Dynamic trading with uncertain exit time and transaction costs in a general Markov market

This paper investigates a dynamic trading problem with transaction cost and uncertain exit time in a general Markov market, where the mean vector and covariance matrix of returns depend on the states of the stochastic market, and the market state is regime switching in a time varying state set. Following the framework proposed by Gârleanu and Pedersen (2013), the investor maximizes his or her multi-period mean–variance utility, net of quadratic transaction costs capturing the linear price impact where trades lead to temporary linear changes in prices. The explicit expression for the optimal strategy is derived by using matrix theory technique and dynamic programming approach. Finally, numerical examples are provided to study the effects of transition cost and exit probability on the wealth process, the trading strategy, turnover rate and the total transaction cost.     

A Markov switching analysis of contagion in the EMS

This paper applies the multivariate version of the Forbes and Rigobon (2002) contagion test, as proposed by Dungey et al. (2005a), to detect contagion effects in the Exchange Rate Mechanism (ERM) of the European Monetary System (EMS). Crisis and non-crisis observations are determined endogenously via a Markov-switching vector autoregression (MSVAR). We show that the MSVAR is suitable for this purpose, as it does particularly well in identifying the 11 realignments of the ERM. We examine whether Denmark’s rejection of the Maastricht Treaty and Italy’s competitiveness problems affected other EMS participants and find evidence for contagion.     

A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns

This paper develops the regime classification algorithm and applies it within a fully-fledged pairs trading framework on minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifically, the highly flexible algorithm automatically determines the number of regimes for any stochastic process and provides a complete set of parameter estimates. We demonstrate its performance in a simulation study—the algorithm achieves promising results for the general class of Lévy-driven Ornstein–Uhlenbeck processes with regime switches. In our empirical back-testing study, we apply our regime classification algorithm to propose a high-frequency pair selection and trading strategy. The results show statistically and economically significant returns with an annualized Sharpe ratio of 3.92 after transaction costs—results remain stable even in recent years. We compare our strategy with existing quantitative trading frameworks and find its results to be superior in terms of risk and return characteristics. The algorithm takes full advantage of its flexibility and identifies various regime patterns over time that are well-documented in the literature.     

Feedback trading in stock index futures: Evidence from South Africa

This study tests whether investors and speculators in stock index futures contracts on the South African stock market use feedback trading strategies. Feedback trading can be destabilizing and impede on the risk mitigation and price discovery functions of futures contracts. Using the Sentana and Wadhwani (1992) model, and accounting for the global financial crisis, we find no evidence of feedback trading in the Top40 futures index or the Top40 mini futures index contracts. Our findings have important implications for investors who wish to use index futures to mitigate risk or exploit arbitrage opportunities and regulators concerned about the destabilizing effects of futures trading.     

Estimating a regime switching pairs trading model

We consider a discrete time pairs trading model which includes regime changes in the dynamics. The prices of the pair of assets, and so their difference or spread, depend on the state of the market, which in turn is modelled by a finite state Markov chain. Different states of the chain give rise to different parameters in the dynamics of the spread. However, the state of the chain is not observed directly but only through the prices or spread. Based on observations of the spread, this paper provides recursive estimates for both the state of the market and all coefficients in the model.     

Feedback trading and the ramadan effect in frontier markets

We examine the presence of the Ramadan effect in feedback trading drawing on a sample of eleven majority Muslim markets for the period of 29/6/2001 to 1/8/2016. Feedback trading is significant in several of these markets, appearing stronger outside, rather than within, Ramadan. These results hold for the full sample period, including before and after the global financial crisis raising the possibility that Ramadan’s widely documented lower volatility is related to the reduced presence of feedback trading during that month. We attribute our findings to Ramadan’s traditionally documented low volumes rendering feedback trading less feasible during that month.     

Non-linear dynamics in international resource markets: Evidence from regime switching approach

This paper examines the nonstationary and nonlinear features of the non-renewable resource markets: the crude oil (US West Texas Intermediate and UK Brent), bituminous coal and natural gas markets. In particular, we achieve this goal by using the Markov switching unit root regression. This approach is attractive because it allows price to switch between stationary and nonstationary regimes (partial nonstationarity). It also allows price to switch between two stationary regimes (varied stationarity) or to switch between two nonstationary regimes (varied nonstationarity). The results of a range of non-linear tests show that the independently and identically distributed (i.i.d.) hypothesis or the random walk hypothesis is untenable for the non-renewable resource prices. The results from the Markov regression indicate that, in the cases of US West Texas Intermediate, UK Brent as well as bituminous coal, prices are characterized by the local nonstationarity in both regimes, and therefore varied nonstationarity is sustained. The price of natural gas is characterized by partial nonstationarity, indicating that this market is inconsistent with the efficient market.     

