry does not increase post‐IPO acquisitiveness but is positively related to the performance of acquirers. Finally, CEO power enhances the performance of acquisitive IPO firms only when faced with intense competitive rivalry.     

Better the devil you know: The influence of political incumbency on Australian financial market uncertainty

The Australian federal election cycle, which occurs approximately every 3 years, causes much media attention and invokes indecision regarding investment decisions in both the real economy and financial markets. This paper constructs measures of political uncertainty and formally explores their relationship with market uncertainty, as measured by implied volatility. The empirical evidence suggests that increasing (decreasing) levels of uncertainty around the election result induce higher (lower) levels of market uncertainty. In a case of the market preferring the devil it knows, an increasing (decreasing) likelihood of the incumbent party, whose economic policies are well-known, winning the election, reduces market uncertainty. The results remain significant even after controlling for a number of macroeconomic variables, and when an alternative GARCH framework is considered.     

动量交易策略及我国股票市场实证分析

动量交易策略指的是事先针对股票收益及交易量设定过滤规则,一旦股票收益或者股票收益和交易量同时满足过滤规则就买入或卖出股票的交易策略。动量交易策略的理论基础是行为金融学。国外投资者已经成功地在实践中应用了该策略。我国股票市场是否存在动量效应,还未形成统一的结论。在总结国内外学者研究方法的基础上,利用目前可用的数据,对我国股票市场在中期条件下动量交易策略的适用性进行了实证研究。但得出的结论并不支持存在动量效应。     

Abnormal trading volume,news and market efficiency: Evidence from the Jamaica Stock Exchange

This paper investigates market efficiency of the Jamaica Stock Exchange (JSE). Together, weak and semi-strong form efficiency claim that historical and newly released public information do not predict future stock price movement. We test both forms of market efficiency by analyzing stock price behavior during times of abnormal trading volume and around the release dates of earnings information. Abnormal trading volume may be driven by liquidity demand or reflect new or private information flow to the market. Using JSE data over the period 2000 to 2021, we find price dynamics consistent with price pressure as firms experience negative abnormal returns on the day of abnormal trading activity but offsetting positive abnormal stock returns on the following day. Further findings show post earnings announcement drift on the JSE. Taken as a whole, the evidence suggests violations of market efficiency and has implications for capital allocation in this emerging market.     

Are there Monday effects in stock returns: A stochastic dominance approach

We provide a test of the Monday effect in daily stock index returns. Unlike previous studies we define the Monday effect based on the stochastic dominance criterion. This is a stronger criterion than those based on comparing means used in previous work and has a well defined economic meaning. We apply our test to a number of stock indexes including US large caps and small caps as well as UK and Japanese indexes. We find strong evidence of a Monday effect in many cases under this stronger criterion. The effect has reversed or weakened in the Dow Jones and S&P 500 indexes post 1987, but is still strong in more broadly based indexes like the NASDAQ, the Russell 2000 and the CRSP.     

Machine learning sentiment analysis,COVID-19 news and stock market reactions

The recent COVID-19 pandemic represents an unprecedented worldwide event to study the influence of related news on the financial markets, especially during the early stage of the pandemic when information on the new threat came rapidly and was complex for investors to process. In this paper, we investigate whether the flow of news on COVID-19 had an impact on forming market expectations. We analyze 203,886 online articles dealing with COVID-19 and published on three news platforms (MarketWatch.com, NYTimes.com, and Reuters.com) in the period from January to June 2020. Using machine learning techniques, we extract the news sentiment through a financial market-adapted BERT model that enables recognizing the context of each word in a given item. Our results show that there is a statistically significant and positive relationship between sentiment scores and S&P 500 market. Furthermore, we provide evidence that sentiment components and news categories on NYTimes.com were differently related to market returns.     

Pre-holiday effects: International evidence on the decline and reversal of a stock market anomaly

The pre-holiday effect is one of the best known of the calendar effect anomalies. This paper extends prior work by examining whether the effect has declined for the U.S., U.K. and Hong Kong markets. For all three markets, the effect is shown to have declined, but only significantly in the U.S. The result is not surprising given the relative sophistication of the market. What is surprising, however, is the reversal of the pre-holiday effect during the period 1991–1997, with the mean return on pre-holiday days becoming negative, and the subsequent elimination of this effect during 1997–2003.     

Modelling the joint behaviour of electricity prices in interconnected markets

The liberalization of energy markets worldwide during recent decades has introduced severe implications for the price formation in these markets. Especially within the European day-ahead electricity markets, increased physical connections between different market areas and a joint effort on optimizing the aggregate social welfare have led to highly connected markets. Consequently, observing the exact same hourly day-ahead prices for two or more interconnected electricity markets in Europe happens frequently. This affects the modelling of such prices and in turn the valuation of derivatives written on prices from these market areas. In this paper, we propose a joint model for day-ahead electricity prices in interconnected markets composed of a combination of transformed Ornstein–Uhlenbeck processes. We discuss the properties of the model and propose an estimation procedure based on filtering techniques. Furthermore, the properties of the model reveal that analytical prices are attainable for, e.g., forwards and spread options.     

