Predictability of stock markets with disequilibrium trading. A commentary paper

The paper focuses on the problem of predictability of stock market returns with disequilibrium trading. It is shown that the predictability of returns may be the consequence of quantity constraints appearing in the markets due to the imposition of administrative restrictions on trade. A relevant test of predictability for the Warsaw Stock Exchange (WSE) based on information referring to disequilibrium states occurrence is proposed. The empirical results of its application to the WSE on a sample containing session-to-session observations from the period January 1995 to December 1999 strongly support the hypothesis of predictability.     

Tick size and market quality: Simulations based on agent‐based artificial stock markets

This paper investigates the way that minimum tick size affects market quality based on an agent‐based artificial stock market. Our results indicate that stepwise and combination systems can promote market quality in certain aspects, compared with a uniform system. A minimal combination system performed the best to improve market quality. This is the first study to analyse tick size systems that remain at the theory stage and compare four types of system under the same experimental environment. The results suggests that a minimal combination system could be considered a new direction for market policy reform to improve market quality.     

Margin practices and requirements during the National Banking Era: An early example of macro‐prudential regulation

The New York stock market was plagued by a series of financial crises during the National Banking Era, culminating in the Panic of 1907. The traditional view holds that the crises were rooted in structural flaws related to trade settlement as well as excessive and indiscriminate margin lending that remained unaddressed until the formation of the Federal Reserve Bank. An examination of the historical record, however, shows that brokers sought to control contagion and spillover effects through reform of the settlement process and by modulating margin lending rates and maintenance requirements according to macroeconomic conditions, counterparty credit‐worthiness and market volatility. Using newly gathered archival data, we show that the New York Stock Exchange enacted macro‐prudential regulations that may have reduced the severity of crises during this period. By providing early evidence of private sector responses to rising systemic risk, the paper addresses an important aspect of early market microstructure.     

Forecasting stock market returns over multiple time horizons

In this paper, we seek to demonstrate the predictability of stock market returns and explain the nature of this return predictability. To this end, we introduce investors with different investment horizons into the news-driven, analytic, agent-based market model developed in Gusev et al. [Algo. Finance, 2015, 4, 5–51]. This heterogeneous framework enables us to capture dynamics at multiple timescales, expanding the model’s applications and improving precision. We study the heterogeneous model theoretically and empirically to highlight essential mechanisms underlying certain market behaviours, such as transitions between bull and bear markets and the self-similar behaviour of price changes. Most importantly, we apply this model to show that the stock market is nearly efficient on intraday timescales, adjusting quickly to incoming news, but becomes inefficient on longer timescales, where news may have a long-lasting nonlinear impact on dynamics, attributable to a feedback mechanism acting over these horizons. Then, using the model, we design algorithmic strategies that utilize news flow, quantified and measured, as the only input to trade on market return forecasts over multiple horizons, from days to months. The backtested results suggest that the return is predictable to the extent that successful trading strategies can be constructed to harness this predictability.     

IPO Placement Risk and the Number of Co‐Managers

Previous studies show that co‐managers mainly affect initial public offering (IPO) aftermarket activities. We investigate the role of co‐managers in IPO pre‐market activities. We argue that co‐managers help reduce IPO placement risk and hypothesize that IPO issuers hire more co‐managers when placement risk is higher. We find the number of co‐managers is positively associated with three proxies for placement risk. IPOs with more price uncertainty and high‐tech IPOs hire more co‐managers, while IPOs in regulated industries hire fewer co‐managers. We also find larger IPOs, recent IPOs, and IPOs with more reputable lead underwriters hire more co‐managers.     

Stock price firm‐specific information on the choice of stock payment in mergers and acquisitions

Previous studies on the choice of stock payment in M&A mainly focus on managerial private information. This study shows that managers also learn new firm‐specific information from financial markets in making this decision. The acquirer's stock price firm‐specific information increases the stock‐payment‐to‐Q sensitivity. The target's stock price firm‐specific information decreases the stock payment probability. Further analyses on deal and firm characteristics as well as shareholder wealth in stock mergers support the managerial learning argument. Overall, this study highlights a new set of information that affects the form of merger payment in mergers and acquisitions.     

