The Effect of Investor Sentiment on Feedback Trading and Trading Frequency: Evidence from Taiwan Intraday Data

Abstract

Although extensive literature has suggested that investor sentiment may be one of the most important factors in explaining investor trading frequency and trading strategies, how individual investors are significantly influenced by sentiment remains underexplored. The feature of numerous individual investors in the Taiwan stock market provides an avenue to examine the relationship of investor sentiment to trading frequency and positive-feedback trading according to intraday data. Using a vector autoregression model to measure feedback trading in one-minute intervals, we find that trading frequency appears to increase in periods of rising market, suggesting that investor sentiment–driven trading increases market trading frequency without relying on past experiences to conduct trading behavior.     

Pairs trading with a mean-reverting jump–diffusion model on high-frequency data

This paper develops a pairs trading framework based on a mean-reverting jump–diffusion model and applies it to minute-by-minute data of the S&P 500 oil companies from 1998 to 2015. The established statistical arbitrage strategy enables us to perform intraday and overnight trading. Essentially, we conduct a three-step calibration procedure to the spreads of all pair combinations in a formation period. Top pairs are selected based on their spreads’ mean-reversion speed and jump behaviour. Afterwards, we trade the top pairs in an out-of-sample trading period with individualized entry and exit thresholds. In the back-testing study, the strategy produces statistically and economically significant returns of 60.61% p.a. and an annualized Sharpe ratio of 5.30, after transaction costs. We benchmark our pairs trading strategy against variants based on traditional distance and time-series approaches and find its performance to be superior relating to risk–return characteristics. The mean-reversion speed is a main driver of successful and fast termination of the pairs trading strategy.     

The profitability of pairs trading strategies: distance,cointegration and copula methods

We perform an extensive and robust study of the performance of three different pairs trading strategies—the distance, cointegration and copula methods—on the entire US equity market from 1962 to 2014 with time-varying trading costs. For the cointegration and copula methods, we design a computationally efficient two-step pairs trading strategy. In terms of economic outcomes, the distance, cointegration and copula methods show a mean monthly excess return of 91, 85 and 43 bps (38, 33 and 5 bps) before transaction costs (after transaction costs), respectively. In terms of continued profitability, from 2009, the frequency of trading opportunities via the distance and cointegration methods is reduced considerably, whereas this frequency remains stable for the copula method. Further, the copula method shows better performance for its unconverged trades compared to those of the other methods. While the liquidity factor is negatively correlated to all strategies’ returns, we find no evidence of their correlation to market excess returns. All strategies show positive and significant alphas after accounting for various risk-factors. We also find that in addition to all strategies performing better during periods of significant volatility, the cointegration method is the superior strategy during turbulent market conditions.     

An analysis of commodity markets: What gain for investors?

In this paper we study whether the commodity futures market predicts the commodity spot market. Using historical daily data on four commodities—oil, gold, platinum, and silver—we find that they do. We then show how investors can use this information on the futures market to devise trading strategies and make profits. In particular, dynamic trading strategies based on a mean–variance investor framework produce somewhat different results compared with those based on technical trading rules. Dynamic trading strategies suggest that all commodities are profitable and profits are dependent on structural breaks. The most recent global financial crisis marked a period in which commodity profits were the weakest.     

Insolvent trading and voluntary administration in Australia: economic winners and losers?

Australian directors who incur debts while their companies are insolvent can be pursued by the corporate regulator for compensation when their companies fail. Under the Australian insolvent trading laws, directors no longer experience ‘true’ limited liability, and as expected, they adjust their behaviour as a result. Identifying director's rational behaviour in an insolvent trading world is difficult as there are no formal economic models of director decision-making under Australian current corporate law. In this paper, we develop such a model primarily for private companies. We incorporate the threat of insolvent trading as well as director's tactical use of voluntary administration to avoid insolvent trading litigation. We show that neither a combination of insolvent trading or voluntary administration can simultaneously ensure creditors-best outcomes, eliminate insolvent trading and reduce director underinvestment.     