Forecasting stock market volatility: Further international evidence

Abstract

This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility in fifteen stock markets. Volatility is defined as within-month standard deviation of continuously compounded daily returns on the stock market index of each country for the ten-year period 1988 to 1997. The first half of the sample is retained for the estimation of parameters while the second half is for the forecast period. The following models are employed: a random walk model, a historical mean model, moving average models, weighted moving average models, exponentially weighted moving average models, an exponential smoothing model, a regression model, an ARCH model, a GARCH model, a GJR-GARCH model, and an EGARCH model. First, standard (symmetric) loss functions are used to evaluate the performance of the competing models: mean absolute error, root mean squared error, and mean absolute percentage error. According to all of these standard loss functions, the exponential smoothing model provides superior forecasts of volatility. On the other hand, ARCH-based models generally prove to be the worst forecasting models. Asymmetric loss functions are employed to penalize under-/over-prediction. When under-predictions are penalized more heavily, ARCH-type models provide the best forecasts while the random walk is worst. However, when over-predictions of volatility are penalized more heavily, the exponential smoothing model performs best while the ARCH-type models are now universally found to be inferior forecasters.     

Unifractality and multifractality in the Italian stock market

Tests for random walk behaviour in the Italian stock market are presented, based on an investigation of the fractal properties of the log return series for the Mibtel index. The random walk hypothesis is evaluated against alternatives accommodating either unifractality or multifractality. Critical values for the test statistics are generated using Monte Carlo simulations of random Gaussian innovations. Evidence is reported of multifractality, and the departure from random walk behaviour is statistically significant on standard criteria. The observed pattern is attributed primarily to fat tails in the return probability distribution, associated with volatility clustering in returns measured over various time scales.     

The long-horizon returns behaviour of the Portuguese stock market1

In the last few years several research studies have challenged the traditional weak-form efficiency tests of the stock market. These studies suggested an alternative to the random walk model, containing temporary and permanent components. If stocks follow such a model then the traditional tests, using returns computed for short intervals would be unable to detect them. To investigate the evidence for such models in the Portuguese stock market ten stock indexes were created. This is a pioneer study of the Portuguese stock market, and uses nominal, real and excess returns, computed for longer horizons. Three methodologies were used: variance ratios, ordinary least squares regressions and weighted least squares regressions. The statistical significance of the results was studied using traditional parametric tests as well as non-parametric tests. The evidence is mixed, as the presence of tendencies towards mean aversion and mean reversion were detected. Results also show that the evidence is very sensitive to the methodology used and the signifcance tests performed. These results, however, do not necessarily reject the weak-form market efficiency hypothesis.     

The impact of industrial incidents on stock market volatility

We examine stock market volatility attributed to industrial incidents involving publicly traded US companies, with contributing factors identified as company violations and safety errors, equipment failure, human error and vandalism. Incidents identified as safety violations elicited the highest costs in terms of equity price reductions, but the volatility effects of these incidents tend to mitigate within two weeks. Incidents caused by vandalism experience the sharpest volatility increases, but reduce within two days. Volatility associated with incidents caused by equipment failure tends to persist for almost four weeks. Injuries cost publicly traded companies $14 million each while fatalities lead to equity market capitalisation reductions of between $465 and $720 million. These results shed light on the equity market's role as a driver for enhanced compliance with health and safety regulation and with industry good practice.     

Predicting premiums for the market, size, value, and momentum factors

This paper investigates the out-of-sample predictability of monthly market as well as size, value, and momentum premiums. We use a sample from each of the US and the Swiss stock markets between 1989 and 2007. Using the Swiss sample provides an important new perspective as the repeated evaluation of the same (US) data set leads to data mining problems. To avoid data mining in our predictability study, we test both statistical significance and robustness in the two samples. Our key results are as follows. We find no robust indication that the market premium is predictable, which is also true for the momentum and value premiums. It cannot be excluded that the results from the US may be caused by data mining in light of the results from the Swiss sample. However, the size premium seems to be somewhat predictable, due to the credit spread. We theorize that there are three possible reasons for this rare evidence for predictability. First, predictability may have disappeared over the last decade, as academic research made the respective information public. Second, predictability seems, as we demonstrate, not to be robust to the choice of methodology. Third, robustness tests in the Swiss sample reveal that many of the supposedly statistically significant interrelations from the US sample may be attributed to randomness, which, in that case, would be data mining. Therefore, we think that future discussions of predictability should address the issue of data mining by applying robustness tests.

Sanctions and the Russian stock market

The article presents the robust estimates of extreme movements and heavy-tailedness properties for Russian stock indices returns before and after sanctions were introduced. The obtained results show that almost for all sectoral indices there was a statistically significant increase in volatility. At the same time there is not enough evidence of structural breaks in heavy-tailedness, though some indications of heavier both right and left tails in the post-imposition period can be observed for some indices. However, we cannot with complete certainty directly link the increase in heavy-tailedness with the imposed sanctions. The latter to a considerable extent could be caused by higher country-specific risks due to geopolitical tensions as well as oil prices volatility. Whatever is the cause, any increases in heavy-tailedness can have grave consequences for corporate management, economic modeling and financial stability analysis.