How does electronic trading affect efficiency of stock market and conditional volatility? Evidence from Toronto Stock Exchange

The present paper investigates informational efficiency and changes in conditional volatility of the TSX before and after the implementation of an automated trading system on April 23, 1997. Using a battery of unit root, stationarity, as well as linear tests, we find that the introduction of electronic trading led to an increase in linearity dependence in TSX daily returns. In addition, when we examined the nonlinearity dependences using powerful econometric tests, we find that electronic trading has increased nonlinear dependencies in return series, which is the main cause of rejecting the Random Walk Hypothesis (RWH). Our results suggest that the automated trading system has negatively affected informational efficiency of the TSX. We also find evidence of long memory following automation which suggests that the introduction of electronic trading has increased the level of persistence of information and trading shocks.     

Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market

We study the impact of algorithmic trading (AT) in the foreign exchange market using a long time series of high‐frequency data that identify computer‐generated trading activity. We find that AT causes an improvement in two measures of price efficiency: the frequency of triangular arbitrage opportunities and the autocorrelation of high‐frequency returns. We show that the reduction in arbitrage opportunities is associated primarily with computers taking liquidity. This result is consistent with the view that AT improves informational efficiency by speeding up price discovery, but that it may also impose higher adverse selection costs on slower traders. In contrast, the reduction in the autocorrelation of returns owes more to the algorithmic provision of liquidity. We also find evidence consistent with the strategies of algorithmic traders being highly correlated. This correlation, however, does not appear to cause a degradation in market quality, at least not on average.     

Insider stock pledging and stock price informativeness: Evidence from India

Insiders’ shares can act as collateral while raising funds from lenders. This study examines the impact of insiders’ stock pledging activities on stock price informativeness using a sample of 1835 Indian firms. Our findings report that insider stock pledging increases the informational efficiency of stock prices. This informational efficiency increases for larger firms with: (1) financial constraints (high leverage and low cash holdings); (2) greater reliance on trade credit; and (3) higher indulgence in related party transactions. We also provide evidence on abnormal share turnover as a trading mechanism through which insider stock pledging is related to stock price informativeness. Our findings are robust across different specifications and after accounting for endogeneity issues.     

基于MF-DFA的中国股指期货价格的多重分形实证研究

本文通过应用多重分形谱分析法和多重分形消除趋势波动分析（MF-DFA）法,研究了新产生的中国股指期货市场的多重分形性。通过对2942个股指期货最后十分钟结算价格的分析,我们发现中国股指期货的收益率具有长程相关性和多重分形性,期货价格波动并不能用单一的标度指数进行充分描述。进一步通过将原始序列和转换后的收益序列进行比较,转换过程包括重排以及相位随机化,我们发现导致中国股指期货市场多重分形性的两种不同成因。研究结果表明,虽然厚尾分布是造成多重分形性的一个方面,但长程相关性才是引起中国股指期货市场多重分形的主要原因。     

Analysing long memory and asymmetries

The paper presents evidence on nonlinearities in Finnish financial time series. The analysis concentrates on the so-called long-memory property which is examined using, various alternative test procedures. This analysis makes use of relatively long monthly Finnish time series which cover the period 1922–1996. The results give some evidence on long memory but one cannot say that the results would overwhelmingly support the existence of long memory in Finnish time series. There are, however, considerable differences between variables and the results are quite sensitive in terms of the treatment of short memory which also applies to different ways of prefiltering the data. Clearly more work is required to obtain more affirmative results in this respect. One way, of doing that is to apply asymmetric time models to find the source of nonlinearity. When that is done with the Finnish data some weak evidence on asymmetry is obtained.     

The informational efficiency and the financial crashes

The evolution of the daily informational efficiency is measured for different stock market indices (Japanese, Malaysian, Russian, Mexican, and the US markets) by using the local entropy and the symbolic time series analysis. There is some evidence that for different stock markets, the probability of having a crash increases as the informational efficiency decreases. Further results suggest that the latter probability also increases for jumping to a less efficient market. In addition, the US stock market seems to be the most structurally efficient and the Russian is the most inefficient, maybe because is a young market, recently established in 1995.     

Long memory in financial markets: A heterogeneous agent model perspective

During last decades, studies on asset pricing models witnessed a paradigm shift from rational expectation and representative agent to an alternative, behavioral view, where agents are heterogeneous and boundedly rational. In this paper, we model the financial market as an interaction of two types of boundedly rational investors — fundamentalists and chartists. We examine the dynamics of the market price and market behavior, which depend on investors' behavior and the interaction of the two types of investors. Numerical simulations of the corresponding stochastic model demonstrate that the model is able to replicate the stylized facts of financial time series, in particular the long-term dependence (long memory) of asset return volatilities. We further investigate the source of the long memory according to asset pricing mechanism of our model, and provide evidences of long memory by applying the modified R/S analysis. Our results demonstrate that the key parameter that has impact on the long memory is the speed of the price adjustment of the market maker at the equilibrium of demand and supply.     