Co‐owned Local Government Organisations: Conditions for Strategy Development

Local governments tend to join resources and create co‐owned organisations to solve the supply of public services. From a strategic perspective on local government cooperation, this paper analyses the conditions for strategy development in a co‐owned municipal organisation. The empirical data stem from a longitudinal case study of a Swedish provider of water and sewerage services. We show that difficulties in formulating and agreeing upon strategies are not dependent on the structural form. Instead, there is an intrinsic dilemma when municipalities are to create a joint strategy at the same time as each municipality is sovereign to decide on all local matters.     

Semi‐transparency,dealership market,and foreign exchange market quality

This paper examines the effects of a semi-transparency event, the introduction of the electronic trading system (EBS), on the market quality of a typical dealership market – the FX market. We find that increasing transparency leads to smaller quote disagreement among dealers and higher trading volume, but the beneficial effects are bigger for uninformed dealers than informed dealers. We also find that information efficiency improves overall in the semi-transparent system; however, informed dealers are found to quote less aggressively in the more transparent market. We conclude that semi-transparency raises market quality in general, but that it is the uninformed dealers who benefit more from this increased quality.     

Risk‐based explanation for the book‐to‐market effect

This paper proposes a risk‐based explanation for the book‐to‐market (B/M) effect. I decompose B/M into net operating asset‐to‐market (NOA/M) and net financing asset‐to‐market (NFA/M) components. Portfolio analysis shows that (i) positive B/M, NOA/M and NFA/M are positively related to future returns and (ii) negative B/M, NOA/M and NFA/M are negatively related to future returns. To the extent that positive B/M, NOA/M and NFA/M act as measures of asset risk and negative B/M, NOA/M and NFA/M act as inverse measures of borrowing risk, the nonlinear relations between B/M, NOA/M and NFA/M and future returns provide some evidence to support the risk‐based explanation for the book‐to‐market effect in stock returns.     

The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading

In financial trading, technical and quantitative analysis tools are used for the development of decision support systems. Although these traditional tools are useful, new techniques in the field of machine learning have been developed for time‐series forecasting. This paper analyses the role of attribute selection on the development of a simple deep‐learning ANN (D‐ANN) multi‐agent framework to accomplish a profitable trading strategy in the course of a series of trading simulations in the foreign exchange market. The paper evaluates the performance of the D‐ANN multi‐agent framework over different time spans of high‐frequency (HF) intraday asset time‐series data and determines how a set of the framework attributes produces effective forecasting for profitable trading. The paper shows the existence of predictable short‐term price trends in the market time series, and an understanding of the probability of price movements may be useful to HF traders. The results of this paper can be used to further develop financial decision‐support systems and autonomous trading strategies for the financial market.     

Asymmetric effects of voluntary disclosure on stock liquidity: evidence from 8‐K filings

This paper examines whether the change in stock liquidity subsequent to voluntary disclosure is different between good news and bad news. Using voluntary 8‐K filings, we find that the increase in stock liquidity is more pronounced for firms with good news disclosure than for firms with bad news disclosure. In addition, such findings are stronger when a firm is less visible and when the short‐selling costs are high, suggesting that these two factors play an important role in increasing stock liquidity. Overall, this paper provides evidence that the tone of voluntary 8‐K news is an important determinant of stock liquidity.     

A State‐Price Volatility Index for China's Stock Market

This study derives a volatility index for China's stock market with similar properties to the Chicago Board Options Exchange Volatility Index (the ‘VIX’). A long‐term benchmark of historic volatility expectations is here presented for China from 1996 to 2011, called the ‘China‐ State‐Price Volatility (SPV)’. Construction of this index involves the use of SPV methodology, using implied volatility calculated from options on the Hang Seng China Enterprise Index (HSCEI). Historic open–high–low–close volatility on the Shanghai Composite Index (SHCI) is also used to extend the benchmark prior to the availability of HSCEI options data. The China‐SPV successfully forecasts realised volatility for the Shanghai Stock Exchange. It also serves as a ‘fear gauge’ in that it monitors daily movements of the SHCI in the same way that the VIX monitors the S&P 500 index (Whaley, 2009). The China‐SPV evidences an increasing relation with the US market in terms of the dynamic correlation of levels and changes with the VIX since 2004.     

