We propose empirical measures of non-execution and picking-off risks and demonstrate that a minimum tick size reduction decreases non-execution risk but increases picking-off risk on the Tokyo Stock Exchange. This results in a higher tendency to submit aggressive orders for some stocks and cancel limit orders for the others. We conclude that our two limit order submission risks are crucial for understanding the results of past empirical studies that examine how minimum tick size reduction impacts limit order submission risks and why traders become aggressive in their order choice. We further show that our proposed measures of non-execution and picking-off risks are better variables than are proxies for the two risks such as spread (which have been suggested by previous empirical studies) or transaction cost measured by the relative tick size when analyzing the determination of the order choice and/or evaluating a minimum tick size reduction policy.

Recent empirical research has documented the clustered volatility and fat tails of return distribution in stock markets, yet returns are uncorrelated over time. Certain agent-based theoretical models attempt to explain the empirical features in terms of investors' order-splitting or dynamic switching strategies, both of which are frequently used by actual stock investors. However, little theoretical research has discriminated among the behavioral assumptions within a model and compared the impacts of the assumptions on the empirical features. Nor has the research simultaneously replicated the return features and empirical features on market microstructure, such as patterns of order choice. This study constructs an artificial limit order market in which investors split orders into small pieces or use fundamental and trend-following predictors interchangeably over time. We demonstrate that, on one hand, the market that features strategies with order splitting and dynamic predictor selection can independently replicate clustered volatility and fat tails with near-zero return autocorrelations. However, we also show that patterns of order choice do not match those found in certain previous empirical studies in both types of economies. Thus, we conclude that, in reality, the two strategies can work to generate the empirical return features but that investors may also use other strategies in actual stock markets. We also demonstrate that the impact of both strategies on the volatility persistence tends to be greater as the number of traders increases in the market; this finding implies that the order-splitting strategy and dynamic predictor selection are more crucial for the empirical phenomena pertaining to larger capital stocks.

This study proposes unexamined technical trading rules, which are dynamically switching strategies among filter, moving average and trading-range breakout rules. The dynamically switching strategy is formulated based on a discrete choice theory consistent with the concept of myopic utility maximization. We utilize the transaction data of the individual stocks listed on the Nikkei 225 from September 1, 2005 to August 31, 2007. We demonstrate that switching strategies produce positive returns and their performance is better than those from the buy-and-hold and non-switching strategies over our sample periods. We also demonstrate equivalent performance for switching with different learning horizons, implying that behavioural heterogeneity of stock investors arises from the coexistence of different strategies with varying degrees of learning horizons. Our result supports several research assumptions and results on agent-based theoretical models that successfully replicate empirical features in financial markets, such as fat tails of return distributions and volatility clustering. However, upon considering the effects of data-snooping bias superior performance disappears.

This paper investigates how the state of the order-book economy influences non-execution and picking-off risks. We utilize data from the limit order book and transactions in individual stocks on the Tokyo Stock Exchange. We demonstrate that, on the one hand, the risk of non-execution increases, while the risk of being picked off, on the other hand, decreases when: 1) the depth on the incoming investor's side becomes thicker, 2) the bid-ask spread becomes narrower, 3) volatility declines, and 4) the depth on the opposite side to the incoming investor becomes thicker. In addition, we report asymmetric determinants of non-execution and picking-off risks between buy and sell limit orders, as well as among our sample firms. We interpret the asymmetry to be attributed to differences in transaction volume and order book thickness between buy and sell sides of the order book as well as among the firms. More transactions lead to higher quote competitions among limit order traders, increasing the thickness of the order book inside of the spread. It then decreases the rate of executions and of being picked off for limit orders existing outside of the spread. Our results suggest that real-time information on order book and transactions is highly valuable to stock investors, who trade individual securities and manage a portfolio of individual stocks, such as ETFs. Our findings assist real stock investors in reducing the monitoring cost, making more profitable order choices among market and limit orders and exposing/hiding/canceling/revising limit orders, and understanding the price formation process in an order-driven market. They are crucial for investors for better risk management in actual stock markets. (C) 2014 Elsevier B.V. All rights reserved.

This paper investigates the expectation formation process of Japanese stock market professionals. By utilizing a monthly forecast survey dataset on the TOPIX distributed by QUICK Corporation, we sort forecasters into buy-side and sell-side professionals. We empirically demonstrate that the buy-side and sell-side professionals use either fundamental or trend-following strategies throughout their expectation formation processes and that they switch between fundamental and trend-following strategies over time. We also discuss that strategy switching can be key in understanding the persistent deviation of the TOPIX from the fundamentals. (c) 2013 Elsevier B.V. All rights reserved.