Heterogeneous real-time trading strategies in the foreign exchange market

The foreign exchange (FX) market is worldwide, but the dealers differ in their geographical locations (time zones), working hours, time horizons, home currencies, access to information,transaction costs, and other institutional constraints. The variety of time horizones is large: from intra-day dealers, who close their positions every evening, to long-term investors and central banks. Depending on the constraints, the different market participats need different strategies to reach their goal, which is usually maximizing the profit, or rather a utility function including risk. Different intra-day trading strategies can be studied only if high-density data are available. Oslen & Associates (O & A) has collected and analysed large amounts of FX quotes by market makers around the clock (up to 5000 non-equally spaced prices per day for the German mark against US$). Based on these data, a set of real-time intra-day trading models has been developed. These models give explicit trading recommendations under realistic constraints. They are allowed to trade only during the opening hours of a market, depending on the time zone and local holidays. The models have been running real-time for more than three years, thus leading to an ex ante test. The test results, obtained with a risk-sensitive performance measure, are presented. All these trading models are profitable, but they differ in their risk behaviour and dealing frequency. If a certain profitable intra-day algorithm is tested with different working hours, its success can considerably change. A systematic study shows that the best choice of working hours is usually when the most important markets for the particular FX rate are active. All the results demonstrate that the assumption of a homogeneous 24-hour FX market with identical dealers, following an identical ‘rational expectation’, is far from reality. To explain the market dynamics, a heterogeneous model of the market with different types of dealers is more appropriate.     

Short-term Dynamics in the Cyprus Stock Exchange

Abstract

This paper investigates the short-term dynamics of stock returns in an emerging stock market namely, the Cyprus Stock Exchange (CYSE). Stock returns are modelled as conditionally heteroscedastic processes with time-dependent serial correlation. The conditional variance follows an EGARCH process, while for the conditional mean three nonlinear specifications are tested, namely: (a) the LeBaron exponential autoregressive model; (b) the Sentana and Wadhwani positive feedback trading model; and finally (c) a model that nests both (a) and (b). There is an inverse relationship between volatility and autocorrelation consistent with the findings from several other stock markets, including the US. This pattern could be the manifestation of a certain form of noise trading namely positive feedback trading or, momentum trading strategies. There is little evidence that market declines are followed with higher volatility than market advances, the so-called ‘leverage effect’, that has been observed in almost all developed stock markets. In out of sample forecasts, the nonlinear specifications provide better results in terms of forecasting both first and second moments of the distribution of returns.     

Optimal commodity asset allocation with a coherent market risk modeling

This paper fills a fundamental gap in commodity price risk management and optimal portfolio selection literatures by contributing a thorough reflection on trading risk modeling with a dynamic asset allocation process and under the supposition of illiquid and adverse market settings. This paper analyzes, from a portfolio managers' perspective, the performance of liquidity adjusted risk modeling in obtaining efficient and coherent investable commodity portfolios under normal and adverse market conditions. As such, the author argues that liquidity risk associated with the uncertainty of liquidating multiple commodity assets over given holding periods is a key factor in formalizing and measuring overall trading risk and is thus an important component to model, particularly in the wake of the repercussions of the recent 2008 financial crisis. To this end, this article proposes a practical technique for the quantification of liquidity trading risk for large portfolios that consist of multiple commodity assets and whereby the holding periods are adjusted according to the specific needs of each trading portfolio. Specifically, the paper proposes a robust technique to commodity optimal portfolio selection, in a liquidity-adjusted value-at-risk (L-VaR) framework, and particularly from the perspective of large portfolios that have both long and short positions or portfolios that consist of merely pure long trading positions. Moreover, in this paper, the author develops a portfolio selection model and an optimization-algorithm which allocates commodity assets by minimizing the L-VaR subject to applying credible operational and financial constraints based on fundamental asset management considerations. The empirical optimization results indicate that this alternate L-VaR technique can be regarded as a robust portfolio management tool and can have many uses and applications in real-world asset management practices and predominantly for fund managers with large commodity portfolios.     

TSFDC: A trading strategy based on forecasting directional change

Directional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. In this paper, we develop a contrarian trading strategy named TSFDC. TSFDC is based on a forecasting model which aims to predict the change of the direction of market's trend under the DC context. We examine the profitability, risk and risk‐adjusted return of TSFDC in the FX market using eight currency pairs. The results suggest that TSFDC outperforms the buy and hold approach and another DC‐based trading strategy.     