Trend‐Following Trading Strategies in U.S. Stocks: A Revisit

We show that previous findings regarding the profitability of trend‐following trading rules over intermediate horizons in futures markets also extend to individual U.S. stocks. Portfolios formed using technical indicators such as moving average or channel ratios, without employing cross‐sectional rankings of any kind, tend to perform about as well as the more commonly examined momentum strategies. The profitability of these strategies appears significant, both statistically and economically, through 2007, but evidence of profitability vanishes after 2007. Market‐state dependence, while clearly present, does not explain the post‐2007 reduction in returns to these strategies.     

Risk‐Adjusted Long‐Term Contrarian Profits: Evidence from Non‐S&P 500 High‐Volume Stocks

Can trading volume help unravel the long‐term overreaction puzzle? With portfolios of non‐S&P 500 NYSE stocks, we show that (1) both the high‐ and low‐volume (abnormal volume) contrarian portfolios earn a much higher market‐adjusted excess return than the normal‐volume contrarian portfolio, (2) however, when leverage‐induced risk is factored in, excess returns from contrarian portfolios with normal‐ and low‐volume stocks are insignificant, (3) only excess returns from high‐volume contrarian stocks are significant and cannot be explained by the time‐varying risk and return framework, and (4) such high‐volume, risk‐adjusted excess returns arise mainly from winner (glamour) stocks.     

A predictive system integrating intrinsic mode functions,artificial neural networks,and genetic algorithms for forecasting S&P500 intra‐day data

There is an abundant literature on the design of intelligent systems to forecast stock market indices. In general, the existing stock market price forecasting approaches can achieve good results. The goal of our study is to develop an effective intelligent predictive system to improve the forecasting accuracy. Therefore, our proposed predictive system integrates adaptive filtering, artificial neural networks (ANNs), and evolutionary optimization. Specifically, it is based on the empirical mode decomposition (EMD), which is a useful adaptive signal‐processing technique, and ANNs, which are powerful adaptive intelligent systems suitable for noisy data learning and prediction, such as stock market intra‐day data. Our system hybridizes intrinsic mode functions (IMFs) obtained from EMD and ANNs optimized by genetic algorithms (GAs) for the analysis and forecasting of S&P500 intra‐day price data. For comparison purposes, the performance of the EMD‐GA‐ANN presented is compared with that of a GA‐ANN trained with a wavelet transform's (WT's) resulting approximation and details coefficients, and a GA‐general regression neural network (GRNN) trained with price historical data. The mean absolute deviation, mean absolute error, and root‐mean‐squared errors show evidence of the superiority of EMD‐GA‐ANN over WT‐GA‐ANN and GA‐GRNN. In addition, it outperformed existing predictive systems tested on the same data set. Furthermore, our hybrid predictive system is relatively easy to implement and not highly time‐consuming to run. Furthermore, it was found that the Daubechies wavelet showed quite a higher prediction accuracy than the Haar wavelet. Moreover, prediction errors decrease with the level of decomposition.     

Real‐Time Forecasting and Political Stock Market Anomalies: Evidence for the United States

Using monthly data from 1953 to 2003, we apply a real‐time modeling approach to investigate the implications of U.S. political stock market anomalies for forecasting excess stock returns in real‐time. Our empirical findings show that political variables, chosen on the basis of widely used model‐selection criteria, are often included in real‐time forecasting models. However, political variables do not contribute systematically to improving the performance of simple trading rules. For this reason, political stock market anomalies are not necessarily an indication of market inefficiency.     

Testing for financial contagion based on a nonparametric measure of the cross‐market correlation

When contagion is defined as a significant increase in market comovement after a shock to one country, we propose a test for financial contagion based on a nonparametric measure of the cross-market correlation. Monte Carlo simulation studies show that our test has reasonable size and good power to detect financial contagion, and that Forbes and Rigobon's test (2002) is relatively conservative, indicating that their test tends not to find evidence of contagion when it does exist. Applying our test to investigate contagion from the 1997 East Asian crisis and the 2007 Subprime crisis, we find that there existed international financial contagion from the two financial crises.