Investigating efficiency in betting markets: Evidence from the Greek 6/49 Lotto

In this paper we investigate the existence of profit opportunities in the Greek market for the 6/49 Lotto. Under the assumption of random number selection we find evidence suggesting that the market is efficient. Because number selection is found to deviate from randomness, we further investigate the existence of profit opportunities due to number unpopularity. The evidence suggests that although unpopular numbers exist, they are not sufficiently unpopular so as to generate positive expected payoffs.     

Round-the-clock market efficiency and home bias: Evidence from the international Japanese government bonds futures markets

This paper examines how information is processed between almost identical international futures markets: London (LIFFE) and Tokyo (TSE) JGB futures. In these markets, variations in open-to-open changes are virtually the same as those of close-to-close changes, suggesting that information is transmitted efficiently across markets with small opening pricing errors. The overall results confirm market efficiency around the clock, yet the intraday U-shaped patterns in volume/volatility of the London JGB futures suggest home bias in international investments, indicating a less global view of trading than expected. Specifically, at the LIFFE open, London investors rush to rebalance portfolios instead of doing so at the TSE close, which is only one hour before the LIFFE opens.     

Universal features of price formation in financial markets: perspectives from deep learning

Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary relation between order flow history and the direction of price moves. The universal price formation model exhibits a remarkably stable out-of-sample accuracy across a wide range of stocks and time periods. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific.

The universal model—trained on data from all stocks—outperforms asset-specific models trained on time series of any given stock. This weighs in favor of pooling together financial data from various stocks, rather than designing asset- or sector-specific models, as is currently commonly done. Standard data normalizations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations improves forecast accuracy, indicating that there is path-dependence in price dynamics.     

Data preprocessing and data parsimony in corporate failure forecast models: evidence from Australian materials industry

The present study, based on data for delisted and active corporations in the Australian materials industry, is an attempt to develop a systematic way of selecting corporate failure‐related features. We empirically tested the proposed procedure using three datasets. The first dataset contains 82 financial economic factors from the corporation's financial statement. The second dataset comprises 73 relevant financial ratios, which either directly or indirectly measure a corporation's propensity to fail, and are conciliated from the first dataset. The third dataset is a parsimonious dataset obtained from the application of combining a filter and a wrapper to preprocess the first dataset. The robustness of this preprocessed dataset is tested by comparing its performance with the first and second datasets in two statistical (logistic regression and naïve‐Bayes) and two machine learning (decision tree, neural network) classes of prediction models. Tests for prediction accuracies and reliabilities, using the computational (ROC curve, AUC) and the statistical (Cochran's Q statistic) criteria show that the third dataset outperforms the other two datasets in all four predicting models, achieving various accuracies ranges from 81 per cent to 84 per cent.     

New entry,strategic diversity and efficiency in soccer betting markets: the creation and suppression of arbitrage opportunities

ABSTRACT

We find that prices offered by competing bookmakers within the same quote-driven soccer (football) betting market provide arbitrage opportunities. However, the management practices of bookmakers prevent informed bettors exploiting these in practice. We identify two groups of bookmakers, ‘position-takers’ and ‘book-balancers’. Position-takers alter their odds infrequently, while actively restricting informed traders. Book-balancers actively manage inventory by adjusting odds, and place few restrictions on their customers. We identify 545 arbitrage portfolios, and find that around 50% would require a bet on the favourite at the position-taking bookmaker. The management practices of position-takers generally prevent these opportunities being exploited in practice.     

Liquidity and stock returns in pure order-driven markets: evidence from the Australian stock market

This paper examines the relationship between liquidity and stock returns in the pure order-driven stock market of Australia. The bid-ask spread, turnover rate, and amortized spread are used as proxies for liquidity. In addition to liquidity, other factors that have been found to influence stock returns, such as beta and size, are also considered. Seemingly unrelated regressions (SUR) and the cross-sectionally correlated timewise autoregressive (CSCTA) model form the methodological basis for this research. A small liquidity premium is found in the Australian market, which persists for the entire year. There is also strong evidence of a negative size effect.     

