Do order imbalances predict Chinese stock returns? New evidence from intraday data

In this paper we examine whether order imbalances can predict the Chinese stock market returns. We use intraday data, a panel data predictive regression model that accounts for persistent and endogenous order imbalances and cross-sectional dependence in returns, and show that order imbalances predict stock returns from 1-minute trading to 90-minute trading. On the basis of this predictability evidence using multiple trading strategies we show that profits persist during the day. These results imply that a source of Chinese market inefficiency is order imbalances.

Interday and intraday volatility: additional evidence from the Shanghai Stock Exchange

After examining both the interday and intraday return volatility of the Shanghai Composite Stock Index, it was found that the open-to-open return variance is consistently greater than the close-to-close variance. Examining the volatility of interday returns and variance ratio tests with five-minute intervals reveals an L-shaped pattern, or more precisely, two L-shaped patterns, starting with a small hump during both the morning and the afternoon sessions, with the morning session having a much higher interday volatility than the afternoon session. This L -shaped interday volatility is supported by the similarly shaped intraday volatility pattern. This result suggests that the high volatility of intraday returns for the market open is not entirely due to the trading mechanisms (call auction in the market opening) but also due to both the accumulated overnight information and the trading halt effect. The five-minute breaks after the auction and blind auction procedures are the two major driving forces which exaggerate the high intraday volatility observed at the market open.

Intraday volatility interaction between the crude oil and equity markets

This paper investigates the price volatility interaction between the crude oil and equity markets in the US using 5-min data over the period 2009-2012. Our main findings can be summarised as follows. First, we find strong evidence to demonstrate that the integration of the bid-ask spread and trading volume factors leads to a better performance in predicting price volatility. Second, trading information, such as bid-ask spread, trading volume, and the price volatility from cross-markets, improves the price volatility predictability for both in-sample and out-of-sample analyses. Third, the trading strategy based on the predictive regression model that includes trading information from both markets provides significant utility gains to mean-variance investors.

Spread trading strategy for intraday short term interest rate futures markets

Neste trabalho analisaram-se estratégias de spread calendário de contratos futuros de taxa de juros de curto prazo (STIR – Short Term Interest Rate) em operações de intraday trade. O spread calendário consiste na compra e venda simultânea de contratos de STIR com diferentes maturidades. Cada um dos contratos individualmente se comporta de forma aleatória e dificilmente previsível. No entanto, no longo prazo, pares de contratos podem apresentar um comportamento comum, com os desvios de curto prazo sendo corrigidos nos períodos seguintes. Se este comportamento comum for empiricamente confirmado, há a possibilidade de desenvolver uma estratégia rentável de trading. Para ser bem sucedida, esta estratégia depende da confirmação da existência de um equilíbrio de longo prazo entre os contratos e a definição do limite de spread mais adequado para a mudança de posições entre os contratos. Neste trabalho, foram estudadas amostras de 1304 observações de 5 diferentes séries de spread, coletadas a cada 10 minutos, durante um período de 1 mês. O equilíbrio de longo prazo entre os pares de contratos foi testado empiricamente por meio de modelos de cointegração. Quatro pares mostraram-se cointegrados. Para cada um destes, uma simulação permitiu a estimação de um limite que dispararia a troca de posições entre os contratos, maximizando os lucros. Uma simulação mostrou que a aplicação deste limite, levando em conta custos de comissão e risco de execução, permitiria obter um fluxo de caixa positivo e estável ao longo do tempo.