Evidence on the Extent and Potential Sources of Long Memory in U.S. Treasury Security Returns and Yields

Unlike equity returns, many fixed-income return measures appear to display long memory. We show that the extent of long memory differs strongly for gross and excess holding period returns on U.S. Treasury securities. Granger and others have argued that long memory may only reflect infrequent structural breaks. We explore the impact of structural instability on tests for long memory and find only weak indications that it lies behind the long memory in our return series. The evidence of long memory remains strong for yield and term premia series even after accounting for a series of potential underlying structural changes.     

Analysis of efficiency for Shenzhen stock market based on multifractal detrended fluctuation analysis

We divided the whole series of Shenzhen stock market into two sub-series at the criterion of the date of a reform and their scale behaviors are investigated using multifractal detrended fluctuation analysis (MF-DFA). Employing the method of rolling window, we find that Shenzhen stock market was becoming more and more efficient by analyzing the change of Hurst exponent and a new efficient measure, which is equal to multifractality degree sometimes. We also study the change of Hurst exponent and multifractality degree of volatility series. The results show that the volatility series still have significantly long-range dependence and multifractality indicating that some conventional models such as GARCH and EGARCH cannot be used to forecast the volatilities of Shenzhen stock market. At last, the abnormal phenomenon of multifractality degrees for return series is discussed. The results have very important implications for analyzing the influence of policies, especially under the environment of financial crisis.     

Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19

In this paper, we study the long memory behavior of the hourly cryptocurrency returns during the COVID-19 pandemic period. Initially, we apply different tests against the spurious long memory, with the results indicating the presence of true long memory for most cryptocurrencies. Yet, using the multivariate test, the series are found to be contaminated by level shifts or smooth trends. Then, we adopt the wavelet-based multivariate long memory approach suggested by Achard and Gannaz (2016) to model their long memory connectivity. The findings indicate a change in persistence for all series during the sample period. The fractal connectivity clustering indicates a similarity among Ethereum (ETH) and Litecoin (LTC), Monero (XMR), Bitcoin (BTC), and EOC token (EOS), while Stellar (XLM) is clustered away from the remaining series, indicating the absence of any interdependence with other crypto returns. Overall, shocks arising from COVID-19 crisis have led to changes in long-run correlation structure.     

Commodity futures returns: more memory than you might think!†

This paper investigates long-range dependence in 14 commodity and 3 other financial futures returns series from 1993 to 2009 and shows that long memory is a pervasive phenomenon in contrast to the extant evidence. Utilizing a semi-parametric wavelet-based estimator with time windows, the results provide overwhelming evidence of time-varying long-range dependence in all futures returns series. Structural break tests indicate multiple regimes of dependence, in the majority of which the persistence parameter is statistically significant. The results also provide evidence of predominantly negative parameter values which are known as anti-persistence. The latter is consistent with investor overreaction to shocks and suggests temporary departures from market efficiency.     

Relative forecasting performance of volatility models: Monte Carlo evidence

Abstract

A Monte Carlo (MC) experiment is conducted to study the forecasting performance of a variety of volatility models under alternative data-generating processes (DGPs). The models included in the MC study are the (Fractionally Integrated) Generalized Autoregressive Conditional Heteroskedasticity models ((FI)GARCH), the Stochastic Volatility model (SV), the Long Memory Stochastic Volatility model (LMSV) and the Markov-switching Multifractal model (MSM). The MC study enables us to compare the relative forecasting performance of the models accounting for different characterizations of the latent volatility process: specifications that incorporate short/long memory, autoregressive components, stochastic shocks, Markov-switching and multifractality. Forecasts are evaluated by means of mean squared errors (MSE), mean absolute errors (MAE) and value-at-risk (VaR) diagnostics. Furthermore, complementarities between models are explored via forecast combinations. The results show that (i) the MSM model best forecasts volatility under any other alternative characterization of the latent volatility process and (ii) forecast combinations provide systematic improvements upon most single misspecified models, but are typically inferior to the MSM model even if the latter is applied to data governed by other processes.     

Testing for long-term memory in yen/dollar exchange rate

This paper examines evidence of long-term memory in the yen/dollar price change as well as in the daily estimate of volatility of the exchange rate series. The methodology used is due to Lo (1989) which is robust to the presence of heteroscedasticity and is applied to a ten year data set. The result shows no evidence of long-term memory in the price change series indicating efficient pricing by the market participants. The volatility series, however, shows evidence of long-term memory which may have implications for traders dealing with long lived assets.     

Unifractality and multifractality in the Italian stock market

Tests for random walk behaviour in the Italian stock market are presented, based on an investigation of the fractal properties of the log return series for the Mibtel index. The random walk hypothesis is evaluated against alternatives accommodating either unifractality or multifractality. Critical values for the test statistics are generated using Monte Carlo simulations of random Gaussian innovations. Evidence is reported of multifractality, and the departure from random walk behaviour is statistically significant on standard criteria. The observed pattern is attributed primarily to fat tails in the return probability distribution, associated with volatility clustering in returns measured over various time scales.     