Financial liberalization and changes in the dynamic behaviour of emerging market volatility: Evidence from four Latin American equity markets

This paper examines whether the dynamic behaviour of stock market volatility for four Latin American stock markets (Argentina, Brazil, Chile and Mexico) and a mature stock market, that of the US, has changed during the last two decades. This period corresponds to years of significant financial and economic development in these emerging economies during which several financial crises have taken place. We use weekly data for the period January 1988 to July 2006 and we conduct our analysis in two parts. First, using the estimation of a Dynamic Conditional Correlation model we find that the short-term interdependencies between the Latin America stock markets and the developed stock market strengthened during the Asian, Latin American and Russian financial crises of 1997–1998. However, after the initial period of disturbance they eventually returned to almost their initial (relatively low) levels. Second, the estimation of a SWARCH-L model reveals the existence of more than one volatility regime and we detect a significant increased volatility during the period of crisis for all the markets under examination, although the capital flows liberalization process has only caused moderate shifts in volatility.     

Liquidity,information, strategic trading in an electronic order book: New insights from the European carbon markets

The electronic limit order book (LOB hereafter) has rapidly become the primary way of trading European carbon assets over the 4 years of the EU ETS programme (2008–2012). In this first attempt of examining the informational content of an electronic order book, we evidence that order flow imbalances have a moderate capacity to predict short term price changes. However, we find that both LOB slope and immediacy costs help to forecast quote improvements and volatility in the next 30 min. Further, we explain why informed trading is highly influential and show that it consists in mixing order splitting strategies and posting fleeting orders once the asymmetric information is reduced (Rosu, 2009). Overall, the consolidated status of the order book mirrors a high level of market uncertainty and a low degree of informational efficiency. In this way, strategic trading can in itself explain some of order book properties, independently of the degree of traders’ sophistication and market competition.     

Stock market dependence in crisis periods: Evidence from oil price shocks and the Qatar blockade

This paper examines the correlation and the dependence patterns of the Qatar stock market with other markets using copula statistical theory and exploiting new datasets covering the period August 1998 to June 2018. To examine the crisis –specific change in the average degree of dependence we decomposed the data into the time periods before and after oil price shocks and the 2017 political crisis among the Gulf Cooperation Council members (i.e. the Qatari blockade). Our findings from the static copula modelling show that the correlations between the Qatari and the other stock markets significantly change after the oil price and the blockade crisis as well. The degree of change in the correlation is time varying and differs from county-group to another. Moreover, our findings reveals that the 2008 global financial crisis has a stronger impact than the price shocks and political crisis. The findings of the paper are of interest and allow for formulating a reliable and dynamic portfolio design framework for investors and risk managers.     

中国房地产市场价格泡沫与空间传染效应

本文使用对数周期性幂律(Log Period Power Law, LPPL)模型对房地产市场价格泡沫进行测度,运用空间计量模型对我国房地产市场价格泡沫和空间传染效应进行研究。LPPL模型认为由价格泡沫产生并最终破裂的金融市场与地震系统具有很多相似之处,即金融资产的价格呈周期性变化规律,价格持续上涨到临界状态直至反转。本文采用2010年6月至2017年11月间我国100个城市的房地产市场数据对各城市房地产价格泡沫进行测度和物理/经济空间传染效应研究。研究发现,LPPL模型能够对我国100个城市房地产价格泡沫进行甄别且主要存在两种泡沫状态:正向泡沫(房价持续上升)和反转泡沫(房价整体下降却存在反转点)。各个城市(地区)房地产价格具有较强的空间传染性;存在正向泡沫区域的空间传染性相较反转泡沫区域更为明显,在考虑经济空间测度而不是物理空间测度的情况下,各城市间的空间传染性更强。与现有文献不同,我们发现反转泡沫区域的新房价格指数特别是二手房价格指数的上升对周边城市的房地产价格指数存在强烈的正向推高影响。最后,本文发现城市的房地产调控政策在一定程度上抑制了房价传统影响(比如信贷、新房、二手房价等)因素的推高影响,但各城市房地产价格之间的联动变化特征应该引起监管部门的注意。