Trading performance and market efficiency: Evidence from algorithmic trading

In India, National Stock Exchange directly identifies algorithmic trading participation. Algorithmic traders possess intraday market timing skills. Results are not motivated by extreme short-term signals or transitory price trading. Magnitude of market timing performance in cross-sectional group of traders shows that they earn profit across all the cases, and maximize while providing liquidity. Volume-weighted-average-price decomposition analysis reports algorithmic traders earn profits through intraday market timing performance for five-minute and one-minute intervals, and it is higher compared to short-term market timing performance across all trader groups. Order imbalance and price delay regressions show that algorithmic trading significantly improves price efficiency.     

Feedback trading and intermittent market turbulence

This paper studies the potential for complex asset return dynamics in a high-frequency, non-fundamental feedback trading model. Price adjustment is driven by the time-varying price impact of net orderflow. In tranquil times feedback trading has no impact on the price level. Given feedback trading intensities, as asset liquidity declines the market progressively becomes stressed and turbulent. Returns and absolute returns persistence are found to display power-law features, and episodes of turbulence are intermittent.     

Cross‐region and cross‐sector asset allocation with regimes

Cross‐region and cross‐sector asset allocation decisions are one of the most fundamental issues in international equity portfolio management. Equity returns exhibit higher volatilities and correlations, and lower expected returns, in bear markets compared to bull markets. However, static mean–variance analysis fails to capture this salient feature of equity returns. We accommodate the nonlinearity of returns using a regime switching model across both regions and sectors. The regime‐dependent asset allocation potentially adds value to the traditional static mean–variance allocation. In addition, optimal allocation across sectors provide greater benefits compared to international diversification, which is characterized by higher returns, lower risks, lower correlations with the world market and a higher Sharpe ratio.     

Insolvency law reform in Australia and Singapore: Directors' liability for insolvent trading and wrongful trading

This article compares reforms to directors' liability for insolvent trading in Singapore and in Australia. We analyse the law in these two countries because they are important Asia‐Pacific trading partners and their laws were originally largely the same—Singapore's law on insolvent trading reflected the law in Australia from the 1960s. However, the law in the two countries has now diverged substantially. The comparison of these two countries therefore represents an interesting case study in how countries differ in their approaches to balancing the competing interests evident in laws that impose personal liability on company directors for insolvent trading. Reform of the prohibition against insolvent trading was a focus of Australia's insolvency law reforms in 2017, which led to the introduction of a safe harbour for directors from liability. Singapore's omnibus insolvency law reforms of 2018–19 include amendments to update Singapore's fraudulent and insolvent trading provisions by introducing a concept of “wrongful trading.” The article finds that there are some areas of convergence between these two jurisdictions when it comes to debates about such provisions but concludes that the different contemporary legislative histories in Australia and Singapore have affected their approaches to reform. Reformers in both jurisdictions have attempted to find an appropriate balance between protecting creditors, discouraging director misconduct, and encouraging entrepreneurship and innovation; however, this comparison suggests that the weight that reformers place on creditor protection compared with the concern that excessive personal liability can make directors unduly risk‐averse is influenced by their existing legislative framework and experience of those laws. Although Australia has shifted away from a strict focus on creditor protection, to give directors more opportunities to engage in restructuring, Singapore's amendments may provide a more creditor‐friendly regime.     

COVID-19 and stock returns: Evidence from the Markov switching dependence approach

This paper aims to investigate the regime-switching and time-varying dependence between the COVID-19 pandemic and the US stock markets using a Markov-switching framework. It makes two contributions to the empirical literature by showing that: (a) the variations of the daily reported COVID-19 cases and cumulative COVID-19 deaths induced asymmetric lower (left) and upper (right) tail dependence with the stock markets, and its left and right tail dependence exhibited significant time-varying trends; and (b) the left and right tail dependence between the stock markets and the pandemic exhibited significant regime-switching behaviours, with its switching probabilities in the higher tail dependence stage all being greater than in the lower tail dependence stage after 1 December 2019. Moreover, given that there is concurrent but significant financial market reaction to any unexpected emergence of a transmittable respirational disease or a natural calamity, the outcomes have some vital implications to market players and policymakers.     

Strategic insider trading around earnings announcements in Australia

This paper tests, within the Australian setting, whether directors strategically time trades in their own firms, around earnings announcements, in the context of impediments to trading in the immediately preceding period. I show that both signed and unsigned trade activity are insignificantly different from zero in the preceding period, and significantly negative and positive after the event. Further, directors in Australia significantly sell following positive earnings news, and buy after negative news, providing evidence of ‘indirect’ trading. Directors’ trades in the longer-term pre-announcement period are also negatively related to the news content sentiment, contrary to expectation. Finally, I find evidence of positive autocorrelation between directors’ trades over the longer-term past, and those executed after earnings announcements, which, in the absence of the ‘short-swing’ rule in Australia, casts doubt over short-term strategic insider trading, more generally.     