Il mercato e-MID: intraday e crisi finanziaria

Il lavoro di tesi ha l’obiettivo di dare seguito e profondità alle verifiche già condotte negli anni scorsi da Monticini e Baglioni, da Angelini e da Vento e La Ganga. Nel dettaglio nella serie storica dei tassi scambiati, sul comparto ON, del mercato e-Mid, riscontrano empiricamente che i tassi scambiati nelle prime ore del mattino sono in media superiori rispetto a quelli del pomeriggio. In pratica emerge un costo orario positivo. Il campione è costituito da 196.571 contratti eseguiti tra il 1 gennaio 2007 ed il 31 marzo 2009, sul comparto ON. Le analisi si sono concentrate sull’intensità del reversal patttern, oltre che su base giornaliera anche su base settimanale (day of the week) e su base mensile (month of the year effect). Le medesime analisi sono state condotte sull’ultimo giorno del periodo di mantenimento, e su particolari giorni che hanno scandito la crisi finanziaria. Per osservare la dinamica del reversal pattern prima e durante la crisi, le medesime verifiche sono condotte su tre sub campioni. Il primo considera tutti i contratti dall’inizio e fino all’8 agosto compreso; il secondo da quest’ultimo e fino al 12 settembre 2008 compreso; l’ultimo dal fallimento della Lehman e fino al 31 marzo 2009. I risultati confermano la presenza del reversal, per tutti i giorni e per tutti i mesi. Tuttavia quando il campione è suddiviso, le stime del primo campione, perdono di significatività, in particolare il giovedì ed il mese di febbraio. Sel medesimo periodo di campionamento al fine di verificare se il reversal pattern sia attribuire in parte anche alla liquidità del mercato, si è indagato su tutte le proposte in bid ed in offer, e dei relativi volumi, ovvero sul bid ask spread, il relativo prezzo medio ed i volumi. Anche in questo caso le analisi si sono concentrate oltre che sul campione originario, anche sui tre sub campioni in cui è stato successivamente suddiviso. . Quando infine si analizzano volumi e proposte, si nota innanzitutto che, al mattino, quando i prezzi scambiati sono maggiori il bid ask spread è minimo. Viceversa il pomeriggio quando il BAS è minore. La stessa dinamica si osserva per lo spread effettivo (SE), il quale è pari alla differenza tra prezzo medio delle contrattazioni e prezzo medio delle proposte. Infine i volumi sia delle proposte che delle contrattazioni, sono maggiori al mattino rispetto al pomeriggio e tendono a decrescere con l’intensificarsi della crisi. Inoltre i volumi in bid sono sempre maggiori rispetto a quelli in offer al mattino piuttosto che al pomeriggio.

Forecasting Intraday Stock Price Trends with Text Mining Techniques

In this paper, we describe NewsCATS (news categorization and trading system), a system implemented to predict stock price trends for the time immediately after the publication of press releases. NewsCATS consists mainly of three components. The first component retrieves relevant information from press releases through the application of text preprocessing techniques. The second component sorts the press releases into predefined categories. Finally, appropriate trading strategies are derived by the third component by means of the earlier categorization. The findings indicate that a categorization of press releases is able to provide additional information that can be used to forecast stock price trends, but that an adequate trading strategy is essential for the results of the categorization to be fully exploited.

A machine learning approach to intraday trading on foreign exchange markets

Foreign Exchange trading has emerged in recent times as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process will be very helpful. In this paper we try to create such a system using Machine learning approach to emulate trader behaviour on the Foreign Exchange market and to find the most profitable trading strategy.

Intraday trading patterns in London listed exchange traded funds

In this paper we examine the intraday trading patterns of Exchange Traded Funds (ETFs) listed on the London Stock Exchange. ETFs have been shown to be characterised by much lower bid–ask spread costs and by lower levels of information asymmetry than individual securities. One possible explanation for intraday trading patterns is that concentration of trading arises at the start of the trading day because informed traders have private information that quickly diminishes in value as trading progresses. Since ETFs have lower trading costs and lower levels of information asymmetry we would expect these securities to display less pronounced intraday patterns than individual securities. We fail to find that ETFs are characterised by concentrated trading bouts during the day and therefore find support for the argument that information asymmetry is the cause of intraday volume patterns in stock markets. We find that ETF bid–ask spreads and volatility are elevated at the open but not at the close. This lends support to the “accumulation of information” explanation that sees high spreads and volatility at the open as a consequence of information accumulating during a market closure and impacting on the market when it next opens.

Modelling intraday trading volume

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An autoregressive conditional filtering process to remove intraday seasonal volatility and its application to testing the noisy rational expectations model

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.

An Autoregressive Conditional Filtering Process to remove Intraday Seasonal Volatility and its Application to Testing the Noisy Rational Expectations Model

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.

Intraday return predictability, portfolio maximisation, and hedging

We examine whether intraday Chinese return predictability is linked to optimal portfolio holding and hedging. We find that: (1) S&P500 futures returns only predict Chinese spot market returns in up to 5-minute of trading with predictability disappearing at higher frequencies of trade; (2) the portfolio weight is maximised at the 5-minute trading frequency, when predictability is the strongest; and (3) when predictability is the strongest, significantly less shorting of the futures is required to minimise risk when a long position is taken in the Chinese market.

Three essays on the intraday dynamics of the foreign exchange market

Containing three essays on the intraday dynamics of the foreign exchange market, the dissertation highlights the role of higher-moments in improving the forecasting ability of exchange rates models while contributing to the literature through the identification of new calendar anomalies in the currency market which has implications for regulators and investors.