“Swimming Ducks Forecast the Coming of Spring”—The predictability of aggregate insider trading on future market returns in the Chinese market

This study systematically examines the ability of aggregate insider trading to predict future market returns in the Chinese A-share market. After controlling for the contrarian investment strategy, aggregate executive(large shareholder)trading conducted over the past six months can predict 66%(72.7%) of market returns twelve months in advance. Aggregate insider trading predicts future market returns very accurately and is stronger for insiders who have a greater information advantage(e.g., executives and controlling shareholders).Corporate governance also affects the predictability of insider trading. The predictability of executive trading is weakest in central state-owned companies,probably because the "quasi-official" status of the executives in those companies effectively curbs their incentives to benefit from insider trading.The predictive power of large shareholder trading in private-owned companies is higher than that in state-owned companies, probably due to their stronger profit motivation and higher involvement in business operations. This study complements the literature by examining an emerging market and investigating how the institutional context and corporate governance affect insider trading.     

Pairs trading in the UK equity market: risk and return

In this paper, we provide the first comprehensive UK evidence on the profitability of the pairs trading strategy. Evidence suggests that the strategy performs well in crisis periods, so we control for both risk and liquidity to assess performance. To evaluate the effect of market frictions on the strategy, we use several estimates of transaction costs. We also present evidence on the performance of the strategy in different economic and market states. Our results show that pairs trading portfolios typically have little exposure to known equity risk factors such as market, size, value, momentum and reversal. However, a model controlling for risk and liquidity explains a far larger proportion of returns. Incorporating different assumptions about bid-ask spreads leads to reductions in performance estimates. When we allow for time-varying risk exposures, conditioned on the contemporaneous equity market return, risk-adjusted returns are generally not significantly different from zero.     

Can commodity futures be profitably traded with quantitative market timing strategies?

Quantitative market timing strategies are not consistently profitable when applied to 15 major commodity futures series. We conduct the most comprehensive study of quantitative trading rules in this market setting to date. We consider over 7000 rules, employ two alternative bootstrapping methodologies, account for data-snooping bias, and consider different time periods. We cannot rule out the possibility that trading rules compliment some other trading strategy or that some traders may have success using a specific rule on its own, but we do conclusively show that none of these rules beat the market any more than expected given random data variation.     

全球股票市场交易费用变动趋势研究

各国证券交易所为提高市场质量和增强国际竞争力,进行了股票市场交易费用结构与费率的频繁调整。本文首先总结了美国、英国、日本、香港等国家和地区交易所现行交易费用的结构特征,并以纽约证券交易所和伦敦证券交易所为案例估算了交易费率的调整比率。其次,本文运用2002年至2010年期间主要海外证券交易所的财务数据和市场数据,分析交易费用变动与交易所经营业绩的互动关系。最后,我们对欧美市场交易费用的大幅下调进行了成因分析,并探讨我国证券交易所内外部环境及经营特点与海外市场存在的差异,提出相关启示和思考。     

A pairs trading strategy based on linear state space models and the Kalman filter

Among many strategies for financial trading, pairs trading has played an important role in practical and academic frameworks. Loosely speaking, it involves a statistical arbitrage tool for identifying and exploiting the inefficiencies of two long-term, related financial assets. When a significant deviation from this equilibrium is observed, a profit might result. In this paper, we propose a pairs trading strategy entirely based on linear state space models designed for modelling the spread formed with a pair of assets. Once an adequate state space model for the spread is estimated, we use the Kalman filter to calculate conditional probabilities that the spread will return to its long-term mean. The strategy is activated upon large values of these conditional probabilities: the spread is bought or sold accordingly. Two applications with real data from the US and Brazilian markets are offered, and even though they probably rely on limited evidence, they already indicate that a very basic portfolio consisting of a sole spread outperforms some of the main market benchmarks.     