Informed trading before analyst downgrades: Evidence from short sellers

This paper studies short-selling prior to the release of analyst downgrades in a sample of 670 downgrades of Nasdaq stocks between 2000 and 2001. We find abnormal levels of short-selling in the three days before downgrades are publicly announced. Further, we show that this pre-announcement abnormal short-selling is significantly related to the subsequent share price reaction to the downgrade, and especially so for downgrades that prompt the most substantial price declines. Our findings are robust to various controls that might also affect short-selling such as pre-announcement momentum, three-day pre-announcement returns, and announcement-day share price. In addition, the results are independent of scheduled earnings announcements, analyst herding, and non-routine events near downgrades. Further evidence suggests that tipping is more consistent with the data than the prediction explanation which posits that short sellers successfully predict downgrades on the basis of public information about firms’ financial health. Finally, we present evidence that downgraded stocks with high abnormal short-selling perform poorly over the subsequent six months by comparison with those with low abnormal short-selling. Overall, our results support the hypothesis that short sellers are informed traders and exploit profitable opportunities provided by downgrade announcements.     

Automated trading with boosting and expert weighting

We propose a multi-stock automated trading system that relies on a layered structure consisting of a machine learning algorithm, an online learning utility, and a risk management overlay. Alternating decision tree (ADT), which is implemented with Logitboost, was chosen as the underlying algorithm. One of the strengths of our approach is that the algorithm is able to select the best combination of rules derived from well-known technical analysis indicators and is also able to select the best parameters of the technical indicators. Additionally, the online learning layer combines the output of several ADTs and suggests a short or long position. Finally, the risk management layer can validate the trading signal when it exceeds a specified non-zero threshold and limit the application of our trading strategy when it is not profitable. We test the expert weighting algorithm with data of 100 randomly selected companies of the S&P 500 index during the period 2003–2005. We find that this algorithm generates abnormal returns during the test period. Our experiments show that the boosting approach is able to improve the predictive capacity when indicators are combined and aggregated as a single predictor. Even more, the combination of indicators of different stocks demonstrated to be adequate in order to reduce the use of computational resources, and still maintain an adequate predictive capacity.     

The January effect across volatility regimes

Using a Markov regime switching model, this article presents evidence of the well-known January effect on stock returns. The specification allows a distinction to be drawn between two regimes: one with high volatility and another with low volatility. We obtain a time-varying January effect that is, in general, positive and significant in both volatility regimes. However, this effect is larger in the high-volatility regime. In sharp contrast with most of the previous literature, we find two major results: (1) the January effect exists for all sizes of portfolio; (2) the negative correlation between the magnitude of the January effect and portfolio size fails across volatility regimes. Moreover, our evidence supports a slight decline in the January effect for all sizes of portfolio except the smallest, for which it is even larger.     

iShares Australia: a clinical study in international behavioral finance

Using iShares Australia returns as a proxy for the influence of overseas investors in the Australian market, we found that U.S.-based investors in the Australian market overreact to contemporaneous and lagged returns of the U.S. equity market, the U.S.-Australian dollar exchange rate, and past iShares Australia returns. In response to changing conditional risk, however, investors behave rationally: increasing (decreasing) expected risk is associated with falling (rising) prices. In light of these findings, we hypothesize that behavioral finance might explain the observed correlations between international equity markets.     

Social networks and managerial rent-seeking: Evidence from executive trading profitability

This study examines whether board social networks are associated with executive trading profitability. Using a sample of US public firms with a history of executive trading from years 2000 to 2015, we find robust evidence that the profitability of executive trading is significantly lower in firms with higher levels of board social networks. The evidence is consistent with our view that board social networks effectively curb executives' private information advantage over outsiders, thus leading to a lower level of managerial rent-seeking. Our research has policy implications for regulators concerned about the role of corporate board in capital markets.     

Evidence of algorithmic trading from Indian equity market: Interpreting the transaction velocity element of financialization

Technology and innovation have been the driving forces behind financialization across the globe. One such technological advent, in the pursuit for minimizing the risk and maximizing the return and in order to adhere to the financial sector changes, is Algorithmic Trading (AT). Though AT is being used extensively across the world, there is a lack of academic research on the evidence of AT in most of the markets. The lack of evidence stems from the ambiguity in definitions of AT and High Frequency Trading (HFT) and their usage interchangeably. The lack of evidence also hinders the understanding and interpretation of the impact of ever-increasing unprecedented growth in the velocity of financial transactions on the social machinery of global economies. We take advantage of the clear definition and identification of AT in the Indian equity market to provide evidence of AT and interpreting it as the transaction velocity element of financialization. We also attempt to decipher the impact of AT, symbolizing the transaction velocity element of financialization, on the price discovery process.     

How Persistent is Stock Return Volatility? An Answer with Markov Regime Switching Stochastic Volatility Models

Abstract:  We propose generalised stochastic volatility models with Markov regime changing state equations (SVMRS) to investigate the important properties of volatility in stock returns, specifically high persistence and smoothness. The model suggests that volatility is far less persistent and smooth than the conventional GARCH or stochastic volatility. Persistent short regimes are more likely to occur when volatility is low, while far less persistence is likely to be observed in high volatility regimes. Comparison with different classes of volatility supports the SVMRS as an appropriate proxy volatility measure. Our results indicate that volatility could be far more difficult to estimate and forecast than is generally believed.