Retail investor information demand – speculating and investing in structured products

We study the impact of retail investor information demand on trading in bank-issued investment and leverage structured products, which are specifically designed for retail investors. Stock-specific information demand positively predicts speculative trading activity. Furthermore, we find a positive relationship between market-wide information demand and order aggressiveness and order uncertainty for speculating and investing activity. Whereas information supply is associated with speculative long positions, information demand does not induce investors to be predominantly long or short. Finally, we do not find retail investor information demand to contribute to an upward price pressure on security prices. In contrast, information supply exerts negative price pressure. Overall, retail investor trading in individual stocks is much more strongly influenced by market-wide information demand instead of firm-specific information demand. This implies a low informational efficiency of retail investor speculation and investing activity.     

Fundamentalists,chartists and asset pricing anomalies

This paper investigates whether excess volatility of asset prices and serial correlations of stock monthly returns may be explained by the interactions between fundamentalists and chartists. Fundamentalists forecast future prices cum dividends through an adaptive learning rule. In contrast, chartists forecast future prices based on the observation of past price movements. Numerical simulations reveal that the interplay of fundamentalists and chartists robustly generates excess volatility of asset prices, volatility clustering, trends in prices (i.e. positive serial correlations of returns) over short horizons and oscillations in prices (i.e. negative serial correlations of returns) over long horizons, often observed in financial data. Moreover, we find that the memory of the learning rule plays a key role in explaining the above-mentioned stylized facts. In particular, we establish that excess volatility of asset prices; volatility clustering and autocorrelation of returns at different horizons emerge when fundamentalists have short memory. However, volatility clustering as well as short-run and long-run dependencies, observed in financial time series, are more pronounced when fundamentalists have longer memory.     

Comovements in volatility in the euro money market

This paper assesses the sources of volatility persistence in Euro Area money market interest rates and the existence of linkages relating volatility dynamics. The main findings of the study are as follows. Firstly, there is evidence of stationary long memory, of similar degree, in all series. Secondly, there is evidence of fractional cointegration relationships relating all series, except the overnight rate. The common long memory factor analysis points to a two-factor volatility curve. The most important factor, in terms of proportion of total variance explained, can be interpreted as a level factor (64% of total variance), while the other as a slope factor (13% of total variance). Impulse response analysis and forecast error variance decomposition finally point to non significant forward transmission of liquidity shocks.     

AUTOMOTIVE RECALLS AND INFORMATIONAL EFFICIENCY

This study provides an empirical test of the informational efficiency of the stock market by exploring the stock price and volume patterns exhibited by Chrysler, Ford, and General Motors around the time of announcement of severe automotive recall campaigns. Because information concerning automotive recalls is released to the public via two distinct methods, which differ only with respect to the number of market participants notified of the recall campaigns, a differential performance analysis of stock returns and trading volume around both events provides evidence of the degree of informational aggregation in the stock market for three closely followed U.S. firms. The results of the study fail to support the definitional notion of informational efficiency with respect to the first public release date of severe recalls, as the vast majority of the stock market's response to recall announcements does not occur until the information is reported to all market participants. Further, tests of differential trading volume around the announcements suggest that some members of the financial community may be trading securities on the basis of the information contained in the first public announcement.     

Strategic trading and learning about liquidity

Many practitioners point out that the speculative profits of institutional traders are eroded by the difficulty in gauging the price impact of their trades. In this paper, we develop a model of strategic trading where speculators face such a dilemma because of incomplete information about time-varying market liquidity. Unlike the competitive market makers that they trade against, informed traders do not know the distribution of liquidity (“noise”) trades. Instead, they have to learn about liquidity from past prices and trading volume. This learning implies that strategic trades and market statistics such as informational efficiency are path-dependent on past market outcomes. Our paper also has normative implications for practitioners.     

Dependence and mean reversion in stock prices: The case of the MENA region

This paper provides empirical evidence on the long memory behavior of the stock markets of Egypt, Jordan, Morocco, and Turkey. To test for long memory in the returns and volatility, we employ the modified rescaled range statistic R/S proposed by Lo [Lo, A.W., 1991. Long-term memory in stock market prices. Econometrica 59, 1279–1313] and the recently proposed rescaled variance V/S statistic developed by Giraitis et al. [Giraitis, L., Kokoszka, P.S. Leipus, R., Teyssiere, G., 2003. Rescaled variance and related tests for long memory in volatility and levels. J. Econ. 112, 265–294]. Further analysis is conducted by employing the ARFIMA (p, d, q) model to estimate the long memory parameters. Egypt and Morocco show evidence of long memory in the return series, while Jordan and Turkey display negative persistence. For the volatility series, long memory is conclusively demonstrated for all markets. Then, we compare the forecasting performance of ARMA and ARFIMA models and find that the ARFIMA model outperforms in out-of-sample forecasting of the markets. Our results should be useful to regulators, practitioners and derivative market participants, whose success depends on the ability to forecast stock price movements in these markets.