国外高频交易的发展现状及启示

证券市场上的高频交易模式大体上分为四类:订单拆分策略、做市交易策略、定量化交易策略和其他策略。研究发现:(1)高频交易降低了买卖价差,提高了市场流动性,而并没有增加市场波动率,甚至反而可能降低了市场波动率;(2)没有发现高频交易者存在系统性抢单行为(并不排除有特定高频交易者存在此类行为);(3)学术研究认为高频交易有导致市场风险的可能性,但是事件调查大多认为高频交易不是引发市场风险的罪魁祸首。本文认为,对高频交易的监管应该注重抓住重点区别对待,以维护公平、透明、高效的市场秩序。     

SEC product-line reporting and market efficiency

This study tests the efficiency of the securities market with respect to non-public segment earnings data for 1967–1969 which was first made public by many firms in 1970 SEC 10-K reports. Trading rule strategies are proposed in which segment-based earnings forecasts are compared to consolidated-based forecasts to anticipate ‘unexpected’ changes in earnings. Using the ‘market model’ to eliminate market related movements in security prices, average monthly abnormal returns conditional on this segment-based strategy are estimated for 1968, 1969, and 1970 for two groups of firms: (1) ‘Non-disclosure’ firms that did not publicly report either segment revenue or profit data prior to 1970, and (2) ‘partial disclosure’ firms that publicly reported segment revenue information, but no segment profits, prior to 1970.The results suggest that the market was not efficient with respect to the non-public segment revenue and profit data of non-disclosure firms for 1968–1969. However, this finding could not be replicated for 1970. The average monthly abnormal returns conditional on the segment-based trading rule strategy were found to be relatively small for the partial disclosure firms. This suggests that segment revenue data can be used to successfully anticipate changes in total entity earnings which would otherwise be ‘unexpected’ if only consolidated data were available.     

Knowing me, knowing you: : Trader anonymity and informed trading in parallel markets

In this paper we empirically analyze whether the degree of trader anonymity is related to the probability of information-based trading. We use data from the German stock market where non-anonymous traditional floor based exchanges co-exist with an anonymous computerized trading system. We use an extended version of the Easley et al. (J. Finance 51 (1996) 1405) model that allows for simultaneous estimation for two parallel markets. We find that the probability of informed trading is significantly lower in the floor based trading system. We further document that the size of the spread and the adverse selection component are positively related to the estimated probabilities of information-based trading.     

Trade Size Preference of Informed Traders in Indian Equity Markets

This study utilized high frequency transactions data to analyze the trade size preference of informed traders in Indian equity markets. It is observed that informed traders at an aggregate level adopt stealth trading strategy, wherein they prefer medium sized trades over large sized trades in order to camouflage their private information. However, the stealth trading behavior varies across stocks, wherein informed traders prefer more large sized trades on firms that are part of an index compared to non-index firms. Trading behavior also varies across other market conditions. It has been noted that informed traders prefer large sized trades during periods of high market thickness, negative returns, and low volatility. This study also provides a rationale for such varied behavior of informed traders.     

Empirical Evidence on the Role of Trading Suspensions in Disseminating New Information to the Capital Market

Abstract:  This paper examines the effect of temporarily suspending the trading of exchange-listed individual stocks. We evaluate whether regulatory authorities can successfully use the mechanism of trading suspension in forcing companies to disclose new and material information to the capital market. Previous studies on trading suspensions mainly concentrate on North-American stock markets and find conflicting results. This study utilizes a new data set comprising of firms listed on Euronext Brussels – an important segment of Europe's leading cross-border exchange. Our results show that suspension is indeed an effective means of disseminating new information. Stock prices adjust completely and instantaneously to the new information released during trading suspensions. We also observe a significant increase in trading volume with the reinstatement of trading. On the other hand, we do not find support for the claim that trading suspensions increase the volatility of stock prices. Overall, our results show the efficacy of trading suspensions in disseminating new information.     

Trading behaviour and market sentiment: Firm-level evidence from an emerging Islamic market

We provide firm-level evidence from an emerging Islamic market that individual investors' trading behaviour causes weekend sentiment. Using data for 285 companies listed on the Dhaka Stock Exchange (DSE) for the period from 2002 to 2019 and applying appropriate econometric techniques, the paper has found evidence of weekend effect both on return and volatility. The results confirm that individual investors' sentiment drives the weekend effect in DSE. ‘Information content theory’ and ‘information processing hypothesis’ work for investors so that the market return and volatility become significantly different on Sunday. The market sentiment effect is significant for smaller firms and low dividend yield firms where individual investors are prevalent, suggesting that trading behaviour of individual investors determines weekend sentiment. A positive feedback relationship exists between returns on Sunday and the previous Thursday for both institutions and individuals. Our results are robust in various alternative specifications.