Do house prices overreact to relevant information? New evidence from the UK housing market

We use recent panel data and various empirical models to investigate the validity of the irrational expectations hypothesis and the feedback theory in the UK housing market. We provide the first empirical evidence to justify the statistically significant and positive feedback causality effect between the changes in bubbles and the contemporaneous changes in house prices. While we have found evidence to support the idea that the irrational expectation hypothesis best fits the UK housing market in the short-run, we failed to find evidence in support of the feedback theory. We observe that an increase in bubbles could cause a subsequent decrease in house prices, ceteris paribus, suggesting that people also learn from their mistakes and attempt to compromise by acting as rationally as possible. Overall, we observe that the causality effects are asymmetrical, being more significant from bubble to house price than they are from house price to bubble

A note on the relationship between high-frequency trading and latency arbitrage

We develop three artificial stock markets populated with two types of market participants — HFT scalpers and aggressive high frequency traders (HFTrs). We simulate real-life trading at the millisecond interval by applying Strongly Typed Genetic Programming (STGP) to real-time data from Cisco Systems, Intel and Microsoft. We observe that HFT scalpers are able to calculate NASDAQ NBBO (National Best Bid and Offer) at least 1.5 ms ahead of the NASDAQ SIP (Security Information Processor), resulting in a large number of latency arbitrage opportunities. We also demonstrate that market efficiency is negatively affected by the latency arbitrage activity of HFT scalpers, with no countervailing benefit in volatility or any other measured variable. To improve market quality, and eliminate the socially wasteful arms race for speed, we propose batch auctions in every 70 ms of trading

An investigation of the behaviour of financial markets using agent-based computational models

PhD ThesisThis thesis aims to investigate the behaviour of financial markets by using agent-based computational models. By using a special adaptive form of the Strongly Typed Genetic Programming (STGP)- based learning algorithm and real historical data of stocks, indices and currency pairs I analysed various stylized facts of financial returns, market efficiency and stock market forecasts. This thesis also sought to discuss the following: 1) The appearance of herding in financial markets and the behavioural foundations of stylised facts of financial returns; 2) The implications of trader cognitive abilities for stock market properties; 3) The relationship between market efficiency and market adaptability; 4) The development of profitable stock market forecasts and the price-volume relationship; 5) High frequency trading, technical analysis and market efficiency. The main findings and contributions suggest that: 1) The magnitude of herding behaviour does not contribute to the mispricing of assets in the long run; 2) Individual rationality and market structure are equally important in market performance; 3) Stock market dynamics are better explained by the evolutionary process associated with the Adaptive Market Hypothesis; 4) The STGP technique significantly outperforms traditional forecasting methods such as Box-Jenkins and Holt-Winters; 5) The dynamic relationship between price and volume revealed inconclusive forecasting picture; 6) There is no definite answers as to whether high frequency trading is harmful or beneficial to market efficiency

The effect of ambiguity on the UK stock market: evidence from a new empirical approach

This study developed a new ambiguity measure using the bid-ask spread. The results suggest that the degree of ambiguity has an impact on the daily UK stock market returns, but ambiguity does not cause changes in the returns. This implies that UK stock prices or returns cannot be predicted using variation in the degree of ambiguity through linear models, such as the VAR model, which was used in the study. The two sets of results in the study show that the degree of ambiguity from the previous two days might affect stock market returns. The authors observe that an increase in the degree of ambiguity two days ago is associated with a positive premium required by the investors. On the other hand, the degree of ambiguity tends to be affected by its past five-day values. Thus, the degree of ambiguity seems to persist for five days until investors update their priors. The intuition behind the result is that the degree of ambiguity can affect the returns of the UK stock market and UK stock market returns can in turn have an impact on the degree of ambiguity. The authors also observe that the degree of ambiguity does not seem to predict stock market returns in the UK when one applies linear models. However, this does not mean that there is no non-linear relationship between the degree of ambiguity and stock market returns or stock returns

Investigating the determinants of dividend policy in emerging markets using a combination of exploratory variables

s. The authors analyze the factors causing dividend policy by utilizing agency cost theory of dividend and transaction cost of dividend by using blue chips companies stock listed in the Indonesian Stock Exchange (IDX) from 2004-2013. They also examine the transaction costs of bid-offer spread and commission as the proxies with agency cost factors of insider ownership and shareholder dispersion. The authors observe that the independent variables affected the dividend policy simultaneously. In addition, they find that the bid-offer spread as a new proxy also had significant effects on the dividend polic

Identification of house price bubbles using user cost in a state space model

This article studies how much variation in house prices results from nonfundamental factors. We propose a relative valuation approach to quantifying a bubble in housing by incorporating the housing User Cost into a state space model. We find that UK house prices were undervalued from January 1995 to May 2001 and subsequently moved into a bubble over the period to October 2012. Our results support the bounded rationality hypothesis in the long run. However, we also find that the irrational and the rational expectation hypotheses can coexist in the short run when explosive bubbles are driven by price dynamics

Investigation of institutional changes in the UK housing market using structural break tests and time-varying parameter models

This paper investigates the effects of institutional changes within the UK housing market in recent decades using structural break tests and time-varying parameter models. This approach is motivated by models of institutional change drawn from the political science literature which focus on the existence of both fast-moving and slow-moving institutional changes and the interactions between them as drivers of the dynamics of asset prices. As a methodological contribution, we use several time-varying parameter models for the first time in investigations of institutional change. Our findings support the existence of both structural breaks and continuous variance in parameters. This contributes to our understanding of the housing market in two respects. Firstly, the dates of structural breaks appear to better match unexpected market shocks rather than remarkable political events, and this supports prior institutional theory. Secondly, assessment of the effect of slow-moving institutional changes shows that people’s biased expectations rather than the economic fundamentals have increasingly played an important role in driving housing prices in the short run although fundamentals continue to drive house prices to converge to their long-run equilibrium

The rise of the machines in commodities markets: new evidence obtained using Strongly Typed Genetic Programming

Abstract Market regulators around the world are still debating whether or not highfrequency trading (HFT) is bene?cial or harmful to market quality. We develop arti?cial commoditiesmarketpopulatedwithHFTscalpersandtraditionalcommoditiestradersusing StronglyTypedGeneticProgramming(STGP)tradingalgorithm.Wesimulatereal-lifecommodities trading at the millisecond timeframe by applying STGP to the S&P GSCI data stamped at the millisecond interval. We observe that HFT scalpers anticipate the order ?ow leading to severe damages to institutional traders. To mitigate the negative implications of HFT scalpers on commodities markets, we propose a minimum resting trading order period of more than 150m