We document the determinants of the expectation heterogeneity of stock price forecasters on TOPIX. Monthly panel data collected by QUICK Corporation in the Nikkei Group via surveys is utilized in the process. We examine the determinants of expectation heterogeneity by categorizing our sample into buy-side and sell-side professionals and demonstrate that the co-existence of different types of professionals contributes to the expectation heterogeneity. We show that buy-side and the sell-side professionals, who possess different business goals, differentiate the information contents as well as their interpretations of the same information in their forecasts, contributing to the expectation heterogeneity. In addition, we investigate the interactive expectation formulation of buy-side and sell-side professionals and find that buy-side professionals incorporate the sell side's ideas regarding future stock prices into their own forecasts, but refer exclusively to their own ideas when relating foreign exchange rates to future stock prices. Meanwhile, sell-side professionals tend to utilize buy-side professionals' ideas about future prices in order to improve their research and ingratiate themselves to their clients, that is, to the buy-side professionals. We demonstrate that this interactive expectation formulation also contributes to the generation of the expectation heterogeneity. (c) 2012 Elsevier B.V. All rights reserved.

This paper conducts an intraday technical analysis of individual stocks listed on the Nikkei 225. In addition to the price-based technical rules popularly examined in the literature, we uniquely propose and statistically investigate technical rules that utilize information regarding (1) the order-flow imbalance and (2) the order-book imbalance. Technical analysis using the imbalance-based trading rules is motivated by the evidence presented first in this paper that short-term returns can be predicted from the information regarding the order-flow and order-book imbalances for more than half of Nikkei 225-listed stocks. However, we demonstrate that no strategies, including limit order trading where trading signals are derived from the order-book imbalance, beat the buy-and-hold strategy within our sample. The results imply that past prices and demand/supply imbalances do not contribute to profiting in intraday trading and that non-execution and picking-off risks are too large for limit order trading to be profitable in our sample. (C) 2012 Elsevier B.V. All rights reserved.

Recent empirical research has documented that the state of the limit order book influences stock investors' strategies. Investors place more aggressive orders when the same side of the order book is thicker, and less aggressive orders when it is thinner. We conjecture and demonstrate that this behavior is related to long memories of trading volume, volatility, and order signs in stock markets. We investigate our conjecture in two types of artificial stock markets: a transparent market, in which agents observe all limit orders on both sides of the book and order volumes at those prices before they trade; and a less transparent market, in which agents observe only the best five bid and ask quotes with the depth available at these limit prices. The first market structure resembles certain actual stock exchanges in the level of pre-trade transparency, such as the Australian Stock Exchange, NYSE OpenBook, and the London Stock Exchange, whereas the second market structure is consistent with stock exchanges such as Euronext Paris, the Toronto Stock Exchange, the Tokyo Stock Exchange, and Hong Kong Exchanges and Clearing. We demonstrate that our long memory results are robust with different levels of pre-trade transparency, implying that the strategy constructed by the state of the order book is key for explaining long memories in many actual stock exchanges. (C) 2011 Elsevier B.V. All rights reserved.

Recent agent-based models have demonstrated that agents' herding behavior causes volatility clustering in stock markets. We examine economies where agents herd on others, yet they have limited sets of information on other agents to imitate. In particular, we conduct experiments on economies with agents with different levels of information sharing where agents can imitate: (1) the strategies of others but with an error, (2) the strategies of only a fraction of agents, or (3) the strategies of others, but update their parameters only by a proportion. In each experiment we change the likelihood that agents make errors to copy the strategy of others, the fraction of agents to herd, or the proportion of the parameter that agents update, in order to examine the effect of the different degrees of information sharing on volatility clustering. We show that volatility clustering tends to disappear when agents have limited information on the strategies of others, and agents need to imitate the strategy details of others in order to generate the clustered volatility.

Recent empirical research has documented asymmetric volatility and volatility clustering in stock markets. We conjecture that a limit of arbitrage due to a borrowing constraint and herding behavior by investors are related to these phenomena. This study conducts simulation analyses on a spin model where borrowing constrained agents imitate their nearest neighbors but switch their strategies to a different one intermittently. We show that herding matters for volatility clustering while a borrowing constraint intensifies the asymmetry of volatility through the herding effect. (C) 2009 Elsevier B.V. All rights reserved.

Recent empirical research has documented long-memories of trading volume, volatility, and order-signs in stock markets. We conjecture that traders' order-splitting is related to these empirical features. This study conducts simulations on an order-driven economy where agents split their orders into small pieces and execute piece by piece to reduce price impact. We demonstrate that we can replicate the long-memories in our order-splitting economy and conclude that order-splitting can be a possible cause for these empirical properties.

This paper introduces an order-driven market with heterogeneous investors, who submit limit or market orders according to their own trading rules. The trading rules are repeatedly updated via simple learning and adaptation of the investors. We analyze markets with and without learning and adaptation. The simulation results show that our model with learning and adaptation successfully replicates long-memories in trading volume, stock return volatility, and signs of market orders in an informationally efficient market. We also discuss why evolutionary dynamics are important in generating these features. (c) 2007 Elsevier B.V. All rights reserved.

This paper explores a possible cause of persistence in stock return volatility. Artificial stock markets are examined with different learning mechanisms, i.e. imitative and experiential learning. The simulation result shows that an economy with imitative learning gives rise to persistence of return volatility while an experiential learning economy does not. We find that volatility becomes persistent as investors learn through imitating the prediction methods of others. Imitation is crucial to producing the persistence in stock return volatility.